Research Repository

The k-NN algorithm is a highly effective method for many application areas. Conceptually the other good properties are its simplicity and easy to understand. However, according to the measurement of the performance of an algorithm based on three considerations (simplicity, processing time, and prediction power), the k-NN algorithm lacks the high-speed computation and maintenance of high accuracy for different k values. The k-NN algorithm is still under the influence of varying k values. Besides, the prediction accuracy fades away whenever k approaches larger values. To overcome these issues, this paper introduces a kd-tree based dual-kNN approach that concentrates on two properties to keep up the classification accuracy at different k values and upgrade processing time performance. By conducting experiments on real data sets and comparing this algorithm with two other algorithms (dual-kNN and k-NN), it was experimentally confirmed that the kd-tree based dual-kNN is a more effective and robust approach for classification than dual-kNN and k-NN.

Morphological operations have been an integral part of the enhancement of digital imaging programs, especially for filtering noise to improve the quality of images by utilizing the two most basic morphological operations: erosion and dilation. The main role of dilation is to fill the defined region in an image with pixels, whereas erosion removes pixels from the region. As we know, the methods of erosion followed by dilation, or dilation followed by erosion, are indeed attractive approaches amongst researchers who deal with filtering noise problems. However, these approaches need more computational time and have a high-percentage chance of losing essential pixel area. To cover these issues, this paper introduces a new approach called an adaptive
morphological operation to boost the performance of image enhancement. Based on 2011, 2013, 2015, and 2016 weather image datasets collected from the WITH radar installed on the rooftop of the Information Engineering building, University of the Ryukyus, the experimental results confirm
that the proposed approach is more efficient than the conventional approaches.

As we know, machine learning algorithms are powerful tools for a variety of application domains, giving widely divergent dimensions, such as reliability, precision, robustness, high-speed solutions, etc. Likewise, the other critical dimension that a well-designed learning algorithm should occupy is strength against unpredictable and phenomenal noise. For this critical dimension, we
introduce a new approach, dual k-nearest neighbors (dual-kNN), to investigate the tolerance level for mislabeling based on different injected-noise levels. Literally, dual-kNN is a reborn algorithm of k-nearest neighbors (k-NN) aiming to reduce the influence of a steady decrease in prediction accuracy over increasing k values. What is more, dual-kNN is proven to have higher classification accuracy in many application domains. For the primary goal of this paper, we mainly emphasize investigating dual-kNN’s resistance level to mislabeled classes. Provably, our empirical experimentations describe how dual-kNN has a higher resistance level to mislabeling than normal
k-NN, density-based kNN, and logistic regression, for noise levels of up to 50%. The practical datasets applied within this paper are medical data files from the University of California, Irvine (UCI) Machine Learning Repository.

The performance of an algorithm is usually measured in three dimensions (simplicity, processing time, and prediction power). In addition, we should take into account the noise resistance level in those measures. For this reason, this paper focuses on investigating the noise-tolerance level of dual k-nearest neighbors (dual-kNN) primarily based on five noisy medical diagnosis problems. Literally, dual-kNN is a reborn version of the k-nearest neighbors (k-NN) algorithm with a new observation idea in the classification process with a collaborative effort between the first and second nearest neighbors of an observed instance. It was recently proven that dual-kNN has high prediction accuracy for a variety of real-world data sets, especially so in unbiased data sets. Thus, in this report, not only the prediction accuracy of dual-kNN is compared with normal k-NN, logistic regression, and the neural network, but we additionally investigate the noise tolerance in the aforementioned approaches. The practical data sets applied in this paper are medical data files from the University of California, Irvine, Machine Learning Repository. In this report, the new approach to dual-kNN commences with better prediction accuracy, and higher noise resistance is presented, in comparison with normal k-NN, logistic regression, and neural networks.

The k-Nearest Neighbors algorithm is a highly effective method for many application areas. Conceptually the other good properties are its simplicity and easy to understand. However, according to the measurement of the performance of an algorithm based on three considerations (simplicity, processing time, and prediction power), the k-NN algorithm lacks the high-speed computation and maintenance of high accuracy for different k values. The k-Nearest Neighbors algorithm is still under the influence of varying k values. Besides, the prediction accuracy fades away whenever k approaches larger values. To overcome these issues, this paper introduces a kd-tree based dual-kNN approach that concentrates on two properties to keep up the classification accuracy at different k values and upgrade processing time performance. By conducting experiments on real data sets and comparing this algorithm with two other algorithms (dual-kNN and normal-kNN), it was experimentally confirmed that the kd-tree based dual-kNN is a more effective and robust approach for classification than pure dual-kNN and normal k-NN.

The k-Nearest Neighbor (k-NN) is very simple and powerful approach to conceptually approximate real-valued or discrete-valued target function. Many researchers have recently approved that K-NN is a high-prediction accuracy algorithm for a variety of real-world systems using many different types of datasets. However, as we know, k-NN is a type of lazy learning algorithms as it has to compare to each of stored training examples for each observed instance. Besides, the prediction accuracy of k-NN is under the influence of K values. Mostly, the higher K values make the algorithm yield lower prediction accuracy according to our experiments. For these issues, this paper focuses on two properties that are to upgrade the classification accuracy by introducing Regional Distance-based k-NN (RD-kNN) and to speed up the processing time performance of k-NN by applying multi-threading approach. For the experiments, we used the real data sets (wine, iris, heart stalog, breast cancer, and breast tissue) from UCI machine learning repository. According to our test cases and simulations carried out, it was also experimentally confirmed that the new approach, RD-kNN, has a better performance than classical kNN.

Classification is a process of discovering and categorizing objects from large data storage that have similar characteristics, properties, and patterns. One of the most widely used classification methods in machine learning is the k-nearest neighbors (k-NN) algorithm. The unique property of k-NN that appeals to researchers is its simplicity, so it can be applied successfully over a wide field. However, according to measurement of the performance of an algorithm based on three considerations (simplicity, processing time, and prediction power), the k-NN algorithm lacks highspeed computation and maintenance of high accuracy for different K values. In other words, k-NN is a heuristic classification approach. Besides, the prediction accuracy fades away whenever K approaches larger values. To overcome these issues, this paper presents a dual-kNN that concentrates on two properties to keep up the accuracy at different K values and upgrade processing time performance. By conducting experiments on real datasets and comparing this algorithm with k-NN, it was also confirmed that the new dual-kNN is an effective and robust
approach to classification.

Nowadays, as traffic jams are a daily elementary problem in both developed and developing countries, systems to monitor, predict, and detect traffic conditions are playing an important role in research fields. Comparing them, researchers have been trying to solve problems by applying many kinds of technologies, especially roadside sensors, which still have some issues,
and for that reason, any one particular method by itself could not generate sufficient traffic prediction results. However, these sensors have some issues that are not useful for research. Therefore, it may not be best to use them as stand-alone methods for a traffic prediction system. On that note, this paper mainly focuses on predicting traffic conditions based on a hybrid prediction
approach, which stands on accuracy comparison of three prediction models: multinomial logistic regression, decision trees, and support vector machine (SVM) classifiers. This is aimed at selecting the most suitable approach by means of integrating proficiencies from these approaches. It was also
experimentally confirmed, with test cases and simulations that showed the performance of this hybrid method is more effective than individual methods.

k-nearest neighbor (K-NN) is a well-known classification algorithm, being feature space–based on nearest-neighbor training examples in machine learning. However, K-NN, as we know, is a lazy learning method. Therefore, if a K-NN–based system very much depends on a huge amount of history data to achieve an accurate prediction result for a particular task, it gradually faces a processing-time performance-degradation problem. We have noticed that many researchers usually contemplate only classification accuracy. But estimation speed also plays an essential role in realtime prediction systems. To compensate for this weakness, this paper proposes correlation coefficient–based clustering (CCC) aimed at upgrading the performance of K-NN by leveraging processing-time speed and plurality rule–based density (PRD) to improve estimation accuracy. For experiments, we used real datasets (on breast cancer, breast tissue, heart, and the iris) from the University of California, Irvine (UCI) machine learning repository. Moreover, real traffic data
collected from Ojana Junction, Route 58, Okinawa, Japan, was also utilized to lay bare the efficiency of this method. By using these datasets, we proved better processing-time performance with the new approach by comparing it with classical K-NN. Besides, via experiments on real-world datasets, we compared the prediction accuracy of our approach with density peaks clustering based on K-NN and principal component analysis (DPC-KNN-PCA).

Estimation and analysis of traffic jams plays a vital role in an intelligent transportation system and advances safety in the transportation system as well as mobility and optimization of environmental impact. For these reasons, many researchers currently mainly focus on the brilliant machine learning–based prediction approaches for traffic prediction systems. This paper primarily addresses the analysis and comparison of prediction accuracy between two machine learning algorithms: Naïve Bayes and K-Nearest Neighbor (K-NN). Based on the fact that optimized estimation accuracy of these methods mainly depends on a large amount of recounted data and that
they require much time to compute the same function heuristically for each action, we propose an approach that applies multi-threading to these heuristic methods. It is obvious that the greater the amount of historical data, the more processing time is necessary. For a real-time system, operational response time is vital, and the proposed system also focuses on the time complexity
cost as well as computational complexity. It is experimentally confirmed that K-NN does much better than Naïve Bayes, not only in prediction accuracy but also in processing time. Multithreading-based K-NN could compute four times faster than classical K-NN, whereas multithreading-based Naïve Bayes could process only twice as fast as classical Bayes.

The increasing need for traffic detection system has become a vital area in both developing and developed countries. However, it is more important to get the accurate and valuable data to give the better result about traffic condition. For this reason, this paper proposes an approach of tracking traffic data as cheap as possible in terms of communication, computation and energy efficient ways by using mobile phone network. This system gives the  information of which vehicles are running on which location and how much
speed for the Traffic Detection System. The GPS sensor of mobile device will be mainly utilized to guess a user’s transportation mode, then it integrates cloud environment to enhance the limitation of mobile device, such as storage, energy and computing power. This system includes three main components: Client Interface, Server process and Cloud Storage. Some tasks are carried out on the Client. Therefore, it greatly reduces the bottleneck situation on Server side in efficient way. Most of tasks are executed on the Server
and history data are stored on the Cloud Storage. Moreover, the paper mainly uses the distance based clustering algorithm in grouping mobile devices on the same bus to get the accurate data.

Cross-border education can be termed as the movement of student, research, academic exchange programs and institutions across national borders with the provision of international education programs. In this case, the secure and speedy education credit transfer and exchange of student records are the important factors for the cross-border education system. Thus, this paper proposes a system called “Blockchain-based Cross-Border Educational Transaction System” for the higher education industry. Blockchain technology is one of the megatrends for recent years. It is potentially a revolutionary means of secure and transparent data sharing and processing in a wide variety of sectors including the education sector. The important concept of blockchain technology is a combination of secured distributed ledger, cryptocurrency and smart contract system. That concept is very appropriate at creating trusted and secured information processing for large and heterogeneous sets. Therefore, the blockchain-based cross-border educational transaction system enables the education industry to transfer and exchange the secure education credit and academic records for students, and other stakeholders such as government organizations, companies, and other institutions. Besides, maintaining educational records in the blockchain can protect from an unexpected natural disaster. This paper will propose and discuss the system framework that consists of three layers. The first layer would be an interface layer for application development. The second layer provides smart contract service, core service of blockchain, for generating trusted education credit, grading and certificate transaction, as well as for secure agreements of exchange and collaborative educational processes. The last layer is a data storage layer including database methodologies and distributed computing methods. The proposed system would overcome the barrier of traditional cross-border transaction system by allowing the globally secure, transparent, and reliable education transaction services and collaborative processes among universities in different regions.

Morphological operations have been an integral part of enhancement of digital imaging programs, especially for filtering noise for improving the quality of image by utilizing the two most basic morphological operations, named as erosion and dilation, altogether. The main role of dilation is to fill the defined region in an image with pixels, while erosion removes pixels from the region. As we know, the method of erosion followed by dilation or dilation followed by erosion is indeed an attractive approach amongst researchers to deal with filtering noise problems. However, this approach needs more computation time and has a high percentage of losing essential pixel area. To cover these issues, this paper introduces a new approach called adaptive morphological operation to boost the performance of image enhancement. Based on 2011,  2013, 2015, and 2016 weather image datasets collected from WITH radar, which is installed on the rooftop of Information Engineering building, University of the Ryukyus, the experimental results confirm that the proposed approach is more efficient than the conventional approach.

The k-NN algorithm is a highly effective method for many application areas. Conceptually the other good properties are its simplicity and easy to understand. However, according to the measurement of the performance of an algorithm based on three considerations (simplicity, processing time, and prediction power), the k-NN algorithm lacks the high-speed computation and maintenance of high accuracy for different k values. The k-NN algorithm is still under the influence of varying k values. Besides, the prediction accuracy fades away whenever k approaches larger values. To overcome these issues, this paper introduces a kd-tree based dual-kNN approach that concentrates on two properties to keep up the classification accuracy at different k values and upgrade processing time performance. By conducting experiments on real data sets and comparing this algorithm with two other algorithms (dual-kNN and k-NN), it was experimentally confirmed that the kd-tree based dual-kNN is a more effective and robust approach for classification than dual-kNN and k-NN.

Unexpected heavy rainfall has been seriously occurred in most parts of the world, especially during monsoon season. As a serious consequence of heavy rainfall, the people in those areas battered by heavy rainfall faced many hardship lives. Without exception, prevention is the best way of minimizing these negative effects. In spite of all, we developed a rainfall series prediction system for different series patterns by applying the dynamic programming approach aiming to acquire the rainfall level of the whole rainfall cycle. The simple idea behind the proposed dynamic programming approach is to find the similarity of two rainfall sequences upon the maximum match of the rainfall level of those sequences. Based on 2011 and 2013 real data sets collected from WITH radar, which is installed on the rooftop of Information Engineering, University of the Ryukyus, the comparison between the conventional approach (Polynomial Regression) and the proposed approach is investigated. These correlation experiments confirm that the dynamic programming approach is more efficient for predicting different rainfall series.

The k-Nearest Neighbors algorithm is a highly effective method for many application areas. Conceptually the other good properties are its simplicity and easy to understand. However, according to the measurement of the performance of an algorithm based on three considerations (simplicity, processing time, and prediction power), the k-NN algorithm lacks the high-speed computation and maintenance of high accuracy for different k values. The k-Nearest Neighbors algorithm is still under the influence of varying k values. Besides, the prediction accuracy fades away whenever k approaches larger values. To overcome these issues, this paper introduces a kd-tree based dual-kNN approach that concentrates on two properties to keep up the classification accuracy at different k values and upgrade processing time performance. By conducting experiments on real data sets and comparing this algorithm with two other algorithms (dual-kNN and normal-kNN), it was experimentally confirmed that the kd-tree based dual-kNN is a more effective and robust approach for classification than pure dual-kNN and normal k-NN.

k-Nearest Neighbor (k-NN) is very simple and powerful approach to conceptually approximate real-valued or discrete-valued target function. Many researchers have recently approved that k-NN is a high prediction accuracy for variety of real world systems using many different types of datasets. However, as we know, k-NN is a type of lazy learning algorithms as it has to compare to each of stored training examples for each observed instance. Besides, the prediction accuracy of k-NN is under the influence of K values. Mostly, the higher K values make the algorithm yield lower prediction accuracy according to our experiments. For these issues, this paper focuses on two properties that are to upgrade the classification accuracy by introducing Regional Distance-based k-NN (RD-kNN) and to speed up the processing time performance of k-NN by applying multi-threading approach. For the experiments, we used the real data sets, wine, iris, heart stalog, breast cancer, and breast tissue, from UCI machine learning repository. According to our test cases and simulations carried out, it was also experimentally confirmed that the new approach, RD-kNN, has a better performance than classical k-NN.

K-nearest neighbor algorithm is one of the most popular classifications in machine learning zone. However, as k-nearest neighbor is a lazy learning method, when a system bases on huge amount of history data, it faces processing performance degradation. Many researchers usually care about only classification accuracy, but the speed of estimation also play an essential role in real time prediction systems. For this issue, this research proposes correlation coefficient- based k-mean clustering for k-nearest neighbor aiming at upgrading the performance of k-nearest neighbor classification by improving processing time performance. For the experiments, we used the real data sets, Breast Cancer, Breast Tissue and Iris, from UCI machine learning repository. Moreover, the real traffic data collected from Ojana junction, Route 58, Okinawa, Japan, was also utilized to show the efficiency of this method. By using these datasets, we prove the better processing performance and prediction accuracy of the new approach by comparing the classical k-nearest neighbor with the new k-nearest neighbor.

Nowadays, as traffic jam is an everyday facing problem in the developed and developing countries, monitoring, predicting and detection current traffic condition systems are playing an important role in research fields. For this case, many researchers have been trying to solve this problem by using many kinds of technologies, especially road side sensors. However, these sensors have already injected with the good and bad things altogether. Therefore, it may not be a better way to use it in stand alone for traffic prediction system. For this issue, the paper mainly focus on predicting traffic condition based on multiple points of view such as the data from road side camera, weather condition, weekday or weekend, rush hour time and special day. The system has three parts: Resources Side (RS), Traffic Prediction Server (TPS) and Display Side (DS).

Intelligent Transportation System (ITS) has been becoming an integral part of life in city, giving at the result of great impact by utilizing the communica-tion, computing and sensor technologies to solve the relating problem of transportation such as traffic congestions. Traffic congestion is used to curse to citizen and an ongoing problem in almost urban areas. The purpose of this paper is mainly to provide the data without noises as much as possi-ble to Traffic Detection System (TDS) based on GPS_enable Mobile phone. The system is constructed into two parts: Client side (Mobile device) and Cloud Backend Server. In this work, the process of transportation mode fil-tering is carried out on the Client side applying Moving Average Filtering method and then the filtering location duplicated data continues to work out on Server side based on the Client’s result. In order to solve the server side is-sue, the distance based clustering method, OPTICS: Ordering Point To Iden-tify the Clustering Structure, is mainly utilized. Afterward, the accuracy of the system is measured by Purity, F-measurement and Entropy method. To execute closeness between GPS points, the distance between them is meas-ured by using Haversine Formula.

As traffic congestion is becoming an everyday facing problem in urban region, traffic prediction and detection systems are playing an important role in city life. The road network sensors were popular in the previous systems. However, these technologies addressed to solve the installation and maintenance cost. Fortunately, the dramatic technology innovation is carrying many crucial solution for transportation agency to provide the relative services efficiently. This paper mainly emphasizes on detecting traffic condition by analyzing the behavior of vehicle primarily based on GPS mobile phone and history data. The system is built into two parts: Client and Cloud Server. On the Client side, the system distinguishes whether a phone carrier is taking a vehicle or walking. To analysis this situation, the Average Moving Filtering method are applied. On the Server side, it detects the traffic status based on checking vehicle’s behavior based on the Client’s result by applying Bayes Classifier.

