.Aanandha Saravanan K

@veltech.edu.in

Associate Professor Eelctronics and Communication Engineering
VelTech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology

.Aanandha Saravanan K


               Download Compact PDF  
https://researchid.co/sarvanme2314

RESEARCH INTERESTS

Wireless Networks,IoT,Network Security
53

Scopus Publications

Scopus Publications

  • Chronic heart disease detection of elder patient’s using machine learning technology integrated with internet of things
    B. Sathyasri, Aanandha Saravanan, G. Aloy Anuja Mary, S. Vanaja, K. C. Sriharipriya
    Multimedia Tools and Applications, 2025
  • Threshold Behavior of Bidirectional Sodium Currents in Sinoatrial Node Cells and Modeling Consequences for Pacemaker Dysfunction
    S. Ramkumar, Martin Joel Rathnam, K. Aanandha Saravanan, T. Nivethitha
    Mathematical Modelling of Engineering Problems, 2025
  • Predicting Pest Infestation Patterns with Graph Convolutional Networks (GCN) in Precision Farming
    Marri Sireesha, Laith Hussein, M. Manju, Prashant Johri, T. Kuppuraj, Aanandha Saravanan K
    2025 International Conference on Automation and Computation Autocom 2025, 2025
    Due to the increases pest impacts on agricultural productivity, effective pest prediction models are required for efficient pest control in precision farming. The framework outlined in this research uses a Graph Convolutional Network (GCN) to estimate pest infestation by exploiting spatial and temporal relationships present in agri-ecosystems. The methodology combines weather, soil, pest, and crop health data into a graph-based representation of spatial units, and the directed connections between them that reflect their proximity and climatic similarity. The GCN converts these graphs to learn such complex relationship as the infestation prediction at various severity levels. The proposed model has produced a better accuracy of 94.8%, better precision of 95.2%, better recall of 94.5% and better F1-score of 94.8% as compared from Random forest, SVM, LSTM, CNN as per the comparison of table 3. Comparing them focuses on graph construction approaches and temporal aspects, mixed P/S and temporal emb has improved the model performance. This framework is practical for further pest management and employing the result without harming the environment. The results show the effectiveness of GCNs for use in pest prediction in precision farming and the opportunity to develop sustainable and scalable data-based agriculture.
  • Cutting Force Prediction in Drilling Operations Using Temporal Convolutional Networks (TCN) and Spindle Speed Data
    Dilli Ganesh V, Mohsin Ikram, Saif O. Husain, Jayanti Ballabh, T. Mohanraj, K Aanandha Saravanan
    2025 International Conference on Automation and Computation Autocom 2025, 2025
    The prediction of forces in drilling is vital for enhancement of machining process, reduction of tool wear rates, and quality improvement of the final product. The present work introduces a new method for force estimation based on Temporal Convolution Networks (TCNs) and trained with spindle speed data. TCNs take advantage of the ability to process sequential data that perform well in capturing short-term and long-term dependencies, and hence are used in time-series analysis to analyze machining environments that are ever changing. The process is real-time acquisition of spindle speed and cutting force signals, filtering of signals to remove noise, and feature extraction for training of the models. To make correct predictions while making efficient computations, a TCN architecture that has been specifically customized with causal and dilated convolutions is used. The proposed model has MAE = 2.15 N and 98.7% inclusive squared coefficient of determination, which is significantly better than traditional machine learning models like ANN, SVM, and LSTM and faster on inference time. Indeed, all of the present model's validation has been done across different spindle speeds and for different types of materials. Also, the TCN model implementation into real-time monitoring allows optimizing the modelling parameters in proximity to changing conditions, which is a feature of industry 4.0. It also strongly supports the practice of sustainable manufacturing, which in turn improves the efficiency of machining. High value is credited to the potential of TCNs as a transformative tool in intelligent machining systems.
