Dr. Abhishek Kumar is currently working as an Assistant director /Associate professor in Computer science & Engineering Department in Chandigarh University, Punjab, India .He is Doctorate in computer science from University of Madras and is doing Post-Doctoral Fellow in Ingenium Research Group Ingenium Research Group Lab, Universidad De Castilla-La Mancha, Ciudad Real, and Ciudad Real Spain. He has done M.Tech in Computer Sci. & Engineering and B.Tech in I.T. from, Rajasthan Technical University, Kota India. He has total Academic teaching experience of more than 11 years along with 2 years teaching assistantship. He is having more than 100 publications in reputed, peer reviewed National and International Journals, books & Conferences He has guided more than 30 M.Tech Projects at national and International level and guiding 6 PhD Scholar. His research area includes- Artificial intelligence, Renewable Energy Image processing, Computer Vision, Data Mining, Machine Learning. He has been Se
RESEARCH, TEACHING, or OTHER INTERESTS
Artificial Intelligence, Engineering, Health Information Management, Energy
227
Scopus Publications
4451
Scholar Citations
37
Scholar h-index
88
Scholar i10-index
Scopus Publications
A secure framework for optimized data transmission using steganographic techniques Priya Batta, Abhishek Kumar, Pramod Singh Rathore Journal of Big Data, 2026 The safety of sensitive information on the way to the destination becomes an area of increasing concern, especially in areas such as healthcare and financial sectors. There is an emergency necessity of ensuring the optimized and safe infrastructure of information interchange. The issue that is discussed in this research is data transmission security by applying an effective steganographic option. The research shows FIEO Optimal Location Enabled Stegochain as an innovative framework that assumes the combination of steganography with FIEO algorithm to find the safest locations places of embeddings in the image. The method guarantees strong embedding and extraction of data because of Discrete Wavelet Transform (DWT) and its reverse (IDWT). The proposed model is more secure, has a reduced data distortion and correlation and Peak Signal-to-Noise Ratio (PSNR) compared to the current methods. The robustness and the effectiveness of the proposed method are validated by the PSNR values which have been 64.297 in the case of random noise and 61.288 in the salt-and-pepper noise condition as analysed in the experiment as well as correlation coefficients which are 0.81 and 0.79 respectively under random noise and salt-and-pepper noise conditions.
SBTM: epileptic seizure prediction from EEG signal using deep learning in blockchain-enabled smart healthcare monitoring with IoT networking Abhishek Kumar, Esha Tripathi, Abhay Kumar Tripathi, Himanshu Kumar Diwedi, Pramod Singh Rathore, Arshiya S. Ansari Scientific Reports, 2026 Epileptic Seizure prediction is highly significant for the identification and reduction of high risks related to serious brain injuries, strokes, and brain tumors. Early and accurate diagnosis is vital for providing intervention measures for enhancing the quality of life of the affected individuals. Numerous techniques have been developed based on Machine vision techniques to predict epileptic seizures. Nonetheless, the acquisition of precise epileptic seizure detection with low false positive rates is challenging. Moreover, the emergence of the Internet of Things (IoT) revolutionized healthcare monitoring with technological improvements, aiming to handle the concerns related to data interoperability, scalability, as well as privacy issues. Hence, this research proposes the Smart Healthcare Monitoring Framework, namely Spizella Optimization-based Bidirectional Short Term Memory Network (SBTM), for determining the seizure states, thereby allowing the provision of remote care. Specifically, the proposed model exploits the Bi-LSTM architecture that captures the temporal dependencies and nonlinear dynamics of EEG signals, making the model highly efficient for predicting the seizure patterns. Besides, the Spizella Optimization is applied for fine-tuning the hyperparameters of the classifier, thereby leading to accurate prediction. Experimental results demonstrate that the proposed SBTM model accomplishes superior results by achieving high accuracy, sensitivity, and specificity equivalent to 97.52%, 97.51% and 98.51% with 90% training, outperforming the state-of-the-art techniques. Moreover, the presented approach significantly improves the remote monitoring, guaranteeing on-time medical care, ensuring data security, and enhancing the overall performance of applications in tech-aided healthcare systems.
