Ramya G

Scopus Publications

Predictive Analysis of PM 2.5 Concentrations for Enhanced Air Quality Monitoring
Abijeet B R., Sanjaykumar S, G. Ramya
2025 International Conference on Emerging Technologies in Engineering Applications Icetea 2025 Proceedings, 2025
Global health remains at serious risk because of fine particulate matter (PM2.5) pollution which continues as a major threat throughout the world. Extremely small PM2.5 particles enter the body by evading natural defence mechanisms to reach deep locations within the lungs and bloodstream, where they contribute to respiratory and cardiovascular illnesses, and even premature death. In response to these growing concerns, this research presents a predictive air quality monitoring system that combines real-time data from environmental sensors with machine learning techniques to forecast PM2.5 concentrations. An evaluation process included five machine learning models namely Linear Regression, Random Forest, Gradient Boosting and Support Vector Regression (SVR) as well as XGBoost to identify which models performed best for prediction purposes. The best performance came from XGBoost model which produced an RMSE of 0.019315 alongside an R2 value of 0.999098. This study demonstrates that integrating low-cost sensor networks with advanced machine learning can offer an efficient, scalable, and accurate solution for real-time air quality monitoring. The proposed system not only supports early health risk detection but also has the potential to inform environmental policies and community-level interventions aimed at reducing the harmful effects of air pollution.
A Sensor-Based Intelligent System for Early Detection and Prevention of Varicose Veins
Pradap E, Kathiron K, G.Ramya, G.Divya
Proceedings of the 6th International Conference on Smart Electronics and Communication Icosec 2025, 2025
Varicose veins, a common condition characterized by swollen and twisted superficial leg veins, can cause significant discomfort and impact quality of life. Early detection and intervention are crucial to mitigate symptom progression and prevent severe complications. This project proposes a novel wearable rehabilitation monitoring and exercise device for the early identification and management of potential varicose vein development. The system utilizes DHT11 temperature sensors for upper and lower body temperature monitoring, a force sensor in the leg to detect pressure variations, and an IR sensor for additional monitoring. Data from these sensors are processed by a NodeMCU microcontroller via WiFi. The core mechanism involves comparing temperature differentials between the upper and lower body and detecting abnormal force patterns in the legs. A significant three-degree difference in these parameters triggers the activation of a coin vibration motor integrated into the device. This localized vibration aims to provide rapid, temporary relief from subtle pain and address potential blood flow restriction in the affected area. The implementation of such a system offers the potential for patients to receive immediate, albeit temporary, therapeutic intervention at home, potentially reducing the need for immediate hospital visits for minor symptom exacerbations.
Intelligent Adaptive Energy Management System for Standalone Electric Vehicle with Dynamic Power flow Optimization and Battery Protection
Hemanth Kumar R, Vijay Sachin M, G.Ramya, G.Divya
Proceedings of the 6th International Conference on Smart Electronics and Communication Icosec 2025, 2025
An advanced energy management system (EMS) designed for a standalone electric vehicle (EV) that is primarily powered by photovoltaic (PV) panels and batteries, with Peltier modules serving as secondary contributors, is presented in this report. The intelligent adaptive PID controller at the heart of the EMS maintains energy balance during load and renewable source fluctuations and directs excess energy to a dump load, ensuring effective power flow management. A nonlinear PID controller is used to control the output voltage within predetermined bounds, improving voltage control performance and guaranteeing steady and dependable operation. In order to maximize overall energy utilization, the EMS is made to handle issues like erratic energy generation and unexpected load demands. Also The EMS ensures global asymptotic stability, which means that the system can adapt and maintain optimal performance in real-time scenarios. Simulation results and hardware implementation validate the robustness and practical viability of the control architecture, confirming its potential for real-world EV applications where efficiency and dependability are crucial. The system's performance can outperform traditional PID and sliding mode controllers, especially under dynamic load conditions.
Hierarchical Graph Convolutional Approach for Detecting Major Depressive Disorder from EEG Signals
Swetha K, Sriraam J, G.Ramya, G.Divya
2025 5th International Conference on Emerging Research in Electronics Computer Science and Technology Icerect 2025, 2025
Major Depressive Disorder (MDD) is a prevalent psychiatric disease that needs to be diagnosed properly at the appropriate time for fruitful intervention. A novel method using a Multi-Granularity Graph Convolution Network (MG-GCN) model to classify depression degree from images of EEG signal is introduced in this work. The architecture consists of image processing techniques such as median filtering and enhancement using ESRGAN to improve the clarity of the signal. A Decision Tree classifier is used to classify depressive states into three levels of severity. Model performance is measured using structural similarity (SSIM), peak signal-to- noise ratio (PSNR), and classification accuracy, reflecting the ability of the model in the extraction and discrimination of functionally relevant brain activity patterns.MG-GCN architecture utilizes multi-level graph structures for preserving strong and weak brain connectivity features to facilitate longer learning of brain topologies. It is implemented with a user-friendly graphical interface in MATLAB, by which EEG images can be uploaded and received with real-time diagnostic results. In addition to real-time prediction, visualization was improved, and performance metrics to assist clinical assessment, quick documentation is also enabled by automatic reporting of the system. The deployment is a clear trend towards making EEG-based psychological diagnosis smart and accessible.
