Engineering, Artificial Intelligence, Human-Computer Interaction, Electrical and Electronic Engineering
46
Scopus Publications
877
Scholar Citations
17
Scholar h-index
25
Scholar i10-index
Scopus Publications
Cloud-enabled automatic modulation classification using deep feature fusion and Moth-Flame Optimized ELM approach Padma Charan Sahu, Bibhu Prasad, Ratnakar Dash, Debendra Muduli, D. Samuel Kollie, Rahul Priyadarshi, Rakesh Ranjan Scientific Reports, 2026 Automatic modulation classification (AMC) plays a vital role in modern wireless communication systems by enabling efficient spectrum utilization and ensuring reliable data transmission. With increasing complexity in communication signals, traditional AMC methods face challenges in accurately classifying modulation types, particularly when deployed in cloud-based environments with scalable resources. This study aims to develop a robust AMC method that leverages deep learning–derived features combined with an optimized Extreme Learning Machine (ELM) classifier to enhance classification accuracy and reliability. Features are extracted using pre-trained deep learning models–Inception V3, ResNet 50, and VGG 16–and concatenated into a comprehensive feature set. These features are input into an ELM whose hidden-node parameters are optimized via the Moth Flame Optimization (MFO) algorithm, resulting in the MFOP-ELM classifier. Additionally, explainable AI techniques, including SHAP value analysis, are applied to interpret model predictions. The approach is evaluated on three cloud-based virtual machines with configurations of vCPU-4/16GB RAM, vCPU-8/32GB RAM, and vCPU-16/64GB RAM. The proposed MFOP-ELM model achieves a classification accuracy of 94.19%, sensitivity of 89.56%, and specificity of 88.76% on the highest configuration (vCPU-16/64GB RAM). Performance comparisons demonstrate that this method outperforms existing state-of-the-art AMC approaches. The integration of deep learning features with an MFO-optimized ELM classifier provides a highly accurate and interpretable solution for automatic modulation classification, effective in both cloud and standalone environments.
Mirror Reality: Exploring the Realm of Digital Twin Systems Panduranga Ravi Teja, Anish Kumar Vishwakarma, Rakesh Ranjan, Bikash Chandra Sahana Generative and Open AI in Industry 5 0, 2026 Digital twin (DT) systems are revolutionizing modern industries by enabling real time, data-driven decision-making through the synchronization of virtual models and physical entities via IoT sensors and networked infrastructure. This chapter explores the evolution of DTs, from their conceptual origins to cutting-edge applications in smart manufacturing, urban planning, and 5G network management, where they enhance monitoring, simulation, and optimization. A key focus is the integration of generative AI (e.g. DeepONet, GANs) and real-time data pipelines, which empower DTs to predict system behaviors and autonomously adapt to dynamic conditions. By employing mathematical modeling and robust data processing, DTs achieve unprecedented accuracy in replicating and optimizing complex systems. The chapter highlights how these advancements address scalability and resilience challenges, particularly in 5G networks, where predictive learning frameworks enable proactive resource allocation and fault mitigation. Through a synthesis of theoretical foundations and practical implementations, this work provides researchers and engineers with critical insights for deploying intelligent, adaptive, and scalable DT ecosystems, setting a foundation for future innovations in Industry 5.0 and beyond.
MalrENSNet: a multi-model ensemble approach for detection of malaria from thin-blood smear images Rakesh Ranjan, Nikhil Dhengre, Shiva Singh Bagri, Bikash Chandra Sahana, Rahul Priyadarshi Engineering Research Express, 2026 Malaria is caused by Plasmodium parasites transmitted through the bites of infected female Anopheles mosquitoes, and remains a significant global health issue. Accurate and timely diagnosis of malaria is essential for effective treatment, yet conventional microscopic examination remains prone to human error and delay. The proposed study introduces an innovative multi-model ensemble approach that integrates the pre-trained backbone models MobileNetV2 and EfficientNetB0 networks to enhance malaria parasite detection performance. The preprocessing steps were applied to the images for outlier detection and removal before being fed into the proposed model to achieve an impressive accuracy of 99%. The proposed ensemble model outperformed various pre-trained Deep Learning (DL) models and state-of-the-art techniques. The model was evaluated using thin-blood smear images from the publicly available NIH Malaria dataset with 27,558 images, demonstrating its potential for application in clinical settings and contributing to more efficient and accurate malaria parasite detection using blood cell smear images. Further, the proposed model was deployed in a Python-based web application to improve user interaction.
