Computer Vision and Pattern Recognition, Artificial Intelligence, Information Systems, Decision Sciences
52
Scopus Publications
545
Scholar Citations
12
Scholar h-index
17
Scholar i10-index
Scopus Publications
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
ResNet-Fused External Attention Network with Fractional Star Piranha Forager Optimization for Forest Fire Detection in Internet of Things Environments Ananth John Patrick, P. D. Mahendhiran, Sajeev Ram Arumugam, Daniya Thavasilingam International Journal of Image and Graphics, 2026 A forest fire is an uncontrolled fire that occurs in natural areas and causes extensive damage to human lives. Therefore, the ResNet-fused External Attention Network with Fractional Star Piranha Forager Optimization (RfEANet_FStPFO) is devised for forest fire detection. Initially, the IoT simulation is performed in a cloud-based environment and routing is done by Star Piranha Forager Optimization (StPFO). StPFO combines Piranha Foraging Optimization Algorithm (PFOA) and Star Fish Optimization Algorithm (SFOA). Besides, the forest fire images are collected from the relevant dataset. These collected images are filtered, and several features are extracted for further processing. Subsequently, the forest fire is detected by ResNet-fused External Attention Network (ResfEANet) and its parameters are tuned by the proposed Fractional Star Piranha Forager Optimization (FStPFO). FStPFO integrates the benefits of fractional calculus and StPFO. The RfEANet_FStPFO attained an accuracy of 96.490%, True Positive Rate (TPR) of 97.691%, and False Positive Rate (FPR) of 2.869% for 90% of training samples. The routing algorithm named as StPFO obtained an end-to-end delay of 0.256[Formula: see text]s (seconds), network life of 0.756, throughput of 97.249 Mbps (Megabits per second), and energy consumption of 3.735 Joules (J) for 1000 rounds with Forest Fire dataset from Mendeley.
Physiotherapy Using Artificial Intelligence Physiotherapy Using Artificial Intelligence, 2026 Empower your practice with this definitive resource that bridges the gap between artificial intelligence and biomechanics, providing the essential tools and knowledge to optimize assessments, personalize treatment plans, and predict recovery outcomes in the rapidly evolving landscape of modern physiotherapy. The integration of artificial intelligence (AI) with biomechanics in physiotherapy represents a transformative shift in the healthcare landscape, driven by rapid technological advancement and an increasing emphasis on personalized, data-driven care. Over the past decade, AI has progressed from theoretical exploration to practical clinical application, enabling enhanced decision-making and improved patient outcomes. This book examines the intersection of artificial intelligence and physiotherapy with a focused emphasis on biomechanics, exploring how AI can optimize biomechanical assessments, support individualized treatment planning, and predict patient progress in clinical settings. As demand grows for AI-driven innovation in rehabilitation, this volume serves as an essential resource for physiotherapists, clinicians, and researchers seeking to understand and adopt these emerging technologies to advance practice and improve rehabilitation outcomes.
Effective Charging Scheduling of Electric Vehicles Using a Hybrid Deep Learning Network J. P. Ananth, Pankaj Kumar, M. Belsam Jeba Ananth, R. Cristin Energy Storage, 2025 Electric vehicles (EVs) are developed by diverse industries as a substitute for vehicles with internal combustion engines, with many compensations that are environment‐friendly. The amount of EVs is likely to rise fast in the approaching ages. However, uncoordinated vehicle charging may significantly stress the power grid. The main objective of the devised model is to minimize the charging time and waiting time for EVs by distributing equal power resources. Therefore, an energy‐aware multi‐objective system in a cloud‐internet of things (IoT)‐based electric vehicular network for a priority‐based charge‐scheduling scheme is proposed here and established as follows. Initially, the network with the EV location as well as the charge station (CS) location is simulated. Then, the charging planning is performed by determining the CS selection using the fractional spotted hyena jellyfish optimization (FSHJSO) considering a multi‐objective function. Subsequently, the charge scheduling is performed using the established hybrid deep learning (DL) approach namely MobileNet neural network (MNN‐Net) based on various objectives. The integration of MobileNet with deep neural network (DNN) forms the MNN‐Net. By employing deep neuro‐fuzzy network (DNFN), the power prediction is done. The efficiency of the developed MNN‐Net is validated with some methods and achieved superior performance with an average waiting time of 11.796 s, distance 0.067 m, available power 53.657 W and number of EVs charged 63.