Enhanced power system fault detection using quantum-AI and herd immunity quantum-AI fault detection with herd immunity optimisation in power systems
M. L. Sworna Kokila, V. Bibin Christopher, G. Ramya
Iet Quantum Communication, 2024
Quantum computing and deep learning have recently gained popularity across various industries, promising revolutionary advancements. The authors introduce QC‐PCSANN‐CHIO‐FD, a novel approach that enhances fault detection in electrical power systems by combining quantum computing, deep learning, and optimisation algorithms. The network, based on a Pyramidal Convolution Shuffle Attention Neural Network (PCSANN) optimised with the Coronavirus Herd Immunity Optimiser, shows promising results. Initially, historical datasets are used for fault detection. Preprocessing, which includes handling missing data and outliers using Adaptive Variational Bayesian Filtering is followed by Dual‐Domain Feature Extraction to extract grayscale statistical features. These features are processed by PCSANN to detect faults. The Coronavirus Herd Immunity Optimisation Algorithm is proposed to optimise PCSANN for precise fault detection. Performance of the proposed QC‐PCSANN‐CHIO‐FD approach attains 24.11%, 28.56% and 22.73% high specificity, 21.89%, 23.04% and 9.51% lower computation Time, 25.289%, 15.35% and 19.91% higher ROC and 8.65%, 13.8%, and 7.15% higher Accuracy compared with existing methods, such as combining deep learning based on quantum computing for electrical power system malfunction diagnosis (QC‐ANN‐FD), electrical power system fault diagnostics using hybrid quantum‐classical deep learning (QC‐CRBM‐FD), applications of machine learning to the identification of power system faults: Recent developments and future directions (QC‐RF‐FD).
Investigation on water stagnant in agricultural field during rainfall using Pi and fuzzy logic controller in smart irrigation system to avoid crop damage
Ridhvi Mahesh, A. S. Vickram, G. Ramya
Aip Conference Proceedings, 2024
Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Ridhvi Mahesh, A. S. Vickram, G. Ramya; Investigation on water stagnant in agricultural field during rainfall using Pi and fuzzy logic controller in smart irrigation system to avoid crop damage. AIP Conf. Proc. 7 May 2024; 2853 (1): 020031. https://doi.org/10.1063/5.0197510 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Residential energy management system (REMS) using machine learning
G. Ramya, R. Ramaprabha
Advances in Computers, 2024
Alzheimer's Disease Prediction Using Machine Learning
Shashwat Mishra, Viplav Sharma, G. Ramya
Proceedings of International Conference on Circuit Power and Computing Technologies Iccpct 2024, 2024
Alzheimer's disease is a progressive and debilitating neurodegenerative disorder that poses a significant public health challenge worldwide. Early detection and prediction of Alzheimer's disease are critical for effective treatment and care. This research paper presents a novel approach for predicting Alzheimer's disease using machine learning methodologies. Our study leverages a diverse dataset that includes clinical, genetic, and neuroimaging data from individuals at various stages of the disease. Through rigorous preprocessing and feature selection processes, we extract relevant features and apply advanced machine learning algorithms. Our results demonstrate the efficacy of this approach in accurately predicting the onset and progression of Alzheimer's disease. The model's high accuracy, sensitivity, and specificity suggest its potential as a valuable tool for clinicians and researchers. Furthermore, our study highlights key factors contributing to the prediction and offers insights into the underlying biological processes of the disease. This research holds promise for early intervention and personalized patient care, ultimately contributing to improved management of Alzheimer's disease and enhancing the quality of life for affected individuals and their families.
