I am currently working as an Assistant Professor at the School of Computer Science, UPES, Dehradun. Prior to this, I completed my PhD in Computer Science and Engineering from IIT (BHU) Varanasi, where I specialized in computer vision and deep learning. My PhD research focuses on hyperspectral image classification, aiming to advance spectral-spatial feature extraction techniques for enhanced classification performance. I pursued my PhD under the supervision of Prof. Ravi Shankar Singh.
EDUCATION
Ph.D : Indian Institute of Technology BHU Varanasi 2025
Master of Technology: Indian Institute of Technology Kharagpur 2017
Bachelor of Technology: Indian Institute of Technology BHU Varanasi 2014
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Vision and Pattern Recognition, Artificial Intelligence, Signal Processing
Efficient task scheduling on the cloud using artificial neural network and particle swarm optimization Pritam Kumar Nayak, Ravi Shankar Singh, Shweta Kushwaha, Prasanth Kumar Bevara, Vinod Kumar, et al. Concurrency and Computation Practice and Experience, 2024 SummaryA difficult problem in the service‐oriented computing paradigm is improving task scheduler policy or resource provisioning.In order to increase the performance of cloud applications, this article primarily focuses on tasks for resource mapping policy optimization. With the aim of reducing makespan and execution overhead and increasing the average resource utilization, we suggested an efficient independent task scheduler employing supervised neural networks in this paper. The suggested ANN‐based scheduler uses the status of the cloud environment and incoming tasks as inputs to determine the optimal computing resource for a given assignment as a result that assembles our goal. We proposed a novel algorithm in this paper that uses a hybrid methodology based on a swarm intelligence algorithm (PSO) in combination with a machine learning technique (ANN). PSO is used to prepare the train and test dataset for the neural network. Results clearly state that suggested work achieves significant improvement to considered algorithms in makespan (45%–55%), average VM utilization (15%–20%), and execution overhead(20%–30%).
Gases/odors classification using K-means, hierarchical clustering and self organizing map Electronics Communications and Networks IV Proceedings of the 4th International Conference on Electronics Communications and Networks Cecnet2014, 2015
RECENT SCHOLAR PUBLICATIONS
From Lab to Field: Robustness Analysis of Plant Disease Recognition Models in Uncontrolled Environment N Nigam, N Sharma, S Yadav, RN Tiwari, V Kumar International Conference on Machine Learning, Image Processing, Network … , 2026 2026
CKGFLNet: A Fast Hybrid Architecture for Hyperspectral Image Classification Leveraging Kaiming-Gaussian Attention V Kumar, RS Singh, N Nigam, S Choudhury, R Kumar International Conference on Machine Learning, Image Processing, Network … , 2026 2026
Lightweight Logarithmic Group Convolution Network with Polarized Attention for Hyperspectral Image Classification S Jain, V Kumar, RS Singh, I Todwal, K Patel International Conference on Computer Vision and Image Processing, 376-390 , 2025 2025
Enhancing Hyperspectral image classification through transformer-based contextualization and novel logarithmic convolutional techniques V Kumar, RS Singh, N Nigam, K Patel, S Jain Infrared Physics & Technology 147, 105826 , 2025 2025 Citations: 9
Demystifying SAR with attention N Patnaik, R Raj, I Misra, V Kumar Expert Systems with Applications 276, 127182 , 2025 2025 Citations: 4
Deep learning for hyperspectral image classification: A survey V Kumar, RS Singh, M Rambabu, Y Dua Computer Science Review 53, 100658 , 2024 2024 Citations: 133