Remora Jaya Optimization-Enabled Deep Quantum Neural Network for Underwater Target Tracking Using Radar Images D. Thiruselvan, J. P. Ananth Cybernetics and Systems, 2025 The Ocean boundaries can be protected by pursuing an underwater uncooperative target and permits for the exploitation of ocean resources. This research design a novel technique, named Remora Jaya Optimization (RJO)-enabled Deep Quantum Neural Network (DQN) by considering the impacts of an unknown underwater environment for effective underwater target tracking in radar images. Here, the image is modified effectively using the RJO algorithm because the input radar signal is applied to the image reconstruction process. The modified image is fed up to the gridding phase, where the image is partitioned into a number of grids. The feature extraction process is carried out to extract the significant features after the gridding step is over. This data augmentation method is carried out to increase the data dimensionality. Accordingly, the augmented result is forwarded to the DQN for target tracking, where the network is tuned efficiently by the algorithm of RJO, which is devised by the integration of Jaya Optimization and Remora optimization algorithm (ROA). Moreover, the RJO-based DQN has achieved a minimum Means Square Error (MSE) of 0.168 and maximum detection rate of 0.914, and minimum MSE of 0.168 based on Vertical-Vertical (VV) polarity. The proposed method showed higher effectiveness in detecting the underwater target system in the marine environment.
An Adaptive Salp-Stochastic-Gradient-Descent-Based Convolutional LSTM With MapReduce Framework for the Prediction of Rainfall S. Oswalt Manoj, Abhishek Kumar, Ashutosh Kumar Dubey, J. P. Ananth International Journal of Interactive Multimedia and Artificial Intelligence, 2025 Rainfall prediction is considered to be an esteemed research area that impacts the day-to-day life of Indians. The predominant income source of most of the Indian population is agriculture. It helps the farmers to make the appropriate decisions pertaining to cultivation and irrigation. The primary objective of this investigation is to develop a technique for rainfall prediction utilising the MapReduce framework and the convolutional long short-term memory (ConvLSTM) method to circumvent the limitations of higher computational requirements and the inability to process a large number of data points. In this work, an adaptive salp-stochastic-gradientdescent-based ConvLSTM (adaptive S-SGD-based ConvLSTM) system has been developed to predict rainfall accurately to process the long time series data and to eliminate the vanishing problems. To optimize the hyperparameter of the convLSTM model, the S-SGD methodology proposed combine the SGD and the salp swarm algorithm (SSA). The adaptive S-SGD based ConvLSTM has been developed by integrating the adaptive concept in S-SGD. It tunes the weights of ConvLSTM optimally to achieve better prediction accuracy. Assessment measures, such as the percentage root mean square difference (PRD) and mean square error (MSE), were employed to compare the suggested method with previous approaches. The developed system demonstrates high prediction accuracy, achieving minimal values for MSE (0.0042) and PRD (0.8450).
Interactive Data Management for Cancer Care: Leveraging Electronic Health Records and Proteomic Data M. Rohini, S. Oswalt Manoj, J. P. Ananth, D. Surendran Targeted Chemotherapy with Personalized Immunotherapy an AI Approach, 2025 This chapter investigates the critical role of data management systems in enhancing cancer treatment through the integration of electronic health records (EHRs) and proteomic data. The complex nature of cancer is examined initially and indicates the essential need for advanced data management to improve both diagnostic precision and treatment outcomes. The chapter highlights the benefits of robust EHRs in oncology, focusing on their role in facilitating personalized treatment plans and ongoing patient monitoring. The next focus is on the aggregation of proteomic data, which offers significant insights into the molecular foundations of cancer, thus supporting the discovery of new therapeutic targets. The chapter also addresses the intricacies of updating cancer EHRs with an emphasis on deploying a strong message passing protocol that preserves data integrity and ensures timely updates. The staging of critical data, strategies for dependable messaging, and error management to uphold data precision and access are also explored. The chapter also outlines the strategies for ensuring high-availability deployment phases and the uninterrupted operations of cancer data management.