Grid Alert Fault Detection Using IOT - LoRa WAN
Pradeep Sudhakaran, G. Ramya, S. Ravi, Pallavi Giri, Amjad Omar Safori
Proceedings of the 2024 International Conference on Innovative Computing Intelligent Communication and Smart Electrical Systems Icses 2024, 2024
The research paper presents an improved fault detection and monitoring system for power distribution systems, with a focus on enhancing reliability and efficiency. The system builds upon established principles in fault detection, as well as insights from recent advancements in IoT and LoRa WAN technology. Power distribution systems face challenges related to transient stability, supply disturbances, and fault detection, particularly in regions with infrastructure limitations. The study introduces a solution that leverages loT devices to enable real-time fault detection and location determination in medium and low-voltage distribution networks. The system's ability to rapidly generate alerts upon fault occurrence facilitates swift corrective actions, thereby minimizing downtime and enhancing the dependability of power delivery. Additionally, the proposed solution is designed to address cost constraints commonly encountered in developing countries, making it a practical and cost-effective option for modernizing existing power grids. The research represents a step towards improving power distribution management, offering network operators an effective means to monitor and manage distribution networks over extended distances, resulting in a more reliable electricity supply.
Predicting Epileptic Seizures with Precision A Comprehensive Study of ML Algorithms
Ranjani M, Ramya G, Divya G, Deepa N, Lalitha B
Tqcebt 2024 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024, 2024
IEEE Epilepsy, a complex neurological disorder, poses a significant challenge in the realm of healthcare. Encephalography (EEG) stands as a widely adopted clinical tool for recording the intricate electrical activity within the brain. This research delves into the application of machine learning techniques for the prediction of epileptic seizures using EEG signals obtained from the UCI Machine Learning Repository. Our study presents a comprehensive evaluation of various ML algorithms, including the SVM Classifier, RF Classifier, Gaussian Naive Bayes, K-Nearest Neighbors, and others. Significantly, the Support Vector Classifier and Random Forest Classifier achieved remarkable accuracies of 0.98, suggesting their potential for precise prediction. Meanwhile, Gaussian Naive Bayes and K-Nearest Neighbors demonstrated robust predictive capabilities, with accuracies of 0.96 and 0.93, respectively. This research contributes substantially to the field of epilepsy prediction, shedding light on the effectiveness of diverse machine-learning models for early seizure detection. These findings hold promise for enhancing patient care and improving their quality of life, marking the beginning of an exciting journey towards refined epilepsy management
IoT-based Posture Pro: Safeguarding the Human Spine from the Onset of Hunchback in the Initial Stages
D Sathish Kumar, G. Ramya, S. Ravi, Jayant Giri, Mahmoud Odeh
8th International Conference on Electronics Communication and Aerospace Technology Iceca 2024 Proceedings, 2024
Transforming 2D to 3D Light: Bringing AutoCAD Drawings to Life with Dialux Shadows and Light
B Lalitha, G. Ramya, S. Ravi, Jayant Giri, Mahmoud Odeh
8th International Conference on Electronics Communication and Aerospace Technology Iceca 2024 Proceedings, 2024
Human Emotion Detection using Deep CNN
Ishan Dixit, Chandra Prakash, G. Ramya, M. Dinesh, Jayant Giri, Ayman Amer
8th International Conference on Electronics Communication and Aerospace Technology Iceca 2024 Proceedings, 2024
Improving the Electrical Machine Efficiency Using Variable Frequency Drive Compared With Multi-Level Inverter by Reduction of Power Loss
G. Ramya, Lalitha B, Sathish Kumar D, Rajkumar Chadge, Mohammad Rasmi Al-Mousa, S. Ravi
2024 IEEE 2nd International Conference on Emerging Trends in Engineering and Medical Sciences Icetems 2024, 2024
Demodulating an acoustic signal stimulated by photo-thermal elastic energy conversion using quartz tuning forks
M. Tamilselvi, T. M. Amirthalakshmi, R. Pavithra Guru, R. Neelaveni, G. Ramya, Yusuf Siraj Usmani, Mohd Zahid Ansari
Optical and Quantum Electronics, 2024
Efficient utilization of water in smart irrigation system using bluetooth and IoT to increase the crop production
Ridhvi Mahesh, G. Ramya
Aip Conference Proceedings, 2023
Comparison on mppt (Flc and P&O) algorithm under varying environmental factors photovoltaic systems to extract peak power
Mohan Krishna kolla, Ramya Guruambeth
Aip Conference Proceedings, 2023
Exploring the Potential of Interplanetary File System for Secure and Transparent Social Media
Shivam Pandey, Mayank Sinha, Ramya. G
Vitecon 2023 2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies Proceedings, 2023
