Optimizing Energy Consumption in Smart Grids Using Demand Response Techniques SwornaKokila M L, Venkatarathinam R, Rose Bindu Joseph P, M. A. Manivasagam, Kakarla Hari Kishore Distributed Generation and Alternative Energy Journal, 2024 Smart grids have developed as a potentially game-changing strategy for controlling the demand and supply of energy. Unfortunately, peak demand is a significant source of grid instability and rising energy prices, making it one of the most critical difficulties in smart grids. During times of high energy demand on the grid, demand response (DR) strategies incentivize consumers to change how they use energy. This study’s overarching goal is to learn how DR methods may be used to help smart grids make better use of their energy resources. The primary research is to develop a smart DR system that can predict times of high energy demand and proactively alter usage to reduce such periods. Machine learning strategies are utilized in the proposed system to estimate peak demand via past data, weather predictions, and other variables. The system will then alter energy use based on real-time data from smart meters along with other sensing devices to meet the projected demand. The simulation model will include several scenarios for testing the DR system’s flexibility, including a range of weather conditions, load profiles, and grid topologies. Several indicators, including peak demand reduction (80.04%), energy savings (38.09%), environmental consequences, and reaction time (<0.4 seconds), are used to evaluate the model’s performance. The output of the method excelled all of the other current methods that were taken into account. The system’s rapid response time and its positive environmental impact further highlight its potential in managing smart grid resources effectively.
AI-Driven IoT Refrigeration Management using SVM and Cloud Computing G. Swathi, Pavithra M. R, P. Epsiba, M. A. Manivasagam, A. Mani, et al. 2024 5th IEEE Global Conference for Advancement in Technology Gcat 2024, 2024 The paper presents an AI-Driven IoT Refrigeration Monitoring (IRM) using support vector machine algorithms (SVM). The improvement in food safety and environmental sustainability has resulted in a paradigm shift in the techniques used for refrigeration. IRM guarantees that refrigeration units have perfect temperature management by seamlessly combining modern sensors, real-time data analysis, and artificial intelligence. The innovative strategy stops food spoiling, improves food safety, cuts down on waste, and promotes environmental responsibility across the supply chain. The intuitive alarm mechanism of the system notifies temperature variations as quickly as possible, which enables immediate remedial steps to be taken. IRM becomes a crucial instrument for preserving fresh foods and promoting environmentally aware behaviors since it bridges the traditional refrigeration rules with the digital world. The structure of the system, its benefits, and its potential to redefine industry norms in terms of safety and sustainability are discussed in depth in the article.
IoT-Driven Telepresence Robots for Telemedicine using AI for Improved Patient Interaction Chitra Sabapathy Ranganathan, D. Sethuraman, M. A. Manivasagam, Soundharya. K, T. Yuvaraj, et al. 2024 1st International Conference on Innovations in Communications Electrical and Computer Engineering Icicec 2024, 2024 Telepresence robots and the Internet of Things (IoT) are transforming the face of telemedicine by opening new avenues for communication between doctors and patients. Improved patient contact and care outcomes may be achieved via the development and implementation of telepresence robots augmented with artificial intelligence (AI) and powered by the IoT. These robotic assistants let patients and doctors communicate more easily with remote consultations, monitoring, and assistance. Robots can collect and analyze data in real-time and communicate with patients on an individual basis due to AI algorithms that enable them to perform. These solutions provide continuous health monitoring and quick reaction to important health changes by using IoT connection. Through perceptive and empathic interactions, it shows that AI-enhanced telepresence robots greatly improve patient engagement and pleasure while also increasing the efficiency of healthcare services. These results support the idea that telepresence robots have a bright future in telemedicine and call for their widespread use and more development in this area.
Detection of Parkinson's Disease using Machine Learning with Feature Analysis from Audio Signals Kuruma Purnima, M.A. Manivasagam, Dudekula Mohammed Kaif, M Vanitha, Nagella Rajesh, et al. 4th IEEE International Conference on Mobile Networks and Wireless Communications Icmnwc 2024, 2024 Background: Early detection of Parkinson's Disease (PD) is crucial for timely intervention and improved patient outcomes. PD affects motor functions and often results in vocal impairments, making voice analysis a promising non-invasive diagnostic approach. Methods: This study evaluates the effectiveness of machine learning algorithms—Decision Tree, Random Forest, Logistic Regression, Support Vector Machine, Naive Bayes, K-Nearest Neighbors, and XGBoost—for PD detection using a dataset of vocal features from the UCI Machine Learning Repository. The dataset, comprising 23 vocal attributes, was preprocessed using the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. Models were assessed using metrics such as Accuracy, F1-Score, Recall, Precision, and ROC-AUC. Results: The Random Forest and Support Vector Machine algorithms demonstrated the highest classification accuracy (96.6%) and superior overall performance. These findings highlight the potential of these methods for distinguishing between PD-positive and PD-negative cases using voice data. Concluding Remarks: This study underscores the potential of machine learning in developing accessible and accurate tools for early PD diagnosis. Further enhancements, such as feature refinement and dataset expansion, could improve these methods' generalizability and robustness, supporting their integration into healthcare systems.