  • Automated Fruit Counting with YOLOv5 Model for Harvest Management in Orchards
    J. Sasi Kiran, Dilli Ganesh V, Navdeep Singh, Haider Mohammed Abbas, R. Saravanan, Aanandha Saravanan K
    2025 International Conference on Automation and Computation Autocom 2025, 2025
    Proper counting of fruits on trees is vital for proper timing & scheduling of harvesting, labor force requirement and making agricultural sector more efficient. Manual methods are costly and time-consuming together with being inaccurate in most if not all aspects hence the need for automation. It also demonstrates how real-time fruit detection and counting can be performed using YOLOv5 with potential in handling orchards in a more efficient manner. YOLOv5 was trained on a dataset of fruits in different settings including clear sun lit conditions, in overcast conditions, and in conditions where fruits are entangled in large vegetation. The proposed model was able to release the highest detection accuracy with higher precision, recall, and F1 scores; 97.5%, 96.1%, respectively and 96.8% for the F1-score. It also attained a mean Average Precision (mAP@0.5) of 98.2%, and further validating its endurance in fruit detection regardless of scenarios. Due to the YOLOv5's real-time indexing that provides about 30 FPS on edge devices, that makes it ideal to be used on drones and mobile autonomous vehicles in large-scale orchards. A comparison with other object detection models like Faster R-CNN, SSD, and Mask R-CNN, proved that YOLOv5 performs better in accuracy as well as in time efficiency. However, there was a small decline in the detection accuracy in cases of partial fruit occlusion or dense foliage. In conclusion, this particular work demonstrates that indeed YOLOv5 can easily solve the problem of automated fruit counting and bring multiple enhancements to the orchard management and precision agriculture.
  • Optimizing Advertising Campaigns through Advanced Deep Learning-Driven Audience Segmentation for Enhanced Targeting and Engagement
    Gunda Nikhil, Anil P S, Saif Obaid, Archana Sehgal, T. Kuppuraj, Aanandha Saravanan K
    2025 International Conference on Automation and Computation Autocom 2025, 2025
    The present work discusses the utilization of deep learning techniques for precise dividing the audience for advertising in order to increase efficiency and effectiveness of campaigns. Hence, the paper employs a diverse dataset of consumers' demographic data, purchase history, social media activity, and reviews and design a hybrid deep learning model using CNN, RNN-LSTM, and attention mechanisms. In all the assessment measurements of accuracy, precision, recall, and Fl-score, this model offers better results compared with the old-shoot segmentation methods. The performance analysis depicts great enhancements in CTR, conversion rate, as well as ROAS all of which highlight a superior engagement of the ads. Indeed, the outcome points to the effectiveness of deep learning in digital marketing that can provide clients with more efficient advertising tools. Nonetheless, the issues regarding computational capabilities and ethical concerns pose viable and useful innovations and solutions, thus contributing to the progression of deep learning theories in audience segmentation and advertising.
  • Big Data and Machine Learning for Climate Change Prediction: An Integrated Approach to Environmental Monitoring
    Azmeera Ramesh, Zufarova Gulmira, Ammar Hameed Shnain, R. Ramya, P. Nagaveni, Aanandha Saravanan K
    2025 International Conference on Automation and Computation Autocom 2025, 2025
    The integration of big data and machine learning (ML) in climate change prediction offers a transformative approach to environmental monitoring, enabling improved accuracy, scalability, and actionable insights. This study presents an integrated framework that combines the strengths of big data technologies and advanced ML algorithms to address the complexities of climate systems. Diverse datasets from satellites, IoT sensors, and historical records were preprocessed using normalization, feature engineering, and dimensionality reduction techniques to ensure data quality and compatibility. ML models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid architectures, were developed to tackle spatial and temporal prediction tasks. The models demonstrated robust performance, with CNNs achieving 93.5% accuracy in deforestation detection and hybrid CNN-RNN models delivering 95% accuracy in cyclone trajectory prediction. Real-time monitoring applications, such as flood risk assessment and air quality forecasting, showcased low latency and scalability, reinforcing the framework's operational viability. Despite these advancements, challenges such as data bias, computational demands, and algorithmic transparency remain significant. To address these, solutions like dataset augmentation, edge computing, and explainable AI are proposed. This research highlights the potential of integrating big data and ML for dynamic and precise climate predictions while emphasizing the need for ethical and equitable deployment of these technologies. By fostering interdisciplinary collaboration and leveraging emerging computational innovations, this study contributes to advancing climate resilience and sustainable environmental management.