E3DNM: An Efficient 3D Node Deployment Model for Underwater Wireless Sensor Networks Jayant Kumar Rout, Priyal Sharma, Kruthika Tummala, Rahul Priyadarshi, Anish Kumar Vishwakarma, Rakesh Ranjan 2026 IEEE 15th International Conference on Communication Systems and Network Technologies Csnt 2026, 2026 Underwater Wireless Sensor Networks (UWSNs) enable a wide range of ocean monitoring applications, including environmental observation, offshore infrastructure inspection, and marine resource exploration. However, reliable deployment of sensor nodes in underwater environments remains challenging due to the three-dimensional monitoring space, acoustic communication constraints, water-current-driven node mobility, and depth-dependent sensing performance. Conventional deployment strategies adapted from terrestrial wireless sensor networks are inadequate for volumetric underwater coverage and often neglect acoustic propagation characteristics and buoyancy-based node actuation. This paper proposes E3DNM, an Efficient 3D Node Deployment Model designed to improve coverage reliability, acoustic connectivity, and energy efficiency in UWSNs. The proposed framework integrates three coordinated mechanisms: a depth-stratified initialization strategy using hexagonal close packing for efficient volumetric coverage, a 3D acoustic virtualforce refinement algorithm incorporating depth-adaptive sensing radii and acoustic propagation constraints, and a buoyancybased mobility compensation mechanism that mitigates currentinduced drift and preserves deployment stability. Extensive simulations in a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$500 {m} \times 500 {m} \times 200 {m}$</tex> underwater monitoring volume with 120 sensor nodes demonstrate that E3DNM achieves 94.8% volumetric coverage, improves network lifetime by 28.9 %, and reduces communication energy consumption by 41.3 % compared with conventional 3D random deployment while maintaining acoustic connectivity in 97.6 % of trials. These results indicate that E3DNM provides a scalable and energy-efficient deployment framework for reliable large-scale underwater sensing applications.
Hybrid Evolutionary Optimization Algorithm for Energy-Efficient Node Deployment in WSNs Priyal Sharma, Abhishek Kumar Singh, Aritro Saha, Rahul Priyadarshi, Anish Kumar Vishwakarma, Rakesh Ranjan 2026 IEEE 15th International Conference on Communication Systems and Network Technologies Csnt 2026, 2026 Efficient node deployment remains a critical challenge in Wireless Sensor Networks (WSNs), where the dual objectives of maximizing coverage and minimizing energy consumption directly influence network lifetime. Conventional random deployment approaches often result in redundant sensing, uneven energy utilization, and premature node failures. To address these limitations, this work presents a hybrid optimization framework that integrates Genetic Algorithms (GA) and Ant Colony Optimization (ACO). The proposed model leverages the global exploration capability of GA with the local exploitation strength of ACO, and formulates deployment as a multi-objective problem considering coverage maximization, energy conservation, and redundancy reduction under connectivity constraints. Simulation studies conducted in MATLAB demonstrate that the hybrid GA-ACO approach consistently outperforms standalone GA, ACO, and random deployment. Specifically, it achieves higher coverage efficiency across varying node densities, balanced energy consumption, and an extended network lifetime of nearly 1300 rounds compared to 900-1100 rounds in competing methods. These results confirm the potential of hybrid metaheuristic optimization in WSN deployment, offering a scalable and energy-aware solution applicable to Internet of Things (IoT) and smart city monitoring scenarios.
