Uzzal Biswas

Work place: Electronics and Communication Engineering Discipline, Khulna University, Khulna-9208, Bangladesh

E-mail: uzzal.biswas@ece.ku.ac.bd

Website: https://orcid.org/0000-0001-8489-3827

Research Interests:

Biography

Uzzal Biswas received a Bachelor of Science degree in Electronics and Communication Engineering with distinction from Khulna University, Bangladesh in 2011. Later in 2011, he joined in Electronics and Communication Engineering (ECE) discipline as a faculty member. In 2014, he received a Master of Science degree in Electronics and Communication Engineering with distinction from Khulna University, Bangladesh. In 2015, he received a President Gold Medal Award from Khulna University for securing 1st position with distinction and obtaining the highest CGPA in the undergraduate program among all disciplines under the Science, Engineering and Technology (SET) School of Khulna University. In 2016, he received the prestigious Endeavour Postgraduate Research Scholarship from the Education Ministry of Australian Government to pursue his PhD degree at the University of New South Wales (UNSW), Sydney, Australia. In 2021, he received a Doctor of Philosophy degree in Biomedical Engineering from UNSW, Sydney, Australia. Currently, he is working as a Professor in the ECE discipline, Khulna University, Khulna, Bangladesh. His research interests are Biomedical Signal Processing, Telehealth-based Remote Patient Monitoring, Data Mining, Time Series Analysis, Machine Learning-based Classification and Prediction Modeling.

Author Articles
A Comprehensive Bibliometric Study on Machine Learning Based Rehabilitation and Stroke Research (1999 - 2022)

By Tasfia Tahsin Humayra Akter Uzzal Biswas Jun Jiat Tiang Abdullah-Al Nahid

DOI: https://doi.org/10.5815/ijem.2025.01.02, Pub. Date: 8 Feb. 2025

In recent years, the rising prevalence of chronic illness has led to an increase in disability of patients. Extensive research has been done to enhance both the functional abilities as well as the quality of the affected individuals’ lives. Researchers have worked on the effects of numerous scholars, keywords and countries of these specific fields. However, a few state-of-the-art bibliometric analyses have been done in this research to reduce the quantitative aspects of the vast research fields of rehabilitation. We have precisely selected 427 core papers from the Web of Science database spanning from 1999 to 2022 where Machine Learning (ML) or Deep Learning (DL) is used in the rehabilitation field. Consequently, our analysis focuses on citation patterns, trend analysis and collaborations between countries or influential keywords offering a detailed overview of global trends in this interdisciplinary domain. Additionally, we visualize the research trends of various authors and countries which provide invaluable insights into research impact as well as collaboration networks. Overall, this paper aims to shape the evolving field of rehabilitation by providing in depth analysis of the citation landscape, key researchers, and international collaborations.    

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