The impact of variable rate technology (VRT) on soil health and crop yield optimization Amol M. Dhepe, Swati Sharma, J. Somasekar, G. Manikandan, Midhun Mathew Kizhakethil, Abid Salati Computational Techniques in Precision Agriculture Advances and Applications, 2026 Variable rate technology (VRT) is a precision agriculture technology that varies agricultural input application according to real-time data, maximizing the use of resources, improving soil health, and increasing crop yield for sustainable farming. This chapter applies a data-driven, geospatial method to measure VRT adoption using remote sensing, Geographic Information System (GIS) mapping, and analysis of soil variability to examine its effects on indicators of soil health and optimization of crop yield across varying agricultural regions. This chapter aims to depict improved soil nutrient balance, reduced input wastage, and improved crop yield reliability. Spatial analysis would be expected to reveal positive relationships between VRT application zones and optimal productivity, promoting site-specific and sustainable soil management approaches. The chapter discovers that VRT enhances crop yield and soil fertility through effective regulation of inputs. Its use promotes sustainable agriculture through environmental minimization, resource conservation, and long-term soil productivity under a range of farm conditions.
A Novel Approach to IoT Device Identification via Anti-Interference Dynamic Integral Neural Network and Multiobjective Fitness-Dependent Optimizer Algorithm E. Anbalagan, M. Kanchana, G. Manikandan, G. Bhuvaneswari, S. Malathi International Journal of Communication Systems, 2025 The Internet of Things (IoT) has observed an accelerated development in the quantity of applications due to the rapid development of information technology. It can be difficult to identify IoT devices in heterogeneous, interference‐prone networks. The accuracy, optimization, and robustness of existing techniques are insufficient for dependable classification and application detection. To overcome this complication, a novel approach for IoT device identification using an anti‐interference dynamic integral neural network (AIDINN) and a multiobjective fitness‐dependent optimizer algorithm (MOFDOA) (IoT‐DTI‐AIDINN‐MOFDOA) is proposed. The input data are collected from the Network Traffic Dataset. Then, the input data are given to feature extraction. By using the synchro‐transient‐extracting transform (STET), the features are extracted from the dataset. Then the extracted features are given to AIDINN for IoT device identification, which classifies known IoT devices and unknown IoT devices. In general, AIDINN does not adopt any optimization techniques to determine the ideal parameters for ensuring an accurate IoT device identification. Hence, an MOFDOA is proposed here to optimize the AIDINN, which precisely constructs the IoT application detection. The performance measures like accuracy, precision, recall, specificity, F measure, computational time, and computational complexity are evaluated. The proposed IoT‐DTI‐AIDINN‐MOFDOA method attains a higher accuracy of 25.23%, 16.12%, and 21.27% and a higher precision of 25.26%, 16.22%, and 26.27% when analyzed with the following existing models: IoT device type detection using deep neural network (IoT‐DTI‐DNN), adversarial attacks and IoT for long short‐term memory (AA‐IoT‐LSTM), and IoT device identification depending on fully connected neural network (IoT‐DI‐FCNN), respectively.
Topological Information Embedded Convolution Neural Network–Dependent Energy Alert-Cluster Head Selection in WSN Sivanantham Elumalai, Senthil Vadivu Mani, Bhuvaneswari Govinda Swamy, Manikandan Gnanasundaram International Journal of Communication Systems, 2025 Energy efficiency is a major challenge in developing wireless sensor networks (WSNs). The cluster head (CH) can be selected at random or depending on one or more criteria that leads to increase the network lifespan directly. Nevertheless, the CH selection creates an optimization problem. For this purpose, a number of researches have been presented so far to select the optimum CH with the help of various optimization methods, but none of them effectively solves this problem. Therefore, a topological information embedded convolution neural network based energy alert‐cluster head selection in wireless sensor network (TIECNN‐EAC‐WSN) is proposed in this paper. In this method, cluster formation and CH selection is performed by topological information embedded convolution neural network (TIECNN). The CH selection is carried out by three features: energy stabilization, minimization of distance among nodes, and minimization of delay during data transmission. Then, the optimal route is selected by using improved manta ray foraging optimization (IMFO). The TIECNN‐EAC‐WSN approach is evaluated with some metrics, such as network lifetime, number of alive sensor node, and energy consumption with different scenrios. The stimulation results show that the proposed TIECNN‐EAC‐WSN method attains 23.20%, 27.22%, and 26.28% higher number of alive sensor node when compared with the existing models: energy‐aware optimization clustering for hierarchical routing in WSN (EAC‐HR‐WSN), energy‐aware clustering depending on fuzzy modeling in WSN utilizing modified invasive weed optimization (FMEAC‐WSN‐IWO), and quantum tunicate swarm approach–dependent energy‐aware clustering mode for WSN (QTSA‐EAC‐WSN), respectively.