AI-based routing algorithms improve energy efficiency, latency, and data reliability in wireless sensor networks Rahul Priyadarshi, Ravi Ranjan Kumar, Rakesh Ranjan, Padarti Vijaya Kumar Scientific Reports, 2025 This paper proposes a modular Artificial Intelligence (AI)-based routing framework for Wireless Sensor Networks (WSNs) that integrates reinforcement learning (RL), supervised learning, and swarm intelligence techniques such as genetic algorithms (GA) and particle swarm optimization (PSO). Unlike conventional approaches that rely on static or standalone algorithms, the proposed framework employs a structured decision-making pipeline that dynamically adapts to real-time changes in network topology, traffic, and energy conditions. Each AI module plays a distinct role-RL handles local routing decisions, while GA and PSO are invoked for global optimization under resource constraints. Simulations conducted in MATLAB R2021b validate the framework's effectiveness, demonstrating improvements in packet delivery ratio, end-to-end latency, and energy efficiency when compared to traditional protocols. While this study is based on synthetic evaluations, it outlines the architectural groundwork for future real-world implementation and discusses deployment challenges such as scalability, resource usage, and security. The results highlight the potential of hybrid AI-based routing strategies to enhance the reliability, adaptability, and sustainability of WSNs in dynamic and resource-limited environments.
Schizophrenia Identification Through Deep Learning on Spectrogram Images Amarana Prabhakara Rao, G. Prasanna Kumar, Rakesh Ranjan, M. Venkata Subba Rao, M. Srinivasulu, E. Sravya Lecture Notes of the Institute for Computer Sciences Social Informatics and Telecommunications Engineering Lnicst, 2024
The future of smart healthcare Rakesh Ranjan, Bikash Chandra Sahana Cognitive Sensors Volume 2 Applications in Smart Healthcare, 2023
Advancement of shopping handcart for supermarket Subhanvali Shaik, Mohammad Jabirullah, Anish Kumar Vishwakarma, Rakesh Ranjan Pdgc 2020 2020 6th International Conference on Parallel Distributed and Grid Computing, 2020
A comprehensive overview of smart healthcare technologies in revolutionizing modern rehabilitation practices R Priyadarshi, R Ranjan, BC Sahana, AK Bhandari, S Kankanala Connection Science 38 (1), 2612458 , 2026 2026 Citations: 1
Cross-Attention based Dual-Stream Framework for Blind Underwater Image Quality Assessment AK Vishwakarma, R Ranjan, BC Sahana ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and … , 2026 2026
An ensemble-based sentiment analysis approach for precision medicine recommendation A Mishra, SK Bisoy, DS Kollie Jr, R Priyadarshi, R Ranjan Scientific Reports , 2026 2026
Mirror Reality: Exploring the Realm of Digital Twin Systems PR Teja, AK Vishwakarma, R Ranjan, BC Sahana Generative and Open AI in Industry 5.0, 157-167 , 2026 2026
Hybrid Evolutionary Optimization Algorithm for Energy-Efficient Node Deployment in WSNs P Sharma, AK Singh, A Saha, R Priyadarshi, AK Vishwakarma, R Ranjan 2026 IEEE 15th International Conference on Communication Systems and Network … , 2026 2026
Self-Organizing Distributed Node Deployment for Reliable Wireless Sensor Networks JK Rout, R Priyadarshi, S Jain, AK Vishwakarma, R Ranjan 2026 IEEE 15th International Conference on Communication Systems and Network … , 2026 2026
E3DNM: An Efficient 3D Node Deployment Model for Underwater Wireless Sensor Networks JK Rout, P Sharma, K Tummala, R Priyadarshi, AK Vishwakarma, ... 2026 IEEE 15th International Conference on Communication Systems and Network … , 2026 2026