  • Energy-Efficient Cloud Computing Through Reinforcement Learning-Based Workload Scheduling
    Ashwini R Malipatil, M E Paramasivam, Dilfuza Gulyamova, Aanandha Saravanan, Janjhyam Venkata Naga Ramesh, Elangovan Muniyandy, Refka Ghodhbani
    International Journal of Advanced Computer Science and Applications, 2025
    —The basis for current digital infrastructure is cloud computing, which allows for scalable, on-demand computational resource access. Data center power consumption, however, has skyrocketed because of demand increases, raising operating costs and their footprint. Traditional workload scheduling algorithms often assign performance and cost priority over energy efficiency. This paper proposes a workload scheduling method utilizing deep reinforcement learning (DRL) that adjusts dynamically according to present cloud situations to ensure optimal energy efficiency without compromising performance. The proposed method utilizes Deep Q-Networks (DQN) to perform feature engineering to identify key workload parameters such as execution time, CPU and memory consumption, and subsequently schedules tasks smartly based on these results. Based on evaluation output, the model brings down the latency to 15 ms and throughput up to 500 tasks/sec with 92% efficiency in load balancing, 95% resource usage, and 97% QoS. The proposed approach yields improved performance in terms of key parameters compared to conventional approaches such as Round Robin, FCFS, and heuristic methods. These findings show how reinforcement learning can significantly enhance the scalability, reliability, and sustainability of cloud environments. Future work will focus on enhancing fault tolerance, incorporating federated learning for decentralized optimization, and testing the model on real-world multi-cloud infrastructures.
  • Machine Learning Applications in Workforce Management: Strategies for Enhancing Productivity and Employee Engagement
    Mano Ashish Tripathi, Joel Osei-Asiamah, Avanti Chinmulgund, Aanandha Saravanan, T Subha Mastan Rao, Ramya H P, Yousef A. Baker El-Ebiary
    International Journal of Advanced Computer Science and Applications, 2025
    —Workforce management is a critical component of organizational success, encompassing employee scheduling, task allocation, and engagement strategies. Traditional methods rely heavily on rule-based systems and manual supervision, leading to inefficiencies and suboptimal workforce utilization. Existing machine learning (ML) approaches, such as supervised learning and statistical models, have improved certain aspects but often fail to dynamically adapt to evolving workforce demands. Additionally, these models struggle with real-time decision-making, requiring constant retraining and manual intervention. This study introduces a reinforcement learning (RL)-based workforce management framework to optimize productivity and employee engagement. Unlike conventional ML models, RL enables adaptive decision-making by continuously learning from interactions within the workforce environment. The proposed method employs deep Q-networks (DQN) and policy gradient techniques to enhance scheduling, task distribution, and incentive structures, leading to a more efficient and responsive workforce management system. The methodology involves collecting real-time workforce data, pre-processing it for feature extraction, and training the RL model using simulated and historical workforce scenarios. The model’s performance is evaluated based on efficiency gains, employee satisfaction, and task completion rates compared to traditional workforce management techniques. Experimental results demonstrate that the RL-based approach significantly improves task allocation accuracy by 18%, reduces scheduling conflicts by 22%, and enhances employee satisfaction scores by 15%. These findings underscore the potential of reinforcement learning in revolutionizing workforce management by fostering data-driven, real-time optimization, ultimately leading to enhanced organizational productivity and employee well-being.
  • Digital Twin-Based Predictive Analytics for Urban Traffic Optimization and Smart Infrastructure Management
    A. B. Pawar, Shamim Ahmad Khan, Yousef A. Baker El-Ebiary, Vijay Kumar Burugari, Shokhjakhon Abdufattokhov, Aanandha Saravanan, Refka Ghodhbani
    International Journal of Advanced Computer Science and Applications, 2025
    —In modern cities, urban traffic congestion remains a persistent issue that causes longer journey times, excessive fuel consumption
  • Early Detection and Forecasting of Influenza Epidemics Using a Hybrid ARIMA-GRU Model
    Kabilan Annadurai, Aanandha Saravanan, S. Kayalvili, Madhura K, Elangovan Muniyandy, Inakollu Aswani, Yousef A.Baker El-Ebiary
    International Journal of Advanced Computer Science and Applications, 2025
  • Real-Time Multi-Scale Object Detection in Surveillance Using Hybrid Transformer Architecture
    Roshan D Suvaris, Rahul Suryodai, S. Narayanasamy, Aanandha Saravanan, Raman Kumar, P N V Syamala Rao M, Elangovan Muniyandy
    International Journal of Advanced Computer Science and Applications, 2025
  • Enhancing emotion prediction using deep learning and distributed federated systems with SMOTE oversampling technique
    V.V. Narasimha Raju, R. Saravanakumar, Nadia Yusuf, Rahul Pradhan, Hedi Hamdi, K. Aanandha Saravanan, Vuda Sreenivasa Rao, Majid A. Askar
    Alexandria Engineering Journal, 2024
  • AR/VR and robotics and their role in leadership
    Preethi D., Valarmathi R. S., Aanandha Saravanan K.