Design and Development of a Mobile Application for Direct Agricultural Market Access G. Manikandan, Nivetha S B, Sri Siva Swathika S T, Narmathaa J B, Srijhha RM Proceedings of International Conference on Visual Analytics and Data Visualization Icvadv 2025, 2025 In developing countries, the conventional agricultural supply chain relies significantly on middlemen, which often leads to farmers receiving merely 20-40% of the retail price. This system not only diminishes farmers' earnings but also raises prices for consumers. This paper investigates the advantages of direct market access through digital platforms, which eliminate intermediaries and allow farmers to connect directly with consumers and retailers. Utilizing these platforms can boost farmers' incomes by as much as 50%, while also improving transparency and efficiency in the supply chain. The research offers a comprehensive examination of the economic, technological, and social effects of direct market access, as well as addressing major challenges concerning platform adoption, scalability, and connectivity in rural areas.
AI-Enhanced Public Safety Lighting for Smart Sustainable Cities Using Cloud Computing Bharat Tidke, S. Pragadeeswaran, R. Poornima Lakshmi, G Bhuvaneswari, G Manikandan, S. Murugan Proceedings of the International Conference on Multi Agent Systems for Collaborative Intelligence Icmsci 2025, 2025 This paper presents a novel method that combines cloud computing infrastructure and Reinforcement Learning (RL) algorithms to improve public safety lighting in smart cities. By dynamically adjusting lighting levels in response to real-time sensor inputs, the proposed system aims to maximize visibility while minimizing energy consumption. The cloud-based framework's RL agents continuously interact with the environment and human activity patterns to learn the best lighting policies. The system utilizes data from multiple sensors, including ambient light and motion detectors, to adjust real-time to daylight levels, weather, and pedestrian traffic variations. By integrating RL, the lighting system can adapt to changing urban dynamics and safety requirements over time, all while reducing energy waste. Compared to conventional static lighting systems, case studies and simulations show how the system can significantly improve visibility and response times while enhancing safety in various urban settings. RL algorithms can be easily deployed and managed throughout extensive smart city networks due to cloud computing network scalability and adaptability. This study advances sustainable urban infrastructure by showcasing the effectiveness of RL-based artificial intelligence (AI) solutions in public safety lighting optimization, thereby laying the groundwork for future smarter and safer cities.
SVM-based Predictive Modeling for Sustainable Solar Solutions in Off-Grid Areas Giriprasad. S, S. Sangeetha, G Manikandan, G Bhuvaneswari, B Meenakshi, S. Sujatha Proceedings of 5th International Conference on Trends in Material Science and Inventive Materials Ictmim 2025, 2025 The rising need for sustainable energy solutions in off-grid regions has prompted the investigation of creative predictive modeling methodologies. This research examines Support Vector Machine (SVM) techniques to enhance solar energy production and use. SVM, a powerful machine learning instrument, assesses diverse environmental variables, such as solar radiation, temperature, and humidity, to precisely forecast solar energy production. It presents a SVM model to forecast solar energy production in off-grid areas, optimizing resource distribution and improving sustainability with analysis of environmental variations and energy consumption data. The model improves decision-making for deploying solar systems in distant areas using historical data and real-time observations. The prediction skills of SVM enable the creation of customized solar energy systems that address the specific energy requirements of off-grid settlements. This technique enhances sustainability by optimizing solar installation efficiency, decreasing dependence on fossil fuels, and mitigating environmental consequences. The results indicate that SVM-based predictive modeling substantially enhances the dependability and efficiency of solar energy systems, making them more feasible for off-grid applications. It highlights the capacity of advanced machine learning methodologies to promote sustainable energy solutions, hence aiding the worldwide shift towards renewable energy sources.
