Basant Agarwal

Basant Agarwal is working as an assistant professor in the department of computer science and engineering at Indian Institute of Information Technology Kota (IIIT-Kota), MNIT Campus, Jaipur, India. He is also affiliated as visiting Research Fellow at the Department of Computer Science, Norwegian University of Science and Technology (NTNU), Norway. He has more than 10 years of experience in research and teaching. He has worked as PostDoc Research Fellow at NTNU, Norway under prestigious Fellowship by ERCIM (European Research Consortium for Informatics and. Mathematics) through “Alain Bensoussan Fellowship Programme” in 2016. He has also worked as Research Scientist at Temasek Laboratories, National University of Singapore (NUS) Singapore. He has authored two books on the topic of sentiment analysis in Springer Book Series: Socio-Affective Computing series and another on Data structures for Packt Publisher, UK. He has published more than 50 reputed conferences and Journal. Dr. Agarwal is editing books for reputed publisher such as Springer, Elsevier, Taylor & Francis, IGI Global, and also managing many special issues for reputed Journals. Dr. Agarwal is also serving as a senior member, technical program committee member, member of editorial board/reviewer board of various renowned international conferences/journals such as Knowledge-based Systems, IEEE Intelligent Systems, Information Processing & Management to name a few.

Publications

A Deep Neural Network Model for Cross-Domain Sentiment Analysis
A Deep Neural Network Model for Cross-Domain Sentiment Analysis
Suman Kumari, Basant Agarwal, Mamta Mittal. © 2022. 17 pages.
Sentiment analysis is used to detect the opinion/sentiment expressed from the unstructured text. Most of the existing state-of-the-art methods are based on supervised learning...
A Deep Neural Network Model for Cross-Domain Sentiment Analysis
A Deep Neural Network Model for Cross-Domain Sentiment Analysis
Suman Kumari, Basant Agarwal, Mamta Mittal. © 2021. 16 pages.
Sentiment analysis is used to detect the opinion/sentiment expressed from the unstructured text. Most of the existing state-of-the-art methods are based on supervised learning...
Entity-Extraction Using Hybrid Deep-Learning Approach for Hindi text
Entity-Extraction Using Hybrid Deep-Learning Approach for Hindi text
Richa Sharma, Sudha Morwal, Basant Agarwal. © 2021. 11 pages.
This article presents a neural network-based approach to develop named entity recognition for Hindi text. In this paper, the authors propose a deep learning architecture based on...
Forensic Investigations and Risk Management in Mobile and Wireless Communications
Forensic Investigations and Risk Management in Mobile and Wireless Communications
Kavita Sharma, Mitsunori Makino, Gulshan Shrivastava, Basant Agarwal. © 2020. 314 pages.
Mobile forensics has grown from a relatively obscure tradecraft to a crucial part of many criminal investigations, and is now used daily by examiners and analysts within local...
Spam Detection on Social Media Using Semantic Convolutional Neural Network
Spam Detection on Social Media Using Semantic Convolutional Neural Network
Gauri Jain, Manisha Sharma, Basant Agarwal. © 2020. 16 pages.
This article describes how spam detection in the social media text is becoming increasing important because of the exponential increase in the spam volume over the network. It is...
Spectral Clustering and Cost-Sensitive Deep Neural Network-Based Undersampling Approach for P2P Lending Data
Spectral Clustering and Cost-Sensitive Deep Neural Network-Based Undersampling Approach for P2P Lending Data
Pankaj Kumar Jadwal, Sonal Jain, Basant Agarwal. © 2020. 16 pages.
Peer-to-peer lending, also known as P2P lending, is the new generation loan disbursement process, where lenders and borrowers communicate through online services. Loans through...
Spam Detection on Social Media Using Semantic Convolutional Neural Network
Spam Detection on Social Media Using Semantic Convolutional Neural Network
Gauri Jain, Manisha Sharma, Basant Agarwal. © 2018. 15 pages.
This article describes how spam detection in the social media text is becoming increasing important because of the exponential increase in the spam volume over the network. It is...