Deepali Mishra
Department of Computer Applications
- DepartmentDepartment of Computer Applications
- DesignationAssistant Professor
- QualificationPh.D. (Pursuing), M.Tech. (IT), MCA, B.Sc. (PCM)
- Emaildeepali.mishra@invertis.org
- Experience14 Years
Ms. Deepali Mishra is an Assistant Professor in the Department of Computer Applications at Invertis University, Bareilly, with 14 years of teaching experience in Computer Applications. She is pursuing a Ph.D. in Computer Science and holds an M.Tech. in Information Technology (Grade A), an MCA, and a B.Sc. in Physics, Chemistry and Mathematics (PCM).
Her academic and technical expertise includes Artificial Intelligence, Machine Learning, Data Analytics, Programming, Computer Networks, Operating Systems, and emerging technologies. Her research interests focus on the application of Artificial Intelligence and Machine Learning to intelligent prediction, data-driven decision-making, cybersecurity, and practical computing applications.
Ms. Mishra has contributed to scholarly research through conferences, IEEE publications, and other academic publications. Her research work includes studies on genetic algorithms, machine learning for crop yield prediction, AI-driven digital content protection, electric vehicle charging demand prediction, forest ecosystem water-use efficiency, the impact of AI on employment, technology usage and mental health, and machine learning-based prediction of PCOS severity.
Her innovation work includes patents titled “An Artificial Intelligence Method for Prediction of Accidents Involving Electric Vehicles” and “AI Based Traffic Flow Analysis Camera.” She has also participated in Faculty Development Programmes, workshops, seminars, and conferences focused on Artificial Intelligence, Data Analytics, Cybersecurity, Generative AI, and emerging technologies.
Through her teaching and research, Ms. Mishra contributes to technology-oriented learning and research in Computer Applications, with an emphasis on applying AI and machine learning techniques to practical and data-driven challenges.
