University of Computer Studies, Yangon

About: University of Computer Studies, Yangon is a education organization based out in Yangon, Myanmar. It is known for research contribution in the topics: Feature extraction & Machine translation. The organization has 511 authors who have published 577 publications receiving 2276 citations. The organization is also known as: Institute of Computer Science and Technology.

An Energy Efficient Cluster-Head Selection for Wireless Sensor Networks

Abstract: Recent advances in wireless sensor networks have led to many new protocols specifically designed for sensor networks where energy awareness is an essential consideration. Clustering is a key routing technique used to reduce energy consumption. Clustering sensors into groups, so that sensors communicate information only to cluster-heads and then the cluster-heads communicate the aggregated information to the base station, saves energy and thus prolonging network lifetime. In this paper, we propose an energy efficient cluster-head selection algorithm for adapting clusters and rotating cluster head positions to evenly distribute the energy load among all the nodes. Our proposed model is extended to the LEACH’s stochastic cluster-head selection algorithm by modifying the probability of each node to become cluster-head based on remaining energy level of sensor nodes for transmission. Simulation results show that our proposed model could better implement load balance and prolong the lifetime of the network.

Sentiment analysis of students' comment using lexicon based approach

Abstract: In education system, students' feedback is important to measure the quality of teaching. Students' feedback can be analyzed using lexicon based approach to identify the students' positive or negative attitude. In most of the existing teaching evaluation system, the intensifier words and blind negation words are not considered. The level of opinion result isn't displayed: whether positive or negative opinion. To address this problem, we propose to analyze the students' text feedback automatically using lexicon based approach to predict the level of teaching performance. A database of English sentiment words is created as a lexical source to get the polarity of words. By analyzing the sentiment information including intensifier words extracting from students' feedback, we are able to determine opinion result of teachers, describing the level of positive or negative opinions. This system shows the opinion result of teachers that is represented as to whether strongly positive, moderately positive, weakly positive, strongly negative, moderately negative, weakly negative or neutral.

Extracting Product Features and Opinion Words Using Pattern Knowledge in Customer Reviews

Abstract: Due to the development of e-commerce and web technology, most of online Merchant sites are able to write comments about purchasing products for customer. Customer reviews expressed opinion about products or services which are collectively referred to as customer feedback data. Opinion extraction about products from customer reviews is becoming an interesting area of research and it is motivated to develop an automatic opinion mining application for users. Therefore, efficient method and techniques are needed to extract opinions from reviews. In this paper, we proposed a novel idea to find opinion words or phrases for each feature from customer reviews in an efficient way. Our focus in this paper is to get the patterns of opinion words/phrases about the feature of product from the review text through adjective, adverb, verb, and noun. The extracted features and opinions are useful for generating a meaningful summary that can provide significant informative resource to help the user as well as merchants to track the most suitable choice of product.

Challenges of Implementing e-Learning in Developing Countries: A Review

Abstract: The rapid developments of internet and communication technologies have materially altered many characteristics and concepts of the learning environment. E-learning has started to make way into developing countries and is believed to have huge potential for governments struggling to meet a growing demand for education while facing shortage of expert teachers, shortage of update text books and limited teaching materials. However, there are many challenges to implement e-learning in developing countries such as poor network infrastructure, lack of ICT knowledge, weakness of content development, etc. The objective of this study is to determine the major challenges of implementing e-learning systems in developing countries. The results of this study will serve as a basic for improving higher education in developing countries.

Web log cleaning for mining of web usage patterns

Abstract: Web usage mining (WUM) is a type of web mining, which exploits data mining techniques to extract valuable information from navigation behavior of World Wide Web users. The data should be preprocessed to improve the efficiency and ease of the mining process. So it is important to define before applying data mining techniques to discover user access patterns from web log. The main task of data preprocessing is to prune noisy and irrelevant data, and to reduce data volume for the pattern discovery phase. This paper mainly focus on data preprocessing stage of the first phase of web usage mining with activities like field extraction and data cleaning algorithms. Field extraction algorithm performs the process of separating fields from the single line of the log file. Data cleaning algorithm eliminates inconsistent or unnecessary items in the analyzed data.