As Traffic congestion is becoming an everyday facing problem in urban region, monitoring road and Traffic prediction system are playing an important role in the city life. The previous Traffic Prediction Systems were implemented depending on the road network sensor. These technologies had been prompted by the need of addressing to solve the problem installation and maintenance cost. Fortunately the dramatic technology innovation is carrying many crucial solution for Transportation agency to provide the relative services efficiently. This paper mainly emphasizes detecting Traffic condition by analyzing the behavior of vehicle primarily based on applying GPS enable Mobile phone and integrating the underlying Transportation network information and history data. The system is built into two parts: Client (Mobile device) and Cloud Backend Server. On the Client side, the system distinguishes whether the Mobile device carrier is taking a vehicle or walking. The Average Moving Filtering method and the measurement of total distance are utilized in analyzing mode of Transportation. The distance of two points (latitude and longitude) is computed by using Haversine Formula. On the Server side, it detects the Traffic status based on checking the behavior of vehicle based on the Client result by applying Bayes Classifier.

Nowadays, monitoring road and traffic prediction in urban area is becoming an important role in developing countries like ours. It is the most important part that getting and performing the accurate traffic data in all Traffic Prediction System.  This paper introduces an approach  of tracking traffic data using the cheapest way and it was computed the traffic data in terms of communication, computation and energy efficient ways. Mobile devices are becoming an important role not only for personal contact, but also for business and environmental sensing application. The GPS sensor of mobile device will be mainly utilized to guess a user’s transportation mode, then it integrates cloud environment to enhance the limitation of mobile device, such as storage, energy and computing power.  This system includes three main components: Client Interface, Server process and Cloud Storage. Some tasks are carried out on the Client. Therefore, it greatly reduces the bottleneck situation on Server side in efficient way. Most of tasks are executed on the Server and history data are stored on the Cloud Storage. Firstly, the user’s transportation mode, motorize or non-motorized, is analyzed on the client side using raw GPS data, instead of submitting frequently raw data to data center. If it is only the motorize mode, some useful traffic data are offloaded to cloud.  On the server side, all motorize mode are not taken into account as traffic data. In this case, the mobile data that comes from the same location are recognized as one proves. Later, these data are used as history data for future prediction to perform more accurate traffic information.

As concurrency systems expand and become more and more popular, there is a growing need for efficient, tolerating stale data and reducing in concurrent writing problem. The use of multiple threads is beneficial in concurrent access files on file server. Threads allow operations from multiple clients to run concurrently and possibly access the same objects. The focus of this thesis is to implement concurrency. The client’s requests are processed by Read_Copy Update techniques and Round Robin fashion and multiple worker threads that operate reading and writing operations on data files in file server. This paper analyses performance evaluation on processing time by single worker thread and multiple worker threads and the results show less processing time in required for multiple worker threads.

Communities among users play the popular role for
days of Social Network and the presence of groups of nodes that
are high tightly connected with each other than with less links
connected to nodes of different groups. So community detection
algorithms are come to be the key to detect the user who are
interact with each other in social media. However, there are still
challenges in considering of some nodes have no any common
node within the same group as well as some nodes have no any
link to the other node. It can be used similarity measure based on
neighborhood overlapping of nodes to organize communities and
to identify outliers which cannot be grouped into any of the
communities. In this paper, we detect communities and outliers
from Edge Structure with neighborhood overlap by using nodes
similarity. The result implies the best quality with modularity
measurement which leads to more accurate communities as well
as improved their density after removing outliers in the network
structure.

Challenges of detecting communities among users’ interactions play
the popular role for days of Social Network. The previous authors proposed for
detecting communities in different point of view. However, similarity based on
edge structure and nodes which cannot group into communities are still motivating.
Considering the community detection is motivating from the similarity
measurement to detect significant communities which are high tightly connected
each other upon the edge structure and outliers which are unnecessary to group
into the communities. This paper is proposed the approach of using similarity
measure based on neighborhood overlapping of nodes to organize communities
and to identify outliers which cannot be grouped into any of the communities
based on Edge Structure. The result implies the best quality with modularity
measurement which leads to more accurate communities as well as improved
their density after removing outliers in the network structure.

Exploring communities and outliers in Social Network is based on considering of some nodes have overlapped neighbor node within the same group as well as some nodes have no any link to the other node or have no any overlapped value. The existing approaches are based on the overlapping community detection method were only defined the overlap nodes or group of overlap nodes without thinking of which nodes might have individual communities or which nodes are outliers. Detecting communities can be used the similarity measure based on neighborhood overlapping of nodes and identified nodes so called outliers which cannot be grouped into any of the communities. This paper proposed method to detect communities and outliers from Edge Structure with neighborhood overlap by using nodes similarity. The result implies the best quality with modularity measurement which leads to more accurate communities as well as improved their density after removing outliers in the network structure.

In this system of plant metadata is used increasingly to improve both the availability and the quantity of the plant information delivered. Dublin Core has been used with other types of plant materials and in applications demanding some complexity. Information Retrieval (IR) is concerned with the process involved in the representation, storage, searching and finding of traditional medicinal plants information which is relevant to a requirement for information desire a human user. This system retrieves information about the Myanmar Traditional Medicinal Plants according to using keyword. This system is related information retrieving based on metadata in Myanmar Traditional Medicinal Plants system. These traditional plants may include leaves, fruit, stem and root.

This paper discusses the results of an on-going study on the effect of project-based learning (PBL)
on students’ learning outcomes in Database Course. Project-based learning can be a deeply successful model
for the teaching and learning of Database, one of the core courses of Computer Science in undergraduate
level. In most database courses, PBL is the active methodology that is widely used. The approach focuses on
the development of a project by student teams that designs and builds a database. Database management and
querying skills are a key element of a robust information system curriculum and active learning is an
important way to develop these skills. This paper represents a survey conducted to a set of students who
employed Project-based learning in Database Course from the University of Computer Studies (Pinlon).
Moreover, this paper describes the empirical results of student perception and to determine the effectiveness
of Project-based learning method.

Assessing the trustworthiness of reviews is a
key issue for the maintainers of opinion such as
Amazon.com. Opinion reviews on products and services
are used by potential customers before deciding to
purchase a product. An important issue that has been
neglected so far is opinion spam or trustworthiness of
online opinions. To the best of our knowledge, there is
still little published study on this topic, although Web
spam and email spam have been investigated
extensively. This paper presents spammer detection
techniques to calculate spam score of each user based
on their rating and review similarity on the target
products. The experiment showed that the presented
technique has comparatively spammer detection with
less computation than others.

Opinion reviews on products are used by potential customers before deciding to purchase a product. Large volumes of reviews are posted to the Internet and detection of fake reviews is now a challenging research problem. Opinion spam or trustworthiness of online opinions is an important issue and this issue has been neglected so far. To the best of our knowledge, there is not many published study on this topic, although Web spam and email spam have been investigated extensively. This paper is presented spammer detection techniques that define the spammer score on rating behaviors of the reviewer. The experiment showed that the presented technique has comparatively effective spammer detection than other techniques based on rating behaviors.

It is now quite common for online user to write reviews on websites and these reviews are read by customer before deciding to purchase a product. Trustworthiness of reviews is now a challenging research problem. There is not many published study on this topic although web spam and email spam has been investigated extensively. Spammer detection techniques that define spam score based on review behaviors of the reviewer are presented in this paper. The experiment showed that the presented technique has comparatively effective spammer detection than other techniques.

Product reviews posted at online shopping sites are
read by potential customer before deciding to purchase a product.
The quality is not control in posting review and trustworthiness of
reviews is now a challenging research problem. There is not many
published studies on t his topic although web spam and email spam
has been investigated extensively. Spammer detection techniques
that define spam score based on spamming behaviors of the reviewer
are presented in this paper. Review similarity is an important factor
to determine spammer. Therefore, all detailed review behaviors of
reviewer are calculated if the reviewer writes similar reviews. Rating
spam score is also calculated for that reviewer and the more
spamming behaviors the reviewers make the more spamming scores
they get. The experiments show that the presented technique has
comparatively effective spammer detection than other technique
based on helpfulness votes alone.

Organizations in both public and private sectors
have become increasingly dependent on electronic
data processing. It is essential to protect the
communication channels and the interfaces of any
system that handles information that could be the
subject of attacks e.g. personal mail, electronic
commerce and other financial transactions.
Protecting the important data is of utmost concern to
the organizations and cryptography is one of the
primary ways to do the job. Public Key
Cryptography is used to protect digital data going
through an insecure channel from one place to
another. This paper will implement a secured
architecture for the various medical records by using
RSA public key encryption algorithm and different
authentication levels with the help of Agent
Technology. RSA algorithm, asymmetric public key
algorithm, is used to encrypt and decrypt the data.
Agent Technology is used to retrieve required data
efficiently and effectively. Since, medical records are
confidential; it needs to be secured over the
communication channel. So that security agent
encrypts the requested data before sending to the
target user. Personal agent is responsible for
requesting data, carrying user preferences and user
authentication to the remote server and bringing
data back to the user. Data agents are used for
generating the relevant data to the user (Personal)
agent through security agent.

Cloud computing is an emerging computing paradigm in which applications, data and IT resources are provided as a service to users over the Internet. One kind of services that can be offered through the cloud is Software as a Service or (SaaS). For SaaS placement in the Cloud, the problem relates to how a composite SaaS should be placed in a Cloud by the Cloud’s providers such that its performance is optimal based on its estimated execution time. The challenges in the SaaS placement process rely on several factors, including the size of the Cloud network, SaaS competing resource requirements, SaaS interactions between its components and SaaS interactions with its data components. This paper proposes a PSO-GA (Particle Swarm Optimizing-Genetic Algorithm) based hybrid algorithm to the composite SaaS placement problem in the Cloud storage for minimizing the execution time and execution cost of applications on the resources provided by cloud.

Cloud computing paradigm contains many
shared resources such as infrastructures, data
storage, various platforms and software. In cloud
storage service, consistency not only influences
the performance and availability of the systems
but also the overall operational cost. This paper
is proposed an analytical model using MM1
queuing for data consistency on private cloud
storage system which is proposed to evaluate the
discarding probability based on update requests
to handle update conflict .Moreover, an
approach is also proposed to improve the
readability after update and consistency of
storage on the cloud environment.

Consistency maintenance is an important
issue in collaborative work systems that are
activated in both traditional distributed system
and cloud system. Collaborative Works are
computer based systems that support groups of
people engaged in a common task (or goal) and
that provide an interface to a shared context.
Consistency maintenance of shared documents
under the constraints of short response time and
support for free and concurrent editing in
distributed environment is one of the
fundamental and challenging issues. In this
paper, we describe a consistency model for
constructing collaborative software development
on cloud.

Consistency preservation is an important problem in collaborative system that is
activated in both traditional distributed systems and cloud based systems for
availability and performance. Especially, cloud storage services still need to
improve the consistency guarantees in client centric approach. This paper
describes a consistency model for constructing collaborative software
development on cloud that intends to evaluate the effectiveness of the system by
using Monotonic Write Consistency and Vector clock timestamp algorithm are
applied to control the collaborative work on cloud in order to provide the time
management. We discuss the effectiveness of our approaches compared to
normal process and process with consistency model.

Cloud computing paradigm contains many shared
resources such as infrastructures, data storage, various
platforms and software. In cloud storage service, consistency
not only influences the performance and availability of the
systems but also the overall operational cost. This paper is
proposed an analytical model using MM1 queuing for data
consistency on private cloud storage system to evaluate the
discarding probability based on update requests to handle
update conflict .Moreover, an approach is also proposed to
improve the readability after update and consistency of
storage on the cloud environment.

Many methods for classification and gene
selection with microarray data have been developed.
Some methods usually give a ranking of genes.
Relevant gene rank criteria is derived from SVM and
based on generalization error bounds with respect to
genes variable .We address feature selection problem
for classification because of small samples with high
dimensionality of genes. The best choice of gene
subset means selection of relevant features that is a
key for building a more accurate classifier. We
propose a new method using minimizing essential
set (MES) generated based on the nearest neighbor
rule. It is related to structural risk minimization and
thus leads to good generalization. The proposed
method is compared to some standard feature
selection methods with three real datasets. Our
approach is computationally efficient with better
classification performance.

Nowadays, microarray datasets are characterized by a large number of gene expression levels for each patient and a relatively small number of patients. Data can grow along two dimensions of fields (called attributes) and the number of cases, abnormal functionalities. Early detection for possible cancer cannot get by large scale data. The problem become feature selection with classification accuracy because of small samples with high dimensionality of genes .The best choice of gene subset means selection of relevant features that is a key for building a more accurate classifier.
The traditional statistical methods with linear function are not capable of discovering nonlinear relationships in microarray data. Data overfitting arises when the number of features is very large. Identifying relevant variables give more insight into the nature of corresponding classification problem and tend to be better predictive performance. Incorporating feature selection is a fairly straightforward procedure for linear or nonlinear support vector machine (SVM) classifiers.

We propose a new method that uses support vector machines with features ranking technique based on recursive feature elimination (RFE).Among various feature selection methods, embedded approach of feature ranking is particularly attractive. A fixed number of top ranked features are selected for design of classifier; a threshold can be set on the ranking criterion. The feature with the smallest ranking criterion is eliminated at each step. The ranking criterion is obtained from the weights of SVM that trained on the subset of features. SVM composes the transformation function and the dot product in the higher dimensional space into a single kernel function. The goal of maximum margin classification in binary SVM is to separate the two classes by a hyperplane. We can decide the optimal hyperplane which reduces the generalization error. The main objectives are to obtain profiles of relevant genes tend to predict the cancer class of unknown patient, prediction accuracy may improve by discarding irrelevant variables and apply the proposed method into an interface that can easily be used by clinicians.
This work is to be optimized which SVM model selection will be particularly important and scalable in microarray data analysis. From our work, experiments on real data sets from UCI machine learning repository can indicate that new method outperforms other feature selection method in terms of classification accuracy. On the other hand, machine learning methods may be able to objectively interpret all available results for the same patient and increase the diagnostic accuracy for each disease.

Identification of cancer genes that might anticipate the clinical
behaviors of different types of cancers is challenging due to the huge
number of genes and small number of patients samples. The new method
is being proposed based on supervised learning of classification like
support vector machines (SVMs).A new solution is described by the
introduction of the Minimized Margin (MM) in the subset criterion,
which permits to get near the least generalization error rate. The
performance of the new method was evaluated with real-world data
experiment. It can give the better accuracy for classification.

The data analysis can be many different points of views from many researchers. New method is evaluated for variable subset relevance with a view to variable selection. The new criteria are derived from support vector approach based on classification problems. This search can be efficiently performed by minimizing the generalization error. Selecting a small subset of features variables not only improves the efficiency of the classification algorithms but also improve the cancer classification accuracy. The process of building classifier is divided into two components (i ) selection of variables features (i .e genes) (ii ) selection of classification method. Our study indicates that the classification problem is more difficult than the binary one for the gene expression data sets. This new method is related to structural risk minimization and thus leads to good generalization. The proposed method is compared to some standard feature selection method with real data sets. This method is computationally efficient with better classification performance.

Identification of cancer genes that might anticipate the clinical behaviors from different types of cancer disease is challenging due to the huge number of genes and small number of patients samples. The new method is being proposed based on supervised learning of classification like support vector machines (SVMs).A new solution is described by the introduction of the Maximized Margin (MM) in the subset criterion, which permits to get near the least generalization error rate. In class prediction problem, gene selection is essential to improve the accuracy and to identify genes for cancer disease. The performance of the new method was evaluated with real-world data experiment. It can give the better accuracy for classification

The data analysis can be many different
points of views from many researchers. New
method is evaluated for variable subset relevance
with a view to variable selection. The new criteria
are derived from support vector approach based
on classification problems. This search can be
efficiently performed by minimizing the
generalization error. Selecting a small subset of
features variables not only improves the efficiency
of the classification algorithms but also improve
the cancer classification accuracy. The process of
building classifier is divided into two components
(i) selection of variables features (i .e genes) (ii)
selection of classification method. This study
indicates that the classification problem is more
difficult than the binary one for the gene
expression data sets. This new method is related to
structural risk minimization and thus leads to
good generalization. The proposed method is
compared to some standard feature selection
method with real data sets. This method is
computationally efficient with better classification
performance.

Big data has become important as many organizations have been collected massive amounts of domain specific information. This information are useful for problems of national intelligence, fraud detection and medical informatics. In statistical computing, extraction of knowledge from large amount of data has required a lot of efforts. Similarly, big data analysis can be used for impressive decision making in business field by some modification in existing machine learning algorithms. Firstly, this paper will address the relationship between Cloud computing using neural networks, associated with services on the basis of cloud computing, such as the infrastructure of the system. Second, we propose the neural based framework designing take part with different data sources and how to assign distinct data mining tasks. Some popular tools for different mining patterns will also be presented for the better performance of predictive learning on big data. Cloud computing is also considered as a type of resource scheduling.

This paper has presented different approach for variables of feature selection algorithm. This criterion is derived from generalization error bounds of the SVM theory: weight vector norm ||w||2 and upper bounds of the leave-one-out error. In addition ,it implements a criterion similar to the SVM-RFE measures the sensitivity of ||w||2 to a variable by computing the change in ||w||2 when this given variable has been removed. Lastly, as it has the lowest time complexity, it may be support for practical applications. Intuitively, one may justify the behavior by the over fitting effects occurring due to the small number of data and the large number of variables. We may improve the performance of our algorithms by using an alternate search strategy combining the feature selection process into an overall optimization problem. Though the data set is related to cancer, the method is generic and can be applied on other large data sets that require feature selection.

This paper has presented different approach for variables of feature selection algorithm. This criterion is derived from generalization error bounds of the SVM theory: weight vector norm ||w||2 and upper bounds of the leave-one-out error. In addition ,it implements a criterion similar to the SVM-RFE measures the sensitivity of ||w||2 to a variable by computing the change in ||w||2 when this given variable has been removed. Lastly, as it has the lowest time complexity, it may be support for practical applications. Intuitively, one may justify the behavior by the over fitting effects occurring due to the small number of data and the large number of variables. We may improve the performance of our algorithms by using an alternate search strategy combining the feature selection process into an overall optimization problem. Though the data set is related to cancer, the method is generic and can be applied on other large data sets that require feature selection.

The purpose of this research paper is to develop the decoding skill in reading of first year students at the Computer University, Taunggyi. English is an international language and today is the age of technology. Therefore, students should have not only a good command of English but also computer knowledge. To catch up with the latest technology, students must be skillful in English language because most of websites, technical books and prescribed texts in every university are written in English. Reading is one of the most important things in learning English. The more the students read many books or passages, the more range of vocabulary they can get. The first stage in reading is phonetic decoding or word attack. This is an important part of early reading experience for many computer students.

This paper aims to provide some functional ways for developing listening skill of students at the University of Computer Studies who want to be confident, interested and improve their listening skill. It also intends to guide the language teachers to take effective techniques in their teaching specifically and thus facilitate their students to triumph over their listening problem. For this purpose, firstly data was gathered from Language teachers of University of Computer Studies (Pang Long) in order to know about the improvement of listening skill of their students. And this study attempts to examine the listening problems of first year computer science and technology students, total 80 were selected for the study. Data was collected by means of questionnaires and interviews. Listening is one of the main supporting skills not only in daily life but also in classroom settings. It is fundamental in the language classroom because it makes a very positive contribution to the students. Thus, a language teacher also needs to help students understand using the efficient ways and authentic materials. There are many effective ways and the functional materials that can make students develop listening skill. The objective of the study is to help the English language teachers and students to overcome the difficulties by showing functional ways for developing English language listening skill in the language classroom.

