Browsing by Author "Ahsan, Tanveer"

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Combining a Rule-based Classifier with Weakly Supervised Learning for Twitter Sentiment Analysis
(IEEE, 2016-10-28) Siddiqua, Umme Aymun; Ahsan, Tanveer; Chy, Abu Nowshed
Microblog, especially Twitter, have become an integral part of our daily life, where millions of user sharing their thoughts daily because of its short length characteristics and simple manner of expression. Monitoring and analyzing sentiments from such massive amount of twitter posts provide enormous opportunities for companies and other organizations to learn about what user think and feel about their products and services. But the ever-growing unstructured and informal user-generated posts in twitter demands sentiment analysis tools that can perform well with minimum supervision. In this paper, we propose an approach for sentiment analysis on twitter, where we combine a rule-based classifier with weakly supervised NaiveBayes classifier. To classify the tweets sentiment, we introduce a set of rules for the rule-based classifier based on the occurrences of emoticons and sentiment-bearing words, whereas several sentiment lexicons are applied to train the Naive-Bayes classifier. We conducted our experiments based on the Stanford sentiment140 dataset. Experimental results demonstrate the effectiveness of our method over the baseline in terms of recall, precision, F1 score, and accuracy.
Mathematical analysis of a metamaterial structure for Electromagnetic (EM) absorption reduction
(International Islamic University Chittagong, 2022-07) Ahsan, Tanveer
The rapid development of communication system such as the second generation (2G) and third-generation (3G) mobile communications, global position system (GPS), WiFi, WiMAX, wireless Bluetooth and Ultra-Wideband (UWB) systems have driven the wireless technology to a revolutionary communications. Besides, high data rate communication system has led to great demand in higher frequency next generation communication system. The wireless devices emit electromagnetic (EM) radiation during active mode of operation which is absorbed by human body. The main challenge of the next generation communication system is to ensure safe use of wireless devices such as mobile phone. Therefore, EM radiation should be controlled towards human body. In this research, a metamaterial structure is developed and the performance of the structure is analyzed. The structure consists of a modified omega-shaped split resonator which can manipulate the behaviour of the EM wave. The mathematical analysis of the proposed antenna shows 63.29% point EM radiation reduction.
The Need for Computer Ethics Course for the Students of CSE/CS or Equivalent Discipline in Bangladesh
(CRP, International Islamic University Chittagong, Bangladesh, 2012-12) Islam, Md. Monirul; Alam, Mohammed Shamsul; Ahsan, Tanveer
Ethics has long been a part of engineering education and practices. Computer Science and Engineering (CSE) is not an exception. In this study, however, it was revealed that very few universities of Bangladesh have a course on Computer Ethics in their respective curriculum of Computer Science and Engineering. But the scenario of other countries are completely different; many universities in the world offer courses like Computer Ethics, Social and Ethical Implications of Computing or courses with similar title and content. There is an increasing trend towards teaching ethics as a major course within CSE departments. This paper examines the necessity of incorporating a Computer Ethics course in the curriculum of CSE in the universities of Bangladesh and suggests an outline for the course. It suggests some topics that can be covered in a Computer Ethics course and offers some practical suggestions also for making the course an effective one. This study also examines ethics in computer education in the light of the Holly Quran and the Sunnah of the Prophet Muhammad (S).
Predicting the Popularity of Online News from Content Metadata
(IEEE, 2016-10-28) Md. Taufeeq Uddin, Md. Taufeeq; Patwary, Muhammed Jamshed Alam; Ahsan, Tanveer; Alam, Mohammed Shamsul
Popularity prediction of online news aims to predict the future popularity of news article prior to its publication estimating the number of shares, likes, and comments. Yet, popularity prediction is a challenging task due to various issues including difficulty to measure the quality of content and relevance of content to users; prediction difficulty of complex online interactions and information cascades; inaccessibility of context outside the web; local and geographic conditions; social network properties. This paper focuses on popularity prediction of online news by predicting whether users share an article or not, and how many users share the news adopting before publication approach. This paper proposes the gradient boosting machine for popularity prediction using features that are known before publication of articles. The proposed model shows around 1.8% improvement over previously applied techniques on a benchmark dataset. This model also indicates that features extracted from articles keywords, publication day, and the data channel are highly influential for popularity prediction.