An IoT-Enabled Hybrid Machine Learning Framework for Early Plant Disease Detection and Crop Health Monitoring Puneet Sapra, Adepu Bixapathi, Sasikala. P, P Joel Josephson, S. Nalini Jayanthi, Sridhar D Proceedings of 2nd International Conference on Visual Analytics and Data Visualization Icvadv 2026, 2026 An important obstacle to sustainable agriculture and global food security is a growing incidence of plant diseases. An IoT-Enabled Hybrid Machine Learning Framework for early plant disease diagnosis and crop health monitoring is presented in this research. To allow real-time monitoring and intelligent disease categorisation, the suggested system combines Internet of Things (IoT) sensors with a hybrid Convolutional Neural Network-Support Vector Machine (CNN-SVM) model. Temperature, humidity, and soil moisture are among the environmental and agricultural data that IoT-based sensors collect and send to a cloud server for analysis. The CNN-SVM hybrid model is used to analyse high-resolution plant leaf images with CNN extracting complicated characteristics and SVM performing effective classification. This hybridisation guarantees accurate operation in a range of field situations, improves detection accuracy, and reduces accidental alarms. With 98.6% accuracy, 98.1 % precision, and 97.9 % F1-score, experimental findings show better performance than traditional machine learning and deep learning models. The framework encourages sustainable crop management by assisting farmers in making decisions and receiving timely disease notifications. For precision agriculture and upcoming smart farming applications, this clever IoT-ML connection offers a practical, affordable, and sustainable solution.
Real-Time Road Accident Prediction in Smart Cities using a Hybrid Random Forest-LSTM Model J. Krishna, Jaya Jain, Arun Raj S.R, P. Joel Josephson, G. Pavan Kumar, P. Dileep Kumar Reddy Proceedings of International Conference on Sustainable Communication Networks and Application Icscn 2025, 2025 Traffic collisions consistently provide substantial challenges to urban safety, resulting in considerable human and economic costs. Conventional accident prediction techniques frequently fail to identify complex patterns in real-time urban traffic settings. The proposed study handles the issue by presenting a hybrid model that combines Random Forest for feature importance evaluation and Long Short-Term Memory (LSTM) networks for sequential accident forecasting. The suggested method captures essential variables including traffic volume, weather conditions, and geolocation data, which are subsequently preprocessed and sorted utilising Random Forest. The highest-ranked characteristics are subsequently converted into temporal sequences to train the LSTM, enabling the model to identify trends before accident occurrences. Experimental analysis on synthesised real-world datasets reveals substantial improvements in performance compared to conventional models. The hybrid RF–LSTM model attains 96.2% accuracy, 94.8% precision, 95.6% recall, 95.2% F1-score, and 0.981 AUC-ROC, outperforming the performance of Logistic Regression, SVM, and Vanilla LSTM baselines. The suggested study presents a scalable, high-precision framework appropriate for real-time implementation in smart city systems. The model's resilience and predictive capability can greatly aid metropolitan authorities in proactive traffic management, thus minimising the probability and severity of road accidents. These results confirm the practical feasibility and superiority of the proposed method in improving road safety.
A Machine Learning-based Smart Water Management Framework for Agricultural Irrigation using a Stacked Ensemble Model Nandini Kannan, Erugu Krishna, P Joel Josephson, Ravi Kant, M V Rathnamma, A Bharathi Proceedings of 5th International Conference on Trends in Material Science and Inventive Materials Ictmim 2025, 2025 Employing SWM in agricultural irrigation enhances efficiency, reduces costs, and boosts crop yield, while simultaneously benefiting the environment. We can now accurately quantify the water requirements of each plant, thanks to technological breakthroughs. The harmonious integration of diverse technologies poses a significant barrier to efficient solid waste management. Agriculture utilises around 70% of global freshwater, making effective water management essential for maintaining a sustainable food and water supply. In their quest for optimal irrigation, they utilised RF, RFE, and SelectKBest for feature selection after processing features with blanks. The ideal irrigation timetable for plants was established using a stacking ensemble technique. The recently constructed EM-MoNet network also facilitated SWM. This model utilised a two-stage transfer learning methodology that included re-training with the target dataset to improve its accuracy. The proposed method shown utility with an accuracy of 98.23% on a local dataset. This study highlights the importance of SWM in precision irrigation, which enhances sustainable agriculture by augmenting crop yields and reducing water consumption.