This paper presents and focuses on the creating environment of the communication of EFL students. Communication skill is mainly important for all of the universities students, as oral speaking and listening are the main concern for communication. In order to listen clearly of a speech, students need to have fluency in the language and many listening exposures. Moreover, native speakers’ conversation and other environment and situation may create to improve the students’ listening and speaking skills. Most students are facing of many difficulties such as having not exposure, shy to speak in public and they are not trust their language abilities to apply outside the classroom. To improve the students’ language skill in classrooms, teachers always monitoring their students’ skills and setting some sample activities and interesting games. In this paper, the authors would like to propose role playing and interactive communicative activities games that can encourage students to improve their listening and speaking skills. Data sources are language teachers’ interviews and questionnaires and classroom observation. This study is conducted some students who are undergraduate from Universities of Computer Studies, Taunggyi and Monywa, 2019-2020 Academic Year.

This paper presents and focuses on the enhancing the oral communication skill of the students at the Universities of Computer Studies. Enhancing the oral communication skill is mainly important for the fourth year and final year students at the Universities of Computer Studies, as oral speaking is the main concern for communication. It also intends to guide the language teachers to take effective activities in their teaching specifically and thus facilitate their students to overcome their speaking tests. For this purpose, firstly data was gathered from language teachers of Universities of Computer Studies (Taunggyi) to know the students’ speaking skill. In order to produce a good speech, the students need to have fluency in the language. Moreover, vocabulary knowledge and pronunciation are components of language in speaking skill. Therefore, this study is conducted some under graduate students from Universities of Computer Studies, 2018-2019 Academic Year. There were one hundred (100) randomly selected the students. To achieve this aim, the needs survey, is done through questionnaires for the students and teachers.

Data preprocessing needs to make data cleaning which routines work to clean the data by filling in missing values, smoothing noisy data, identifying, removing outliers, and resolving inconsistencies. Data transformation operations are additional data preprocessing procedures that would contribute towards the success of the mining process. Data normalization transforms data values for different database attributes into a uniform set of units or into a uniform scale range. Classification is the process of finding the common properties among different entities and classifying them into classes. A decision tree is built from a set of training data having attribute values and a class name. In this paper, the original dataset is transformed using three different normalization methods such as min-max, z-score and decimal scaling normalization. The normalized dataset is evaluated using decision tree and analyses number of leaf nodes and accuracy. Comparisons between the three normalization methods were discussed in this paper.

The forecasting of rice production is considered as one of the real world complex problems due to its non-deterministic nature and uncertain behavior. Fuzzy time series relies on real time fuzzy values for the input and provides more accuracy in pattern and gives more accurate result. In this paper, the system presents the method to forecast of agricultural production based on fuzzy time series. The system applies forecasting technique using the fuzzy set theory and fuzzy time series. Trapezoidal membership function is used to calculate the fuzzy set and the centroid method is used for defuzzification module. To illustrate the forecasting process, the time series data of rice production in Bago Division is collected and forecast for the next year rice production in Bago Division.

As Ozone layer depletes in the stratosphere, the harmful ultraviolet rays are entering to the Earth’s atmosphere. As the UV rays are increasing, the dangers of them are greater. So, we need to take care of ourselves much better. The system “Arduino UV Care” acts as a measurement tool and sends the information to the users. This system is designed with the use of the Arduino, the ultraviolet sensor (UVM30A), Nokia 5110 LCD is used to display the UV index from the sun being experienced in a particular place and at a particular time and GSM Sim 900 A Module for sending and receiving messages. The purpose of the system is to prevent people from the dangers caused by high UV index rays as it could lead to things like sunburn, skin cancer etc. It is also measured to guide people, so they can take adequate protective measures, like the use of sunscreens, sunglasses, hats etc. on a day out.

This paper describes ultrasonic blind walking stick with the use of Arduino. Smart cane is an innovative stick designed for visually impaired person for improved navigation. It combines with two devices by wireless module (NRF24L01). It is smart cane and watch. Smart cane is using an Ultrasonic sensor with an Arduino for detecting the obstacles. This cane is integrated with ultrasonic sensor along with obstacle sensing. The cane can automatically detect an obstacle and give the user feedback response by vibrating the walking stick. If the user forgets the stick away from the watch he wore in limited distance, the stick not only will give a warning sound but also will send message to connected phone number with cane. The connected one can know the location of the blind via GPS in the watch. Visually impaired person can also know the location of the stick by pressing the button on the watch.

Fingerprints were one of the first forms of
biometric authentication to be used for law
enforcement and civilian applications. Contrary to
popular belief, despite decades of research in
fingerprints, reliable fingerprint recognition is an
open problem. Extracting features out of poor
quality prints is the most challenging problem
faced in this area. So it is essential to enhance the
prints prior to feature extraction. This paper
proposes an approach for fingerprint image
enhancement based on the Gabor Filter. Ridge
orientation, one of the important parameters of the
filter, is estimated using the Sobel operator. But,
the estimation of ridge frequency, which is another
important parameter, is eliminated by giving a
fixed value that leads to a fast enhancement
process.

Two applications of great importance in the area of
image processing are noise filtering and image
enhancement. The aim of noise filtering is to
eliminate noise and effects on the original image,
while corrupting the image as little as possible.
Poisson, Gaussian and Salt and Pepper noises exist
in many practical applications and can be
generated by various sources, including the number
of man made phenomena, a noisy sensor. This
system provides these three methods of noise. Use
can test these noises and then implemented the two
filtering methods of medium and bilateral filters.
The medium filtering and bilateral filtering are in
order to perform the noise filtering as well as to
achieve the enhancement of the image.

Some digital images need a large amount of memory space, so that precious memory in computer is waste. Image compression is an essential technique for storage and network transmission. This paper proposes implementation of image compression using Haar wavelet transform. The input image is acquired by some digital imaging device such as a digital camera, a scanner or imaging sensor. If the input image is color image (RGB image), it is need to change as grayscale image. The resultant image is transformed by using Haar wavelet transform method. Select non-negative threshold value (Є). The resultant transformed image is compared with non-negative threshold value (Є). When the values of transformed image are less than threshold value (Є), it is need to replace zero in the value of transformed image.

Among the developed technologies in day-to-day life, the vehicle tracking system is widely used to identify the vehicle location. In this paper, a tracking system is described use of GPS and GSM technologies to track the location of the vehicle. This tracking system takes the latitude and longitude data from GPS and send it through GSM module to desired mobile phone using mobile communication. When there is the vehicle stolen case, the user makes a phone call to the GSM module implemented with the microcontroller. It then sends this place information with the google map link to the user mobile phone. The microcontroller Arduino Uno is used as the main part of the system that gets the location of the vehicle from GPS. This system is implemented for the purpose of locating the stolen vehicle and supporting the taxi agency to detect the location of its vehicles and can be useful for other purposes.

This paper proposes the design and implementation of a microcontroller-based single-based automatic voltage regulator(AVR).

The main goal of this project is to develop and implement a
prototype of two-axis solar tracking system based on a PIC
microcontroller. The parabolic reflector or parabolic dish is
constructed around two feed diameter to capture the sun’s energy.
The focus of the parabolic reflector is theoretically calculated
down to an infinitesimally small point to get extremely high
temperature. This two axis auto-tracking system has also been
constructed using PIC 16F84A microcontroller. The assembly
programming language is used to interface the PIC with two-axis
solar tracking system. The temperature at the focus of the
parabolic reflector is measured with temperature probes. This
auto-tracking system is controlled with two 12V, 6W DC gear
box motors. The five light sensors (LDR) are used to track the sun
and to start the operation (Day/Night operation). Time Delays are
used for stepping the motor and reaching the original position of
the reflector. The two-axis solar tracking system is constructed
with both hardware and software implementations. The designs of
the gear and the parabolic reflector are carefully considered and
precisely calculated.

Nowadays, Information Technology is rapidly improving to retrieve and integrate information from multiple data source especially on the Internet. To facilitate the information retrieval system to be more efficient and effective, the system implements information retrieval which the documents related with the music information. Information Retrieval (IR) addresses the problem of retrieving specific information from a collection of documents. The Information Retrieval System has the task of retrieving text documents that answer a user query. In this system, users run queries for retrieving data about composer name, artist name, song title and album title. The system imposes music information retrieval system by applying vector space model and cosine similarity measures. By using this system, the user can instantly retrieve and get the information of music information.

: Digital identity plays the main role in the digital world. Thus, many researchers emphasize the determination of the accurate identity of users. This paper proposes the blockchain-based digital identity management system in Myanmar, to decrease the use of national identity document. The occurrence of duplicated identity numbers in Myanmar is the main issue, and another problem is how to recover the lost national identity card. Trusted humans and organizations that have the responsibility for the entire activity need secure data management. Blockchain technology built on the internet could use current identity data in a peer-to-peer, interoperable way to eliminate third parties. Using the benefit of blockchain, the unique and persistent identity number of the citizen will be stored in the blockchain network. The user could store encrypted private, personal, and sensitive data, as well as share verifications about the information on the blockchain.

Functionality on mobile device is ever richer in daily life. Mobile devices have limited resources like battery life, storage and processor, etc. Nowadays, Mobile Cloud Computing (MCC) bridges the gap be-tween the limited capabilities of mobile devices and the increasing user demand of mobile applications by of-floading the computational workloads from local de-vices to the remote cloud. Deciding to offload some computing tasks or not is a way to solve the limitations of battery life and computing capability of mobile de-vices. Application offloading is energy efficient only under various conditions for determining where/which code should be executed. This paper presents a Com-putational Offloading Decision Algorithm (CODA) , to save the battery life of mobile devices, taking into ac-count the CPU load, state of charge, network band-width and transmission data size. The system can take decision which method should be offloaded or not based on different context of the mobile device to ob-tain minimum processing cost. Numerical study is carried out to evaluate the performance of system. Exper-imental result will demonstrate that the proposed algo-rithm can significantly reduce energy consumption of mobile device as well as execution time of application.

Educational prospects for the regions
which are the outside of the main capital in Myanmar are
reluctant to reach to the goal. The educational,
socioeconomic developments of the local people in the Shan
state has been encountered as a lot of challenges in everyday
life and the vast areas are beyond the opportunities.
According to 2014 official census statistics, the percentage of
illiterate in the urban area is only 15% of the population of
the Shan state whereas 42.1% are illiterate in the rural area.
It is only about 19.75% of primary school students in Shan
State reach high school. And also 3.43% of the total
population of Shan state who completed the highest level of
education such as College and University. This is our main
contribution of the research for the ethnic students weak in
Myanmar language and English language consequently they
have rare chances to connect to the science and technology.
As lends a hand of ICT, this paper describes the preliminary
approach for the creation of the machine readable
dictionary, can provide the meaning of Myanmar, English
and Pa-O language words vice versa. In this paper we
proposed dictionary building design process model and the
framework for the development of the multilingual
dictionary that used the traditional building method, such as
theory driven process. As Pa-O language is such a kind of an
under-resourced language, the data resources are rarely
hard to find. Hitherto, there is no officially printed dictionary
for Pa-O language to Myanmar language and also English
language as well. As a preliminary approach we performed
the data collection, the data identification stairs of the
proposed design process model in this paper. We collected
26174 Pa-O words with related meaning of Myanmar words
and analyzed these words, we discovered the four inference
observations presented in this paper. We noticed that Pa-O
language have 33 consonants same as Myanmar language
whereas the phonetics, some vowels, medial are dissimilar.
The Pa-O language words started with the consonant of [A,
အ] are observed as the most widely used word about 4782
words and the second most are started with the consonant of
[Ta, တ] about 2612 words. Our purpose is being the
University of Computer Studies (Taunggyi) in the Shan state,
we craft with the support of Information technology
innovation to this region. It enforces not only widen the ICT
sectors but also for arising homogeneous developing and the
enormous prosperity of the economic, education and social
sectors in all areas of mountain and land in Myanmar.

Functionality on mobile devices are ever richer
in daily life. Mobile devices have limited resources
like battery life, storage and processor, etc. Offloading
some computing tasks from mobile devices to the
cloud is a way to solve the limitations of battery life
and computing capability of mobile devices. Application
offloading is energy efficient only under various
conditions. This paper proposes an Enhanced Computation
Offloading Algorithm, to extend the life time
of mobile devices, partition the job as a graph and
taking into account the CPU load, state of Charge,
wireless network bandwidth, transmission data size.
Based on the inputs, the system decide whether to
offload the application to the cloud or not. Offloading
is an effective method for extending the life time
of mobile devices by executing some components of
applications remotely.(E.g. processing applications
on mobile cloud). Result will demonstrate that the
algorithm can significantly reduce energy consumption
of mobile device as well as execution time of
application.

Cloud computing is a scalable distributed computing environment in which a large set of virtualized computing resources, different infrastructures, various development platforms and useful software are delivered as a service to customers as a pay-as-you-go manner usually over the Internet. In cloud computing, virtual machines offer unique advantages to the computing community, such as Quality of Service (QoS) guarantee, performance isolation, easy resource management, and the on-demand deployment of computing environments. Virtual machines need to be schedule on the cloud for maximize utilization, do the job faster and consume less energy. This system presents the VM scheduling algorithms using backfilling and gang scheduling approaches to maximize the VM resource utilization.

Cloud computing is deployed a large
set of virtualized computing resources in
different infrastructures and various
development platforms. One of the key
challenges in cloud computing system is virtual
resources and virtual machines (VMs) are
rapidly provision in order to meet the cloud
user’s requirement. To address this challenge,
this system contributes two level scheduling
systems: (i) virtual resource allocation and
scheduling on private cloud infrastructure and
(ii) real time scheduling that is invoked for
multimedia applications running on virtual
machines. First is resource level scheduling and
second is application level scheduling. This
system analyzes first level scheduling steps by
applying an analytical performance model using
Stochastic Markov chain. Moreover, a real time
scheduling algorithm is presented for
application level to analyze real time
multimedia applications running on
virtualized servers. According to performance
evaluation, this system describes the detail
analysis of virtual resources and allocation steps
based on the criteria such as user request
completion probability, mean response time.
Then, this system also shows the analysis results
for real time applications running on virtualized
servers. This scheduling algorithm contributes
to reduce the rate and ratio of missing deadline.
As a testbed infrastructure, this system
evaluates and analyzes an academic-oriented
private cloud system which is implemented
using Eucalyptus open source system.

Cloud computing is a scalable distributed
computing environment in which a large set of
virtualized computing resources, different
infrastructures, various development platforms
and useful software are delivered as a service to
customers as a pay‐as‐you-go manner usually
over the Internet. In cloud computing, virtual
machines (VMs) are used as a computing
resource. Parallel jobs of user request in cloud
need to allocate these resources. Therefore,
parallel jobs require a mechanism to scheduling
the executions order as well as resource
allocation. In this paper, the proposed algorithm
schedules two phases. First phase is priority
based job scheduling and second is resource
allocation phase. The proposed algorithm aims
to real time jobs to meet their deadline and
effective and efficient resource allocation. As a
cloud Testbed, this system implements an
Eucalyptus cloud infrastructure along with Xen
virtualization technology as VM Monitor
backend.

Cloud computing is deployed in a large set of virtualized computing resource in different infrastructures and various development platforms. Virtualization technology plays a vital role in cloud computing system to provide virtual resource and Virtual Machines (VMs) which are provision in order to meet the requirement of the cloud user. To analyse the provision of user request, we study resource allocation and scheduling steps on Eucalyptus private cloud architecture by applying an analytical performance model using Markov chain. This model intends to analyze the performance of the system for Infrastructure resource of private cloud. Moreover, this model also analyzes the effects of variations in workload such as user request arrival rates, service time and system capacity (number of NCs in cloud system) on IaaS cloud service by applying the proposed model. According to analytical performance evaluation, the effective mean response time of the system and the number of user servicing through the system (throughput) are measured to improve the performance of IaaS services in the system.

Cloud computing is deployed a large set of
virtualized computing resources in different
infrastructures and various development platforms.
One of the significant issues in cloud computing
system is the scheduling of virtual resources and
virtual machines (VMs). To address this issue, this
paper proposed an efficient approach for virtual
machines scheduling in VM management also
called EVMSA (Efficient Virtual Machines
Scheduling Algorithm) that provides the effective
and efficient resource allocation. The proposed
approach is going to test the evaluation on
Eucalyptus open source private cloud architecture.
The contributions of this paper are: the proposed
scheduling algorithm will provide to improve the
resource utilization such as CPU, Memory, and
Disk. Then it will also minimize the turnaround
time of VMs.

Many tourists and local people are visiting everywhere in Myanmar. The most interesting places are emphasized for touring to them especially Southern Shan State. The city guide system is proposed to apply Spherical law, shortest path algorithm and GPS services for guiding to city. This system is divided by three
parts. Firstly, Spherical Law of Cosines is calculated the distances plus earth radius in order to provide the exact distance between current location and destination. Secondly, Dijkstra’s algorithm is used the current position point and destination point as input which are retrieved by GPS system to calculate the possible paths between current and destination point. Lastly, this system can display the detailed information like shortest route to help users in navigating places easily and faster. Then, this system is efficient and effective for city directory which help users abilities to locate, navigate and get information about the places such as hotels, restaurants, petrol stations, hospitals, markets, pagodas and etc.

Face recognition (FR) is a very challenging problem
and up to date. Due to dissimilarity in human pose,
facial expressions, hairstyle, image style and lighting condition,
the problem is very difficult. This paper proposes the
method of the face recognition focus on still image in frontal
view. The facial feature is extracted from the face in
frontal view or near frontal view. The silent feature region
also extract from the two eyes and the mouth. Singular
value decomposition approach is applied for detecting the
matching point pairs. Some experiments have been done to
confirm the effectiveness of our system.

Nowadays, the world is in knowledge age. Variety of new technologies and supports are being evolved for education. One of the most well known systems is recommendation system. Recommender system uses the opinions of a community of users to help individuals in that community more effectively. In this paper, the system propose a multi agent recommendation system for education service mainly on information retrieving and recommending course (subject) lists. This recommendation system is based on item-based collaborative filtering method for recommending related subjects on user’s interested subjects and multidimensional association rule method for providing knowledge about the relationship between the users and degree courses. These techniques made as a form of agent and perform separately in its corresponding sections. The system provides agent based information extraction by user’s preferences. This system provides information and recommendation to users to help them to decide which are interested or to be chosen.

Face recognition is an active research area that
has many real-life applications such as bank/
store security, database face recognition, access
control and so on. Each application has each own
criteria. In terms of background, faces and
application, the numerous presented systems show
different capabilities and strengths in aspects of
recognition performance. In this paper, we
proposed Recognition of real time face images in
frontal view using eigenface. Face recognition is
performed by projecting a new face image into
face space and then comparing its position in the
face space with those of known face. After that we
find the best match in a face database. The
eigenface approach does provide a practical
solution that is well fitted to the task of face
recognition.

In this paper we present a matching process of handwriting between exhibits and specimen of Myanmar handwriting documents. This is also a method to identify the writer of Myanmar handwriting documents. Many methods have been reported for handwriting-based writer identification. Most such techniques assume that the written text is fixed. There are many methods for writer identification or signature verification. They are the kinds of content dependent identification methods. But our method is a content independent method. In our method, we take the handwriting as an image containing some special texture, and writer identification is regarded as texture identification. We apply the well-established multi-channel Gabor filtering technique to extract features and a Weighted Euclidean Distance classifier to fulfill identification task. The result of this paper will confirm whether handwriting of specimen is the true writer of the exhibit.