Leveraging AI to Promote Sustainable Energy Distribution Raja R. Vinston, K. Fouzia Sulthana, Subha Priyadharshini A, R. Kotteeswaran, G. Manikandan, Joel M. Robinson Achieving Sustainability in Multi Industry Settings with AI, 2025 The demand for sustainable energy solutions is more important than ever as the globe struggles to address the twin issues of accelerated climate change and depleting conventional energy sources. In order to provide a stable, sustainable future for future generations, the search for greener, more efficient energy systems is not just about innovation but also about survival. The increasing significance of data science and artificial intelligence (AI) provides a path toward change and a ray of hope in this dire situation. “Leveraging AI to Promote Sustainable Energy Distribution,” the chapter's title, delves into the revolutionary potential of artificial intelligence (AI) and data science to transform the global energy distribution and management landscape. This chapter seeks to shed light on the various ways that artificial intelligence (AI) might support the creation of sustainable energy infrastructures by a thorough examination of AI-driven techniques, ranging from peer-to-peer energy trading to predictive maintenance and grid optimization.
Making clinical decisions to treat patients by using health information technology S. Ruban, S. Prabagar, C. Moorthy, J. P. Manimozhi, M. Robinson Joel, G. Manikandan Responsible AI for Digital Health and Medical Analytics, 2024 A Clinical Decision Support System (also known as a CDSS) is a computer programme that offers guidelines, suggestions, patient-specific data, and evidence-based information to help healthcare practitioners make clinical choices. In order to provide suggestions that can increase clinical outcomes, lower medical mistakes, and improve patient care, CDSS incorporates knowledge from a variety of sources, including medical literature, guidelines for clinical practice, electronic medical records (EHRs), and information about patients. A large body of knowledge, including clinical recommendations, best practices, expert opinions, and medical literature, is included into CDSS. Patient-specific data, including medical histories, test results, diagnosis, and prescription lists, can be accessed through connections with electronic health records (EHRs) as well as other healthcare information systems.
Cloud-Based Foot Pressure Analysis for Diabetic Care Using ANN Pradeepa H, Raveendra N Amarnath, V G Sivakumar, Thamizhamuthu R, G Manikandan, G Bhuvaneswari Proceedings of the 5th International Conference on Data Intelligence and Cognitive Informatics Icdici 2024, 2024
STUDY ON THE USE OF POLYMERIC TREATMENT WITH RICE HUSK SILICA ON DIRECT TENSION BEHAVIOUR AND ADHERENCE OF SISAL FIBRE IN CEMENTICIOUS COMPOSITES Journal of Environmental Protection and Ecology, 2024
Mining spatially co-located objects from vehicle moving data European Journal of Scientific Research, 2012
RECENT SCHOLAR PUBLICATIONS
Navigating the cloud: Unraveling anomalies using self-attention based conditional generative adversarial network approach in data center networks A Poobalan, G Manikandan, G Bhuvaneswari International Journal of Information Technology & Decision Making , 2026 2026
An Integrated SARIMAX and Agentic Retrieval-Augmented Generation Framework for Pharmaceutical Sales Forecasting and Intelligent Decision Support G Manikandan, JB Narmathaa, SB Nivetha, SSS ST, RM Srijhha, ... 2026 3rd International Conference on Research Methodologies in Knowledge … , 2026 2026
PyKFHNet: Pyramid Deep Kronecker Forward Harmonic Network for Breast Cancer Detection Using Histopathological Images V Anitha, RB Laxmi, K Abinaya, P Jose, G Manikandan, MR Joel Biomedical Materials & Devices, 1-22 , 2026 2026 Citations: 1
Hybrid Deep Learning Model with Optimization Algorithm for Precise Skin Disease Prediction and Classification D Kumar, G Bhuvaneswari, G Manikandan, PV Sena, P Deepa Architecture Image Studies 6 (4), 312-327 , 2025 2025