Detecting Jamming and Spoofing Attacks on Unmanned Aerial Vehicles with Advanced Neural Network Models Veernapu Sudheer Kumar, Gajendrasinh Natvarsinh Mori, Suresh Kumar K R, P Joel Josephson, N. Kumar, R Nithya 2025 Global Conference in Emerging Technology Ginotech 2025, 2025 Security issues have been highlighted by the widespread deployment of UAVs in both civilian and military settings due to their susceptibility to signal jamming and spoofing. Traditional UAV autopilot systems put cybersecurity last on the list of priorities. An increasing concern in smart city security systems is the vulnerability of UAVs to GPS spoofing and jamming, which can lead to signal loss, hacking, or hijacking. Because of these problems, this study suggests using the Attn-BiLSTM model for preventative and attack detection purposes. Normalisation of z-scores is the first step in preprocessing. Attribute selection using chi-square and correlation analyses. Using a CNN to extract spatial-temporal information and a BiLSTM to process them are necessary for UAV signal integrity prediction. Important aspects for the safety of UAV communications are brought to light by the attention mechanism's expansion. The proposed method achieves a prediction accuracy of 99.18%, which is higher than four leading deep learning models, according to the experimental results. By protecting UAVs against jamming and spoofing assaults, this study improves their operational security and reliability in dangerous areas, demonstrating the significance of AI in UAV cybersecurity.
Crop Disease and Pest Management in Agriculture via UAV Remote Sensing and Advanced Machine Learning Models A. Punitha, S Jayamangala, P Joel Josephson, K. Bharathi, Sindhu. V, Kamlesh Singh 3rd International Conference on Integrated Circuits and Communication Systems Icicacs 2025, 2025 Pests and diseases greatly reduce crop quality and yield; therefore, IA relies on effective pest and disease control. UAVs have become a crucial remote sensing (RS) tool for agricultural process monitoring and management. This study will examine major advances in this field using bibliometric methodologies including author co-occurrence and keyword co-contribution studies. The suggested technique involves preprocessing, feature extraction, and model training. Data quality improves with preprocessing. UAV images are used for feature extraction, focusing on canopy structure and height. PPO is trained the prediction model. Compared to ultramodern GANs and LSTM networks, the recommended model wins. The model consistently outperforms competitors with 91.17 percent accuracy. The study suggests employing UAVs in smart farming to reduce pests and diseases. The suggested model's accuracy and reliability improve crop quality and production by solving agricultural monitoring and management problems.
Automated Leaf Disease Detection using a Hybrid CNN-BiLSTM Model for Smart Agriculture Jamuna Deepakraj, Tegil J John, S. Sathiyanathan, P Joel Josephson, Priyanka H D, D. Suganthi Proceedings of 8th International Conference on Computing Methodologies and Communication Iccmc 2025, 2025 The mitigation of crop losses and the sustainability of agriculture rely on the prompt identification of foliar diseases. In large-scale agriculture, conventional identification methods such as expert eye inspections are inefficient, susceptible to errors, and labour-intensive. A growing number of individuals are seeking automated methods to monitor plant health, given that the majority of Indians are employed in agriculture. This study presents a hybrid DL strategy for leaf disease detection, encompassing preprocessing, segmentation, feature extraction, and model training. Initially, images are processed to enhance their quality and uniformity. The impacted regions of the leaf are subsequently categorised by K-Means clustering. The classification accuracy is improved by utilising several feature extraction methods. The proposed model, CNBiLS, integrates bidirectional LSTM layers with convolutional layers to leverage the spatial and sequential information in image data. When evaluated against contemporary state-of-the-art models, CNBiLS exhibited superior performance, achieving an exceptional 99.84% classification accuracy. This result underscores the model's accuracy in identifying various leaf diseases. Ultimately, CNBiLS offers a precise, scalable, and robust automated system for detecting leaf diseases, equipping farmers with timely information to manage illnesses effectively, so enhancing both the quality and yield of their crops.
