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3. Patterns of Information Seeking Behaviour of Law Students in Digital Environment: A Study

Author

Das, Rajesh Kumar and Jadab, Anwesha

Subjects
Information Seeking Behaviour, Digital Environment, Law Students, Bangladesh, Bibliography. Library science. Information resources
Abstract

Nowadays the enormous growth of modern information communication technologies and its massive use have influenced information users all over the world. Such a digital environment has drastically changed the information seeking patterns of information users of every community. These also tend law students to use various legal information sources and services in digital environments while seeking information. But there have been few empirical user studies on the aspect of digital information seeking behaviours of law students in either law or library and information science literatures. This paper aims to draw out patterns of information seeking behavior of students of law in digital environments at the University of Dhaka. A stratified random sample survey was conducted for this study. The results show that students prefer the electronic format of information rather than printed format. Major e-resources used by them and the influential factors of use were also identified in this study. This study also identified some crucial problems for seeking information and provides suggestions for the development of electronic legal information systems.

Published
2017
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11. Application of Artificial Intelligence and Machine Learning in Libraries: A Systematic Review

Author

Das, Rajesh Kumar and Islam, Mohammad Sharif Ul

Subjects
Computer Science - Digital Libraries, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

As the concept and implementation of cutting-edge technologies like artificial intelligence and machine learning has become relevant, academics, researchers and information professionals involve research in this area. The objective of this systematic literature review is to provide a synthesis of empirical studies exploring application of artificial intelligence and machine learning in libraries. To achieve the objectives of the study, a systematic literature review was conducted based on the original guidelines proposed by Kitchenham et al. (2009). Data was collected from Web of Science, Scopus, LISA and LISTA databases. Following the rigorous/ established selection process, a total of thirty-two articles were finally selected, reviewed and analyzed to summarize on the application of AI and ML domain and techniques which are most often used in libraries. Findings show that the current state of the AI and ML research that is relevant with the LIS domain mainly focuses on theoretical works. However, some researchers also emphasized on implementation projects or case studies. This study will provide a panoramic view of AI and ML in libraries for researchers, practitioners and educators for furthering the more technology-oriented approaches, and anticipating future innovation pathways.

Published
2021

17. Analysis of Bangla Transformation of Sentences Using Machine Learning

Author

Das, Rajesh Kumar, Sammi, Samrina Sarkar, Kobra, Khadijatul, Ajmain, Moshfiqur Rahman, khushbu, Sharun Akter, Noori, Sheak Rashed Haider, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Troiano, Luigi, editor, Vaccaro, Alfredo, editor, Kesswani, Nishtha, editor, Díaz Rodriguez, Irene, editor, Brigui, Imene, editor, and Pastor-Escuredo, David, editor

Published
2023
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18. A Simulation of Nanofluid Flow with Variable Viscosity and Thermal Conductivity Over a Vertical Stretching Surface

Author

Dey, Debasish, Das, Rajesh Kumar, Borah, Rupjyoti, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Dutta, Paramartha, editor, Chakrabarti, Satyajit, editor, Bhattacharya, Abhishek, editor, Dutta, Soumi, editor, and Piuri, Vincenzo, editor

Published
2023
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40. Prediction of Bangladeshi Urban Children's Mental Health for the Effect of Mobile Gaming Using Machine Learning.

Author

Hasan, Tanveer, Bonny, Moushumi Zaman, Das, Rajesh Kumar, Sultana, Salma, Islam, Md. Touhidul, and Sattar, Abdus

Subjects
MOBILE games, CHILDREN'S health, MENTAL health services, MACHINE learning, YOUNG adults, MOBILE health, RURAL children
Abstract

The popularity of mobile gaming is increasing globally, fueled by technological advancements, high-quality smartphones with gaming features, and widespread internet access. A large number of young people and also children are mainly involved in different types of mobile gaming, primarily online mobile gaming, which is a matter of concern for society. This addiction leads to various psychological issues, including mental health problems, loneliness, introversion, insomnia, and a lack of self-control. Bangladesh's lack of public mental health facilities exacerbates this challenge, particularly in rural areas. To address this issue, we have developed a machine learning model to predict the level of mental health issues faced by Bangladeshi urban children due to gaming addiction. A total of 1996 data were collected from urban parents about their children's gaming activities and categorized them as 'Serious,' 'Partial,' and 'Normal.' The dataset was then split into a 70:30 ratio for training and testing purposes. K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Multinomial Naïve Bayes, & Random Forest were applied as the machine learning approaches for prediction. Here, the Support Vector Machine gives good accuracy (92.75%), and other classifiers have different accuracy. The research highlights the significance of addressing gaming addiction in children and the potential of machine learning technology in predicting mental health issues, particularly in the absence of public mental health services. The study concludes by proposing the expansion of the research to include smartphone and mobile gaming addiction among Bangladeshi rural children and ensure a holistic approach to tackling the broader issue of technology-related addiction. [ABSTRACT FROM AUTHOR]

Published
2024
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