    Impact of New Technology on Next Generation Leadership, 2024
  • Hyperelliptic curve based authentication for the internet of drones
    Aloy Anuja Mary Gnanaraj, Farithkhan Abbasali, Aanandha Saravanan Kumar, Sathyasri Bala Subramanian, Murugan Chinnathambi
    International Journal of Reconfigurable and Embedded Systems, 2024
  • Digital Transformation in Healthcare: An Engineering Perspective on Metaverse, Game Theory, Artificial Intelligence, and Blockchain
    B. Sathyasri, G. Aloy Anuja Mary, K. Aanandha Saravanan, Murali Kalipindi
    Healthcare Services in the Metaverse Game Theory AI Iot and Blockchain, 2024
  • Spinach Classification and Its Health Benefits Suggestion using Deep learning
    Kirubasri G, Sreesubha S, Vidyasree G, Anitha Elavarasi S, Aanandha Saravanan K
    IEEE International Conference on Electronic Systems and Intelligent Computing Icesic 2024 Proceedings, 2024
  • Modelling for Predicting and Optimizing of the Tensile and Flexural Properties of Sisal/Carbon Fibre Nano-Composite through RSM
    K. Aanandha Saravanan, N. Arunachalam, P. Uma, K. Saravanan, Jothi Arunachalam
    Journal of Environmental Nanotechnology, 2024
  • IOT Enabled Cloud Based Risk Monitoring System in Food Supply Chain
    B. Sathyasri, G. Aloy Anuja Mary, C. Ambika Bhuvaneswari, K. Aanandha Saravanan, S. Vishnu Kumar
    Lecture Notes in Networks and Systems, 2024
  • Efficiently Listing URLs from Active Hidden Servers in the Onion Routing Network
    C Ambika Bhuvaneswari, E D Kanmani Ruby, Helina Rajini Suresh, V Diana Ershia, M Prabha, K Anandha Saravanan
    Proceedings of the 5th International Conference on Smart Electronics and Communication Icosec 2024, 2024
  • AI Driven Anomaly Detection in Network Traffic Using Hybrid CNN-GAN
    Vuda Sreenivasa Rao, R. Balakrishna, Yousef A. Baker El-Ebiary, Puneet Thapar, K. Aanandha Saravanan, Sanjiv Rao Godla
    Journal of Advances in Information Technology, 2024
  • Explainable Artificial Intelligence Method for Identifying Cardiovascular Disease with a Combination CNN-XG-Boost Framework
    J Chandra Sekhar, T L Deepika Roy, K. Sridharan, Natrayan L, Dr.K.Aanandha Saravanan, Ahmed I. Taloba
    International Journal of Advanced Computer Science and Applications, 2024
  • A Hybrid DBN-GRU Model for Enhanced Sentiment Analysis in Product Reviews
    Shaista Khan, J Chandra Sekhar, J. Ramu, Yousef A.Baker El-Ebiary, K.Aanandha Saravanan, Kuchipudi Prasanth Kumar, Prajakta Uday Waghe
    International Journal of Advanced Computer Science and Applications, 2024
  • Enhanced Quantitative Financial Analysis Using CNN-LSTM Cross-Stitch Hybrid Networks for Feature Integration
    Taviti Naidu Gongada, B. Kumar Babu, Janjhyam Venkata Naga Ramesh, P. N. V. Syamala Rao M, K. Aanandha Saravanan, K Swetha, Mano Ashish Tripathi
    International Journal of Advanced Computer Science and Applications, 2024
  • Federated LSTM Model for Enhanced Anomaly Detection in Cyber Security: A Novel Approach for Distributed Threat
    Aradhana Sahu, Yousef A.Baker El-Ebiary, K. Aanandha Saravanan, K. Thilagam, Gunnam Rama Devi, Adapa Gopi, Ahmed I. Taloba
    International Journal of Advanced Computer Science and Applications, 2024
  • Energy optimized quorum system MAC protocol for wireless sensor networks
    Kumar Aanandha Saravanan, Gnanaraj Aloy Anuja Mary, Balasubramanian Sathyasri, Abbas Ali Farithkhan
    Aip Conference Proceedings, 2023