Face recognition (FR) is a very challenging prob-lem and up to date, there is no technique that provides a robust solution to all situations and applications that face recognition may encounter. This paper proposes a face recognition system which considers a grey scale image from frontal side. Face recognition and facial expression recognition are carried out using maximum likelihood. Facial appearance matching is improved by facial expression matching. We present semiautomatic approach to face recognition from facial expressions. The goal of the work presented here in, is to develop face recognition techniques by using statistical analy-sis for preprocessing step and a probabilistic model for classification step. In our current implementation, the face is divided into 16 facial feature points by using bounding box.

Different chemical structures of alcohols are chemical founds in cosmetic products such as lotions, shampoos, and conditioners. The correct usage of alcohol in cosmetic production is the important factor for product quality. If experts know the correct name and compound of the alcohol in short time, they can easily applied this alcohol in good quality cosmetic production. Machine learning algorithms are less costly way to classify alcohols according their attributes obtained by QCM sensors so that an informed decision about cosmetic products can be made automatically. In this paper, Support Vector Machine (SVM) learning algorithm is performed to classify type of alcohols based on their features obtained from QCM sensor for cosmetic production development. The QCM sensor dataset for alcohol classification is used in this paper and the five labels of alcohols are 1-octanol alcohols, 1-propanol alcohols, 2-butanol alcohols, 2-propanol alcohols and 1-isobutanol alcohols.

Cross-border education can be termed as the movement of student, research, academic exchange programs and institutions across national borders with the provision of international education programs. In this case, the secure and speedy education credit transfer and exchange of student records are the important factors for the cross-border education system. Thus, this paper proposes a system called “Blockchain-based Cross-Border Educational Transaction System” for the higher education industry.
Blockchain technology is one of the megatrends for recent years. It is potentially a
revolutionary means of secure and transparent data sharing and processing in a wide variety of sectors including the education sector. The important concept of blockchain technology is a combination of secured distributed ledger, cryptocurrency and smart contract system. That concept is very appropriate at creating trusted and secured information processing for large and heterogeneous sets. Therefore, the blockchain-based cross-border educational transaction system
enables the education industry to transfer and exchange the secure education credit and academic records for students, and other stakeholders such as government organizations, companies, and other institutions. Besides, maintaining educational records in the blockchain can protect from an unexpected natural disaster.

This paper will propose and discuss the system framework that consists of three layers. The first layer would be an interface layer for application development. The second layer provides smart contract service, core service of blockchain, for generating trusted education credit, grading and certificate transaction, as well as for secure agreements of exchange and collaborative educational processes. The last layer is a data storage layer including database methodologies and distributed computing methods.
The proposed system would overcome the barrier of traditional cross-border transaction system by allowing the globally secure, transparent, and reliable education transaction services and collaborative processes among universities in different regions.

This study aimed to reveal that the internship program is important in
employability of engineering graduates in Myanmar. In Myanmar, an internship program is the best approach to explore career opportunities in the technological industry but most of engineering students do not have opportunities to take internship program. In our neighboring countries such as Singapore, Malaysia, India, Vietnam, etc., information technology and computer studies are very much in demand among businesses; the internship is likely to be very good compensated. But in our country, Myanmar, we have many challenges for the successful implementation of internship program of engineering institutes as well as other kinds of universities. Moreover, the engineering institutes in Myanmar are reformed their curriculum that internship program is a part of the curriculum. So, the final
year engineering students cannot pass their final examination without finishing their
internship program successfully. In this study, the major challenges of internship program, especially in engineering institutes, are discussed as well as it proposed the feasible solution for the problem of getting rare opportunities to be the interns.
This study also analyzed the employability of engineering graduates of a public
university in computer studies and it show that employability is improved for the graduates who have chances of internship program. This study also accessed the performance of interns evaluated by the manager or supervisor of different companies where interns deployed. Qualitative type of research methodology is used to analyze primarily and required data are collected also qualitatively from the University of Computer Studies (Taunggyi), which is one the IT engineering institute in Myanmar. The results proved that the capabilities of IT engineering graduates are drastically improved after attending internship program and the employability of engineering graduates are in progress. This study also showed that the academic performance is moderately related with internship performance.

Outcome-Based Education (OBE) is a student-centric teaching and learning methodology in which the course delivery, assessments are planned to achieve stated objectives and outcomes.   Outcome-based education focuses on: (i) Student assessments are designed to measure the learners’ achievement of the learning outcomes (ii) Backward design of curriculum where courses and learning experiences are designed to help learners to achieve the learning outcomes (iii) Constructive alignment of learning outcomes, curriculum, teaching and learning methods, and student assessments. In OBE system, the student evaluation result is decided by educational stakeholders. Thus, the important problem of traditional OBE system can be absent to evaluate the student learning, abilities and states by educational stakeholders. As a next problem, society is unable to effectively evaluate the teachers and students as well as student evaluation results. Moreover, traditional document records can be destroyed in the case of natural disasters or wars.

            To fulfill those requirements, this paper designs a system, entitled “Outcome-based Education System using Blockchain Technology”. The important concept of Blockchain technology is a combination of secured distributed ledger, cryptocurrency and smart contract system. Blockchain is a reliable mechanism, and the development of blockchain brings significant benefits to education including providing a secure data processing platform, cost-saving, immutable and enhancing trust and transparency. Therefore, blockchain technology is applied to the traditional OBE system for higher education. In this case, this research focuses on the university curriculum and student credit system specialized in computer science and technology as a case study. The intended outcomes of this system would be (i) ensure the secure, reliable and robustness services for credit transfer and industry relationship (ii) support unique standardization for student data between all institutions and universities linkage.

Environmental impacts concern as the garment
industry expands in Myanmar. In this research, it is
focused on the survey of the Initial Environmental
Review (IER), Assessment of Environmental Impacts
and development of proposed mitigation measure for
reducing environmental impacts for Garment
industry, Hlaing Thar Yar Township of Yangon City.
The impacts of garment industry’s production process
were analyzed and assessed based on the data from
IER survey, criteria for the assessment of
environmental impact and the effects of significant
environmental impact. According to the results of IER
survey, even though the staffs and employees do not
have knowledge about environmental impacts, they
are able to reduce the pollution from their industry by
adopting solid waste management, energy
conservation, and water reducing process in their
industry. Finally, the proposed mitigation measure is
described in this research to reduce environmental
impacts in their industry.

The sample soybean was collected from Meiktila
Township, Mandalay Region, and Myanmar and carried
out on analysis. The main aim of this research work is
to determine the antioxidant activity, mineral and
protein contents of soybean. The mineral content in
soybean was measured by Energy Dispersive X-ray
Fluorescence (EDXRF) Spectrophotometer. The
antioxidant activity of ethanol extract of sample was
done by 1-1-Diphenyl-2-picryl hydrazyl (DPPH) radical
scavenging assay. The moisture and ash contents of
sample were done by AOAC method. The percentage of
moisture and ash content of sample were found to be
7.89 and 0.8. The extraction and isolation of protein by
using Trichloroacetic Acid (TCA) and acetate buffer
method. The protein percentage of soybean was 37.94.
This isolated protein was confirmed qualitative
determination test, which gives rise to Biurest test,
Millon’s test, Formaldehyde test, Xanthoproteic test and
Hopkin’s-Cole test respectively.

ဤစာတမ််းသည် မ ်းမ ်း (အင််းလ ာ်း) ၀တထုတ
မ ာ်း(၁)မှ စစ်သာ်းမယာ်း၊ ဘ၀ အမမှာင်၊ အ မ်ကလူကက ်း၊
ဟူမသာ ၀တထု(၃)ပ ဒ်တွင် မတွွေ့ရ သည ်ဘ၀အမတွ်း
အမမင်နှင ်ရသတ ို့က မလ လာ သ ်းသပ်တင်မပထာ်းမသာ
စာတမ််း မြစ်သည်။ ရသစာမပ အြွ ွေ့တစ်ခ မြစ်မသာ
၀တထုသည် ဒ ဌမလာက က သာ အမမခတည် မရ်းြွ ွေ့ မသာ
မ ကာင ် နှစ်သက်မ ွေ့အာရ က သာမက ဘ၀အသ အမမင်
ဆင်မခင်တ တရာ်း၊ မလာက န ယာမ တရာ်း တ ို့က လည််း
မပ်းစွမ််း န င်ပါသည်။ ထ မျှမက အသ ညဏ်က လည််းပ ၍
ထွန််းမမပာင်မစ ပါသည်။ ဤစာတမ််း တွင ် ၀တထုတ သမဘာ၊
ရသ စာမပ သမဘာ၊ မ ်းမ ်း(အင််းလ ာ်း)၏ ၀တထုတ မမပာစကာ်း
မ ာ်းမှ ရရှ ခ စာ်းရမသာ ဘ၀အမတွ်းအမမင်နှင ် ရသဟူ၍
အပ င််း(၃) ပ င််း ခွ ကာ တင်မပထာ်း ပါသည်။

ဤစာတမ််းသည် မမန်မာက ဗ ာမ ာ်းရရ်းဖွ ွဲ့ရာတွင် အလ ဉ််းသင ်
သလ ိုပန််းကရလ်းမ ာ်းအရ ကာင််း ထည ်သွင််းဖွ ွဲ့ဆို ရလ ရ ရသာ
စာဆ ိုတို၏ို့ က ဗ ာပရ ယာယ ် က ို တင်မပလ ိုမြင််း
မဖစ်ပါသည်။ က ဗ ာ ပရ ယာယ်တွင် ပရေဠ မဖင် ဖွ ွဲ့ဆ ိုမြင််းမ ်း
လည််းရ ရ ကာင််း သ ရ ရစလ ိုပါသည်။ အင််းဝရြတ်န င ်
ရတာင်သူရြတ် စာဆ ိုတ ိုို့၏ က ဗ ာမ ာ်းက ို အရလ လာြံအမဖစ်
သတ်မ တ်ပါ သည်။ ပန််းကရလ်းမ ာ်းက ို မည်သ ိုို့ မည်ပံို
ပရေဠ ဝ က်ဖွ ွဲ့ဆ ို ထာ်းရ ကာင််းက ို
ရလ လာတင်မပထာ်းပါသည်။ စာဆ ိုတိုို့ ၏
ဖန်တ ်းမှုအတတ်ပညာတစ်ရပ်မဖစ်ရသာ က ဗ ာ ပရ ယာယ်မဖင ်
ြ န ်ထ ို်းရလ လာတင်မပမည် မဖစ်ပါသည်။

This research intends to decide the fondness of
Intermediate Students in performing Personal Computer
and paper-based reading assignments, and to what
extent PC and paper-based reading impact on their
understanding rate, exactness and comprehension. The
test was carried on at the University of Computer
Studies, Pyay. The members were 82 students of
Computer Science and Computer Technology. Two
kinds of information were gathered in this research.
First, the Questionnaires for Online Reading
Comprehension were used to collect information about
the members’ perspectives on their PC and paper-based
reading exercises. Second, one test was conducted with
12 chipping subjects to comprehend their understanding
rate, exactness and comprehension in both PC and
paper-based reading tests. The consequences of the
research proposed that almost all students preferred
paper-based reading to PC reading. Moreover, the
study shows that reading speed on the PC was almost
12% quicker than paper-based reading for these
students.

This study is aimed to present the investigation and
classification of grammatical errors, to examine the error
sources and to do the evaluation of those errors made by
the students at the University of Computer Studies,
Panglong and Technological University, Taunggyi. This
study is done to survey what the most grammatical errors
the students made are in writings and how much the
analysis could provide the teachers to develop the
competence of students’ language in teaching. For the
study, 30 written samples of the students were collected
for the data and the errors were identified, categorized
and analyzed. Due to the result, most of the errors that the
students made are subject- verb agreement, incorrect use
of tenses, word order of sentence formation, limited
knowledge of vocabulary, preposition. The finding
highlights that the teachers need to give adequate
feedbacks on students’ errors in writing so that the
students can avoid the errors by learning their mistakes.

This research paper focuses on the use of classroom
activities to reinforce EFL students’ four skills in the
student-centered course. Students play the center role in
the student-centered learning process. They actively
engage in classwork activities and collaborate with
their peers under the guidance of the teachers in the
student-centered classrooms. Moreover, students are
active participants for their educational goal, while
teachers are educators and they acts as facilitators or
activators in the student-centered learning. Eventually,
a student-centered classroom is a place for students to
create a positive environment for learning, where they
can choose and do classroom activities effectively.
Thus, student-centered learning environment is one of
the most effective ways to help students develop their
four skills independently. The competency in four skills
is also vital for EFL (English as a Foreign Language)
students and the integration of the four skills in their
learning process helps them to be active learners.
Therefore, developing students’ competency in four
skills means reinforcing their communicative
competence so that they can communicate in real life
situations.

Since English is used as a medium of teaching,
reading skill is very important for engineering students
to study their respective subjects. However, most of the
first-year engineering students got low test scores on
reading and they are suffered from reading for their
failure in the achievement of reading comprehension.
For these reasons, it is necessary to find out the
students’ needs and wants about the reading skill and to
support them. As to the research methodology, a
questionnaire including nine questions is responded by
fifty students of first-year engineering students. The
results were analysed through descriptive statistic and
the findings show that most of the students need more
help from their teacher and their teacher needs to
support effective strategies to improve their reading
skills. So, the aim of this research paper is to improve
first-year engineering students’ reading skills through
effective strategies.

The study is concerned with students’ perceptions of
their experiences in making oral presentation test at the
university of computer studies, Thaton (UCST). The
research aims to examine students’ attitudes towards
making oral presentations by identifying benefits
obtained and the challenges faced regarding the three
perspectives: personal traits, deficiency in presentation
skills, and apprehension of peers and teacher. The
participants of the study were 85 fifth-year students
from three sections and six teachers. A four-point Likert
Scale questionnaire composed with 35 items and semistructured
interview were carried out to collect the dada
for analysis. The results obtained were described in
percentages. The findings showed that students
experienced a medium level of difficulties in conducting
oral presentation. Analysis of qualitative dada indicated
students’ challenges such as stress and lack of practice
in oral presentation skills. This study recommends that
students need more exposure and experience of
presentation skills in university.

This purpose of this study is to show the impact of
cooperative activities on raising learners’ interest in
interpreting language. To lead sophisticated design
and method, grouping strategies including supported
tools are integrated into cooperative activities. Data
are gathered from 100 numbers of students at UTYCC
to show students’ comprehension laguage level,
interest in interpreting skill and other lanugage areas
providing reading ability. In research design,
questionnaries are handed out to express their selfconcepts
and achievement tests including pre-test and
post-test have been done to know their enhancement in
interpreting skill. In this study, mean values and
standard deviation indicate that cooperative activities
have large effect on growing the interest’s rate of
learners and developing interpreting abiltiy.

This paper intends to promote learners’ acquisition
skill for the speaking a foreign language in the
classroom by applying effective ways. Nowadays, many
effective strategies and activities are used to teach and
learn four skills in the language classroom. Teachers
are always observing to fill students’ needs fruitfully
and motivate their students’ interest. As English is the
second language and it’s a foreign language, the role of
English (International Language) is the essential need
to communicate with each other with the development of
Information and Communication Technology.
Therefore, students also need to improve their language
acquisition skill in speaking. This research paper
suggests how to improve students’ English language
acquisition skill in speaking.

The objective of doing this research paper is to
investigate the actual situation of the students’ needs,
wants and lacks in writing skill and to develop their
writing ability by means of effective teaching
approaches. As writing is one of the most valuable
learning tool to give an idea or message, students need
to focus on and master in this skill. Although fourth year
students have been learning English since their very
beginning of learning, they still face problems in writing.
Most of the students have no confidence and they are
afraid of making mistakes in writing activities. In this
research paper, a survey on selected students from fourth
year is carried out and questionnaires are given to find
out the current situation of the students ’weakness and
interest. Based on the findings of needs analysis, effective
teaching approaches for improvement of students’
writing skill are presented in this research. It is hoped
that this research paper will give lots of support for
development of students’ writing skill through effective
approaches which are interesting and useful for the
students’ writing skill and help learners to become
competent writers.

This paper focuses on the study of the
effectiveness of using task-based language
teaching approach in second language teaching
and learning. In the research, the teacher teaches
vocabulary, reading and speaking skills by using
task-based language teaching method proposed by
Ellis (2003) and examines this method is effective
or not in teaching and learning English. This
paper aims to find out the effectiveness of taskbased
language teaching approach in teaching
vocabulary, reading and speaking in English. The
objectives of the paper are to present the approach
of task-based language teaching and to examine
whether this method can help or not to develop
students’ ability in learning English and to present
students’ development in three stages. This present
research is used Pre-task, During-task and Posttask
methods by Ellis (2003). It was observed that
students’ reading skill, speaking skill, thinking skill
and presentation skill have been improved in
learning English by using task-based teaching
method.

The main purpose of this paper is to improve
the English language learning of engineering students
by classroom techniques. In the global context, English
is one of the most dominating languages of the world. It
impacts on every field of work. In the field of
engineering education also, not only almost all
engineering subjects are being taught in English but
also many works of research and studies are recorded
in English. In Myanmar country, because English is not
mother tongue, most engineering students are reluctant
to learning English. Although it is known that mastering
in English is essential not only for education, but also
for future careers, students have been bored in learning
English until now. To develop the students’ learning
skills, the role of teacher is very essential. Teachers can
inspire students to learn well, and motivate them to keep
their academic goals on track. There are a variety of
different teachings styles. Teacher who uses teaching
techniques effectively may well encourage the students
to be more interested in learning.

The main concern of this study is to examine the
theoretical knowledge of Third year and Fourth year
English specialization students of Mandalay University
of Foreign Languages by exploring the translation
methods commonly employed in “The Choice”, the
translation of the Myanmar Short Story, “Yway-Chal-
Paing-Khwint” of SoeHlaing Tint translated by Zaw
Tun. The research methods used in this study are both
qualitative and quantitative. The data for this study
were collected from 48 Third year English
specialization students and 61 Fourth year English
specialization students from Mandalay University of
Foreign Languages. The translation methods were
investigated based on the theory of Newmark (1988).
The research found that both Third year and Fourth
year English specialization students were weak in the
knowledge of semantic translation method and
idiomatic translation methods because their study and
practice of translation methods do not go beyond the
classroom. They need to make more practice in the outof-
the- classroom situations. The translator used 5 out
of 8 translation methods in the translation of the short
story. The most frequently used translation method is
semantic translation. It was found that using semantic
translation method has a positive effect on the
translation text because it can give the readers the
aesthetic pleasure with the atmosphere of the original
taste of the story.

As online learning development goes digital, it
becomes imperative for organizations and educational
institutes to digitalize their learning and development
content. At the present time, many universities face
educational problems because the covid-19 virus spread
around the world. Thus, universities and education
centers close until certain period. Most of the
universities try to overcome this obstacle with the help
of technology such as using various Learning
Management System (LMS). Success with online
learning requires careful thought and preparation into
how that learning will take place. On the other hand,
online education works best for students who have
strong self-discipline, motivation and time management
skills. The main objective of this paper is to study some
issues and motivation of students on online learning
from Mon state. To know positive attitudes of the
students, questionnaire was used for collecting data.
The key findings of the present study shows 80% of
students have positive attitudes on online learning. But
some students pointed out their difficulties for online
learning from their home place.