Analysis of forward error correction schemes for different coding rates in free-space communication Amit Kumar Garg, Vinita Tiwari, Tarun Kumar, Siddhartha Varshney, Abhishek Kumar Proceedings of SPIE the International Society for Optical Engineering, 2026 FSO communication provides high band-width, low-latency data transmission over optical beams to offer an alternative mode for communication over wires. It will suffer severely from disturbances brought about by atmospheric disturbances due to turbulence, rain and fog which will result in an increased Bit Error Rate. Thus, a solution offered was the correction of errors without the overhead of retransmission in systems like LDPC, Reed Solomon, Polar, Turbo code, among others. In this paper, we do the comparative analysis of these FEC techniques for FSO systems. The study high lights LDPC codes as one of the most efficient FEC technique to the superior error correction capability, low decoding complexity, and near optimal performance in challenging conditions. We analysis the performance of LDPC codes for various coding rates and iterations by running MATLAB simulations and present optimal configurations that maximize coding gain. The results indicate that an LDPC code rate of 2/3 with 12 decoding iterations provides the optimal compromise between error correction performance and bandwidth efficiency. In this way, this paper illustrates the effectiveness of LDPC codes as a powerful method to enhance the reliability of the FSO system and gives some valuable suggestions on its use in modern communication technologies.
Machine learning and hybrid intelligence for wind energy optimization: A comprehensive state-of-the-art review Ashutosh Kumar Dubey, Abhishek Kumar, Isaac Segovia Ramírez, Fausto Pedro García Márquez Expert Systems with Applications, 2026 Wind energy plays a pivotal role in the global transition toward sustainable energy. However, its intermittent and stochastic nature presents challenges in achieving optimal performance, reliability, and seamless grid integration. Recent advances in machine intelligence—including machine learning (ML), deep learning (DL), and reinforcement learning (RL)—offer powerful tools to address these challenges across forecasting, control, maintenance, and diagnostics. This systematic review provides a comprehensive evaluation of how machine intelligence has contributed to the optimization of wind energy systems. These techniques have been applied to enhance turbine-level performance, reduce power losses, predict faults, and maximize energy yield under uncertain and dynamic conditions. Particular emphasis is placed on hybrid models that combine data-driven algorithms with physical dynamics and domain heuristics, enabling real-time, predictive, and autonomous wind farm operations. Furthermore, the study critically examines integration barriers such as noisy SCADA data, regulatory compliance, computational costs, and sustainability trade-offs. The findings highlight that multi-objective optimization—balancing energy production, system resilience, and cost efficiency—is central to the most successful implementations. Hybrid frameworks, explainable artificial intelligence (AI), edge computing, and transfer learning are identified as key enablers for scalable deployment. This review offers a comprehensive roadmap for the application of machine intelligence in advancing wind energy optimization and provides actionable insights for researchers, engineers, and policymakers committed to developing intelligent, adaptive, and sustainable wind power infrastructures.
AI-driven Healthcare Innovations: Applications in Neurology and Medicine AI Driven Healthcare Innovations Applications in Neurology and Medicine, 2026 AI-driven Healthcare Innovations presents a timely and authoritative exploration of how artificial intelligence (AI) is transforming modern clinical practices and medical research. Positioned at the intersection of healthcare, data science and computational intelligence, this book provides a comprehensive context for understanding the growing role of AI in diagnosis, treatment and decision-making within neurology and broader medical domains. The book systematically examines core AI techniques, including machine learning (ML), deep learning (DL) and intelligent optimization, and demonstrates their practical deployment across neurological disorders, medical imaging, predictive analytics and personalized care. Emphasis is placed on real-world clinical workflows, data acquisition and preprocessing, model interpretability and performance evaluation. In addition, we also address ethical considerations, regulatory challenges and data security issues critical to healthcare adoption. By combining theoretical foundations with applied case studies and future research directions, this book serves as a valuable resource for researchers, clinicians, graduate students and industry professionals seeking to leverage AI-driven innovations to improve patient outcomes and advance next-generation healthcare systems.
Preface AI Driven Healthcare Innovations Applications in Neurology and Medicine, 2026
Microplastic Monitoring Using Artificial Intelligence Microplastic Monitoring Using Artificial Intelligence, 2026 Revolutionize your approach to environmental protection with this groundbreaking resource, which details how to replace labor-intensive manual analysis with deep learning and explainable AI (XAI) to achieve precise, real-time identification and scalable monitoring of microplastic pollution. AI-driven microplastic monitoring sits at the intersection of environmental science, artificial intelligence, and data analytics, representing a rapidly developing frontier in both research and industry. Microplastic pollution, which has become a critical environmental and public health concern, is challenging to monitor using traditional techniques due to the vast scale, complexity, and minute size of microplastics. Conventional methods, such as manual filtration, microscopic examination, and chemical analysis, are often labor-intensive, time-consuming, and limited in their ability to provide real-time, large-scale data. This book is a groundbreaking exploration of how artificial intelligence, particularly deep learning and explainable AI (XAI), is revolutionizing microplastic research. It highlights innovative applications of deep learning for precise identification and classification of microplastics, while emphasizing the role of XAI in providing transparency and interpretability to AI-driven methods. By integrating these approaches with advanced sensing technologies and predictive models, the book addresses key limitations of traditional methods, offering robust solutions for scalable and accurate monitoring. Additionally, the book considers the ethical, regulatory, and policy implications of deploying AI in environmental science, providing a balanced perspective on the potential benefits and challenges. With contributions from leading researchers and practitioners, this book is an essential resource for environmental scientists, data scientists, policymakers, and technologists committed to sustainable solutions for combating microplastic pollution.