Empowering Autonomous Vehicles to Make Challenging Options in Unexpected Circumstances with Hybrid Learning Sajeev Ram Arumugam, Sheela Gowr P, Ananth J P, Sankar Ganesh Karuppasamy, Palani S, Elavarasi J Proceedings of 3rd International Conference on Sustainable Computing and Data Communication Systems Icscds 2025, 2025 Autonomous vehicles (AVs) must navigate challenging and unexpected circumstances while guaranteeing security and competence. Prescribed rule-based classifications strive to handle the large unpredictability of virtual driving situations. In the proposed work, a novel hybrid architecture enables autonomous vehicles to make human-like choices in unexpected scenarios by using a combination of deep learning and data-driven planning techniques. The framework combines VOLOv7-based perception, multimodal transformers for fusing LiDAR, radar, and camera data, and a dual-policy approach using DAgger and Decision Transformer to obtain both sensitive and deliberate decision-making behaviors. An ensemble voting mechanism combines policy outputs to improve reliability. The proposed work is trained and evaluated using the Waymo Open Dataset and CARLA simulator. The proposed work attains a collision rate of 3.4%, route completion of 97.2%, and an average intervention frequency of 0.4.
A deep survey on quantum computing technologies J. P. Ananth, G. Raghuraman, S. P. Premnath, A. Francis Alexander Raghu Quantum Computing and Artificial Intelligence Training Machine and Deep Learning Algorithms on Quantum Computers, 2023
Comparative study of human recognition for video surveillance International Journal of Applied Engineering Research, 2015
Parallel processing agents for data mining with cloud computing & multi-agent systems International Journal of Applied Engineering Research, 2015
Optimizing Feature Extraction of Hand Written Characters Using Zoning International Journal of Applied Engineering Research, 2014
Racah moments based interactive image retrieval system European Journal of Scientific Research, 2011
Recognizing and estimating rainfall using cloud images S. Oswalt Mano, J. P. Ananth, V. Kavitha, J. SahayaAru Proceedings of the International Conference on Recent Advances in Space Technology Services and Climate Change 2010 Rsts and Cc 2010, 2010
RECENT SCHOLAR PUBLICATIONS
AI-driven Healthcare Innovations: Applications in Neurology and Medicine A Kumar, P Batta, JP Ananth John Wiley & Sons , 2026 2026
ResNet-Fused External Attention Network with Fractional Star Piranha Forager Optimization for Forest Fire Detection in Internet of Things Environments AJ Patrick, PD Mahendhiran, SR Arumugam, D Thavasilingam International Journal of Image and Graphics, 2850005 , 2026 2026
Hybrid DeepSentX Framework for AI-Driven Requirements Insight and Risk Prediction in Multilingual Sports Using Natural Language Processing S Alalasandra Ramakrishnaiah, Y Abdullah Rabi, A John Patrick, ... Big Data 14 (2), 67-86 , 2026 2026
Interactive Data Management for Cancer Care: Leveraging Electronic Health Records and Proteomic Data M Rohini, SO Manoj, JP Ananth, D Surendran Targeted Chemotherapy with Personalized Immunotherapy: An AI Approach, 375-390 , 2025 2025
An Adaptive Salp-Stochastic-Gradient-Descent-Based Convolutional LSTM With MapReduce Framework for the Prediction of Rainfall. SO Manoj, A Kumar, AK Dubey, JP Ananth International Journal of Interactive Multimedia and Artificial Intelligence … , 2025 2025 Citations: 3
Empowering Autonomous Vehicles to Make Challenging Options in Unexpected Circumstances with Hybrid Learning SR Arumugam, JP Ananth, SG Karuppasamy, S Palani, J Elavarasi 2025 3rd International Conference on Sustainable Computing and Data … , 2025 2025 Citations: 1