History, challenges, and opportunities in tissue engineering M. Gokul Varshan, P. Joel Josephson, Bijaya Bijeta Nayak, Venkatesan Hariram, K. Balachandar Handbook of Research on Advanced Functional Materials for Orthopedic Applications, 2023
Stretchable Elastomer-Phase Change Composites for Passive Thermal Management in Wearable Electronics SSM M. Chennakesavulu1,*, S. John Leon2, P. Joel Josephson3, V. Parimala4 ... Journal of Polymer & Composites 14 (2), 30-41 , 2026 2026
An IoT-Enabled Hybrid Machine Learning Framework for Early Plant Disease Detection and Crop Health Monitoring P Sapra, A Bixapathi, PJ Josephson, SN Jayanthi 2026 International Conference on Visual Analytics and Data Visualization … , 2026 2026
Adaptive Power Quality Control in PV-Wind Distributed Generation Systems Integrating Batterybased Energy Storage PJ Josephson, R Manikandan, P Jamuna, S Dhivagar, M Hariprabhu 2026 International Conference on Communication, Computing and Emerging … , 2026 2026
Real-Time Road Accident Prediction in Smart Cities using a Hybrid Random Forest–LSTM Model J Krishna, J Jain, AR SR, PJ Josephson, GP Kumar, PDK Reddy 2025 International Conference on Sustainable Communication Networks and … , 2025 2025
Automated Leaf Disease Detection using a Hybrid CNN-BiLSTM Model for Smart Agriculture J Deepakraj, TJ John, S Sathiyanathan, PJ Josephson, P HD, D Suganthi 2025 8th International Conference on Computing Methodologies and … , 2025 2025 Citations: 2
Detecting Jamming and Spoofing Attacks on Unmanned Aerial Vehicles with Advanced Neural Network Models VS Kumar, GN Mori, SK KR, PJ Josephson, N Kumar, R Nithya 2025 Global Conference in Emerging Technology (GINOTECH), 1-6 , 2025 2025 Citations: 2
A Machine Learning-based Smart Water Management Framework for Agricultural Irrigation using a Stacked Ensemble Model N Kannan, E Krishna, PJ Josephson, R Kant, MV Rathnamma, A Bharathi 2025 5th International Conference on Trends in Material Science and … , 2025 2025
Stochastic gradient boosted distributed decision trees security approach for detecting cyber anomalies and classifying multiclass cyber-attacks JC Sekhar, R Priyanka, AK Nanda, PJ Josephson, MJD Ebinezer, TK Devi Computers & Security 151, 104320 , 2025 2025 Citations: 14
Crop Disease and Pest Management in Agriculture via UAV Remote Sensing and Advanced Machine Learning Models A Punitha, S Jayamangala, PJ Josephson, K Bharathi, K Singh 2025 3rd International Conference on Integrated Circuits and Communication … , 2025 2025 Citations: 1
An effective PDE-based thresholding for MRI image denoising and H-FCM-based segmentation S Kollem, S Peddakrishna, PJ Josephson, S Cheguri, G Srilakshmi, ... International Journal of Experimental Research and Review 44, 51-65 , 2024 2024 Citations: 1
Automated face recognition using deep learning technique and center symmetric multivariant local binary pattern JC Sekhar, PJ Josephson, A Chinnasamy, M Maheswari, S Sankar, ... Neural Computing and Applications 37 (1), 263-281 , 2024 2024 Citations: 10
Analysis of acoustic channel characteristics in shallow water based on multipath model Y Durgachandramouli, A Sailaja, PJ Josephson, TN Prasad, KE Prasad, ... International Conference on Cognitive Computing and Cyber Physical Systems … , 2024 2024 Citations: 9
Augmenting Cervical Cancer Analysis with Deep Learning Classification and Topography Selection Using Artificial Bee Colony Optimization K Ramu, A Ananthanarayanan, PJ Josephson, NRR Paul, P Tumuluru, ... SN Computer Science 5 (6), 703 , 2024 2024 Citations: 38
