Jaydip Sen

Praxis Business School, India

Jaydip Sen is a professor in the Department of Data Science, Praxis Business School, Kolkata, India. His research areas include security and privacy issues in wireless ad hoc and sensor networks, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has published more than 200 papers in reputed indexed journals, refereed international conference proceedings, and 18 book chapters. He has authored three books and edited ten volumes. He is the editor of Knowledge Decision Support Systems in Finance and serves on the technical program committees of several high-ranked international conferences of the Institute of Electrical and Electronics Engineers (IEEE) and the Association for Computing Machinery (ACM). He has contributed to several standardization efforts of the IEEE, including the 802.16m standards and the 3GPP LTE standards. Prof. Sen has been listed among the top 2% of scientists in the world by Stanford University for the last four consecutive years (2019–2022).

Jaydip Sen

7books edited

10chapters authored

Latest work with IntechOpen by Jaydip Sen

Recent times are witnessing rapid development in machine learning algorithm systems, especially in reinforcement learning, natural language processing, computer and robot vision, image processing, speech, and emotional processing and understanding. In tune with the increasing importance and relevance of machine learning models, algorithms, and their applications, and with the emergence of more innovative uses–cases of deep learning and artificial intelligence, the current volume presents a few innovative research works and their applications in real-world, such as stock trading, medical and healthcare systems, and software automation. The chapters in the book illustrate how machine learning and deep learning algorithms and models are designed, optimized, and deployed. The volume will be useful for advanced graduate and doctoral students, researchers, faculty members of universities, practicing data scientists and data engineers, professionals, and consultants working on the broad areas of machine learning, deep learning, and artificial intelligence.

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