Web-based examination system (WES) is the
examination system where examinations are held online
via Internet. This paper presents the online examination
system for testing English grammar. It is intended for
testing English Grammar as Multiple Choice Questions
online in schools and universities. The system is
implemented for three English grammar levels, basic,
intermediate and advance levels. The role of
administrator is adding new question into the system. To
add new questions, the question with three options
including one right answer must be added. User needs to
register to the system and login with by using registered
user email and password to take exam and view result.
On choosing a level test, user have to answer the
questions within the time limit. When the user finished
answering all questions, he

Credit Unit System has been applied for two
academic years at University of Computer Studies
(Taunggyi). This paper would like to express the
relationship between credit unit and assessment in
credit unit system, has been studied for two years.
Students’ assessment is very important and it may
definitely affect individual’s overall performance and
academic achievements of the students. Students’ credit
unit can also affect performance level of students. In
this regard, the data was collected from the fourth
year,third year and second year students in University
of Computer Studies (Taunggyi) to analyze that how
effectively they are performing their assessment for
achieving their academic. Principal of this paper is to
check the qualification of credit unit based on the
assessment mark. Credit unit is totally equal to
assessment’s mark. This paper was developed by
analysis todatabase subject, software engineering
subjectin University of Computer Studies (Taunggyi)
that has been using credit system for two years
successfully.

The relation of student involvement and subject
achievement in teaching and learning of University of
Computer Studies (Taungoo) was studied in this paper.
The study analyzed the teaching styles of the subject and
the student assessment data of 85 students attending fifth
year between 2019-2020 academic years. The research
data were evaluated and monitored by using two
indicators: student involvement indicators and subject
achievement indicators. Qualitative data was
thematically coded and quantized then entered in
statistical package for social science (SPSS) alongside
the quantitative data then was analyzed descriptively and
inferentially and presented using statistical tables. Result
of the research indicates that student involvement has a
positive impact to academic achievement.

Tha-Na-Kha flower was investigated by using the
Energy Dispersive X-ray Fluorescence (EDXRF)
detection technique with Fundamental Parameter (FP
balance) method, the Shimadzu EDX-7000 spectrometer
and analysis software. Potassium element was the largest
element contained in Tha-Na-Kha flower sample. Other
elements such as calcium, phosphorus, sulphur, iron,
rubidium, copper, zinc, strontium, manganese, titanium
and nickel were also observed as trace elements. Little
amount of molybdenum was found in Tha-Na-Kha
flower. The advantages of elements were presented.

Physico-chemical parameters have been analyzed in
water sample collected every four months for one year
from Naungtone Lake. The objective of this work was to
analyze annual variations of Naungtone Lake water
quality. The results revealed that some of the parameters
such as total dissolved solid (TDS), chloride, chemical
oxygen demand (COD), biochemical oxygen demand
(BOD) and sulphate were not agreement with
recommended by the European legislation. Therefore,
water from Naungtone Lake is not adequate for human
consumptions or industrial purposes and needs to be
purified to apply for drinking water.

In this study, the experimental works have been
done in three portions, physical examination,
determination of elemental constituents and toxic metal
analysis. Water quality assessment has been made to
assess the quality of water sample obtained from 4th
Street of Southern Pyi-Thar-Yar near Myanmar
Aerospace Engineering University (MAEU), Meiktila
Township in Mandalay Region. Most of the methods
used in this investigation are from the World Health
Organization guidelines limits for drinking water. The
water sample has been tested in Water and Sanitation
Department in Mandalay City Development Committee.
Upon the whole considering the various parameters
studied, the observed values indicate that the quality of
tube-well water from Southern Pyi-Thar-Yar in Meiktila
Township, Mandalay Region is satisfactory.

Bioplastics are an increasingly well known starch
based plastics. Firstly, starch was extracted from corn
by cold extraction method. The qualitative test of
extracted corn starch was performed by various
methods such as iodine test, alcohol test, Fehling’s test
and Molisch’s test. Bioplastic film was successfully
prepared by combination of two biopolymer, corn
starch and potato peel in the presence of glycerol. The
water soluble capacity of prepared bioplastic film was
also measured. The prepared bioplastic film was easily
soluble in water. Biodegradability of prepare bioplastic
film was determined by soil burial test. The
physicomechanical properties such as thickness, tensile
strength, % elongation at break and tear strength of
prepared bioplastic were investigated. The prepared
bioplastic film was characterized by using Fourier
Transform Infrared Spectroscopy (FT IR) and
Thermogrivimetry-Differential Thermal Analysis (TGDTA).
The morphology of prepare bioplastic film was
characterized by Scanning Electron Microscope (SEM)
technique. The decomposition temperatures of
bioplastic film obtained from Thermogravimetric
analysis was found to be 471.10 °C.

Plastic can cause damage to the environment.
Plastics are not easily broken down by micro-organisms
and therefore most are not biodegradable. This leads to
waste disposal problems. When plastics burn, they can
produce toxic gases such as carbon monoxide, hydrogen
cyanide and hydrogen chloride. In this research work,
“Top Choice” carrying plastic bag sample (S1) and “D”
carrying plastic bag sample (S2) were collected from
Mingalar market, Chan Aye Thar Zan Township,
Mandalay. The solubility of plastic samples was
investigated. For the solubility tests, the organic
solvent such as n-hexane, toluene, benzene, ethyl
acetate, dichloromethane, ethanol and methanol were
chosen. Starch was extracted from wheat flours. The
blend films with starch and polyvinyl alcohol were
prepared by casting method. Glycerol was used as
plasticizer. The different amount of solvent (water),
starch and glycerol were investigated to get the
optimum condition. Biodegradability of the prepared
film was tested by water absorption test, moisture
absorption test and soil burial test.

In the present study, the root of Millettia extensa
(Benth.) Benth. ex Baker, Myanmar name Wunu, was
selected to evaluate the total phenolic content and its
radical scavenging potential. Firstly, the phytochemical
constituents of this sample were carried out. Moreover,
total phenolic content of the root of analyzed sample
was evaluated by the Folin-Ciocalteau reagent using
UV Visible spectrophotometer (UV- 1800,
SHIMADZU, UV spectrophotometer) at 765 nm. The
total phenolic content of this selected sample was
determined as 40.53  0.058 mg gallic acid equivalent
(GAE) per g dry weight. Furthermore, the free radical
scavenging potential of MeOH extract of root of M.
extensa (Benth.) was analyzed by 2,2-diphenyl-1-
picrylhydrazyl Assay method. IC50 value of MeOH
extract of the root of analyzed sample was 25.75
μg/mL. The root of M. extensa (Benth.) was found to be
considerable radical scavenging potential which is
comparable to standard ascorbic acid (4.92 μg/mL).

The aim of this research is to study the chemical
constituents present in the roots of Derris elliptica
Benth. called Say-min in Myanmar, which is known as
natural insecticidal plant. To investigate the chemical
constituents of the roots, phytochemical screening tests
were performed and it showed that the presence of
alkaloids, free sugar, saponins, glycoside, terpenoid,
phenolic groups and flavonoid compounds. Then
antibacterial activities of Say-min roots were examined
with the extracts of five solvent systems on three tested
microorganisms, Bacillus subtilis, Staphylococcus
aureus and Pseudomonas aeruginosa by Agar Well
Diffusion methods. Column chromatographic
separation was performed to isolate the chemical
constituents by Thin Layer Chromatographic (TLC) and
Fourier Transform Infrared (FT IR) spectroscopic
techniques and a pure bioactive isolated compound may
be identified as elliptinol. This research work can
determine the phytochemical constituents of the roots of
Derris elliptica Benth. and isolate elliptinol, a
derivative compound of rotenoids group which is
effective as insecticidal properties.

In Myanmar, sand soap “Thae-Suppya” is found in
many places, especially in the dry zone. It is used for
manufacture of caustic soda and suppya. This sample
was collected from Myinthi village in Tada U Township,
Mandalay Region. Physical properties such as moisture,
pH, bulk density and texture were examined. In addition,
the major components such as chloride, sulphate,
carbonate, bicarbonate, sodium, potassium, organic
matter, exchangeable calcium and magnesium in this
sample were also determined by using recommended
standard methods. The water soluble carbonate salts
were prepared from sand soap. The carbonate salt was
converted to caustic soda crystal by using commercial
quick lime. Finally, Natural soap was produced by a
basic hydrolysis reaction on a mixture of palm oil.

The main aim of this research paper is to
investigate the antimicrobial activities of leaves of
Panax pseudoginseng Wall. Panax pseudoginseng
Wall. is not only a well-known medicinal herb but also
a very good sex tonic. The qualitative phytochemical
screening of leaves of Panax pseudoginseng Wall. was
performed by using standard methods. The mineral
elements of leaves of Panax pseudoginseng Wall. were
analyzed by Energy Dispersive X-ray Fluorescence,
EDXRF Spectrometer. Seven minerals were found in
leaves of Panax pseudoginseng Wall. The antimicrobial
activities of the crude extract of leaves of Panax
pseudoginseng Wall. in various solvent systems were
tested by agar well diffusion method on six selected
organisms, such as Bacillus subtilis, Staphylococcus
aureus, Pseudomonas aeruginosa, Bacillus pumilus,
Candida albicans and Escherichia coli. The ethanol
crude extract of these leaves showed high activities on
all tested organisms except n-hexane and ethyl acetate
crude extracts. These antimicrobial activities of leaves
of Panax pseudoginseng Wall. can be a significance of
antimicrobial medicines to treat infection.

In this research work, the three soil samples were
collected from May-ni-gone Village, Loikaw Township.
The important physicochemical properties such as
moisture, pH, texture, organic matter content, available
nitrogen, phosphorus, potassium, exchangeable calcium
and magnesium of the collected soil samples were
determined. The moisture and texture of the soil
samples were determined by oven drying method and
pipette method respectively. The pH value was
measured by pH meter. Available nitrogen was
determined by permanganate method and Truog’s
method was used for available phosphorus. Flame
photometer was used for the determination of available
potassium. Moreover, the exchangeable calcium and
magnesium of the samples were examined by EDTA
titration method and Eriochrome Black T is used as
indicator.

In this research work, the peels of Citrus maxima
were selected for chemical analysis. Firstly, the
sample was tested by phytochemical screening which
gave rise to positive results for glycoside, phenolic
compound, polyphenol, lipophenol, flavonoid,
alkaloid and carbohydrate. After that, Pectin was
isolated from peels of citrus maxima by using
different concentrations of hydrochloric acid and
different refluxing times. Finally, the isolated
compound was characterized by FTIR (Fourier
Transform Infrared) spectroscopic method.

Currently population, urbanization and
modernization are still going to increase for problems
of sewage disposal and contamination of surface waters
like lakes. Natural water is getting contaminated due to
weathering of rocks, leaching of soils and mining
processing, etc. Water samples were collected from
Paleik Inn (lake), Sint-kaing Township, Mandalay
Region, in summer 2017, 2018 and 2019 for this
research work. Water quality of Paleik Inn (Lake) was
assessed by various parameters such as BOD, COD, pH,
total solid, total hardness, total alkalinity etc.
Furthermore, the heavy metals contents, especially Pb,
Cu and Cd were examined by Atomic Absorption
Spectroscopy (AAS) at Department of Chemical
Technology, Pyin Oo Lwin Township, Mandalay Region.
In addition, bacteriological examination of water
samples was carried out at Public Health Laboratory,
Mandalay.

The present study was conducted for quantitative
determination of flavonoids in spinach using
spectrophotometric method. Quercetin was used as the
standard for calibration. Phytochemical screening of
the selected sample revealed the presence of phenolic,
glycoside, reducing sugar, tannin, steroid, polyphenol,
terpene, saponin, flavonoid and lipophenol. Qualitative
tests for flavonoids such as ferric chloride test,
Shinoda’s test, lead-acetate test were also performed. In
addition, physicochemical parameters such as total ash
and water soluble ash content of the selected sample
were also determined. Furthermore, the elemental
analysis of the selected sample was carried out by
EDXRF (Energy Dispersive X-ray Fluorescence)
method.

Crotalaria pallida Aiton. (family –Fabaceae) is used
for treating urinary problems and fever. The
phytochemical constituents, the total phenolic and
mineral contents, antioxidant and antimicrobial activity
of the stem of Crotalaria pallida Aiton. were reported.
The phytochemical analysis by standard methods
showed the presence of all tested phytochemical
constituents in Crotalaria pallida. The mineral contents
by EDXRF displayed that potassium, calcium, iron,
silicon, sulfur, manganese, phosphorus, zinc, titanium,
copper, rubidium were detected in Crotalaria pallida.
The result of total phenolic content of Crotalaria pallida
using Folin Ciocalteu’s reagent was found to be 47.6 mg
GAE/g. The evaluation of antimicrobial potential by
agar well diffusion method revealed that the methanolic
extract of Crotalaria pallida showed high activity
against Staphylococcus aureus (25 mm), medium
sensitivity against Bacillus cereus (17 mm), Shigella
boydii (14 mm) and Salmonella typhii (19 mm). The
antioxidant activity by 2,2-diphenyl-1-picryl-hydrazyl
(DPPH) scavenging assay exhibited that Crotalaria
pallida extract showed moderate activity with EC50
62.77 g/ml.

The present research aimed to analyze the
nutritional composition, minerals and microbiological
results of soybean from Naga Self-Administration Zone
intended for human consumption, for selection purposes
regarding their nutritional properties. The standard
procedures were followed to analyze the nutritional
compositions of soybean seed powder. The minerals (
calcium, magnesium and potassium) contents were
determined by Atomic Absorption Spectrophotometer.
Microbiological properties were also determined. The
result showed that soybean contained highest amount of
protein 50.34% and lowest amount of ash 6.75%.
Among the minerals tested soybean contained the
highest amount of potassium 48716 mg/L at all.
Microbiological results showed that acceptable value
(<103 cfu/g). Considering the nutrient contents and
microbiological analysis of the sample, soybean should
be an inexpensive source of nutrients that could be used
in the management of protein-energy malnutrition and
to improve status of functional foods.

Making appropriate decision can succeed in all
organizations. In the area of personnel management,
optimum number of personnel can be proposed by
applying the linear programming techniques. The
purpose of this paper is to show how linear
programming methodology can help to design optimum
number of teaching staffs in the institute. In this paper,
to maximize the teaching staff with respect to the
departments and faculties, the data sets for University
of Computer Studies (Sittway) are sought. In this paper,
the constraints of problem, specified objective,
structured mathematical model are detailed. Systematic
review was done by identifying the data from the
department of student affairs in UCS (Sittway). To solve
the problem, simplex method can be used or Excel
Solver can be used to solve the LP model. From these
results, the institute can achieve the optimum number of
teaching staff in respective faculties

In recent years, queue models have been many
interesting problem and popular topic that have been
applied to the various research areas in real world
applications. Queue Models ‘greatest success in the
real-world application areas have been in
communications and data networking. In this study, a
delay predictor for waiting customers outside restaurant
is developed based on queue models such as Enter
Service Rate of Last Customer and Head of Waiting
Line are used to estimate the condition of waiting in the
restaurant and the effect on the out of waiting status.
For this work, Cox Model among survival analysis
methods is used to calculate the service rate and delay
time as Status of Customer Service and Gradient
Boosting Regression Tree (GBRT) is used. The
experimental results of this system show that the well
work of this predictor and this proposed design is
applied for real-world applications.

Coronavirus disease 2019 (COVID-19) has started
in China. The Covid-19 are spreading around the world
who are living people and animals. Although the test kit
is donated from China to Myanmar, there are not
enough in all area. Therefore, Coronavirus Disease
(COVID-19) detection system using Histogram Oriented
Gradients and Feed forward Network is proposed for
diagnosing as COVID-19 instead of using test kits.
These system is divided into three parts. Firstly, the
chest X-ray image of the patient is preprocessed by
using median filter that reduces noise. Secondly, the
feature descriptor is extracted by using Histogram
Oriented Gradients (HOG) as feature extraction step.
Lastly, feed forward neural network is used as a
classifier for classifying COVID-19 patient or normal
patient. The proposed system is tested on. Experiments
are carried out on open database of COVID-29 and
give the correct performance in terms of 100 percent of
accuracy for training set and 80.45 percent of accuracy
for test set.

Nowadays, computer assisted approaches for analyzing the images have increased because the manual interpretation of the image is a time consuming process and suitable to human errors. So, this system is implemented as the computer assistance classification system for analyzing endoscope image about gastric cancer. To classify the gastric cancer stage, this system is based on image processing technique. Before classification, this system extracts geometrical features and first-order (FO) statistic features from the endoscope image. By using these features, this system classifies the gastric cancer stage according to the support vector machine (SVM) classifier. So, this system helps the physician by identifying the gastric cancer stage.

Automatic fingerprint identification system, AFIS is essential and most reliable biometric technology and the improvements in the performance are always necessary important for the security process of national and worldwide applications. With the combination of the emergence of various sensor technologies and the problems of the Big Data era, the requirement of health care mechanisms in today’s world lead to the researchers emphasizing the contactless security mechanism and sensor interoperability problems. The transmission of a large amount of image processing data and the requirement of the high volume of storage for image processing face many troubles in cyber applications. Therefore, this paper proposes a framework for contactless fingerprint for 2D images and the recognition is intended to the cross-matching to solve the different sensors problems. In addition, the proposed system reduces the complexity by proposing effective features to increase the performance by using a machine learning classifier. The experiments are performed by using the public dataset of the PolyU database and the results are compared with the previous systems and achieve acceptable outcomes.

In the last decade, content-based image retrieval (CBIR) was an important research subject. In this paper, a set of image features is proposed for image retrieval based on color and texture features. For color feature, color statistics values are calculated from HSV and LAB color spaces. For texture feature, Haralick feature is extracted from RGB and Gray scale image. A set of proposed features is applied Columbia Object Image Library (COIL-100) for image retrieval. The input of our system is the query image and the output is the relevant images for a query image. Precision and recall are measured to show the properties of proposed feature in content based image retrieval. In experiment, the combination of color and texture feature has the high precision(98.72%) and recall(97.25%) value in content-based image retrieval (CBIR) system.

The human activity recognition system is automatically identified as human activity from the input video stream. It is a vital task in computer perception because it has many application areas such as healthcare, security, entertainment, and tactical scenarios. The system provides a way to automatically recognize human activity in the input video stream by distinguishing the Oriented Gradient Features (HOG) and K Nearest Neighbor (KNN) types. Functional features can be extracted from the input video frames with the HOG feature and can be organized to form an activity pattern. The empirical results and its accuracy indicate that the proposed system applies to recognition of human activity in real life. This system has been completed using the MATLAB programming software and the evaluation results for the system quality are measured by the confusion metric that is precision, recall and fitness measures.

Nowadays, the human signature plays an important role in biometric authorization. Signature verification can be performed online or offline. With the help of modern computers, signatures are treated as images and recognized with the technique of computer vision and neural network. Signature detection is a very challenging active research because of the possible signature appearance variation in illumination, occlusions, etc. Although there are many research papers in signature recognition, there are a lot of gaps to implement the robust signature recognition system. In this paper, offline signature recognition and verification is proposed. In our system, there are two main portions: feature extraction and classification. The features are extracted using invariant central movement and modified Zernike movement as there are a large amount of variation in size, translation, rotation and shearing parameters in a signature. Multilayer perceptron neural network is applied to recognize signatures.