Mobile-Le Harmonic Fusion Network for Object Recognition and SiamMoT Based Multi-Object Tracking Using Video Surveillance J Shajeena, B Govindasamy, M Gnanasundaram, M Robinson Joel Cybernetics and Systems, 1-31 , 2025 2025
A nonlinear autoregressive recurrent forward harmonic network for rainfall forecasting S Pal, S Palaniyandi, A Al-Abri, R Joel, G Bhuvaneswari, G Manikandan Journal of Hydrology, 134208 , 2025 2025 Citations: 1
EGAN-F-MIRNet: Enlighten Generative Adversarial Network-Fusion-MIRNet for Image Enhancement of MRI Brain Image R Kavitha, R Arunadevi, K Ishwarya, S Athinarayanan, MR Joel, ... Biomedical Materials & Devices, 1-22 , 2025 2025
Multi-Algorithm ML Framework for Intrusion Detection and Resilient Security in Mobile Ad Hoc Networks S Ramya, JKS Al-Safi, BJ Ashwin, G Manikandan, R Revathi 2025 5th Asian Conference on Innovation in Technology (ASIANCON), 1-6 , 2025 2025
EARLY FIRE HAZARD PREDICTION FRAMEWORK IN SMART CITIES USING DEEP LEARNING WITH ANTLION OPTIMIZATION ALGORITHM RSM Nagar, GT Kavaraipettai, T District Journal of Theoretical and Applied Information Technology 103 (14) , 2025 2025
A Novel Approach to IoT Device Identification via Anti‐Interference Dynamic Integral Neural Network and Multiobjective Fitness‐Dependent Optimizer Algorithm E Anbalagan, M Kanchana, G Manikandan, G Bhuvaneswari, S Malathi International Journal of Communication Systems 38 (7), e70041 , 2025 2025 Citations: 1
LinguAIsts@ DravidianLangTech 2025: Misogyny Meme Detection using multimodel Approach R Arthi, J Pavithra, A Lekhashree, G Dhanyashree, K Arivuchudar, ... Proceedings of the Fifth Workshop on Speech, Vision, and Language … , 2025 2025
codecrackers@ DravidianLangTech 2025: Sentiment Classification in Tamil and Tulu Code-Mixed Social Media Text Using Machine Learning LK VP, G Manikandan, M Raj Proceedings of the Fifth Workshop on Speech, Vision, and Language … , 2025 2025 Citations: 1
SVM-based Predictive Modeling for Sustainable Solar Solutions in Off-Grid Areas S Sangeetha, G Manikandan, G Bhuvaneswari, B Meenakshi, S Sujatha 2025 5th International Conference on Trends in Material Science and … , 2025 2025 Citations: 2
Design and development of a mobile application for direct agricultural market access G Manikandan, SB Nivetha, SSS ST, JB Narmathaa, RM Srijhha 2025 International Conference on Visual Analytics and Data Visualization … , 2025 2025 Citations: 3
AI-Enhanced Public Safety Lighting for Smart Sustainable Cities Using Cloud Computing B Tidke, S Pragadeeswaran, RP Lakshmi, G Bhuvaneswari, ... 2025 International Conference on Multi-Agent Systems for Collaborative … , 2025 2025 Citations: 2
Achieving Sustainability in Multi-Industry Settings With AI RR Vinston, R Kotteeswaran, K Fouzia Sulthana, G Manikandan, ... 2025 Citations: 15
Leveraging AI to Promote Sustainable Energy Distribution RR Vinston, KF Sulthana, R Kotteeswaran, G Manikandan, JM Robinson Achieving Sustainability in Multi-Industry Settings With AI, 37-62 , 2025 2025 Citations: 28
Integrating blockchain IoT and 6G technologies for secure efficient and sustainable smart city applications enhancing urban living through innovation S Cloudin, MR Joel, G Manikandan, IM Blessy Building Tomorrow's Smart Cities With 6G Infrastructure Technology, 483-508 , 2025 2025 Citations: 6
Making clinical decisions to treat patients by using health information technology S Ruban, S Prabagar, C Moorthy, JP Manimozhi, MR Joel, G Manikandan Responsible AI for Digital Health and Medical Analytics, 87-112 , 2025 2025 Citations: 2
Important Concerns With Comorbidities and Type 2 Diabetes in Clinical Decision Support Systems Based on Mobile Solutions S Ruban, S Anitha, G Bhuvaneswari, G Manikandan Impact of Digital Solutions for Improved Healthcare Delivery, 231-256 , 2025 2025
MOST CITED SCHOLAR PUBLICATIONS