Design and Development of EEG Signal based Driver Activity Recognition and Monitoring System
Ramachandran. R, G. Ramya, Anita Titus, M. Balaji, Bharaneedharan. M
Proceedings of the 2023 International Conference on Innovative Computing Intelligent Communication and Smart Electrical Systems Icses 2023, 2023
EHLM: Empirical Design of Novel Road Curve and Lane Identification Scheme using Effective Hybrid Learning Methodology
S. Sudha Mercy, G. Ramya, R. Sankar, L. Saravanan, Krishnamoorthi K
Proceedings of the 2023 International Conference on Innovative Computing Intelligent Communication and Smart Electrical Systems Icses 2023, 2023
A Robust Real Time Train Delay Prediction System to Support Passengers by using Fuzzy based Learning Scheme
S. Padma, Shankar. K, G. Ramya, G. Saranya, Bharaneedharan. M
Proceedings of the 2023 International Conference on Innovative Computing Intelligent Communication and Smart Electrical Systems Icses 2023, 2023
Strong and stable Data communication Using Artificial Intelligence method in Mobile Ad-Hoc Networks
G. Ramkumar, Anitha G, R. Thandaiah Prabu, P. Nirmala, G. Ramya
Proceedings of the 2022 International Conference on Innovative Computing Intelligent Communication and Smart Electrical Systems Icses 2022, 2022
COVID-19 Identification and Detection from CT-Images using AI Based Ensemble Model
R. Thandaiah Prabu, P. Nirmala, G. Ramya, G. Ramkumar, Anitha G
Proceedings of the 2022 International Conference on Innovative Computing Intelligent Communication and Smart Electrical Systems Icses 2022, 2022
An Artificial Neural Network Classifier for palm Motion categorization based on EMG signal
Anitha G, R. Thandaiah Prabu, P. Nirmala, G. Ramya, G. Ramkumar
Proceedings of the 2022 International Conference on Innovative Computing Intelligent Communication and Smart Electrical Systems Icses 2022, 2022
Global MPP Tracking for Partial shaded PV System using Fractional Order Extreme Seeking controller
R. Ramaprabha, G. Ramya
Iop Conference Series Materials Science and Engineering, 2020
Analysis of photovoltaic fed modular multilevel converter with reduced switch count under source failure condition
Journal of Electrical Systems, 2020
Performance analysis of photovoltaic fed grid tied modular multilevel converter
UPB Scientific Bulletin Series C Electrical Engineering and Computer Science, 2020
Harmonic analysis of reduced device count multilevel converter
B. Lalitha, K. Lakshmi, G. Ramya, R. Sampathkumar, M. Moinuddeen
Advances in Mathematics Scientific Journal, 2020
Implementation and Harmonic Analysis on Single Carrier Modulation Technique for Modular Multilevel Converter
R. Ramaprabha, G. Ramya
2019 IEEE 1st International Conference on Energy Systems and Information Processing Icesip 2019, 2019
Analysis and Determination of Switching Angles Based on Half-Height Method for Hybrid Modular Multilevel Converter for Reduced Total Harmonic Distortion
G. Ramya, R. Ramaprabha
Proceedings of the 4th International Conference on Electrical Energy Systems Icees 2018, 2018
Analysis of synchronization algorithm for grid connected photovoltaic modular multilevel converter using positive sequence detector and phase locked loop
G. Ramya, R. Ramaprabha, K. N. Dinesh Babu
Proceedings Tima 2017 9th International Conference on Trends in Industrial Measurement and Automation, 2017
Implementation of photovoltaic fed single phase nine level hybrid cascaded modular multilevel inverter with reduced number of devices
R. Ramaprabha, G. Ramya
Proceedings of the International Conference on Power Electronics and Drive Systems, 2017
A review on designand control methods of modular multilevel converter
Ramya G, Ramaprabha R
International Journal of Power Electronics and Drive Systems, 2016
Fuzzy logic controller for partial shaded photovoltaic array fed modular multilevel converter
Ramya Guruambeth, Ramaprabha Ramabadran
Iet Power Electronics, 2016
Realization of a photovoltaic fed sparse alternating current (AC)-link inverter
R. Ramaprabha, G. Ramya, U. Ashwini, A.H. Fathima Humaira
Journal of Engineering Research, 2016
Design methodology of P-Res controllers with harmonic compensation technique for modular multilevel converter fed from partially shaded PV array
G Ramya, R Ramaprabha
Proceedings of the International Conference on Power Electronics and Drive Systems, 2015
Switching loss and THD analysis of modular multilevel converter with different switching frequency
G Ramya, R Ramaprabha
Proceedings of the International Conference on Power Electronics and Drive Systems, 2015
Performance enhancement of photovoltaic system using soft switched multi-phase boost converter
Journal of Electrical Systems, 2014
Comparative analysis of soft switching two-phase boost converter for photovoltaic system
G. Ramya, R. Ramaprabha
Proceedings of IEEE International Conference on Circuit Power and Computing Technologies Iccpct 2013, 2013

Ramya G

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