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Deepali Virmani

Work place: Department of Computer Science Engineering, Bhagwan Parshuram Institute of Technology, New Delhi-110089, India

E-mail: deepalivirmani@gmail.com

Website:

Research Interests: Data Mining, Machine Learning

Biography

Dr. Deepali Virmani is Head of the Department and Professor in the Department of Computer Science and Engineering at Bhagwan Parshuram Institute of Technology affiliated to GGSIPU, New Delhi. Dr. Deepali Virmani has received her B.Tech. degree in Computer Science from MDU, Rohtak, M.Tech. degree in Information Technology, from GGSIPU, and the Ph.D. degree in Computer Science from Delhi University,India. She has an innovative work experience of more than 19 years in both research and academics. She has published more than 90 research papers in International journals/National journals/International conferences of repute. She works in a multi-disciplinary environment involving sensor networks, web intelligence, data mining and intelligent information retrieval systems and machine learning applied to various real-world problems. Dr. Virmani has more than 500+ academic citations index as per Google Scholar .She has guided more than 70 B.Tech Projects. Presently, she is guiding many Ph.D. scholars registered with reputed universities like GGSIPU and UPTU. She is branch counselor of BPIT-IEEE student chapter and BPIT- CSI student branch. She is the remote center coordinator for IIT Bombay, Spoken Tutorial IIT Bombay, IIT Kharagpur, and Virtual Labs IIT Delhi.  She is the associate editor of journal Open Computer Science De Grutyer and also on the reviewer board of various IEEE transactions, Elsevier and Springer journals. She has organized many professional activities like FDPs, workshops, expert lectures and conferences. She has been the session chair in National/International conferences. Her papers have won Best Paper Award at various International Conferences. She has won Best Researcher Award and Best Faculty Award at BPIT. She has been awarded Excellence in Research Award by International Research Awards on New Science Inventions.

Author Articles
Ensem_SLDR: Classification of Cybercrime using Ensemble Learning Technique

By Hemakshi Pandey Riya Goyal Deepali Virmani Charu Gupta

DOI: https://doi.org/10.5815/ijcnis.2022.01.07, Pub. Date: 8 Feb. 2022

With the advancement of technology, cybercrimes are surging at an alarming rate as miscreants pour into the world's modern reliance on the virtual platform. Due to the accumulation of an enormous quantity of cybercrime data, there is huge potential to analyze and segregate the data with the help of Machine Learning. The focus of this research is to construct a model, Ensem_SLDR which can predict the relevant sections of IT Act 2000 from the compliant text/subjects with the aid of Natural Language Processing, Machine Learning, and Ensemble Learning methods. The objective of this paper is to implement a robust technique to categorize cybercrime into two sections, 66 and 67 of IT Act 2000 with high precision using ensemble learning technique. In the proposed methodology, Bag of Words approach is applied for performing feature engineering where these features are given as input to the hybrid model Ensem_SLDR. The proposed model is implemented with the help of model stacking, comprising Support Vector Machine (SVM), Logistic Regression, Decision Tree, and Random Forest and gave better performance by having 96.55 % accuracy, which is higher and reliable than the past models implemented using a single learning algorithm and some of the existing hybrid models. Ensemble learning techniques enhance model performance and robustness. This research is beneficial for cyber-crime cells in India, which have a repository of detailed information on cybercrime including complaints and investigations. Hence, there is a need for model and automation systems empowered by artificial intelligence technologies for the analysis of cybercrime and their classification of its sections.

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