  • Enhanced Plagiarism Detection Through Advanced Natural Language Processing and E-BERT Framework of the Smith-Waterman Algorithm
    Franciskus Antonius, Myagmarsuren Orosoo, Aanandha Saravanan K, Indrajit Patra, Prema S
    International Journal of Advanced Computer Science and Applications, 2023
  • Utilizing Multimodal Medical Data and a Hybrid Optimization Model to Improve Diabetes Prediction
    A. Leela Sravanthi, Sameh Al-Ashmawy, Chamandeep Kaur, Mohammed Saleh Al Ansari, K. Aanandha Saravanan, Veera Ankalu. Vuyyuru
    International Journal of Advanced Computer Science and Applications, 2023
  • Tunnel based Fund Transfer over the Internet using AES Algorithm
    Aanandha Saravanan K, Prabha M, Ambika Bhuvaneswari C, MuthuKumaran D, Aloy Anuja Mary G, Sathyasri B
    IEEE 9th International Conference on Smart Structures and Systems Icsss 2023, 2023
  • IoT based Detection and Monitoring for Coronary Artery Disease
    Aanandha Saravanan K, Vignesh Prasanna N, Ezilarasan M R, Aloy Anuja Mary G, Sathyasri B, MuthuKumaran D
    IEEE 9th International Conference on Smart Structures and Systems Icsss 2023, 2023
  • Research on Quality Control of Core Process of Hydraulic Engineering Materials Based on Improved Particle Swarm Optimization
    Aanandha Saravanan K, M Udhayakumar, Abdulsattar Abdullah Hamad, D. Jeyasimman, Gosu Nageswara Reddy, Anbu C
    2023 2nd International Conference on Smart Technologies for Smart Nation Smarttechcon 2023, 2023
  • Machine Learning based Early Detection and Monitoring of Parkinson Disease
    Aanandha Saravanan K, Aloy Anuja Mary G, Sathyasri B, Muthukumaran D, Prabha M, Ambika Bhuvaneswari C
    IEEE 9th International Conference on Smart Structures and Systems Icsss 2023, 2023
  • Artificial Intelligence Scoring Algorithm for English Translation Based on DNN
    R Krishna Priya, Sabarmathi K R, Aanandha Saravanan K, Niranjanamurthy M, Aashish A. Gadgil, Azarudeen K
    2023 2nd International Conference on Smart Technologies for Smart Nation Smarttechcon 2023, 2023
  • Forest Wild Fire Detection using Deep Learning Approach
    Aanandha Saravanan K, MuthuKumaran D, Aloy Anuja Mary G, Sathyasri B, Prabha M, Ambika Bhuvaneswari C
    IEEE 9th International Conference on Smart Structures and Systems Icsss 2023, 2023
  • Integrating Transfer Learning and Deep Neural Networks for Accurate Medical Disease Diagnosis from Multi-Modal Data
    Chamandeep Kaur, Abdul Rahman Mohammed Al-Ansari, Taviti Naidu Gongada, K. Aanandha Saravanan, Divvela Srinivasa Rao, Ricardo Fernando Cosio Borda, R. Manikandan
    International Journal of Advanced Computer Science and Applications, 2023
  • Unleashing the Potential of Artificial Bee Colony Optimized RNN-Bi-LSTM for Autism Spectrum Disorder Diagnosis
    Suresh Babu Jugunta, Yousef A.Baker El-Ebiary, K. Aanandha Saravanan, Kanakam Siva Rama Prasad, S. Koteswari, Venubabu Rachapudi, Manikandan Rengarajan
    International Journal of Advanced Computer Science and Applications, 2023
  • Intellectual transport system for human safety using machine learning approach
    K. Aanandha Saravanan, S. Ramkumar, M. Revathy, K. Mohanthi, T.N. Prabakar, Venugopal, G.V. Raja
    Measurement Sensors, 2022
  • Diagnosis of cardiovascular disorder by CT images using Machine learning technique
    K. Nithyakalyani, S. Ramkumar, S Rajalakshmi, K. Aanandha Saravanan
    2022 International Conference on Communication Computing and Internet of Things Ic3iot 2022 Proceedings, 2022
