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Dr. Kanhaiya Sharma
VIT-AP University, Symbiosis International University Symbiosis Institute of Technology, Anurag University
About
Kanhaiya Sharma is a renowned researcher and educator with an M.Tech. (2011) and Ph.D. (2021). With 16 years of experience, he excels in the Computer Science & Engineering department at Symbiosis Institute of Technology. Dr. Sharma's impactful contributions include 60+ research papers, focusing on low-cost wireless solutions, Artificial Intelligence, Fuzzy Logic, Big Data, 5G communication, and Microstrip Antenna design.
Employment
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VIT-AP University Associate Rofessor2026 - Present
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Symbiosis International University Symbiosis Institute of Technology Assistant Profssor2022 - 2026
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Anurag University Assistant Professor2021 - 2022
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Sandip University Assistant Professor & Head of the department2020 - 2021
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Pandit Deendayal Petroleum University Full time Doctoral student2017 - 2021
Education
Education history is unavailable.
Projects & Funding
Projects & funding information is unavailable.
Publications (61)
- Review of Hybrid and Data-Efficient Methods in Medical Image Segmentation Save
- Fabric-based wideband wearable textile antenna for microwave cancer detection with AI-assisted analysis Save
- Reducing energy consumption in air conditioning systems, a fuzzy logic-based optimization approach Save
- A hybrid rule-based NLP and machine learning approach for PII detection and anonymization in financial documents Save
- Experimental investigation of flexible eight-port asymmetric fed MIMO antenna with narrow-super-widebandn-s characteristics for future applications including internet of things Save
- Modeling and simulation of an effectual triangular slotted UWB flexible antenna for breast cancer detection and healthcare monitoring Save
- Compact wearable microstrip antenna design using hybrid quasi-Newton and Taguchi optimization Save
- A DOA-Driven Adaptive Framework for Smart Traffic and Street Lighting in WSN Save
- A novel methodology for makeup invariant face recognition based on directional gradient and local derivative descriptors (DGLDD-FR) Save
- Improved method for stress detection using bio-sensor technology and machine learning algorithms Save