Digital signal controller DSC, a hybrid of microcontrollers and digital signal processors, is widely used in high performance industrial servo systems due to its rich of control oriented peripherals, its inherent computational power due to its single cycle multiply-accumulate MAC unit, to get very fast cycle times and wide closed-loop current control bandwidths. This paper discusses the use of DSC in high performance PMSM control systems from practical point of view. Firstly, sensing phase current of PMSM motor is filtered using infinite impulse response (IIR) filter. After that, all phase currents are transformed into two phase currents by Clarke and Park transform, in which the required sine and cosine values are calculated real time due to single cycle MAC. Finally, discrete proportional integral (PI) controller is implemented using DSP to generate control voltage. In this work, TMS320F28335 DSC is used to make performance comparison between DSC and general purpose controller.

There is still a growing interest in memory forensics technology today. There are several Cyber Forensics techniques and tools available to help combat Cyber Crime. Among them, memory forensics refer to the analysis of volatile data in a computer’s memory dump. Volatile data is the data stored in RAM on a computer while it is running. Memory forensics can also be triggered the running process of the system such as open connections, recent executed commands in a computer. This paper analyses the memory to detect malware by using volatility tools. This tool is popular volatile memory software analyzer. It can help us to recover useful information stored on the memory of the computer. This extracting scheme focuses on applied operations of memory forensics in Kali Linux machine to detect Trojan malware R2D2 attacks. This paper provides a generalized framework and step by step analysis to retrieve useful information from memory.

Numerous reports with respect to online extortion in assortments media make distrust for leading exchanges on the web, particularly through an open system, for example, the Internet, which offers no security at all. Web based banking-which offers the financial types of assistance through web changed the business exchange of banks radically, additionally diminishing the expense and improving the straightforwardness for the client. On the web/Internet banking administrations comprise of data enquiry warnings and installment move. The issue with Internet-Banking application is that they send information legitimately to client in plain content structure, trading off with security. Accordingly, innovation for security is fundamental to help secure web based business on the Internet. This system presents the online banking security control by using Token Less Two Factors Authentication (TFA) / Syfer-Lock’s Authentication Solution. TFA is used for the secure confirmation code for the editing of the user critical information such as password, username, pin and so on.

Healthcare system in the developing country like Myanmar did not come up the power of information technology yet. Electronic healthcare system can provide more effective and enhance the current healthcare system of Myanmar. Security becomes an important issue when providing electric healthcare system because patient sensitive data is collected and shared by different users and organizations. Personal identifiable information and the patient health record are needed to protect from unauthorized use, disclosure and destruction. To be more efficient and effective healthcare service, electronic healthcare system is provided to change current traditional paper based healthcare system and a secure architecture is proposed for maintaining, transmitting and retrieving patient sensitive information by using cryptographic process. Electronic Health Record is kept in encrypted format instead of plain text record. Key Management Center (KMC) provides the key sharing process between client and Electronic Health Record Server when the client requests the sensitive data.

This paper proposed the usages of Mininet in various adoptions and methods. The emulator is used in sdn related controllers, switches and hosts in implementing the instant virtual network. Usage of Mininet adoption in varies with different purposes. The researchers apply it for SDN projects and testbed, and the students and teachers adopt it for labs and projects and IT services apply it for many sdn applications. By using Mininet, user can deploy host, switch and controllers run on VM (virtual machine). The virtual mode strategy of Mininet has been conducted for the purpose of prototyping and emulating for the network traffic analysis. In this paper Layer 2,3 and 4 flow entries are matched in the OVS flow entries and tested with QoS applied in the modification of the packets. The wireshark is used for the analyzing of the packet traffic in the ethernet port of the OVS switch.

It is convinced that practical hands-on skills are of paramount importance to network technology students. But limitation in using expensive real network devices in hands-on lab becomes a huge barrier for interactive and effective learning. Here comes the role of network simulators as they offer functionality that closely mimics a real network with significantly low cost. For our students in Universities of Computer Studies, Packet Tracer (PT) has been integrated as a learning aid in computer networking field for several years. As there are many learners who want to use an alternative one like Graphical Network Simulator (GNS)3 other than PT, they try to compare and choose between the two simulators. Already set qualitative criteria which have been used in GNS3 and PT evaluation is presented to be helpful in the work of choosing a suitable network simulator. In this paper, we reveal some measurable criteria like CPU and memory utilization by applying different routing protocols to simulation environments implemented on PT and GNS3 to be more apparent in the performance evaluation of these two simulators. It is also intended to examine how the complex network topology affects the CPU and memory utilization of the two simulators.

Greenhouse deployment of farms gives hope for the farmers on higher crop yield, through lowering risks against pests, insects and adverse climatic conditions. Automation of greenhouse benefits the farmers in various ways by detection of soil and water quality and environment temperature. Smart greenhouses are introduced to cultivate any plants in any seasons and to reduce the employee costs. In this paper, a smart greenhouse system is implemented by using microcontroller Arduino Mega, relays and Wi-Fi module with various sensors namely moisture, temperature and light sensors. Our system is aimed to be capable of providing fully automatic environmental controls. System monitoring is possible with the support of ThingSpeak open-source Internet of Thing platform

Recommender system helps the users to choose something actually want or need. So, this system uses the user-based collaborative filtering method for building movies recommendation system. This system knows the user’s interest and recommends items which are particular interest of the user. For this target, choosing appropriate similarity measure is a key to success the recommender system. New Pearson correlation coefficient (NewPCC) is one of the most popular similarity measures for collaborative filtering recommender system, to evaluate how much two users are correlated. By using NewPCC similarity method, this system finds out the recommended movie from all movies.

Enormous amount of information is published daily via online and print media, but it is not easy to tell whether the information is a true or false. The extensive spread of fake news has the potential for extremely negative impacts on individual and society. Therefore, fake news detection has become an emerging research that is attracting tremendous attention. The purpose of the proposed system is to detect fake news with the help of text analysis using n-gram features and machine learning classification techniques. We investigate the feature extraction techniques of term frequency, term frequency –inverse document frequency. Classification of fake or real news is performed using Passive Aggressive Classifier (PAC), Naïve Bayes (NB) and Support Vector Machine (SVM) classifiers. The proposed system is evaluated using three publicly available datasets. Performance of the different classifiers is measured with precision, recall, f-measure and accuracy score. According to the analysis upon the three different datasets with three classifiers, PAC is the strongest classifier among the other two, SVM is stronger than NB.

Digital watermarking has become a significant
discipline in the information process community. In
current research trend, frequency based watermarking
technique and transform based watermarking
techniques are lacked from a problem of geometrical
attack. The aim of the study is to propose a feature
extraction based video watermarking technique. The
extracted first order histogram feature of image are
used by applying the principle component analysis. For
embedding process, the histogram features and groups
the same number of histogram value which are called
component. According to the component, the embedded
feature are selected based on the key value. The
obtained results show that the propose approach offers
good imperceptibility and generates watermarking
videos robust against various attacks with high quality
watermark.

Communication is essential for human lives and it has two major parts; verbal and non-verbal communication. This study focuses on, one of the nonverbal communication categories termed as Computer Mediated Communication (CMC). The aim of the study is to conduct two surveys, emoji survey and personality traits inventory for sample of participants in different country. Based on the survey results, final aim to estimate the association between user personality traits and emoji by using correlation analysis. The results reveal that the country base analysis on the personal traits and emoji that have shown the significant and strong relationship between the HEXACO personality traits.

Today, people don’t necessarily go outside for buying their daily use products. There are many doorto-door delivery services and online shopping to fulfill their needs. However, the expectation of the products and the reality will not be the same every time. In such cases, honest reviews comments of real customers will help to avoid buying poor quality products. This paper emphasizes classifying the customer’s sentiment on Myanmar cosmetic products and we identified three sentiment polarities: positive, negative, and neutral. We collected and annotated the customer’s comments from Facebook pages of the most popular cosmetic products in Myanmar. The experiment is performed by Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) model on the customer’s comments datasets. According to the experiment, the LSTM model performs better than the RNN model. The average accuracy of the LSTM model is 91% and the RNN model is 89%.

Text document classification methods have been rapidly implemented and developed in recent years. The more useful and understand classifiers are needed to get the high accuracy of classification result on the large amount of text document due to the increasing number of complex documents and text documents. The main expected goal of classification on text documents is to exactly categorize the text piece into the various predefined categories. Because of rapidly improvement of using information technology, the automatically classification task on the online text document is become a main role for the discovery and collection information among the large amount documents. Moreover, the various methods of classification and algorithms for classification are improved by previous researchers however these methods and algorithms needed to filter the documents data before classification of these documents. Keeping these requirements, this system implements the idea problem of classifiers on unseen data classification for text documents that are not homogenous. In this proposed system, the filtering step is used before classification step on the text documents to get the better performance of text documents classification. C4.5, Support Vector Machine and Naïve Bayes from the machine learning approaches are used in the classification step of the system, the text documents the structure of the filter is based on dataset (training and testing) then these dataset are processed by the filters without changing behavior of the document structure. Data Discretization is a popular and useful step in the process, since it is easier for classifiers with discrete attributes value rather than continuous attributes value. In the preprocessing step of this system, data discretization is used to discretize the value of numerical attributes in the text document dataset into nominal attributes. Newsgroup data are employed to test the unseen test data and the performance efficiency of classification used filters are analyzed. As the experimental result, the detail results of various classification methods and the classification accuracy result of these classifier are analyzed and describe in this system.

Social Network Analysis (SNA)is the process of investigative social structures through the use of networks and graph theory. One of the most applicable features of graphs representing the real-world datasets is their community structure. Community detection is an important task in social network analysis. Communities are intuitively characterized as “unusually densely knit” subsets of a social network. When in real datasets, many vertexes belong to many communities, therefore overlapping community detection becomes a prominence topic in social analytic research fields. In this paper, we present a new overlapping community detection method based on local seed selection and local seed expansion approach. In our proposed method, we first partitioned the graph into disjoint clusters, and then select the node with highest eigenvector centrality value as seed node for each cluster and then expand these seed node to obtain the overlapping communities. We test our algorithm on small scale real-world datasets. Experimental results show that our presented algorithm outperforms in accuracy than other two popular overlapping community detection methods.

The effect of hate speech can cause an unprecedented rate of vulnerability not only to public emotions but also political trends. It also increases discrimination between different racist groups. So, this paper addresses the problem of spreading hate speech over social network by autonomously detection the posts/tweets of the network users. Firstly, we first perform the pre-processing of language context using NLP tools and then exploit deep learning model called bidirectional recurrent neural network (Bi-RNN) in order to detect if the tweets are vulnerable hate speech or not. The system is then implemented according to proposed architecture and tested with popular Twitter dataset for analysis of hate speech. The experimental works are executed and measured with evaluation metrics called precision, error rate and processing time.

In this era, behavioral detection is mostly vital for every surveillance system. The group of people sometime can be dangerous in some situation. There is a solution to detect the group of people and their directions for various places with association or not, which is a KLT feature tracker with collective transition prior. The video frames are used for doing the crowd analysis purpose. This system aims to detect the number of group from crowd video. KLT (Kanade-Lucas-Tomasi) feature tracker is used to generate tracklets from crowd video. Collective Transition is used for group detection. Kmeans clustering is used for finding dynamics of K in individuals for consecutive time in coherent filtering.

Image clustering is a crucial but challenging task in
machine learning and computer vision. Existing
distance measure methods for vectors are also essential
and important in image clustering. The structure of the
feature forms as the vectors and then different types of
distance measures are applied in image clustering to get
successful image groups for information management.
To highlight the different type of distance measure
methods, we propose to perform comparative analysis
of distance measurements in image clustering. In this
comparative study, Gray Level Co-occurrence Matrix
(GLCM) Feature and K-means Clustering algorithms
with different distance measure methods are used to
compare the clustering results according to different
distance measurements. The main output of proposed
system is the number of image clusters. In experiment,
clustering results are measured by means of clustering
accuracy or purity and performed on the Brodatz
texture images dataset to show the properties of
different distance measurements for image clustering.

Today agricultural production has been using sensing technology and machine-based diagnosing techniques to make farms as smart farming. It can save much time and energy of the farmers and stay connected with them if something unusual pattern is found from the sensed data. In this paper, we propose a plant diagnosis approach to analyze the plant images. We firstly apply sparse subspace clustering (SSC) algorithm in order to reduce the dimensions of high dimensional data captured form sensor elements of smart farm. We then use multi-class support vector machine to classify diseases depending on their symptoms. Of many plant diseases, we focus only on fungal diseases that are main disease-causing organism. The experiment is performed with high dimensioned images collected from sensor elements as well as the high-pixel produced camera. The results have shown that our approach is superior to other contemporary approaches in terms of accuracy and processing time.

The aspiration of this paper is to presents the heuristic function is the important role in artificial intelligence (AI) algorithms. It describes the use of these search algorithms in two types, uninformed and informed search. Firstly, it finds out the solution of the shortest path or cost optimization in uninformed search. Then, some informed search strategies will be operated based on the additional information. Informed search is developed to reduce the amount of search by making the intelligence choice for expansion. To achieve effective outcomes, the decision making depends on the heuristic function. And then the heuristic search to reduce the memory requirement by keeping the generated nodes in memory and pruning some of them away when the memory runs out. This research emphasizes to attention the role of problem-specific information to get optimal solution more efficiently than uninformed strategies.

Today, the diagnosis of diseases is a vital and intricate job in medicine. Medical diagnosis is regarded as an important yet complicated task that needs to be executed accurately and efficiently. Regrettably all doctors do not possess expertise in every sub specialty and moreover there is a shortage of resource persons at certain places. In this situation, an automatic medical diagnosis system is beneficial by bringing all of them together. For this diagnosis system, many classifiers are essential and needed for disease classification. So, this system is proposed as the performance comparison system about classifiers to know which classifier is more effective than other. To compare the performance, this system classifies the heart disease dataset by using Knearest neighbor(KNN), naive bayesian (NB) and decision tree (DT) classifiers.

Information retrieval on the web is significant and furthermore complex activity for web mining. Because of enormously increased the number of websites on the Internet, the execution of PageRank Algorithm should be easy and faster in operation. So the system proposes the fast execution time in PageRank with the reduction of iteration step rather than the original PageRank Algorithm. The proposed system contains two main parts. The first part is to create simple website using mostly HTML, CSS and also bootstrap, front-end framework for web design in the demonstration of the proposed system. The second part is to make the line number extractor using simple web crawler to get the required information for proposed PageRank Algorithm. Then, the PageRank algorithm is executed by using the data from web crawler. Finally, the convergence value is taken to specify the iteration count during the operation of PageRank Algorithm. The main point of proposed system is faster execution time than the original PageRank Algorithm with reduction in iteration steps.

Stock price prediction is an important topic in finance and economics which has predicted the stock trends in an efficient manner can minimize the risk of loss and maximize profit. There is a challenging task in stock price prediction is owing to the complexity patterns behind time series. To solve these types of problems, the time series analysis will be the best tool for forecasting the trend or even future. In this paper, time series analysis model: Autoregressive Integrated Moving Average (ARIMA) and PROPHET has been used extensively for time series forecasting in the field of Myanmar Stock prices. Data is prepared for time series analysis by performing data preprocessing steps such as time stamp conversion, stationary identification and stationary treatment. To find the most accurate forecast model and the most suitable forecasting period, the error analysis of PROPHET and ARIMA methods are performed and compared on the same dataset (daily, weekly, monthly Myanmar stock prices). Based on the analysis results, PROPHET outperforms ARIMA for three periods and both of the two models are suitable for short-term prediction (daily and weekly prediction). The aim of this paper is able to support for Myanmar stock prediction and helps researchers in the field of time series modeling, economic analysis and investments.

Due to the essential requirement of the transmission of large volume data streams in cyber security environments, the improvements of anti-spam filtering methods become hot spot research fields in today technology periods. The emails in today transactions becomes non-stationary data, and disturbed by the spammers in various ways and the normal data stream mining mechanisms need to be improved to get acceptable performance and concept drip mechanism are popular research interest area in big data era. Therefore, this paper proposed an ensemble mechanism for email spam filtering procedure based on the concept drift detection technique recurrently, and Random Forest Classifier adaptively. The experiments are carried out by using the standard real-world datasets of enorn dataset and apache assassin dataset. The proposed system attained better performance than the popular data stream classification algorithms due to the recurrently and adaptively detection of concepts. The experimental results are evolved by using different assembling of classifiers and different detectors.

The classification of music is a relatively research area and there are interesting areas for future explorations, including; in the feature extraction stage, and the use of classifiers that is a main idea of the active research. Although Myanmar’s music has many similarities with other music styles in the district but the ethnic music styles differ depend on their cultural musical instruments. In this paper, the problem of music classification and highly similar of cultural music style of Myanmar’s ethnic music is examined. The experiments are conducted by using the combination of timbre features and combining the nine major features. For this work, in the use of classification methods, Sparse Representation Classifier and k-Nearest Neighbours classifier are commonly used which is to compare the classification results. Moreover, it shows that MFCC (FC4, FC5, FC6) feature combination gives 79% of best classification result with the use of SRC classifier. When all feature combinations are used, the SRC provide the best classification accuracy of 81.64% f or Shan ethnic songs than other ethnic songs.

Understanding customers helps business to provide tailored services. Clustering analysis help all business owner to gain a coherent understanding of their customers. In order to maximize the value of each customer to the business, cluster customers into segments based on their income and spending score. For this purpose, K-means is used. K-means clustering is an unsupervised learning technique which mainly deals with identifying the structure or pattern of the data. In this type of algorithms, labeled data is absent (or the dependent variable is absent). Clustering in Customer data is the process of dividing a company’s customers into groups that reflect similarity among customers in each group. A different number of clusters is tested with k-means classifier. In this experiment, up to 30 clusters is tested with the algorithm. This paper aims to improve the performance of the clustering results by measuring clustering quality with Silhouette scores. The best number of clusters is determined with Silhouette scores: the higher the better.

Software development is a collaborative process where teams of developers work together in order to complete tasks. Developers have to make decisions based on data which can be modified by other developers. To prevent wrong decisions, the system has to make sure that the data is consistent between developers and modifications on one developer are propagated to other developers. Developers can download the source code files that are stored in cloud storage server. And then they modify their source code and upload these update source code file to the cloud storage server to see others developers. However, they can read others developers’ source code file but they can’t write or modify it. Petri Net Model has been presented to compute control flow complexity for this system. The result can be shown that the nine metric values are upward trends when the number of developers increases in CSCW. This paper focuses on the complexity of proposed consistency control model by using Petri net-based representation. Moreover, an effort has been made to evaluate the control flow complexity measure in terms of Weyuker’s properties that is fulfilled by any complexity measure to be suitable for this system.

Teamwork and collaboration are the two main factors for software development methodologies. From previous software engineering works, the students were keen to collaborate in teams for the advancement of their projects and to learn how to use resources which are not only used in the educational environment but also in the industry. The various machine-learning algorithms are used to test student learning from the software engineering cooperation. The analysis of Student Learning of Software Engineering Teamwork prediction methods is performed: Support Vector Machine and K-nearest neighbors. The key objective of this article is to present a comparative analysis to Assess and Predict Student Knowledge in Computer Engineering Teamwork with different exiting classification systems. The experiment performed different classification methods on Data for Software Engineering Teamwork.

