Munish Kumar | Computer Science | Research Excellence Award

Dr. Munish Kumar | Computer Science | Research Excellence Award

Associate Professor | Maharaja Ranjit Singh Punjab Technical University | India

Dr. Munish Kumar is a distinguished researcher in artificial intelligence, machine learning, deep learning, and computer vision, with a strong focus on real-world applications in forensic document analysis, speech and language processing, intelligent transportation systems, multimedia analytics, and smart surveillance. His research interests include handwriting and writer identification, document forgery detection, facial emotion recognition, human activity and gait analysis, speech recognition, and transfer learning–based lightweight AI models. He possesses advanced research skills in CNNs, hybrid CNN-BLSTM architectures, support vector machines, feature extraction, pattern recognition, data analytics, and AI-driven image and signal processing. His impactful contributions have earned him academic recognition through high citation impact and consistent publication in reputed international journals and conferences, reflecting significant awards and honors through scholarly influence. According to Scopus, Dr. Kumar has published 182 documents, received 7,393 citations, and achieved an h-index of 43, underscoring his sustained research excellence, global visibility, and leadership in advancing intelligent and forensic AI systems across interdisciplinary domains.

 

Citation Metrics (Scopus)

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Citations

7,393

Documents

182

h-index

43

Citations

Documents

h-index

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Featured Publications


A survey of deep learning and its applications: A new paradigm to machine learning


– Archives of Computational Methods in Engineering, 2020 (Citations: 1530)


Face detection techniques: a review


– Artificial Intelligence Review, 2019 (Citations: 587)


Transfer learning for image classification using VGG19: Caltech-101 image data set


– Journal of Ambient Intelligence and Humanized Computing, 2023 (Citations: 457)


A healthcare monitoring system using random forest and internet of things (IoT)


– Multimedia Tools and Applications, 2019 (Citations: 389)