Browsing by Author "Bandan, Sheikh Sadi"

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A Comparative Study for Measuring the Quality of Dhaka City Transportation System
(IEEE, 2023-09-01) Rejuan, Md Arifur Rahman; Bandan, Sheikh Sadi; Rakib, Md. Abdur; Assaduzzaman, Md
Dhaka is the capital of Bangladesh and one of the most populous countries in the world. The population of Dhaka has grown at a huge rate in the last few decades and is expected to grow similarly in the coming decades. As a result, population density is increasing, meaning that a large population has to be accommodated in a small area. This is disrupting other activities of Dhaka city. Among the disrupted activities, the condition of the Dhaka Transportation System is absolutely pathetic. Because of this, traffic jams are constantly being created in Dhaka city and people spend their necessary time sitting in traffic jams for hours. Our research paper discusses the Dhaka Transport System. We have worked with 104 data sets and a research paper based on feedback from all of them. By discussing with them, several problems were found, such as: overpopulation, more vehicles, non-observance of the traffic system, not driving carefully, poor mechanical condition of vehicles etc. All these problems have been discussed in the research paper and solutions have been given to make the transportation system in Dhaka city work well in a systematic way.
A Deep Learning Approach for Bengali News Headline Categorization
(IEEE, 2023-08-08) Bandan, Sheikh Sadi; Sunve, Sabid Ahmed; Romel, Shaklian Mostak
A huge amount of information and data is now available in seconds through the internet. Due to the availability of internet in the world, as the amount of online news is increasing on the one hand, people are interested in reading news from online news portals i.e., Facebook, Twitter, WhatsApp, Telegram, Instagram, blogs etc. Along with the increase in the amount of digital data in news portals, the number of readers is also increasing, thus the need for data classification for digital data is also increasing day by day. There are various methods of data classification, such as machine learning, deep learning, etc., as well as various data mining algorithms. Data is classified using these algorithms, so that people can make sense of the news just by reading the news headlines. Natural language processing methods are used to classify data in any language for such problems. In this paper, Bangla news is classified into 6 categories using deep learning algorithm. The categories are International, National, Sports, Entertainment, Politics and IT. In deep learning, BiLSTM and GRU algorithms have been used to classify. BiLSTM has an accuracy of 83.42% and GRU has an accuracy of 80.01%. BiLSTM has been found to have the highest accuracy among deep learning algorithms.
Enhancing Sentiment Analysis using Machine Learning Predictive Models to Analyze Social Media Reviews on Junk Food
(Daffodil International University, 2023-12-20) Ajmain, Moshfiqur Rahman; Khatun, Mst. Farhana; Bandan, Sheikh Sadi; Rejuan, Arifur Rahman; Ria, Nushrat Jahan; Noori, Sheak Rashed Haider
In the last few years, the Use of social media has increased immensely. People share different types of opinions on social media like Facebook posts, comments, tweets etc. Sentiment analysis involves the process of categorizing these opinions. The aim of this study, find out the customer’s attitudes toward the restaurant. Nowadays sentiment review is gaining grip. The benefits of this sentiment analysis for restaurants is how customers like their food and as a result, the business of Bangladeshi restaurants will be more developed. The study focuses primarily on customers’ behavior, tastes, preferences, conversations, reviews, and objections. For this purpose 500 data are collected. There are six attributes in the dataset and based on customer reviews they are satisfied or unsatisfied. This exploration uses different classifiers of ML to develop review analysis like SVM, Random Forest, K-nearest neighbors, Decision Tree, Logistic Regression and XGBoost Classifier. And Comparing these algorithms’ performances, XGBOOST gives the greatest accuracy which is 83%.