A novel machine learning framework for diagnosing the type 2 diabetics using temporal fuzzy ant miner decision tree classifier with temporal weighted genetic algorithm G Bhuvaneswari, G Manikandan Computing 100 (8), 759-772 , 2018 2018 Citations: 58
Hybrid methodology-based energy management of microgrid with grid-isolated electric vehicle charging system in smart distribution network K Kalaiselvan, R Saravanan, B Adhavan, GS Manikandan Electrical engineering 106 (3), 2705-2720 , 2024 2024 Citations: 46
Mining spatially co-located objects from vehicle moving data G Manikandan, S Srinivasan Eur. J. of Sci. Res 68 (3) , 2012 2012 Citations: 33
An efficient algorithm for mining spatially co-located moving objects G Manikandan, S Srinivasan American Journal of Applied Sciences 10 (3), 195-208 , 2013 2013 Citations: 31
Leveraging AI to Promote Sustainable Energy Distribution RR Vinston, KF Sulthana, R Kotteeswaran, G Manikandan, JM Robinson Achieving Sustainability in Multi-Industry Settings With AI, 37-62 , 2025 2025 Citations: 28
An intelligent intrusion detection system for secure wireless communication using IPSO and negative selection classifier G Bhuvaneswari, G Manikandan Cluster Computing 22 (Suppl 5), 12429-12441 , 2019 2019 Citations: 28
Traffic control by bluetooth enabled mobile phone G Manikandan, S Srinivasan International Journal of Computer and Communication Engineering 1 (1), 66 , 2012 2012 Citations: 28
An analysis of security challenges in internet of things (iot) based smart homes MR Joel, G Manikandan, G Bhuvaneswari 2023 Second International Conference on Electronics and Renewable Systems … , 2023 2023 Citations: 26
A smart speed governor device for vehicle using IoT G Bhuvaneswari, G Manikandan Webology 19 (2) , 2022 2022 Citations: 26
Mining of spatial co-location pattern implementation by FP growth G Manikandan, S Srinivasan Ind. J. Comput. Sci. Eng 3, 344-348 , 2012 2012 Citations: 26
Artificial intelligence to the assessment, monitoring, and forecasting of drought in developing countries G Manikandan, G Bhuvaneswari, MR Joel 2023 International Conference on Circuit Power and Computing Technologies … , 2023 2023 Citations: 25
Recognition of Ancient stone Inscription Characters Using Histogram of Oriented Gradients G Bhuvaneswari, G Manikandan Proceedings of International Conference on Recent Trends in Computing … , 2019 2019 Citations: 23
SVM-RFE enabled feature selection with DMN based centroid update model for incremental data clustering using COVID-19 M Robinson Joel, G Manikandan, G Bhuvaneswari, P Shanthakumar 2023 Citations: 21
Enhancement and Development of Next Generation Data Mining Photolithographic Mechanism D Geetha, V Kavitha, G Manikandan, D Karunkuzhali Journal of Physics: Conference Series 1964 (4), 042092 , 2021 2021 Citations: 19
Blockchain technology’s role in an electronic voting system for developing countries to produce better results IM Blessy, G Manikandan, MR Joel 2023 3rd International Conference on Innovative Mechanisms for Industry … , 2023 2023 Citations: 18
Achieving Sustainability in Multi-Industry Settings With AI RR Vinston, R Kotteeswaran, K Fouzia Sulthana, G Manikandan, ... 2025 Citations: 15
Chronological bald eagle optimization based deep learning for image watermarking G Suresh, G Bhuvaneswari, G Manikandan, P Shanthakumar Expert Systems with Applications 238, 121545 , 2024 2024 Citations: 14
Marine Weather Forecasting to Enhance Fisherman's Safety Using Machine Learning MR Joel, G Manikandan, M Nivetha International Journal of Scientific Research in Science, Engineering and … , 2023 2023 Citations: 14
Sign Language Detection and Recognition Using Media Pipe and Deep Learning Algorithm MSH Ms. E J Honesty Praiselin, Dr G Manikandan, Ms. Vilma Veronica International Journal of Scientific Research in Science and Technology 11 (2 … , 2024 2024 Citations: 13
Enhanced Ai-Based Machine Learning Model for an Accurate Segmentation and Classification Methods G Manikandan, BT Hung International Journal on Recent and Innovation Trends in Computing and … , 2023 2023 Citations: 13
