Vinayak Gupta

About

Vinayak Gupta received the M.Tech. degree from the School of Computer Science and Technology, Bennett University, where he honed his expertise in artificial intelligence and machine learning. He is currently an Assistant Professor with the Department of Computer Science and Engineering, ITS Engineering College, Greater Noida. His academic journey is centered around developing ethical AI systems, ensuring transparency, and building trust-based AI models that prioritize human welfare. Through his research and projects, he aspires to contribute meaningfully to advancements in technology while fostering innovation in a collaborative environment. As an active student member of IEEE, he is deeply engaged in innovative research with a special focus on AI’s transformative impact in healthcare, particularly through computer vision and explainable AI.

Employment

• ITS Engineering College Assistant Professor

Education

• Bennett University Master Of Technology
  2023 - 2025
• Galgotias University Bachelor of Technology
  2019 - 2023

Projects & Funding

Projects & funding information is unavailable.

Publications (20)

• A BiLSTM-driven framework for operational PM2.5 forecasting: integrating meteorological kinematics for urban air quality management
  Frontiers in Climate 2026 DOI: 10.3389/FCLIM.2026.1855755
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• A survey of clinical decision support systems
  Artificial Intelligence in Biomedical Sciences and Healthcare 2026 DOI: 10.1201/9781003741770-10
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• Analysing the Constant Power Model and the Quantified Energy Crossover of Python Data Structures
  2026 3rd International Conference on Advancements and Key Challenges in Green Energy and Computing Akgec 2026 2026 DOI: 10.1109/AKGEC68790.2026.11485864
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• Causally informed explainable artificial intelligence for cognitive risk stratification in hearing-impaired adults
  The Egyptian Journal of Otolaryngology 2026 DOI: 10.1186/S43163-026-01065-9
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• Fog Computing Model for Evolving Smart Healthcare Applications
  Advances in Science Technology and Innovation 2026 DOI: 10.1007/978-3-031-96265-3_13
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• LogHyper-IB: Spatio-Temporal Hypergraph Representation Learning via Information Bottleneck for Anomaly Detection in Distributed Systems
  IEEE Access 2026 DOI: 10.1109/ACCESS.2026.3682603
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• MEDIAN AGGREGATION OF SHAP VALUES FOR ROBUST FEATURE SELECTION IN IMBALANCED BREAST CANCER PREDICTION MODELS
  Franklin Open 2026 DOI: 10.1016/J.FRAOPE.2026.100691
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• Neuromorphic Approaches to Urban Traffic Management with Spiking Neural Networks
  International Conference on Communication, Computing and Emerging Technologies (IC3ET) 2026 DOI: 10.1109/IC3ET64989.2026.11467541
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• PhysioGen-KAN: A Transparent and Uncertainty-Aware Probabilistic Framework for Adaptive Physiological Signal Processing
  IEEE Open Journal of the Computer Society 2026 DOI: 10.1109/OJCS.2026.3692803
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• Correction: Dash et al. Improving Object Detection in High-Altitude Infrared Thermal Images Using Magnitude-Based Pruning and Non-Maximum Suppression. J. Imaging 2025, 11, 69
  Journal of Imaging 2026 DOI: 10.3390/jimaging12080371
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