Computation Offloading for Image Compression in Mobile Edge Computing Using a Deep Belief Network Based on the Markov Approximation Algorithm NN Alleema, A Chaturvedi, AK Nanda, PJ Josephson, AM Buttar, ... Mobile Networks and Applications 29 (2), 433-447 , 2024 2024
A Constructive Role for Social Science in the Development of Automated Vehicles Based on LFM-BiGRU Approach J Srikanth, N Nitheesha, S Khetree, PJ Josephson, D Akila, V Divya 2024 3rd International Conference for Innovation in Technology (INOCON), 1-6 , 2024 2024
Advanced Insights in Nursing Science: Applying ARIMA-CLSTM for Concept Analysis T Thirumalaikumari, V Kumar, PJ Josephson, AK Mavliya, HA Basha 2024 3rd International Conference for Innovation in Technology (INOCON), 1-6 , 2024 2024
Unveiling the Energy-Based Validation and Verification (EVV) Method for Perceiving and Averting Rank Inconsistency Attacks (RIA) for Guarding IoT Routing K Ramu, N Gomathi, SK Suman, PJ Josephson, M Vadivukarassi, ... SN Computer Science 5 (2), 249 , 2024 2024 Citations: 34
Predicting Chronic Diseases and Measuring Drug Effectiveness in Diabetes using Machine Learning and Statistical Methods to aid Clinical Decision Making BC Anil, A Sharma, SP Singh, PJ Josephson https://bpasjournals.com/library-science/index.php/journal/article/view/975 , 2024 2024
Cardiac Bioengineering Analysis of Electrophysiological Signals Driven by Deep Learning AK Singh, R Karthikeyan, PJ Josephson, P Singh Journal of Artificial Intelligence and Metaheuristics (JAIM) , 2024 2024
Artificial neural networks-based improved Levenberg–Marquardt neural network for energy efficiency and anomaly detection in WSN M Revanesh, SS Gundal, JR Arunkumar, PJ Josephson, S Suhasini, ... Wireless Networks 30 (6), 5613-5628 , 2023 2023 Citations: 64
MOST CITED SCHOLAR PUBLICATIONS
Artificial neural networks-based improved Levenberg–Marquardt neural network for energy efficiency and anomaly detection in WSN M Revanesh, SS Gundal, JR Arunkumar, PJ Josephson, S Suhasini, ... Wireless Networks 30 (6), 5613-5628 , 2023 2023 Citations: 64
IoT battery management system in electric vehicle based on LR parameter estimation and ORMeshNet gateway topology PS Kumar, RN Kamath, P Boyapati, PJ Josephson, L Natrayan, ... Sustainable Energy Technologies and Assessments 53, 102696 , 2022 2022 Citations: 57
Augmenting Cervical Cancer Analysis with Deep Learning Classification and Topography Selection Using Artificial Bee Colony Optimization K Ramu, A Ananthanarayanan, PJ Josephson, NRR Paul, P Tumuluru, ... SN Computer Science 5 (6), 703 , 2024 2024 Citations: 38
Unveiling the Energy-Based Validation and Verification (EVV) Method for Perceiving and Averting Rank Inconsistency Attacks (RIA) for Guarding IoT Routing K Ramu, N Gomathi, SK Suman, PJ Josephson, M Vadivukarassi, ... SN Computer Science 5 (2), 249 , 2024 2024 Citations: 34
A novel algorithm for real time task scheduling in multiprocessor environment J Josephson, R Ramesh Cluster Computing 22, 13761-13771 , 2018 2018 Citations: 16
Stochastic gradient boosted distributed decision trees security approach for detecting cyber anomalies and classifying multiclass cyber-attacks JC Sekhar, R Priyanka, AK Nanda, PJ Josephson, MJD Ebinezer, TK Devi Computers & Security 151, 104320 , 2025 2025 Citations: 14
