| Qualifications : | Ph.D. (Thapar University), M.Tech.-CSE (Pbi University) | |
| Designation : | Dean (Academic Affairs), Managing Director (Anveshan Foundation), Professor | |
| Teaching Interests : | Database Management Systems, Software Engineering, Soft Computing Techniques, Big Data | |
| Office Address : | Room No. 201, IT Block | |
| Office Phone Number : | 273 | |
| Email : | [email protected] | |
| Research Areas : | Machine Learning, Deep Learning, Hyperspectral Imaging | |
| Bio-Sketch: | Prof. Arun Sharma is Professor in Department of Information Technology Indira Gandhi Delhi Technical University for Women (IGDTUW), Delhi. He completed his PhD Degree from Thapar University in 2009. He has a teaching experience of more than 27 years. His research interests include Machine Learning, Data Science and Software Engineering. He has published more than 135 research papers in SCI/SCIE/SCOPUS index journals and conference proceedings. So far, 11 students have been awarded PhD degree under his supervision. He is also the Coordinator of Centre of Excellence - Artificial Intelligence, established at the University with the support of DST CURIE. He is also having the additional responsibility as Managing Director - IGDTUW Anveshan Foundation (Incubation Centre recognized as NIDHI TBI by DST). In past, he has served HoD-IT, Founder HoD – AI and Data Sciences, HoD-CSE, Dean-Examination Affairs, Dy. Dean-Examination Affairs, Convener – BoS, Admission Nodal Officer for JAC 2020-23, MCA Admission Coordinator 2018 at IGDTUW.
Recent SCI/SCIE/SCOPUS Journal Publications of 2023 Total Clarivate IF (2023): 60.6 (SCI/SCIE: 9, SCOPUS: 4)1. Bala, R., Goel, N., Sharma, A., CTNet: Convolutional Transformer Network for Diabetic Retinopathy Classification, Neural Computing and Applications (SCI Impact Factor 6.0) 2. Jaiswal, G., Rani, R., Mangotra, H., Sharma, A., Integration of hyperspectral imaging and autoencoders: Benefits, applications, hyperparameter tuning and challenges, Computer Science Review, 2023, 50, 100584 (SCI Impact Factor 12.9) 3. Panigrahi, M., Bharti, S., Sharma, A., A reputation-aware hierarchical aggregation framework for federated learning, Computers and Electrical Engineering, 2023, 111, 108900 (SCI Impact Factor 4.3 ) 4. Mangotra, H., Srivastava, S., Jaiswal, G., Rani, R., Sharma, A., Hyperspectral imaging for early diagnosis of diseases: A review, Expert Systems, 2023, 40(8), e13311 (SCI Impact Factor 2.81) 5. Panigrahi, M., Bharti, S., Sharma, A., FedDCS: A distributed client selection framework for cross device federated learning, Future Generation Computer Systems, 2023, 144, pp. 24–36 (SCI Impact Factor 7.5) 6. Bala, R., Goel, N., Sharma, A., Comparative Analysis of Diabetic Retinopathy Classification Approaches Using Machine Learning and Deep Learning Techniques, Archives of Computational Methods in Engineering, 2023 (SCI Impact Factor 9.7) 7. Panigrahi, M., Bharti, S., Sharma, A., A review on client selection models in federated learning, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2023 (SCI Impact Factor 7.56) 8. Singh, H., Kaur, B., ...Singh, A, Sharma, A., Framework for suggesting corrective actions to help students intended at risk of low performance based on experimental study of college students using explainable machine learning model, Education and Information Technologies, 2023 (SCI Impact Factor 5.5) 9. Tyagi, P., Agarwal, K., Jaiswal, G., Rani, R., Sharma, A., Forged document detection and writer identification through unsupervised deep learning approach, Multimedia Tools and Applications, 2023 (SCI Impact Factor 3.6) 10. Sharma, A., Wadhwa, K., Singh, S., ...Wadhwa, S., Machine Learning-Based Breast Cancer Prediction Model, International Journal of Performability Engineering, 19(1), pp. 55-63 (SCOPUS) 11. Singh, S., Sharma, A., State of the Art Convolutional Neural Networks, International Journal of Performability Engineering, 2023, 19(5), pp. 342–349 (SCOPUS) 12. Sinha, K.K., Mathur, M., Sharma, A., Suitability Index Prediction for Residential Apartments Through Machine Learning, International Journal of Performability Engineering, 2023, 19(7), pp. 434–442 (SCOPUS) |
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