Deep Learning and Federated Learning in Air Quality Forecasting: Trends, Insights, Challenges, and Future Perspectives N Sarkar, PK Keserwani, R Ranjan, MC Govil Archives of Computational Methods in Engineering, 1-40 , 2026 2026
Quantitative Electroencephalographic (qEEG) Characterisation and Biomarker Identification of Generalised Paediatric Seizure Using Spectral Features A Kumari, K Jha, T Kumar, L Tiwari, R Ranjan, P Kumar Annals of Neurosciences, 09727531251413457 , 2026 2026
MalrENSNet: A Multi-Model Ensemble Approach for Detection of Malaria from Thin-Blood Smear Images R Ranjan, N Dhengre, SS Bagri, BC Sahana, R Priyadarshi Engineering Research Express , 2026 2026
Energy-Efficient Coverage Maximization in WSNs Using Hybrid Genetic–Firefly Algorithm R Priyadarshi, V Khanna, R Ranjan, S Sundaram 2025 IEEE 17th International Conference on Computational Intelligence and … , 2025 2025 Citations: 1
VascuDR-Net: A Dual-Stage U-Net for Vessel Segmentation and Diabetic Retinopathy Diagnosis AK Upadhyay, J Singh, P Mishra, A Pandey, S Kumar, R Ranjan 2025 IEEE 17th International Conference on Computational Intelligence and … , 2025 2025
HindGK: A LLM-Based Question Answering System for Hindi General Knowledge SR Laskar, D Saxena, R Badhani, AK Vishwakarma, R Ranjan 2025 IEEE 17th International Conference on Computational Intelligence and … , 2025 2025
A Proximity-Aware Framework for Enhancing Rider Safety Through Real-Time Road Hazard Detection AK Upadhyay, R Yadav, GC Awasthi, A Yadav, D Shah, R Ranjan 2025 IEEE 17th International Conference on Computational Intelligence and … , 2025 2025
Dynamic Node Placement for Enhanced Coverage in WSNS with Machine Learning Framework S Baderia, P Sharma, R Varshney, R Priyadarshi, AK Vishwakarma, ... 2025 IEEE 17th International Conference on Computational Intelligence and … , 2025 2025 Citations: 2
Cloud-enabled automatic modulation classification using deep feature fusion and Moth-Flame Optimized ELM approach PC Sahu, B Prasad, R Dash, D Muduli, DS Kollie Jr, R Priyadarshi, ... Scientific Reports , 2025 2025 Citations: 1
uvaNet: A Deep Learning Framework for Precise Detection of Potato Leaf Diseases in Sustainable Agricultural Production A Verma, D Koundal, S Jain, A Doegar, R Ranjan Potato Research, 1-30 , 2025 2025
Enhancing IoT Security in SDN Environments with Game Theory and Reinforcement Learning-Driven Dynamic Resource Allocation PR Teja, AK Vishwakarma, G Alotibi, A Albalawi, BC Sahana, R Ranjan IEEE Transactions on Consumer Electronics , 2025 2025 Citations: 1
An efficient algorithmic framework to minimize the summand matrix in binary multiplication A Verma, M Prateek, SN Shivhare, TP Singh, A Kumar, R Ranjan, ... Automatika 66 (4), 22-31 , 2025 2025
EFRNet: A Deep Hybrid Model for End-to-End Knee Osteoarthritis Detection and Progression Assessment A Kumar, R Ranjan, S Srivastava 2025 47th Annual International Conference of the IEEE Engineering in … , 2025 2025 Citations: 1
MOST CITED SCHOLAR PUBLICATIONS
Deep learning models for diagnosis of schizophrenia using EEG signals: emerging trends, challenges, and prospects R Ranjan, BC Sahana, AK Bhandari Archives of Computational Methods in Engineering 31 (4), 2345-2384 , 2024 2024 Citations: 87
Motion artifacts suppression from EEG signals using an adaptive signal denoising method R Ranjan, BC Sahana, AK Bhandari IEEE Transactions on Instrumentation and Measurement 71, 1-10 , 2022 2022 Citations: 86
Ocular artifact elimination from electroencephalography signals: A systematic review R Ranjan, BC Sahana, AK Bhandari Biocybernetics and Biomedical Engineering 41 (3), 960-996 , 2021 2021 Citations: 84
