Sabyasachi Pramanik

Sabyasachi PramanikSabyasachi Pramanik is a Professional IEEE member. He obtained a Ph.D. in Computer Science and Engineering from the Sri Satya Sai University of Technology and Medical Sciences, Bhopal, India. Presently, he is an Assistant Professor, Department of Comuter Science and Engineering, Haldia Institute of Technology, India. He has many publications in various reputed international conferences, journals, and online book chapter contributions (Indexed by SCIE, Scopus, ESCI, etc.). He is doing research in the field of Artificial Intelligence, Data Privacy, IoT, Network Security, and Machine Learning. He is also serving as the editorial board member of many international journals. He is a reviewer of journal articles from IEEE, Springer, Elsevier, Inderscience, IET, and IGI Global. He has reviewed many conference papers, has been a keynote speaker, session chair and has been a technical program committee member in many international conferences. He has authored a book on Wireless Sensor Network. Currently, he is editing 6 books from IGI Global, CRC Press, EAI/Springer and Scrivener-Wiley Publications. Read More Read Less

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Software Defined Networking for AD Hoc Networks37 %
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Machine Learning Adoption in Blockchain-Based Intelligent Manufacturing44 %
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Software Defined Networking for AD Hoc Networks37 %
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Cyber Security and Digital Forensics
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02 Aug 2022
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Data Driven Mathematical Modeling in Agriculture6 % NR
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An Interdisciplinary Approach to Modern Network Security11 % NR
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Machine Learning Adoption in Blockchain-Based Intelligent Manufacturing6 % NR
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Handbook of Research on Data-Driven Mathematical Modeling in Smart Cities38 % NR
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20 Jan 2023
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Cyber Security and Network Security10 % NR
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Multidisciplinary Approach to Modern Digital Steganography43 % NR
Publisher: IGI Global
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24 May 2021
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Multidisciplinary Approach to Modern Digital Steganography38 % NR
Publisher: IGI Global
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24 May 2021
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Methodologies and Applications of Computational Statistics for Machine Intelligence38 % NR
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Methodologies and Applications of Computational Statistics for Machine Intelligence43 % NR
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Data-Directed Mathematical Modeling13 % NR
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