Preface AI Modernisation Techniques for Contemporary Trends, 2026
AI Modernisation Techniques for Contemporary Trends Pramod Singh Rathore, Abhishek Kumar, Surbhi B. Khan, Faheem Masoodi, Fatima Asiri, T. Rajasanthosh Kumar AI Modernisation Techniques for Contemporary Trends, 2026
Proceedings - 2025 13th International Conference in Software Engineering Research and Innovation, CONISOFT 2025 Proceedings 2025 13th International Conference in Software Engineering Research and Innovation Conisoft 2025, 2025
Framework for Improving the Security of Blockchain Nikhil Dehal, Abhishek Kumar, Sachin Ahuja Conference Proceedings 4th IEEE International Conference on Technology Engineering Management for Societal Impact Using Marketing Enterpreneurship and Talent Temsmet 2025, 2025
An exploration on pattern analysis for bitcoin address behavior Ankita Parihar, Amit Kumar, Abhishek Computational Methods in Science and Technology Proceedings of the 4th International Conference on Computational Methods in Science and Technology Iccmst 2024, 2025
Blockchain Technology to Combat Fake Products in the Luxury Market Nikhil Dehal, Abhishek Kumar, Sachin Ahuja Conference Proceedings 4th IEEE International Conference on Technology Engineering Management for Societal Impact Using Marketing Enterpreneurship and Talent Temsmet 2025, 2025
Solar Energy Optimization Using Generative Artificial Intelligence Solar Energy Optimization Using Generative Artificial Intelligence, 2025
Preface Generative Artificial Intelligence in Finance Large Language Models Interfaces and Industry Use Cases to Transform Accounting and Finance Processes, 2025
Preface Sustainable Smart Homes and Buildings with Internet of Things, 2024
Preface Internet of Medicine for Smart Healthcare, 2024
Blockchain technology, Bitcoin, and IoT P. Srinivas Kumar, Jyotir Moy Chatterjee, Abhishek Kumar, Pramod Rathore, R. Sujatha Quality Assessment and Security in Industrial Internet of Things, 2024
Demystifying Graph Data Science: Graph algorithms, analytics methods, platforms, databases, and use cases Demystifying Graph Data Science Graph Algorithms Analytics Methods Platforms Databases and Use Cases, 2022
Parkinson's disease prediction using adaptive quantum computing Srinivasa Rao Swarna, Abhishek Kumar, Pooja Dixit, T.V.M. Sairam Proceedings of the 3rd International Conference on Intelligent Communication Technologies and Virtual Mobile Networks Icicv 2021, 2021
Utilization of infrared warm imaging for the fast analysis of yield illness Assistant Professor, Computer Science & Engineering , Institute of Engineering & Technology Chitkara University ,punjab, India,, Abhisheh Kumar*, Palvadi Srinivas Kumar, Research Scholar, Department of Computer Science & Engineering, Sri Satyasai University of Technology, Medical Sciences, Sehore, Madhya Pradesh., Dr Rashmi Agrawal, Professor , Faculty of Computer Applications Manav Rachna International Institute of Research, Studies, India. International Journal of Engineering and Advanced Technology, 2019
Implementing medical data processing with ann with hybrid approach of implementation Journal of Advanced Research in Dynamical and Control Systems, 2018
An adaptive method for edge preserving denoising Neeraj Bhargava, Arpit Kumar Sharma, Abhishek Kumar, Pramod Singh Rathoe Proceedings of the 2nd International Conference on Communication and Electronics Systems Icces 2017, 2017
A secure framework for optimized data transmission using steganographic techniques P Batta, A Kumar, PS Rathore Journal of Big Data 13 (1), 9 , 2026 2026
Explainable AI for Transparent and Trustworthy Medical Decision Support A Kumar, D Chinnathambi, RJ Ramírez, A Quezada, PS Rathore Morgan Kaufmann , 2026 2026
AI-driven Healthcare Innovations: Applications in Neurology and Medicine A Kumar, P Batta, JP Ananth John Wiley & Sons , 2026 2026
Brain-Computer Interfaces for Neurorehabilitation J Ramírez, A Kumar, PS Rathore, P Batta, S Ahuja IGI Global , 2026 2026
A Comprehensive Review of Machine Learning Approaches for Wind Turbine Site Suitability, Prediction and Reliability Analysis IU Haq, A Kumar Archives of Computational Methods in Engineering, 1-44 , 2026 2026