Efficient task scheduling on the cloud using artificial neural network and particle swarm optimization PK Nayak, RS Singh, S Kushwaha, PK Bevara, V Kumar, R Medara Concurrency and Computation: Practice and Experience 36 (6), e7954 , 2024 2024 Citations: 3
Advances in Parallel Techniques for Hyperspectral Image Processing Y Dua, V Kumar, RS Singh High-Performance Medical Image Processing, 197-221 , 2022 2022 Citations: 2
Morphologically dilated convolutional neural network for hyperspectral image classification V Kumar, RS Singh, Y Dua Signal Processing: Image Communication 101, 116549 , 2022 2022 Citations: 65
Compression of multi-temporal hyperspectral images based on RLS filter Y Dua, RS Singh, V Kumar The Visual Computer 38 (1), 65-75 , 2022 2022 Citations: 19
Convolution neural network based lossy compression of hyperspectral images Y Dua, RS Singh, K Parwani, S Lunagariya, V Kumar Signal Processing: Image Communication 95, 116255 , 2021 2021 Citations: 68
Parallel lossless HSI compression based on RLS filter Y Dua, V Kumar, RS Singh Journal of Parallel and Distributed Computing 150, 60-68 , 2021 2021 Citations: 23
Comprehensive review of hyperspectral image compression algorithms Y Dua, V Kumar, RS Singh Optical Engineering 59 (9), 090902-090902 , 2020 2020 Citations: 137
MOST CITED SCHOLAR PUBLICATIONS
Comprehensive review of hyperspectral image compression algorithms Y Dua, V Kumar, RS Singh Optical Engineering 59 (9), 090902-090902 , 2020 2020 Citations: 137
Deep learning for hyperspectral image classification: A survey V Kumar, RS Singh, M Rambabu, Y Dua Computer Science Review 53, 100658 , 2024 2024 Citations: 133
Convolution neural network based lossy compression of hyperspectral images Y Dua, RS Singh, K Parwani, S Lunagariya, V Kumar Signal Processing: Image Communication 95, 116255 , 2021 2021 Citations: 68
Morphologically dilated convolutional neural network for hyperspectral image classification V Kumar, RS Singh, Y Dua Signal Processing: Image Communication 101, 116549 , 2022 2022 Citations: 65
Parallel lossless HSI compression based on RLS filter Y Dua, V Kumar, RS Singh Journal of Parallel and Distributed Computing 150, 60-68 , 2021 2021 Citations: 23
Compression of multi-temporal hyperspectral images based on RLS filter Y Dua, RS Singh, V Kumar The Visual Computer 38 (1), 65-75 , 2022 2022 Citations: 19
Enhancing Hyperspectral image classification through transformer-based contextualization and novel logarithmic convolutional techniques V Kumar, RS Singh, N Nigam, K Patel, S Jain Infrared Physics & Technology 147, 105826 , 2025 2025 Citations: 9
Demystifying SAR with attention N Patnaik, R Raj, I Misra, V Kumar Expert Systems with Applications 276, 127182 , 2025 2025 Citations: 4
Efficient task scheduling on the cloud using artificial neural network and particle swarm optimization PK Nayak, RS Singh, S Kushwaha, PK Bevara, V Kumar, R Medara Concurrency and Computation: Practice and Experience 36 (6), e7954 , 2024 2024 Citations: 3
Advances in Parallel Techniques for Hyperspectral Image Processing Y Dua, V Kumar, RS Singh High-Performance Medical Image Processing, 197-221 , 2022 2022 Citations: 2
From Lab to Field: Robustness Analysis of Plant Disease Recognition Models in Uncontrolled Environment N Nigam, N Sharma, S Yadav, RN Tiwari, V Kumar International Conference on Machine Learning, Image Processing, Network … , 2026 2026
CKGFLNet: A Fast Hybrid Architecture for Hyperspectral Image Classification Leveraging Kaiming-Gaussian Attention V Kumar, RS Singh, N Nigam, S Choudhury, R Kumar International Conference on Machine Learning, Image Processing, Network … , 2026 2026
Lightweight Logarithmic Group Convolution Network with Polarized Attention for Hyperspectral Image Classification S Jain, V Kumar, RS Singh, I Todwal, K Patel International Conference on Computer Vision and Image Processing, 376-390 , 2025 2025