Selection of Trust Nodes for Efficient Data Transmission in MANET K. Gunasekaran, D. Regan, Basavaraj G Kudamble, M. A. Manivasagam 2nd IEEE International Conference on Advanced Technologies in Intelligent Control Environment Computing and Communication Engineering Icatiece 2022, 2022 Reliable Routing through Trust Node Selection scheme is proposed to implement improved security measures and efficient routing in MANET. The friend list is created for each and every node and this task is performed for identifying the node ratings through the challenging process for its neighbor nodes. Challenge is a process carried out for determining the ratings obtained for nodes to prove their integrity and honesty. The node challenge process is carried between the nodes through the count of control messages that have been processed. From the consequences of node ratings if the nodes achieve a certain value then the nodes come under the friend node list else the node falls under the unfriend node list and isolates from the routing process. Finally, the data transmission is done through reliable and trusted routes by utilizing a key management model for encrypting the data. Recreation investigation is supported obtainable intended for demonstrating the effectiveness of the future outline.
Signal strength based self reconfiguration to ensure reliability in wireless sensor networks M.A. Manivasagam, T.V Ananthan Indonesian Journal of Electrical Engineering and Computer Science, 2018 <span lang="EN-US">Providing reliability in Wireless sensor networks is considered to be a challenging task, due to the limited capabilities in terms of energy, power and memory. The applications of these systems run in sensors with low level programming abstractions, limited capabilities and routing protocols. In this paper, we propose a strategy to adjust radios in the sensor network depending on the signal strength of the neighboring nodes to ensure reliability using self reconfiguration (S2R2). Redundancy-based reliability is achieved by performing encoding/decoding either at the source and the destination node or each pair of communicating sensor nodes from the source to the destination. Along with the reliability, the link and the stability of the link are checked. The stability of the route makes the route a valid one to send data. Simulation analysis shows that the proposed mechanism performs better in terms of stability and reliability compared to the existing mechanism</span>
Selection of Trust Nodes for Efficient Data Transmission in MANET K Gunasekaran, D Regan, BG Kudamble, MA Manivasagam 2022 Second International Conference on Advanced Technologies in Intelligent … , 2022 2022.0 Citations: 2
An efficient crop yield prediction using machine learning MA Manivasagam, P Sumalatha, A Likitha, V Pravallika, KV Satish, ... International Journal of Research in Engineering, Science and Management 5 … , 2022 2022.0 Citations: 8
Early Diagnosis of Alzheimer’s Disease using Soft Computing Based Deep Learning B Geethavani, RM Mallika, DW Albert, MA Manivasagam Solid State Technology 64 (2) , 2021 2021.0 Citations: 2
Signal Strength Based Self Reconfiguration to Ensure Reliability in Wireless Sensor Networks MA Manivasagam, TV Ananthan Indonesian Journal of Electrical Engineering and Computer Science 10 (2 … , 2018 2018.0
An Adaptive Self Reconfiguration Mechanism for Improving Reliability in WSN MA Manivasagam, TV Ananthan SCOPUS IJPHRD CITATION SCORE 9 (2), 441 , 2018 2018.0
An efficient self-reconfiguration and route selection for wireless sensor networks MA Manivasagam International Journal of MC Square Scientific Research 9 (2), 192-199 , 2017 2017.0 Citations: 16
Reliable and Efficient Self Reconfiguration WSN design (RESR) to Mitigate Link Failures MA Manivasagam, TV Ananthan
Design of self reconfigurable wireless sensor networks for critical applications MA Manivasagam Chennai , 0
NATURAL DISASTER PREDICTION USING MACHINE LEARNING MA Manivasagam, C Ramya, R Bhumika, S Roshan, B Nirmal, ...
MOST CITED SCHOLAR PUBLICATIONS
An efficient self-reconfiguration and route selection for wireless sensor networks MA Manivasagam International Journal of MC Square Scientific Research 9 (2), 192-199 , 2017 2017.0 Citations: 16
An efficient crop yield prediction using machine learning MA Manivasagam, P Sumalatha, A Likitha, V Pravallika, KV Satish, ... International Journal of Research in Engineering, Science and Management 5 … , 2022 2022.0 Citations: 8
Selection of Trust Nodes for Efficient Data Transmission in MANET K Gunasekaran, D Regan, BG Kudamble, MA Manivasagam 2022 Second International Conference on Advanced Technologies in Intelligent … , 2022 2022.0 Citations: 2
Early Diagnosis of Alzheimer’s Disease using Soft Computing Based Deep Learning B Geethavani, RM Mallika, DW Albert, MA Manivasagam Solid State Technology 64 (2) , 2021 2021.0 Citations: 2
Signal Strength Based Self Reconfiguration to Ensure Reliability in Wireless Sensor Networks MA Manivasagam, TV Ananthan Indonesian Journal of Electrical Engineering and Computer Science 10 (2 … , 2018 2018.0
An Adaptive Self Reconfiguration Mechanism for Improving Reliability in WSN MA Manivasagam, TV Ananthan SCOPUS IJPHRD CITATION SCORE 9 (2), 441 , 2018 2018.0
Reliable and Efficient Self Reconfiguration WSN design (RESR) to Mitigate Link Failures MA Manivasagam, TV Ananthan
Design of self reconfigurable wireless sensor networks for critical applications MA Manivasagam Chennai , 0
NATURAL DISASTER PREDICTION USING MACHINE LEARNING MA Manivasagam, C Ramya, R Bhumika, S Roshan, B Nirmal, ...