Emerging smart agricultural practices using artificial intelligence A Kumar, JP Verma, R Jain John Wiley & Sons , 2025 2025 Citations: 2
Remora Jaya optimization-enabled deep quantum neural network for underwater target tracking using radar images D Thiruselvan, JP Ananth Cybernetics and Systems 56 (4), 367-392 , 2025 2025 Citations: 1
NAVT-net neuron attention visual taylor network for lung cancer detection using CT images L Jimson, JP Ananth Computational Biology and Chemistry 115, 108363 , 2025 2025
Effective Charging Scheduling of Electric Vehicles Using a Hybrid Deep Learning Network RC J P Ananth, Pankaj Kumar, M. Belsam Jeba Ananth Energy Storage 7 (1) , 2025 2025 Citations: 1
Blockchain in healthcare: a comprehensive exploration of security, interoperability, and data integrity M Rohini, SO Manoj, JP Ananth, D Surendran 2024
Hybrid deep architecture for intrusion detection in cyber‐physical system: An optimization‐based approach SR Arumugam, PM Paul, BJJ Issac, JP Ananth International Journal of Adaptive Control and Signal Processing 38 (9), 3016 … , 2024 2024 Citations: 12
Image enhancement and blur pixel identification with optimization-enabled deep learning for image restoration SP Premnath, PS Gowr, JP Ananth, SR Arumugam Signal, Image and Video Processing 18 (5), 4525-4540 , 2024 2024 Citations: 9
Video Summarization using Deep Convolutional Neural Networks and Mutual Probability-based K-Nearest Neighbour. DJP Ananth Journal of Experimental & Theoretical Artificial Intelligence 35 (8) , 2023 2023 Citations: 2
Root disease classification with hybrid optimization models in IoT DFS Jayapalan, JP Ananth Expert Systems with Applications 226, 120150 , 2023 2023 Citations: 7
A deep survey on quantum computing technologies JP Ananth, G Raghuraman, SP Premnath, AFA Raghu Quantum Computing and Artificial Intelligence: Training Machine and Deep … , 2023 2023
Loan eligibility prediction using adaptive hybrid optimization driven-deep neuro fuzzy network ICGL Sindhuraj, AJ Patrick Expert Systems with Applications 224, 119903 , 2023 2023 Citations: 12
Deep learning based loan eligibility prediction with Social Border Collie Optimization GL Infant Cyril, JP Ananth Kybernetes 52 (8), 2847-2867 , 2023 2023 Citations: 5
Hybrid optimization algorithm enabled deep learning approach brain tumor segmentation and classification using MRI S Deepa, J Janet, S Sumathi, JP Ananth Journal of Digital Imaging 36 (3), 847-868 , 2023 2023 Citations: 69
Entropy weighted and kernalized power K-means clustering based lesion segmentation and optimized deep learning for diabetic retinopathy detection JGR Elwin, KS Kumar, JP Ananth, RR Kumar International Journal on Artificial Intelligence Tools 32 (01), 2250044 , 2023 2023 Citations: 3
MOST CITED SCHOLAR PUBLICATIONS
Hybrid optimization algorithm enabled deep learning approach brain tumor segmentation and classification using MRI S Deepa, J Janet, S Sumathi, JP Ananth Journal of Digital Imaging 36 (3), 847-868 , 2023 2023 Citations: 69
Taylor kernel fuzzy C-means clustering algorithm for trust and energy-aware cluster head selection in wireless sensor networks S Augustine, JP Ananth Wireless Networks 26, 5113-5132 , 2020 2020 Citations: 49
FACVO-DNFN: Deep learning-based feature fusion and Distributed Denial of Service attack detection in cloud computing ES GSR, R Ganeshan, IDJ Jingle, JP Ananth Knowledge-Based Systems 261, 110132 , 2023 2023 Citations: 47
