Dr. Nidhi Arora
Kalindi College, University of Delhi, Madurai Kamaraj University, Makhanlal Chaturvedi National University of Journalism and Communication, University of Delhi
0009-0009-6413-8432
About
Dr. Nidhi Arora, is professor Department of computer science, Kalindi College, University of Delhi, India. She has started her teaching carrier in 2001 in University of Delhi, India. A Ph.D. from Department of Computer Science, University of Delhi, her primary research areas are Social Networks, Nature Inspired Computing and Machine learning. She has published many peer reviewed research articles in International journals of repute, book chapters, and has also presented many research papers in various international conferences of ACM, Springer and IEEE. Dr. Nidhi Arora has delivered many talks on latest research topics such as “Data Sciences”, “e-content development“ and “ Deep Learning” to name a few in various FDPs and workshops. She has quite actively organized and convened UGC sponsored National conference, FDPs and many seminars and workshops in Kalindi college for teachers as well as students. A passionate teacher and an avid researcher with 23 years of teaching experience in DU, She has been part of many administrative assignments, committees, on the board of Technical Programme Committee of reputed international conferences , and acted as a reviewer to review articles in many
springer and IEEE journals.
Employment
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Kalindi College, University of Delhi Professor2010 - Present
Education
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Madurai Kamaraj University M.Phil2008 - 2010
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Makhanlal Chaturvedi National University of Journalism and Communication MIT1999 - 2001
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University of Delhi B.Sc (Computer Science)1996 - 1999
Projects & Funding
Projects & funding information is unavailable.
Publications (21)
- Prophesying Credit Card Frauds Using Predictive and Deep Transfer Learning: A Comprehensive Experimental Perspective Save
- A robust deep learning ensemble framework for accurate brain tumor classification Save
- An enhanced predictive modelling framework for highly accurate non-alcoholic fatty liver disease forecasting Save
- Deep Learning based Road Traffic Assessment for Vehicle Rerouting: An Extensive Experimental Study of RetinaNet and YOLO Models Save
- An Efficient Deep Learning Model Using Harris-Hawk Optimizer for Prognostication of Mental Health Disorders Save
- Multi-criteria decision making (MCDM) in diverse domains of education: a comprehensive bibliometric analysis for research directions Save
- An ensemble deep learning model for automatic classification of cotton leaves diseases Save
- Effective Groundnut Crop Management by Early Prediction of Leaf Diseases through Convolutional Neural Networks Save
- Diabetes mellitus prediction using machine learning within the scope of a generic framework Save
- Automatic Diseases Classification and Detection in Castor Oil Plant Leaves Using Convolutional Neural Network Save