Medical image enhancement in health care applications using modified sun flower optimization SN Krishnan, D Yuvaraj, K Banerjee, PJ Josephson, TCHA Kumar, ... Optik 271, 170051 , 2022 2022 Citations: 14
Automated face recognition using deep learning technique and center symmetric multivariant local binary pattern JC Sekhar, PJ Josephson, A Chinnasamy, M Maheswari, S Sankar, ... Neural Computing and Applications 37 (1), 263-281 , 2024 2024 Citations: 10
Analysis of acoustic channel characteristics in shallow water based on multipath model Y Durgachandramouli, A Sailaja, PJ Josephson, TN Prasad, KE Prasad, ... International Conference on Cognitive Computing and Cyber Physical Systems … , 2024 2024 Citations: 9
History, challenges, and opportunities in tissue engineering MG Varshan, PJ Josephson, BB Nayak, V Hariram, K Balachandar Handbook of research on advanced functional materials for orthopedic … , 2023 2023 Citations: 7
Exploratory data analysis based on micro grids generation for control communication and monitoring via wireless sensor network B Jain, S Sirdeshpande, MS Gowtham, PJ Josephson, MK Chakravarthi, ... 2022 2nd International Conference on Advance Computing and Innovative … , 2022 2022 Citations: 5
Automated Leaf Disease Detection using a Hybrid CNN-BiLSTM Model for Smart Agriculture J Deepakraj, TJ John, S Sathiyanathan, PJ Josephson, P HD, D Suganthi 2025 8th International Conference on Computing Methodologies and … , 2025 2025 Citations: 2
Detecting Jamming and Spoofing Attacks on Unmanned Aerial Vehicles with Advanced Neural Network Models VS Kumar, GN Mori, SK KR, PJ Josephson, N Kumar, R Nithya 2025 Global Conference in Emerging Technology (GINOTECH), 1-6 , 2025 2025 Citations: 2
Wireless Level Monitoring of Interfacing Two-Tank System through User Datagram Protocol R Pilli, T Rajkumar, KN Shreenath, KRP Kumar, PJ Josephson, B Pant 2023 IEEE International Conference on Integrated Circuits and Communication … , 2023 2023 Citations: 2
Crop Disease and Pest Management in Agriculture via UAV Remote Sensing and Advanced Machine Learning Models A Punitha, S Jayamangala, PJ Josephson, K Bharathi, K Singh 2025 3rd International Conference on Integrated Circuits and Communication … , 2025 2025 Citations: 1
An effective PDE-based thresholding for MRI image denoising and H-FCM-based segmentation S Kollem, S Peddakrishna, PJ Josephson, S Cheguri, G Srilakshmi, ... International Journal of Experimental Research and Review 44, 51-65 , 2024 2024 Citations: 1
Routing Path Selection and Data Transmission in Industry‐Based Mobile Communications Using Optimization Technique RK Bharti, V Bhoopathy, P Bhanarkar, KL Ambashtha, K Priya, CAD Durai, ... Wireless Communications and Mobile Computing 2022 (1), 5431413 , 2022 2022 Citations: 1
Stretchable Elastomer-Phase Change Composites for Passive Thermal Management in Wearable Electronics SSM M. Chennakesavulu1,*, S. John Leon2, P. Joel Josephson3, V. Parimala4 ... Journal of Polymer & Composites 14 (2), 30-41 , 2026 2026
An IoT-Enabled Hybrid Machine Learning Framework for Early Plant Disease Detection and Crop Health Monitoring P Sapra, A Bixapathi, PJ Josephson, SN Jayanthi 2026 International Conference on Visual Analytics and Data Visualization … , 2026 2026
Adaptive Power Quality Control in PV-Wind Distributed Generation Systems Integrating Batterybased Energy Storage PJ Josephson, R Manikandan, P Jamuna, S Dhivagar, M Hariprabhu 2026 International Conference on Communication, Computing and Emerging … , 2026 2026