MapReduce and Optimized Deep Network for Rainfall Prediction in Agriculture OM Ananth JP The Computer Journal 63 (6), 900-912 , 2020 2020 Citations: 47
Illumination‐based texture descriptor and fruitfly support vector neural network for image forgery detection in face images R Cristin, JP Ananth, V Cyril Raj IET image processing 12 (8), 1439-1449 , 2018 2018 Citations: 30
Deep maxout network for lung cancer detection using optimization algorithm in smart Internet of Things MP Ramkumar, PD Mano Paul, B Maram, JP Ananth Concurrency and Computation: Practice and Experience 34 (25), e7264 , 2022 2022 Citations: 29
A modified rider optimization algorithm for multihop routing in WSN S Augustine, JP Ananth International Journal of Numerical Modelling: Electronic Networks, Devices … , 2020 2020 Citations: 17
AN ADAPTIVE ENERGY EFFICIENT DATA GATHERING IN WIRELESS SENSOR NETWORKS SPP S.Balakrishnan, J P Ananth, L.Ramanathan International Journal of Pure and Applied Mathematics 118 (20), 2501-2510 , 2018 2018 Citations: 16
FWS-DL: forecasting wind speed based on deep learning algorithms SO Manoj, JP Ananth, M Rohini, B Dhanka, N Pooranam, SR Arumugam Artificial intelligence for renewable energy systems, 353-374 , 2022 2022 Citations: 15
Design of Grover’s Algorithm over 2, 3 and 4-Qubit Systems in Quantum Programming Studio DS Diana Jingle, Shylu Sam, Mano Paul, Ananth Jude International Journal of Electronics and Telecommunications 68 (1), 77-82 , 2022 2022 Citations: 14
Deep learning in data analytics DP Acharjya, A Mitra, N Zaman Springer International Publishing , 2022 2022 Citations: 13
Hybrid deep architecture for intrusion detection in cyber‐physical system: An optimization‐based approach SR Arumugam, PM Paul, BJJ Issac, JP Ananth International Journal of Adaptive Control and Signal Processing 38 (9), 3016 … , 2024 2024 Citations: 12
Loan eligibility prediction using adaptive hybrid optimization driven-deep neuro fuzzy network ICGL Sindhuraj, AJ Patrick Expert Systems with Applications 224, 119903 , 2023 2023 Citations: 12
Logo based pattern matching algorithm for intrusion detection system in wireless sensor network JP Ananth, S Balakrishnan, SP Premnath International Journal of Pure and Applied Mathematics 119 (12), 753-762 , 2018 2018 Citations: 12
Network Intrusion Detection and Mitigation Using Hybrid Optimization Integrated Deep Q Network GSRE Selvan, T Daniya, JP Ananth, KS Kumar Cybernetics and Systems 55 (1), 107-123 , 2022 2022 Citations: 11
Identification of IoT Device From Network Traffic Using Artificial Intelligence Based Capsule Networks JPASK H. Azath, M. Devi Mani, G. K. D. Prasanna Venkatesan, D. Sivakumar Wireless Personal Communications , 2022 2022 Citations: 11
A high security framework through human brain using algo mixture model deep learning algorithm S Balakrishnan, JP Ananth, L Ramanathan, R Sachinkanithkar, ... Deep Learning in Data Analytics: Recent Techniques, Practices and … , 2021 2021 Citations: 10
Image enhancement and blur pixel identification with optimization-enabled deep learning for image restoration SP Premnath, PS Gowr, JP Ananth, SR Arumugam Signal, Image and Video Processing 18 (5), 4525-4540 , 2024 2024 Citations: 9
CH selection and compressive sensing‐based data aggregation in WSN using hybrid Golden circle‐inspired optimization R TP, V Srinadh, MP P, JP Ananth International Journal of Communication Systems, e5574 , 2023 2023 Citations: 9
IoT enabled lung cancer detection and routing algorithm using CBSOA‐based ShCNN ES Gnanasigamani Samuel Raj, I Diana Jeba Jingle, B Maram, ... International journal of adaptive control and signal processing 37 (1), 224-243 , 2023 2023 Citations: 9
