Dr. Dilwar Hussain Mazumder
National Institute of Technology Nagaland, GIMT Guwahati, Rajiv Gandhi University, Jorhat Engineering College (Dibrugarh University)
0000-0002-6316-2977
About
Dr. Dilwar Hussain Mazumder received the B.E. and M.Tech. degree in Computer Science and Engineering from Jorhat Engineering College, Assam, India, and Rajiv Gandhi University, Arunachal Pradesh, India, in 2008 and 2012, respectively. He pursued the Ph.D. degree in Computer Science and Engineering from National Institute of Technology Nagaland, India in 2019. His research interests include computational methods for gene selection in cancer prediction, data analytics and bioinformatics with emphasis to drug discovery. He has published several research papers in reputed journals and prestigious conferences. He is having more than
18 years of experience in Teaching and Research. Currently, he is working as an Assistant Professor at the Department of Computer Science and Engineering, National Institute of Technology Nagaland, India.
Employment
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National Institute of Technology Nagaland Assistant Professor Grade - I2013 - Present
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GIMT Guwahati Assistant Professor2008 - 2013
Education
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National Institute of Technology Nagaland Ph.D.2014 - 2019
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Rajiv Gandhi University M.Tech.2010 - 2012
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Jorhat Engineering College (Dibrugarh University) B.E.2004 - 2008
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S. S. College Class 122002 - 2004
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Algapur Public HS School Class 102000 - 2002
Projects & Funding
Projects & funding information is unavailable.
Publications (22)
- A Comprehensive Survey of Studies on Predicting Anatomical Therapeutic Chemical Classes of Drugs Save
- MLCNNF: A Multi-Label Convolutional Neural Network Framework for Predicting Adverse COVID Drug Reactions From the Chemical Structure Save
- A Review on Application of Machine Learning Techniques Coupled With E-Nose in Healthcare, Agriculture, and Allied Domains Save
- Feature Rank Aggregation for Effective Biomedical Data Prediction Using Particle Swarm Optimization Save
- MLCNN-LSTM-COV: A Multi-Label Convolutional Neural Network and Long Short-Term Memory-Based Framework for Predicting Adverse COVID Drug Reactions Save
- A novel neural network-based nearest neighbor approach for drug function prediction from chemical structures Save
- MLCNN-COV: A multilabel convolutional neural network-based framework to identify negative COVID medicine responses from the chemical three-dimensional conformer Save
- K1K2NN: A novel multi-label classification approach based on neighbors for predicting COVID-19 drug side effects Save
- Inceptionv3-LSTM-COV: A multi-label framework for identifying adverse reactions to COVID medicine from chemical conformers based on Inceptionv3 and long short-term memory Save
- Improving cancer prediction using feature selection in spark environment Save