The shortest path algorithm (SPA) is one of the most fundamental and important in combinational problems. SPA, a problem in graph theory, has commonly applied in communications, transportation, Artificial Intelligence, Geographic Information System and electronics problems. The problem of finding a path between two vertices (or nodes) in a graph is a key role of SPA tricky to minimize the weights of constituent edges. Thus we would like to implement and highlight the advantages of a shortest path algorithm, Dijkstra’s Algorithm (DA), which has been widely used in engineering calculation such as in Google maps. In this paper, we approve the constancy of this algorithm, also known as label algorithm, using only on positive weights for travelling tracking process in Myanmar cities. The criterion values are referred from Google map data. This paper presents not only the implementation of applicable tracking system but also the minimum cost of this applied algorithm with graph data structure including finite set of vertices. Finally, we also present the review of implementation in these ages as stated by complexity and where we can apply this algorithm as a future work.

Every day a large number of human lives and properties are lost due to emergency event. If the fire breaks out, it’s important to realize that the fire truck searched the right place being on fire with the right way as soon as possible. In this proposed system the Nearest Neighbor Query (NNQ) method can be used to know the nearest fire station from the place being fire if the fire news comes to the station. After getting the using the nearest fire station, A* algorithm is used to calculated the optimal route. This system also checks the way to the fire area whether it can reaches or not by using the optimal shortest path. If fire engine cannot reach destination point because of street type, the system finds water resources near the emergency place using KNN method. The fire station can get sufficient information for current emergency place by using this system.

Parsing is a necessary mechanism for many natural language processing applications, such as machine translation, question answering, knowledge extraction and information retrieval. In this paper, we propose the syntax investigation system which can investigate the syntax errors in the PHP program. This system takes each string from a PHP program as input and determines it as a correct message or error message by using the top down parsing approach. The predictive parser is used to construct the parse table, which determines the action of the input string based on the grammar production rules. These grammar rules were reconstructed if the left recursion rules are occurred to avoid the left recursion problem in the top down parsing approach. As a result, our system can reproduce the correct messages, error messages, and location errors. It was confirmed that our system can be useful to investigate the syntax effectively in PHP programming language.

In human life, people travel to everywhere about work, visit, study and economy. So, they choose to reach with minimum distance by the shortest path. This paper was aimed to find the shortest path that the people were reached quickly from a starting place to the target place in the sparse graph with weighted edges by using Dijkstra’s algorithm. The calculated results of the shortest path from Yadanabon bridge(Y) to Kaunghmudaw pagoda (P) in Sagaing and the shortest path from Sagaing 700 Anniversary Football Pitch(S) to Maha Sigon Gyi Pagoda (M) in Parami quarter, Sagaing were 10.5 km and 950 m, respectively. Dijkstra’s algorithm was very useful method to find the shortest paths of traffic information systems, road maps, network and so on. This method runs faster than Floyd’s algorithm to solve the problems for the sparse graphs with weighted edges.

Blockchain is a revolutionary technology that enables the creation of a decentralized environment, which serves as an immutable ledger. It provides a public infrastructure for building decentralized applications. Blockchain-based applications that are used in a wide variety of sectors ensure transparency and trust between all parties involved in the interaction. Therefore, blockchain technology is applied to the educational certification system to enhance the higher educational process. The proposed framework aids the educational credit records and academic certificates that can be transferred digitally to create a globally trusted among all stakeholders such as universities, companies, other educational institutions, and organizations. By applying blockchain technology, the first achievement of the proposed framework more trust, transparent and reliable services for credit transfer and industry relationship. Moreover, this provides distributed storage, security, and traceability, to efficiently monitor the student assessment data can be traced anytime anywhere and support the trusted higher education system between all institutions and universities linkage.

A content management system (CMS) is a system
used to manage the contents of a web site. CMS
evolved as an alternative to such web-authoring tools.
A CMS is software that helps to make websites easier,
faster, browser compatible and responsive website
with the powerful features. There are several CMS
based on usage, functionalities, compatibility and
platforms. There is no CMS that is not best suited for
every user. The features of a CMS system include
web-based publishing, format management, revision
control, indexing search and retrieval. There is a lot
of variety among the CMS options. These CMS
names: Joomla, Word press, Drupal, Typo3,
Serendipity, Dotclear, Impress Pages and Chamilo.
These CMSs are available based on functionalities,
usage and platforms. In this paper, we study to
analyze these CMSs on the usage, function, available,
default, integrated feature and browser compatibility
with different platforms. This paper attempt to analyze
the pros and cons of these CMS that user can choose
the best CMS according to the application
requirement.

The Dijkstra’s algorithm is solved the shortest path problem (SPP) or minimum spanning tree (MSP). A transportation management system is necessary to provide the shortest path from a specified origin location to other destination location. In this paper, a transportation network from town to town in Loilem District is constructed with the shortest path routes. A numerical description of a transportation network is used to demonstrate the efficiency of the system motioned. Using Dijkstra’s algorithm, analysis shows that the best route which provides the shortest distance will be from node A-B-C-E-G (location**Loilem-Pang Long- Laihka – Mong Naung- Mong Hsu).The result gives total distance of 116miles. Simillary, the result gives the shortest distance from Loilem to Kyaing Taung is 100miles. A TORA software (version 2006) was used in the analysis.

The intention of this paper is to understand the student how to work disk scheduling policies in operating system. Nowadays, modern technology and high performance computing processors are rapidly evolved. Moreover, the speed of processor and main memory are also increased rapidly but secondary memory is still relatively slow. Thus the performances of disk I/O are also vital and many researchers are interested in schemes for improving that performance. Disk scheduling is crucial because multiple I/O requests may be by different operations and only one I/O request can be run at a time by the disk controller. It is hoped that this paper may provide an opportunity for all computer students to learn theories and pratical experience of allocating I/O device for various processes. In this research, the C programming language is used for practical interface and calculates total head movement performance of FIFO, SSTF, SCAN, and C-SCAN in disk scheduling algorithms.

Nowadays, the spread of new display devices with new screen sizes has drastically changed web-based technologies. Each of these devices have variations in size, functionality, orientation, screen resolution, color, etc. These situations challenge the web design landscape. Therefore, web designers have to deal with demands of new web technologies such as responsive and interactive web designs in order to handle various kinds of device-related variations that will be able to respond to user’s behaviors and environments based on screen sizes, platforms and orientations. In this paper, we study and review state-of-the-art web technologies regarding responsive front-end web design. We additionally dig some popular tools that has emerged to confront the current challenges of latest responsive web technologies. We afterwards present a statistical comparison to current popular responsive framework such as bootstrap, foundation and semantic UI, that have been widely used in various applications: business areas, software development process, web-based visualization areas and other web-based areas.

This paper aims the analysis of using Eclectic Approach with the collaboration of ICT to EFL learners in every university, especially in all universities of computer studies. It is the latest innovative approach on teaching and learning English in Education System around the world as a new paradigm due to the demands of new age and new generation. The Eclectic Approach itself is a conceptual approach by extracting and applying the intrinsic worth of various approaches and methodologies in order to get the profound awareness of the language. Thanks to the use of Eclectic Approach integrated with technology it has been expected not only to comprehend the target lessons but also to arouse the motivation and intention of the learners. It also encourages the performances and abilities of the learners to achieve the expected outcomes or skills on the current assessment system. The course of the paper is composed with the integration of the views of the researchers and just over sixteen-year experiences of the author as a language teacher in several Universities of Computer Studies (UCSs). In this paper it is widely expressed the impacts of Eclectic Approach for the learners and the teachers, the challenges of applying this approach in the classroom and the encouragement to the language teachers to use alternative approaches instead of using traditional methods. In this paper the term students and learners are interchangeably used.

Since the Internet has become popular, the consumer oriented e-commerce market has grown rapidly. Online shopping may stimulate a shift from the need for personal travel to demand for the distribution of goods. Transportation plays an important role in online shopping for everyone and every place. The propose system will provide a product delivery system for online shopping market. In this system, the new branches can be opened in Mandalay region and goods will be delivered to customers who live in Mandalay region. This system will use Dijkstra’s algorithm to find distances between cities of Mandalay region. When the customer orders the goods then the system will find the nearest branch from the customer’s address and find the shortest paths from nearest branch to the customer address by using A* search algorithm. This system will also arrange the transportation paths for each branch. This system is implemented by using Java programming.

Today, everyone uses mobile devices. Therefore, mobile applications are more popular than others. For the mobile device, android is one of the top operating systems, being one out of more than billions of devices. This paper is based on two-tiered system. The presentation logic and some application logic are resided at the client. The remaining application logic and the data logic are resided at the server site. The system in this paper is a native Android application that provides the users with the information about Information and Communication Technology (ICT) centers and Foreign Language centers in Pakokku city. At the client site, the user can see the course information, address and phone number of the respective center and can also make registration. At the server site, the admin can update every piece of information for the respective center and can do deleting the respective course.

Zinc Tin Oxide, ZTO thin films were synthesized by sol-gel spin-coating method. They are aimed to be exploited as interfacial layer in perovskite solar cells. The structural property of ZTO was studied by X-ray Diffractometry, XRD, the optical transmission of ZTO films were examined by UV-vis spectroscopy, UV-1600, surface morphology of ZTO films were investigated by Atomic Force Microscopy, AFM and wettability of ZTO on perovskite film were measured by content angle measurement. XRD pattern indicates that ZTO has cubic structure at 700°C. In addition, the optical band-gap energy of ZTO film is about 3.48 eV. From AFM microphotographs, the shape of grains are popcorn-like on ZTO films were observed. The result of wettability measurement observe that the contact angle decreases at the lower concentration of ZTO film, and the surface becomes more hydrophilic. According to the experimental results, ZTO might be promising, credible and applicable in use for perovskite solar cells.

Electronic devices and appliances have become very common in this recent year of technology especially with fast development in smartphones. In this paper, the design of Home Automation System compatibly with Local housing and good features for home automation via remote access are presented. Bluetooth Based Home Automation System Using Android and Arduino is design and implemented. In this research work a part of smart home technology which using Bluetooth in a mobile device is used, so it will cheap and efficient to use. This paper describes about home automation system which would use to enable home lighting, garage door motor, water pumping motor and smoke detection using a smart phone application with Bluetooth wireless technology. The system included three main components: an Arduino microcontroller for connecting the appliances, a Bluetooth module for signal transfer, and a smartphone with the Android application to control home appliances. Bluetooth communication technology and controlled system is that the operating range is low, but it can control from anywhere inside of home, by using smart phone application we can control household appliances and provide security to decrepit peoples. The idea of paper is to control home appliances to avoid the dangerous of electric shock and convenience of decrepit and physically disable people, who can easily access and control the home appliances by staying at particular place and access them remotely without the help of other people. By using this system, our home automation works smartly by providing increased quality of life, and comforts to users.

In a multiprogramming system, multiple processes exist concurrently in main memory. Each process alternates between using a processor and waiting for some event to occur, such as the completion of an I/O operation. The processor or processors are kept busy by executing one process while the others wait. The key to multiprogramming is scheduling. The aim of processor scheduling is to assign to execute by the processors over time, in a way that meets system objectives, such as response time, throughput and processor efficiency. This paper analysis the four scheduling policies First-Come-First-Served (FCFS), Round-Robin (RR), Shortest Process Next (SPN) and Shortest Remaining Time (SRT) and gives the result which policy is the best among them. A comparative analysis is made on the basis of data generated through the simulation using exponentially generated random numbers.

The hospital staff serves as patient registration and appointment scheduling in today’s healthcare domain. It makes time consuming and somehow hectic. Usually, patients have to come to the hospital and have to fill out patient registration forms and have to wait appointment. Over the past, most of works have been done by using online healthcare appointment system like m-commerce, e-commerce, telemedicine etc. Multi-agent system is most suitable for healthcare paradigm, as the properties of agent based systems deals with heterogeneous multiple agents. This information retrieval system based on multi-agent contains agents that have information about the personal medical history, the hospital, departments of each hospital and doctors of each department. This system provides as an agent that gives the patient by selecting desired hospital, desired date and desired time, etc. This multi-agent based system collaborates with the agents of hospital, department and doctor for that appointment. The personal agent allows the access the offered services for appointment and view medical history. The visited user can search and view doctor information with time and date, hospital information and health news. The system in this paper is implemented with PHP programming language, for providing a robust, user friendly appointment for the patient.

This paper attempts to achieve automatic school bell by using an Arduino UNO as a main processor. Automatic School Bell has been designed and constructed based on Real time clock RTC IC DS1307 which works on I2C protocol. The design is in four modules; power supply, RTC IC DS1307, LCD device and Arduino UNO modules. While the Arduino UNO forms the main control element, Real time clock means it runs even after power failure. When power is reconnected, it displays the real time respective to the time and duration it was in off state.16×2 LCD module to display the time in – (hour, minute, seconds, date, month and year) format. An Alarm option is also added and we can set up the alarm time. Once alarm time it saved in internal EEPROM of arduino, it remains saved even after reset or electricity failure. Real time clocks are commonly used in our computers, houses, offices and electronics device for keeping them updated with real time.

Feature detection, description and matching are one of an essential component of many computer vision applications. In order to detect any object in the given scene it is important to know the key features that describe that object. A number of feature detection algorithm have been developed in recent year. However, the computational complexity and accuracy of feature matches limits the applicability of these algorithms. There are four widely used feature detection algorithms, Harris, SURF (Speeded-Up Robust Features), FAST (Features from Accelerated Segment) and FREAK (FAST Retina Key Point) feature detection algorithms. In this paper presented some practical approaches to detecting features, description method and also discuss feature matching.

This paper presents a theoretical and practical approach to the effective communication in teaching and motivation of the students in learning environment. This study based on some under graduate students from University of Computer Studies, Yangon and University of Computer Studies, Taunggyi. It aims to develop learners’ communication skills and to express their speaking in a natural way. Communication skills and task motivation have a great impact in teaching and learning aspects. Teachers need to create a comfortable situation in the classrooms using multimedia to be effective teaching and learning processes. Most students are afraid of speaking in front of others because of lack of practice and opportunity to use English language. Using different types of demonstration aids in teaching allows students to communicate in an active way. Hence, it is hoped to fulfill the needs of learners to motivate and give practice to be good learning results.

Using the stochastic processes called Markov Chains are usually used in modelling many practical problems. In this paper, to predict the immediate future, the data sets for Computer University (Mandalay) are sought. The moving averages for the data are found and grouped them into five different states for the grades. Then Markov chains calculations are applied to the data to create a 5*5 transitional probability matrix. Using this transition matrix, a system of equations are solved by using Chapman-Kolmogorov Equations and found five steady states that will be the same in every row of the matrix. They are variables that represented the probability that a grade for a given year would fall into one of the five states. When this information is used, the actual data to these equations can be applied and the next grade for the near future be predicted. Near future grades using this method are able to be successfully predicted.

As the population increases, the roads network becomes complicated and massive. Finding desired location is becomes difficult tasks. After finding a location people gets confused to reach that location because of the routes comprised a different mode. This problem is even more important for people who may need to visit unfamiliar parts of the metropolis. In case of the fire stations sometimes it is difficult to find the specialized fire station and its shortest path to reach hence takes more time to reach it. In this paper, we tried to solve this problem by representing the shortest path facility for finding the nearest location of the fire stations from fire location. We used the ArcGIS software and the A* algorithm to provide the shortest path from one location to another.

Mobile malware performs malicious activities like stealing private information, sending messages, SMS, reading contacts can harm by exploiting the data. Malware spreads around the world infects not only end user applications, but also large organization service provider’s systems. Malware characterize is a vital component that works together with malware identification to prepare the correct effective malware antidote. Malware feature category is also important to reduce costs and time for malware identification. They may have many features in every mobile android application. This system proposed a score-based detection for Android malware. The advantage of this system is that it uses only manifest files to detect malware. Therefore, in this work is to explain the criteria for characterize, this system need to process characterize different features from the manifest file of permission request. According to the experiment on different categories; the results show that the proposed features characterize is applicable as a lightweight approach.

This paper evaluates the effectiveness of active learning implemented in Unified Modeling Language (UML) subject at the University of Computer Studies, Taunggyi. Several learning activities were implemented during the class. The effectiveness of these activities was investigated using questionnaires to explore student attitude and to measure student perceptions to learning. Results showed that active learning made a valuable approach to teach the UML course. For instructors wanting to do well in UML (Unified Modeling Language) course, use only the traditional lecturing may not be effective. This paper intends an active learning approach to supplement traditional lecturing for teaching the UML course. The use of various active learning techniques and their implementation in the classroom is discussed. Feedback from the students suggests that students like the active learning-based approach and report increased levels of subject matter understanding and ability to apply the knowledge in real world application.

Online social networks (OSNs) are more popular in todays and privacy concerns is also growing in a daily basics. University students, especially for computer university students must understand their privacy and security of personal information sharing on the OSNs. Most students are not fully understand what are personal information and how to prevent them from misusing it. Personal information sharing behaviors of students via OSNs are dertermined in this paper and it is the initial study to help the policy maker to establish or develop different types of information security law to protect information of individual on social networking sites. It can also study what type of student personal information are shared in OSN. A survey questionary was used to collect data from 201 students.

Data analysis is the process of evaluating data using analytical and statistical tools to discover useful information and aid in many decision making sectors. There are several data analysis methods including data mining, text analytics, business intelligence and data visualization. The goal is to transform raw data into understandable useful information. For developing country, data analysis is strongly needed to support useful information to decision maker. The census is most definitely important. The census helps us to see how our country is changing over the periodic. Census data are useful to lawmakers, marketing companies, political groups and even nosy neighbors curious about the family next door. In this paper, the 2014 Myanmar population and housing census Mon state, Thaton district report is used to compare and analysis in three sectors demographic characteristics, literacy rate and economic characteristic. Manual and REDATAM on-line process tool provided by department of population from ministry of labour, immigration and population is used for analysis. This analysis intends to know education and economic conditions of people and highlight some issues in this region.

The exponential growth of mobile devices and server virtualization and cloud computing technologies are the key computing trends which need new networking architecture. Nowadays, Software Defined Networks (SDN) has established a lot of attention as a new technology which provides more flexibility than conventional network which has programmability, configurability and manageability from its unique character of centralized software control. Therefore, this paper focus on enhance system availability using SDN concept for IP network. Since the controller is centralized, it will be a potential single point of attack and failure. The SDN controller affects the overall availability of SDN. To overcome the single point of failure, SDN controller is replicated with active-active replication. The SDN controller can suffer software failure as well as hardware failure. To prevent system failures caused by software, software rejuvenation can be applied. The impact of failure on SDN controllers is illustrated the analytic model and evaluate with the steady-state system availability through the use of numerical analysis.

Mobile device functionality is ever richer in daily life, but they still lack the required amount of resources when it comes to handling resource-hungry applications. Nowadays, Mobile Cloud Computing (MCC) bridges the gap between the limited capabilities of mobile devices (battery life, network bandwidth, storage capacity, processor performance) and the increasing user demand of mobile applications, by offloading the computational workloads from local devices to the remote cloud. This paper provides an overview of the back-ground, techniques, and research areas for computation offloading. Moreover, the issues, existing solutions, and approaches are presented.

At present, Internet of Things (IoT) technology is interested in the researchers, the graduated people and under graduate students who want to do their products with IoT. The Internet of Things (IoT) is the network of physical objects or things embedded with electronics, software, sensors, and network connectivity which enables these objects to collect and exchange data. Internet of Things will provide smart agriculture, smart cities, smart healthcare, smart homes and smart transport and other important applications. The aim of this paper is to know not only the Internet of Things (IoT) applications that can apply in Shan State but also how the Internet of Things works. These smart applications will be beneficial for the government and countryside.