  • Log System for Mass Crowd and Live Tracking Using Radio Frequency Based on IoT
    Nivetha A. S, Ramkumar S, S. Rajalakshmi, K. Aanandha Saravanan, M. Revathy
    2022 International Conference on Communication Computing and Internet of Things Ic3iot 2022 Proceedings, 2022
  • Performance analysis of ICA algorithm for Blind Source Separation
    M.R. Ezilarasan, J. Britto Pari, K. Aanandhasaravanan, N. Vignesh Prasanna, D. Balaji
    8th International Conference on Smart Structures and Systems Icsss 2022, 2022
  • Cumulative Sum Algorithm for Threshold Optimization in Cognitive Radio Networks
    Anuja Mary G Aloy, A Farith Khan, S Vishnukumar, K Aanandha Sarvanan, B Sathyasri
    8th International Conference on Smart Structures and Systems Icsss 2022, 2022
  • Women Safety Maneuver in Real Time Scenarios
    K. Aanandha Saravanan, B. Sathyasri, G. Aloy Anuja Mary, A. Farithkhan, N. Vignesh Prasanna, M. R. Ezilarasan
    8th International Conference on Smart Structures and Systems Icsss 2022, 2022
  • An Artificial Intelligence-Based Quorum System for the Improvement of the Lifespan of Sensor Networks
    Suresh Ponnan, Aanandha K. Saravanan, Celestine Iwendi, Ebuka Ibeke, Gautam Srivastava
    IEEE Sensors Journal, 2021
  • Cluster head election –A novel dynamic methodology for large scale wireless sensor networks
    International Journal of Advanced Science and Technology, 2020
  • Prioritized scheduling scheme for critical and non-critical information packets
    K Aanandha Saravanan, Dr P. Suresh
    International Journal of Engineering and Technology Uae, 2018
  • Dynamic and efficient traffic load balancing strategy for real time sensor networks
    Journal of Advanced Research in Dynamical and Control Systems, 2018
  • Efficient biometric recognition methodology using Guided Filtering and SIFT feature matching
    International Journal of Engineering and Technology Uae, 2018
  • Adolescent sheltered tracking to evade quandary via observant using android app
    S. Ravikumar, S. Alex David, C. Saranya Jothi, V. Usha, K. Aanandha Saravanan
    2017 IEEE International Conference on Smart Technologies and Management for Computing Communication Controls Energy and Materials Icstm 2017 Proceedings, 2017
  • Process control of ice plant system- precise full range bidirectional automatic control of expansion valve with unidirectional pulses
    International Journal of Applied Engineering Research, 2016
  • An Improvised Effective Oceanography Monitoring Using Large Area Underwater Sensor Networks
    Burla Rajesh, K. Aanandha Saravanan
    Geostatistical and Geospatial Approaches for the Characterization of Natural Resources in the Environment Challenges Processes and Strategies, 2015
  • Proficient modus operandi for scrutinize air pollution using wireless sensor network
    Burla Rajesh, Ankit Agarwal, K. Aanandha Saravanan
    2014 International Conference on Circuits Power and Computing Technologies Iccpct 2014, 2014
  • An improvised effective oceanography monitoring using large area underwater sensor networks
    Proceedings of the 16th International Association for Mathematical Geosciences Geostatistical and Geospatial Approaches for the Characterization of Natural Resources in the Environment Challenges Processes and Strategies Iamg 2014, 2014
  • Cracker industry fire monitoring system over cluster based WSN
    Journal of Engineering and Applied Sciences, 2014