AttnEffNet-B4: an attention-augmented EfficientNet-B4 framework with fourier transformation for robust multi-disease diagnosis P Kumar, D Kumar, A Kumar, PS Rathore Scientific Reports , 2026 2026
Optimizing Wind Turbine Site Selection Using Machine Learning: Techniques, Applications, and Case Studies I Ul Haq, A Kumar Solar Energy Optimization Using Generative Artificial Intelligence, 305-327 , 2026 2026
Artificial Intelligence and Robotics: Transformative and Computational Algorithms for Enhanced Problem Solving SK Krishnadhas, AK Tamilarasan, PS Rathore, A Kumar Walter de Gruyter GmbH & Co KG , 2026 2026
Evaluating machine learning models for cardiovascular disease prediction: balancing accuracy and minority class detection Sheetal, SS Deora, IU Haq, SZ Rufai, A Kumar Archives of Computational Methods in Engineering 33 (2), 2451-2469 , 2026 2026 Citations: 4
Blockchain Innovations for a Sustainable Circular Economy P Batta, A Kumar Blockchain Innovations for a Sustainable Circular Economy, 1-11 , 2026 2026
Blockchain Innovations for a Sustainable Circular Economy A Kumar, P Batta, PS Rathore, TA Kumar, JRJ Ramírez Springer Nature , 2026 2026
SBTM: epileptic seizure prediction from EEG signal using deep learning in blockchain-enabled smart healthcare monitoring with IoT networking A Kumar, E Tripathi, AK Tripathi, HK Diwedi, PS Rathore, AS Ansari Scientific Reports , 2026 2026 Citations: 2
Machine Learning Approach for Identifying Optimal Wind Turbine Sites in Jammu and Kashmir IU Haq, A Kumar 2025 International Conference on Electrical, Communication, and Computing … , 2025 2025
The Role of Machine Learning in Advancing Therapeutics for Alzheimer’s Disease: A Comprehensive Study AK Bali, S Ahuja, A Kumar 2025 2nd International Conference on Artificial Intelligence for Innovations … , 2025 2025
Privacy preserving data analytics in DEI empowered IoV P Batta, A Kumar, PS Rathore Discover Computing 28 (1), 332 , 2025 2025
Multiscale feature fusion for automated classification of digital breast tomosynthesis volumes using 3D deep learning S Ravi, A Kumar, PS Rathore Discover Computing 28 (1), 300 , 2025 2025 Citations: 1
Solar Energy Forecasting Using Voting Regression Model IU Hassan, A Kumar 2025 2nd International Conference on Advanced Computing and Emerging … , 2025 2025
Blockchain and Federated Learning Synergy for Privacy-Focused DeepFex Solutions A Kumar, P Batta, TA Kumar, SO Manoj Springer , 2025 2025 Citations: 1
Quantum Protocols in Blockchain Security A Kumar, P Batta, DN Le Springer , 2025 2025
Blockchain and the Water Supply Chain: Opportunities, Challenges and Innovations A Kumar, P Batta, SO Manoj, D Chinnathambi, S Ravi John Wiley & Sons , 2025 2025
MOST CITED SCHOLAR PUBLICATIONS
Study and analysis of SARIMA and LSTM in forecasting time series data AK Dubey, A Kumar, V García-Díaz, AK Sharma, K Kanhaiya Sustainable Energy Technologies and Assessments 47, 101474 , 2021 2021 Citations: 496
Energy-efficient cluster head selection through relay approach for WSN PS Rathore, JM Chatterjee, A Kumar, R Sujatha The Journal of Supercomputing 77 (7), 7649-7675 , 2021 2021 Citations: 191
A novel hybrid approach of SVM combined with NLP and probabilistic neural network for email phishing A Kumar, JM Chatterjee, VG Díaz International Journal of Electrical and Computer Engineering 10 (1), 486 , 2020 2020 Citations: 147
Machine learning implementation on medical domain to identify disease insights using TMS SM Sasubilli, A Kumar, V Dutt 2020 International Conference on Advances in Computing and Communication … , 2020 2020 Citations: 144
Parkinson’s disease prediction using adaptive quantum computing SR Swarna, A Kumar, P Dixit, TVM Sairam 2021 Third International Conference on Intelligent Communication … , 2021 2021 Citations: 133
Concepts of circular economy for sustainable management of electronic wastes: challenges and management options AL Srivastav, Markandeya, N Patel, M Pandey, AK Pandey, AK Dubey, ... Environmental Science and Pollution Research 30 (17), 48654-48675 , 2023 2023 Citations: 130
