Neha Mittal
Meerut Institute of Engineering and Technology, J P Institute of Engineering & Technology, BIT, Jhansi, JP Institute of Engineering and Technology
About
Dr. Neha Mittal is a Professor of the Electronics & Communication Engineering Department at the MIET, Meerut, India. She received B. Tech degree in Electrical & Electronics Engineering from UPTU, M. Tech. degree in Digital Communication from GBTU, Lucknow and Ph.D. from Banasthali University, Rajasthan, India in 2016. She has published more than twenty (20) international ESCI/SCIE/Scopus journal and conferences paper. She is recipient of two funded workshop from TiHAN, IIT Hyderabad. She has secured five Indian patent rights. She received the Research Award in 2025, at MIET, Meerut, UP, India. Her current research interest focus on the development of AI powered and IoT enabled decision support system for sustainable agriculture. She is also working on designing and implementing AI model for diverse applications of image, signal and data analysis and classification
Employment
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Meerut Institute of Engineering and Technology Professor and Head2021 - Present
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Meerut Institute of Engineering and Technology Associate Professor2018 - 2020
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J P Institute of Engineering & Technology Associate Professor2016 - 2018
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JP Institute of Engineering and Technology Assistant Professor2010 - 2016
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Vidya College of Engineering Meerut UP India Sr. Lecturer2008 - 2009
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Radha Govind Group of Institutions Lecturer2004 - 2008
Education
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BIT, Jhansi M.Tech2008 - 2010
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Krishna Institute of Engineering & Technology Department of Electronics and Communication Engineering B.Tech.2000 - 2004
Projects & Funding
Projects & funding information is unavailable.
Publications (28)
- Explainable Vision Transformer Enabled Convolutional Neural Network for Plant Disease Classification Save
- IoT-Enabled Soil Analytics System: A Smart Approach for Sustainable Agriculture Save
- Nanotechnology Revolutionizes Communication Engineering Save
- Apple Leaf Disease Recognition Using Intelligent Techniques Save
- Skin Lesion Detection Save
- CNN-based plant disease recognition using colour space models Save
- Efficient Rice Disease Classification Using Intelligent Techniques Save
- Facial Emotion Recognition using Image Filtering Techniques Save
- Recognition of Apple Leaves Infection Using DenseNet121 with Additional Layers Save
- Skin Lesion Detection: An Analysis of Advanced Computational Approaches Save