A fuzzy neural network approach for automatic K-complex detection in sleep EEG signal R Ranjan, R Arya, SL Fernandes, E Sravya, V Jain Pattern Recognition Letters 115, 74-83 , 2018 2018 Citations: 69
A comprehensive roadmap for transforming healthcare from hospital-centric to patient-centric through healthcare internet of things (IoT) R Ranjan, B Ch Engineered Science 30, 1175 , 2024 2024 Citations: 52
Exploring the frontiers of unsupervised learning techniques for diagnosis of cardiovascular disorder: A systematic review R Priyadarshi, R Ranjan, AK Vishwakarma, T Yang, RS Rathore IEEE Access 12, 139253-139272 , 2024 2024 Citations: 49
Cardiac artifact noise removal from sleep EEG signals using hybrid denoising model R Ranjan, BC Sahana, AK Bhandari IEEE Transactions on Instrumentation and Measurement 71, 1-10 , 2022 2022 Citations: 41
AI-based routing algorithms improve energy efficiency, latency, and data reliability in wireless sensor networks R Priyadarshi, RR Kumar, R Ranjan, PV Kumar Scientific reports 15 (1), 22292 , 2025 2025 Citations: 38
Development of e-health monitoring system for remote rural community of India M Jabirullah, R Ranjan, MNA Baig, AK Vishwakarma 2020 7th International conference on signal processing and integrated … , 2020 2020 Citations: 26
An efficient facial feature extraction method based supervised classification model for human facial emotion identification R Ranjan, BC Sahana 2019 IEEE International Symposium on Signal Processing and Information … , 2019 2019 Citations: 23
Design of voice-controlled smart wheelchair for physically challenged persons K Joshi, R Ranjan, E Sravya, MNA Baig Emerging Technologies in Data Mining and Information Security: Proceedings … , 2018 2018 Citations: 23
Multiresolution feature fusion for smart diagnosis of schizophrenia in adolescents using EEG signals R Ranjan, BC Sahana Cognitive Neurodynamics 18 (5), 2779-2807 , 2024 2024 Citations: 21
A comprehensive overview of transformative potential of machine learning and wireless sensor networks in sustainable urban development R Priyadarshi, R Ranjan, AK Vishwakarma, RR Kumar 2024 International Conference on Intelligent Systems for Cybersecurity (ISCS … , 2024 2024 Citations: 20
An insight into wearable devices for smart healthcare technologies K Verma, P Preity, R Ranjan 2023 13th International conference on cloud computing, data science … , 2023 2023 Citations: 19
Automatic detection of mental health status using alpha subband of EEG data R Ranjan, BC Sahana 2022 IEEE International Symposium on Medical Measurements and Applications … , 2022 2022 Citations: 19
Real time eye blink extraction circuit design from EEG signal for ALS patients R Ranjan, R Arya, P Kshirsagar, V Jain, DK Jain, AK Sangaiah Journal of Medical and Biological Engineering 38 (6), 933-942 , 2018 2018 Citations: 18
Schizophrenia identification through deep learning on spectrogram images A Prabhakara Rao, G Prasanna Kumar, R Ranjan, M Venkata Subba Rao, ... International Conference on Cognitive Computing and Cyber Physical Systems, 3-11 , 2023 2023 Citations: 17
Efficient e-KYC authentication system: Redefining customer verification in digital banking K Verma, R Kumar, AP Rao, R Ranjan 2023 9th International Conference on Signal Processing and Communication … , 2023 2023 Citations: 15
A computer-aided prediagnosis system for health prediction based on personal health data R Ranjan, K Verma, BC Sahana 2023 IEEE 12th international conference on communication systems and network … , 2023 2023 Citations: 13
A machine learning framework for automatic diagnosis of schizophrenia using EEG signals R Ranjan, BC Sahana 2022 IEEE 19th India Council International Conference (INDICON), 1-6 , 2022 2022 Citations: 13