Learning Management Systems (LMS) allow communication and interaction between teachers and students in virtual spaces. Using Learning Management Systems (LMSs) in higher education has facilitated the communication between students and teachers, and raised new challenges as well. The fast growing technologies have changed the ways of teaching and learning in educational institutions. The aim of this paper is to introduce the role of LMS in the learning and teaching processes from students and teachers perspectives. Teachers can organize their classes and post different documents, assignments, tests, etc. The student can answer the test that is created by the teacher. After taking the test (tutorial), the system provides the result of the tutorial that the student took and the pdf file to download for that tutorial and teachers’ lecture slides and documents. LMS can increase motivation of learners, promote learning, encourage interaction, provide feedback and support can be provided during the learning process.

Secure Shell (SSH) service allows people to connect to a local and remote computer with strong password authentication scheme. Brute force attacks on the SSH service have been used more frequently to compromise accounts and passwords. Kali has brute forcing tools to perform brute force attacks against SSH servers. In the proposed work, the three network login crackers of Kali such as hydra, ncrack, and medusa will be used to crack passwords of a specific SSH server which is set up in the University of Computer Studies, Pinlon. All three attacks can break a target machine’s password authentication successfully. However, ncrack cannot crack machines with password authentication disabling. Hydra and Medusa can brute force any open SSH daemon port of the machine.

There is no doubt that big data are now rapidly expanding in all science and engineering domains. While the potential of these massive data is undoubtedly significant, fully making sense of them requires new ways of thinking and novel learning techniques to address the various challenges. Big Data Fuzzy Supervised Learning has been the main focus of latest research efforts. Large-scale data sets are collected and studied in numerous domains, from engineering sciences to social networks, commerce, biomolecular research, and security. particularly, digital data, generated from a variety of digital devices, some traditional learning methods suffer from a loss of information. First, this paper focuses on the analysis on the machine learning techniques and highlights some promising learning methods in recent studies and then discussions about the challenges and possible solutions. The proposed solution comes through integration of fuzzy rules and binary classification which provide problem solving for big data learning.

This paper presents the technological advantages that would help in daily choses of cleaning. For the convenience of most of the people who are extremely busy in these choses, the need of the project has come up. Thus, this has resulted in coming up with an objective of making an autonomous cleaning machine. It is designed to build an autonomous floor cleaning robot that can move itself without continuous human guidance. This cleaner is electro mechanical machine with ultrasonic sensor and IR sensors. Ultrasonic sensor is used for obstacle detection and a pair of IR sensor is used for detecting the surface below the robot without falling down. The proposed system has two main sections for cleaning, Vacuum section and Mopping section. Vacuum section consists of a broom which is attached to the robot to scratch the floor and a vacuum pump is used for sucking the dust particles on the way. Mopping section consists of the water pump with container and the mopped roller. Water pump drips the water on the floor and roller is mopped to clean the floor. All hardware and software operations are controlled by Arduino ATmega2560 microcontroller. This robot can perform dust sweeping and mopping simultaneously. Solar panel is used to charge the power to 12V battery and it is the power source for this proposed cleaning robot. L298N motor driver is used to drive the gear motors and relays are used as switches the motor driver, vacuum cleaner and water pump. The user can set the time to clean the floor for a specific space as it is consisted of the timer function. After fulling the timer, the cleaning process is automatically stop by switching the relays in this system and buzzer is turned on to activate the alarm to the user. This cleaning robot is very useful in improving life style of mankind.

In this paper, we analyzed the data in Moodle
logs of University of Computer Studies (Taunggyi)
to evaluate the interaction of the students with
LMS and to predict performance of the students.
In our approach, Moodle logs were downloaded
in Microsoft Office Excel format and a simple
and effective offline solution using Microsoft
Excel tools is provided. Logs relating Faculty of
Computer Systems and Technologies (FCST)
courses were collected separately for first and
second semester during 2017-2018 Academic
Year. Students activities within online
environment were then extracted and depicted
using some graphical representations.

The career success is essential throughout the
whole life for the people. No matter what the
interest is extrinsic or intrinsic, life without career
interest is like a ship goes without the rudder. One
of the challenges of the development in higher
education is the students decline their deep
learning skills, critical thinking, innovation,
competency because of the students attending
university without interest, ambition, motivation
and endeavors. This paper use informal method
for measuring interest that is preparation of test
sheet with 30 questions that should wrap the
required target. We only focus on 3 questions that
make sense for extracting assumption from the
outcomes. We tested only with 127 of first year
students from University of Computer Studies,
Taunggyi. The facts from the findings are the
students have no interest in computer technology
because no one chooses the career of the network
engineer. The 47% of the students chose IT
professional 52 % of the students will not chose
IT. The 62% of the first year students did not know
what IT is even though they have attended
Computer University. The 80% of the students feel
satisfactory (utility) that the university grants
ample support to their career interest.

Malware are spreading around the world and infecting not only for end users but also for large organizations and service providers. There is a real need of dimension reduction approach of malware features for better detection. This system describes for malware detection and characterization framework which is based on Static Approach by only analyzing the Manifest File of android application. This system also describes a Feature Selection Approach which is also based on Manifest File Analysis for the purpose of dimension reducing of malware features. Firstly, a number of Permission-Based Features are extracted by disassembling the Manifest File of Android application. Then, feature dimensions are reduced by proposed Score-based Approach. The results getting from Correlation and Information Gain are used to compare the results of Score-Based Features Selection. According to the experimental results, proposed a light-weight approach can perform as equal as other feature selection methods. After feature selection, manifest file analysis based on malware classification and characterization results are also described in this system. The classification results tested by without reducing features and the results obtained by reduced features are compared to determine which methods or classifiers are the best to detect malware.

Today, 802.11 networks are becoming more popular among the millions of Laptop users community due to the mobility and ease of use. However, 802.11 networks attracts the lower layers of the open system interconnection (OSI) protocol stack to render the network unusable because of trusted and untrusted traditional boundaries. MAC layer attacks in 802.11 network are known as one of the weakest points of wireless networks because of unprotected management frames. According to these motivation, a defensive approach using MAC layer fingerprinting is proposed based on Probe request, Authentication, Association, Deauthentication, and Disassociation frames. A fingerprint is based on the behavior of a station (STA). Each STA’s behavior varies due to implementation of differences 802.11 protocol. The aim is to design and implement a wireless network security system to detect MAC layer attacks using passive fingerprinting. Experiments are implemented with different STAs with a real time set-up using Kali linux environment.

Wireless network attacks have greatly increased in the past few years. Now, it is time for attackers to intercept secure web connection over the internet. With the passage of time, attackers are getting smarter and smarter. They can now even intercept secure web connections with the help of proxy tools and digital certificates. While it is equally important to stay wireless networks, every wireless clients should know how hackers attack wireless networks and how to prevent these networks. The objective of this paper is to gain a better understanding of different wireless network attacks and the basic strategy adopted by hackers for list of wireless network attacks. This paper highlight different types of wireless network attacks, various tools or methods commonly used by attackers, technical terms associated with each type of attack and how a computer user can detect such attacks.

Software Engineering (SE) education intend to prepare students for their professional responsibility as software engineers. Software engineers are the individuals who are not only responsible of developing software from the beginning phase until its deployment but also describe about various phases involved in a software project. The increasing globalization of software development presents a unique challenge to Computer Engineering and Computer Science education. To better the computer students for this changing world, computer universities and instructors should develop and be able to formally evaluate pedagogy to teach software engineering course to address these challenges and to make students more competitive in today’s global environment. Nevertheless, SE education trends towards to be too theoretical using traditional methods. Therefore, Case Method (CM) is recognized necessary for SE education to improve practicality whereas the students can be exposed to real scenarios and learn to apply those theories through discovery. This paper presents a survey conducted to a set of students who employed CM in learning SE from the University of Computer Studies, Taunggyi. Moreover, this paper describes the empirical results of student perception and to determine the effectiveness of using CM approach.

We now live in the time of big data era. During these recent years, a huge amount of data consisted of text, images, audio, video and other file types is rapidly increasing and changing. When using these massive data, it is also important that these usage must not harm the privacy of data owners. That is why so many researches are worked on for this topic. Here is one of these works for privacy preservation in big data. This research constructs upon the well-known swarm intelligence technique, Particle Swarm Optimization (PSO), for clustering similar data. The novel cloud infrastructure, MapReduce Hadoop, is also applied to effectively handle the huge amount of so-called Big Data. Our approach is tested by using a novel UCI Adult dataset.

The bandwidth and latency of a memory system are strongly dependent on the manner in which accesses interact with the “3-D” structure of banks, rows, and columns characteristic of contemporary DRAM chips. There is nearly an order of magnitude difference in bandwidth between successive references to different columns within a row and different rows within a bank. This paper introduces memory access scheduling, a technique that improves the performance of a memory system by reordering memory references to exploit locality within the 3-D memory structure. Conservative reordering, in which the first ready reference in a sequence is performed, improves bandwidth by 40% for traces from five media benchmarks. Aggressive reordering, in which operations are scheduled to optimize memory bandwidth, improves bandwidth by 93% for the same set of applications. Memory access scheduling is particularly important for media processors where it enables the processor to make the most efficient use of scarce memory bandwidth.

Gas leakage is a major problem with industrial sector, residential premises, and gas powered vehicles like CNG Compressed Natural Gas buses, LPG, Liquefied Petroleum Gas cars etc. One of the preventive methods to stop accidents associated with the gas leakage is to install a gas leakage detection device at vulnerable places. The primary aim of this paper is to present such a device that can automatically detect and stop gas leakage in vulnerable premises. In particular gas sensor has been used which has high sensitivity for propane (C3H8) and butane (C4H10). The system detects the leakage of the LPG using a gas sensor and uses the GSM to alert the person about the gas leakage via SMS. When the LPG concentration in the air exceeds a certain level, the gas sensor senses the gas leakage and the output of the sensor goes LOW. The detection is done by ATmega328 microcontroller and the buzzer is turned ON. The system then alerts the user by sending an SMS (Short Message Service) to the programmed mobile number.

Higher education plays a fundamental part in encouraging the capacity for innovation and creativity in the development of a country. In rural area, there are lack of basic infrastructures, especially, electricity, teaching aids, knowledges, skillful technical experts and human resources. There are also many requirements to establish the campus network infrastructure. In this paper, the appropriate IT infrastructure and services for higher education in rural area in Myanmar are proposed to fulfill these requirements in an effective manner. With the proposed infrastructure design, how much effectiveness and its advantages are also described in conclusion.

Machine Translation is one of Natural Language Processing (NLP) tasks which are modern computational technologies. The machine translation is to translate text from one natural language to another. This involves accounting for the grammatical structure of each language and using rules, and grammars to transfer the grammatical structure of the source language (SL) into the target language (TL). This paper presents Myanmar to Kayah-Li for translating well-structured Myanmar sentences into well-structured Kayah-Li sentences using a rule- based machine translation.

Agriculture is an important sector in developing countries where the majority of rural population depends on it. Among them, maize production is the major farming in Loilem District of Shan State. ICT based agricultural knowledge management can increase production and productivity of maize smallholders in Shan State. In the recent past, the maize farmers are difficult to get the desired information and services related to farming. On the other hand, those in change of the agricultural sector in Loilem District cannot timely distribute and updated information to farmers such as new varieties release, emergence of new threats or diseases, how to protect from bacterial attacks, weather forecast, pricing control and warning alerts etc. Nowadays, mobile devices are used by everyone, including farmers and people living in countryside. So, this paper proposes a mobile based application for maize smallholder to help them in their maize production activities which include maize production information, new products, weather update, daily market prices and news of latest information.

These days, the IT world is lit by lighting of the use of virtualization technology in various aspects. We observe that, we can not truly benefit from virtualization if we neglect the process of virtual machine resource allocation. Motivated by this issue, we propose a model of resource management scheme for virtual machines in server virtualization framework. This paper presents a study on hypervisor-based virtualization technology in order to get flexible management of physical resources of a host. In this work, we created three different settings of resource pools for three level of priority of virtual machines in accordance with different level of CPU and memory resource requirement. The results can provide several guidelines for server virtualization administrators.

This paper attempts to achieve a digital thermometer by using a Arduino UNO as a main processor. A digital thermometer has been designed and constructed based on LM35 as the temperature sensing device. The design is in four modules; power supply, temperature sensor, LCD device and Arduino UNO modules. While the Arduino UNO forms the main control element, the temperature sensor senses the temperature to be measured and converts it to a corresponding analogue voltage. The measured temperature is displayed on a 16 by 2 Character LCD incorporated in the system.

In this paper, we review options for wireless technologies such as Wi-Max, Satellite, Wi-Fi, and also describe their tradeoffs. Then, we propose a system that provides long distance, low cost, point-to-point Wi-Fi backhaul link for a wireless distribution system (WDS) which is particularly suited for rural coverage. This proposed wireless network will be an ICT tool for rural development.

This paper presents a new text to text data hiding steganography using arbitrary two bits including least significant bits. It also presents a novel algorithm to hide a large amount of text in cover text file without affecting the cover format, meaning and font types, by using position file in which all indexes are stored according to the secret message with respect to the cover text file. The size of secret message and cover text file can vary the processing time, while extracting secret message back from cover files using position file. Unlimited amount of secret message can be hidden in a cover text file because the indexes are used as much time as we want with round loop function. But the more the size of secret message, the more the size of position file. In this research work, various secret message and cover files are used to analysis of extracting time. This proposed system is implemented by using MatLab Programming Language.

Myanmar Language being morphologically rich and complex language, morphological analysis is an essential preprocessing phrase for effective and efficient Myanmar language processing tasks and information retrieval. As a part of Myanmar Language Analysis project, a language model that can represent the morphological feature based on the recurrent neural network (RNN) is developed. Two other models, tri-gram with KN and baseline model using the current available systems are also developed. The language models are trained and tested their performance in segmentation and tagging using three different testing corpora.

This paper focuses on the design consideration for private cloud deployment model in academic environment for authors’ institution. Conceptual benefits of deploying private cloud system is to provide virtualized computing resources to users rather than accessing physical computing resources. The users of the academic cloud will consist of faculties and students in the institution and they would access computing resource as they need to utilize elastically and flexibly on demand. For deployment model for academic cloud environment, we choose an open source software platform “CloudStack” that pools computing resources to build private cloud as well as to manage the network, storage, and compute nodes for cloud Infrastructure. This system is expected to increase the efficient usage of computing resources, utilization of servers and decreasing the power consumption for physical machines by accessing virtual machines from cloud servers. Another goal is to minimize the operation and maintenance cost for computing resources.

In Myanmar, necessary e-government services are not fully implementing yet. Several challenges and issues are still facing in e-government developments such as the new trend of technology, legal enforcement, and individual awareness. Currently, some ministries and institutions in Myanmar are implementing their own systems independently. In the last decade, infrastructure and software development for the e-government system has invested. However, the dedicated open framework for e-government services still needs to consider. The author proposes an open framework called “Blockchain-based Open Framework for e-Government Services” in this paper. That will be helpful for the standard implementation of Myanmar e-government services efficiently and effectively. Blockchain technology is one of the megatrends for recent years. It is potentially a revolutionary means of secure and transparent data sharing and processing in a wide variety of sectors. The important concept of Blockchain technology is a combination of secured distributed ledger, cryptocurrency (or) bitcoin and smart contract system. That concept is very appropriate at: (i) creating trusted and secured information processing for large and heterogeneous sets of stakeholders; (ii) creating trusted audit trails of information; (iii) creating robustness and cost-saving data processing platform. The government agencies around the world are looking forward to Blockchain technology for secured e-government services efficiently and effectively. The proposed framework would consist of five layers. Development platform Layer (Layer 1) is the interface layer for application development. There are several e-Government applications depends on the services categories of G2G, G2C, G2B, and G2EservicesThe most e-government services activate depends on the legal law and regulations of individual government. Blockchain-based Services Layer (Layer 2) provides core services of cryptocurrency, blocker service, shared distributed ledger, smart contract, auditing, identification, verification, cybersecurity, payment, authentication, and authorization. Data standardization is the most important part of the e-government system. All data of ministries are storing in heterogeneous and distributed forms. AI-based machine learning algorithms, database methodologies, and standardization methods are involved in Data Standardization and Distribution Layer (Layer 3). Data Storage Service Layer (Layer 4) and Secure and Distributed Infrastructure Layer (Layer 5) are underlying layers for data center services, storage service, distributed computing services, and secure protocol services. The intended outcomes of the proposed framework would be (i) ensure the secure, reliable, and robustness services (ii) support unique standardization for data between all institutions and ministries (iii) provide interoperability and efficiency for collaborative processes between sub-systems.

Face Recognition(FR) is an active research area that has many real-life applications such as
airport security, bank/store security, access control, and so on. Each application has each own criteria and so on. In terms of background faces and application, capabilities, and strengths in each aspect of recognition performance. In this paper, we proposed recognitions of real-time face images in frontal view using a hybrid method of geometric features and template matching. The facial features are extracted from the face in frontal view. The silent feature region also extracts from the two eyes and the mouth. After passing the feature-based method, the new face image is projecting into face space and then comparing its position in the face space with those of known face. After that, we find the
best match in a face database.

Estimation and analysis of traffic jams play a vital role in an intelligent transportation system and advances safety in the transportation system as well as mobility and optimization of environmental impact. For these reasons, many researchers currently mainly focus on the brilliant machine learning-based prediction approaches for traffic prediction systems. This paper primarily addresses the analysis and comparison of prediction accuracy between two machine learning algorithms: Naïve Bayes and K-Nearest Neighbor (K-NN). Based on the fact that optimized estimation accuracy of these methods mainly depends on a large amount of recounted data and that they require much time to compute the same function heuristically for each action, we propose an approach that applies multi-threading to these heuristic methods. It is obvious that the greater the amount of historical data, the more processing time is necessary. For a real-time system, operational response time is vital, and the proposed system also focuses on the time complexity cost as well as computational complexity. It is experimentally confirmed that K-NN does much better than Naïve Bayes, not only in prediction accuracy but also in processing time. Multithreading- based K-NN could compute four times faster than classical K-NN, whereas multithreading – based Naïve Bayes could process only twice as fast as classical Bayes.

Grid access control mechanism is aimed at verifying the identity of an entity, controlling certificates and to restrict from unauthorized accesses to grid resources. Hence, it plays a vital role to get the system availability as well as to prevent the attackers who tries to get the unauthorized accesses to resources. In fact, this paper proposes the system that provides the secure certificate framework to offer access control. The main contribution of this paper is creating two types of certificate and using counting process to secure Authorization, Authentication and Access Control service that is adapted to traditional RSA algorithm for Grid Application.

As traffic congestion is becoming an everyday facing problem in urban region, traffic prediction and detection systems are playing an important role in city life. The road network sensors were popular in the previous systems. However, these technologies addressed to solve the installation and maintenance cost. Fortunately, the dramatic technology innovation is carrying many crucial solution for transportation agency to provide the relative services efficiently. This paper mainly emphasizes on detecting traffic condition by analyzing the behavior of vehicle primarily based on GPS mobile phone and history data. The system is built into two parts: Client and Cloud Server. On the Client side, the system distinguishes whether a phone carrier is taking a vehicle or walking. To analysis this situation, the Average Moving Filtering method are applied. On the Server side, it detects the traffic status based on checking vehicle’s behavior based on the Client’s result by applying Bayes Classifier.