Multi model implementation on general medicine prediction with quantum neural networks SA Kumar, A Kumar, V Dutt, R Agrawal 2021 Third International Conference on Intelligent Communication … , 2021 2021 Citations: 124
Machine learning and big data implementation on health care data G Sasubilli, A Kumar 2020 4th International Conference on Intelligent Computing and Control … , 2020 2020 Citations: 108
Improving health care by help of internet of things and bigdata analytics and cloud computing SM Sasubilli, A Kumar, V Dutt 2020 International Conference on Advances in Computing and Communication … , 2020 2020 Citations: 107
Predictive intelligence for healthcare outcomes: An ai architecture overview SR Burri, A Kumar, A Baliyan, TA Kumar 2023 2nd International Conference on Smart Technologies and Systems for Next … , 2023 2023 Citations: 106
Artificial intelligence techniques for the photovoltaic system: A systematic review and analysis for evaluation and benchmarking A Kumar, AK Dubey, I Segovia Ramírez, A Muñoz del Río, ... Archives of Computational Methods in Engineering 31 (8), 4429-4453 , 2024 2024 Citations: 83
An approach for classification using simple CART algorithm in WEKA N Bhargava, S Dayma, A Kumar, P Singh 2017 11th International Conference on Intelligent Systems and Control (ISCO … , 2017 2017 Citations: 75
Predicting hospital readmission risk for heart failure patients using machine learning techniques: a comparative study of classification algorithms VR Burugadda, PS Pawar, A Kumar, N Bhati 2023 Second International Conference on Trends in Electrical, Electronics … , 2023 2023 Citations: 74
A holistic methodology for improved RFID network lifetime by advanced cluster head selection using dragonfly algorithm PS Rathore, A Kumar, VG Díaz IJIMAI 6 (2), 48-55 , 2020 2020 Citations: 74
IoT-based ECG monitoring for arrhythmia classification using Coyote Grey Wolf optimization-based deep learning CNN classifier A Kumar, SA Kumar, V Dutt, AK Dubey, V García-Díaz Biomedical Signal Processing and Control 76, 103638 , 2022 2022 Citations: 69
Exploring the effectiveness of optimized convolutional neural network in transfer learning for image classification: A practical approach SR Burri, S Ahuja, A Kumar, A Baliyan 2023 International Conference on Advancement in Computation & Computer … , 2023 2023 Citations: 66
Application of Deep Neural Networks and Machine Learning algorithms for diagnosis of Brain tumour S Wani, S Ahuja, A Kumar 2023 International Conference on Computational Intelligence and Sustainable … , 2023 2023 Citations: 64
An efficient ACO-PSO-based framework for data classification and preprocessing in big data AK Dubey, A Kumar, R Agrawal Evolutionary Intelligence 14 (2), 909-922 , 2021 2021 Citations: 64
IoT based arrhythmia classification using the enhanced hunt optimization‐based deep learning A Kumar, SA Kumar, V Dutt, S Shitharth, E Tripathi Expert Systems 40 (7), e13298 , 2023 2023 Citations: 63
Prevention of DDoS attacks using an optimized deep learning approach in blockchain technology B Ilyas, A Kumar, MA Setitra, ZEA Bensalem, H Lei Transactions on Emerging Telecommunications Technologies 34 (4), e4729 , 2023 2023 Citations: 62
RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)
Method Of Data Transmission In A Cluster Network
INDIAN PATENT OFFICE
System And Method For Cluster Head Selection And Cluster Formation For Improving Radio Frequency Identification
Network
INDIAN PATENT OFFICE
202111022269
Iot Enabled Wall Climbing Robot For Security
IP AUSTRALIA /GRANTED
2021101471
An Artificial Intelligence And IoT Based Method For Prevention Of Security Attack On Cloud Medical Data
IP AUSTRALIA/ GRANTED
2021102115
Iot Based Generic Framework For Computer Security Using Artificial Immune System
IP AUSTRALIA /GRANTED
2021102104
Podium with display facility, box and glass holder
INDIAN PATENT OFFICE/GRANTED
346057-001
Hexa Tube LED Bulb
INDIAN PATENT OFFICE/GRANTED
356883001
SMART SHOPPING CART
INDIAN PATENT OFFICE
202111061690
