received her PhD degree in Information and Communication
Engineering from Anna University, College of Engineering Guindy, Chennai. She has
teaching and research experience of over 23 years in Electronics and Communication
Engineering. She loves to learn and teach cutting-edge techniques in Signal Processing
related subjects. As a part of her PhD, she worked on “Performance Analysis on Multimodal
Biometric System-based Authentication”. Her research includes Computer Vision, Pattern
Recognition, VLSI Signal Processing, Remote Sensing and Machine Learning for video
analytics with a focus on human tracking, multi-resolution video processing, biologically-
inspired spatial-temporal filtering, hyperspectral image processing, Natural language
processing, etc.,
She is a recognized supervisor at Anna University and currently guiding nine research
scholars (PhD) at Anna University, Chennai. She has received and successfully completed the
ISRO respond-funded project titled “Design
RESEARCH, TEACHING, or OTHER INTERESTS
Artificial Intelligence, Engineering, Signal Processing, Human-Computer Interaction
10
Scopus Publications
634
Scholar Citations
12
Scholar h-index
15
Scholar i10-index
Scopus Publications
Energy-Efficient Street Lighting System Using Piezoelectric Harvesting and Motion-Activated Dimming R. Gayathri, R. Sathesh Raaj, Sasikala N, C. Bennila Thangammal 2025 5th Asian Conference on Innovation in Technology Asiancon 2025, 2025 The current reliance on electricity to power street lights leads to significant energy consumption, and while solar panels are a sustainable alternative, they are still insufficient to fully meet the energy demands of an average street light. On a sunny day, a solar panel can produce only 0.4 kWh of electricity, whereas the required energy for an average street light is 1.2 kWh. Additionally, street lights remain lit even in the absence of vehicles or pedestrians, resulting in unnecessary energy wastage. This paper proposes a solution by integrating Piezoelectric Transducers, which convert mechanical stress into electricity, and Infrared (IR) Sensors for detecting vehicle or pedestrian movements. The system dynamically adjusts the brightness of street lights using a Light Dimming Module, thereby reducing energy consumption during low-traffic periods. With the incorporation of Piezoelectric energy harvesting and motion detection, the need for external energy sources is minimized, and excess energy generated during high traffic can be utilized for other purposes. This approach not only enhances energy efficiency but also contributes to the reduction of overall energy consumption in street lighting systems.
Engineering for All: Empowering Students with Visual and Hearing Impairments through Inclusive Learning R. Gayathri, Sherwin Jayanth Roy, Shivakumaran SR, Shyam Sundar S Proceedings 3rd International Conference on Artificial Intelligence and Machine Learning Applications Healthcare and Internet of Things Aimla 2025, 2025 Modern learning technologies tend to lack equitable and accessible learning opportunities, especially for the sensory and communicative disabled. Conventional practices are hampered by poor real-time support, poor sign language interpretation, and limited multimodal content access, resulting in immense educational inequalities. In addition, the lack of built-in, automated support features impedes customized learning and early intervention. To meet these pivotal challenges, this research introduces a novel, AI-based inclusive learning platform that is powered by cutting-edge deep learning and multimodal processing. By combining MediaPipe for high-fidelity sign language recognition and cloud-based AI chatbots for interactive query answering, our system adapts automatically to varied learning requirements, reducing dependency on manual interventions and latency. Experimental tests prove that the outlined platform outperforms traditional learning technologies in accuracy, responsiveness, and end-user satisfaction. This innovation presents an affordable and scalable platform for transforming inclusive education, especially in dynamic and culturally diverse learning settings. Thorough testing over multiple user profiles and educational contexts confirms the efficacy of the system, indicating its readiness for real-world applications. This study highlights the capacity of AI-powered solutions to revolutionize education and create an inclusive learning environment.
AI-Enhanced Farming: A Real-Time Monitoring App powered by IoT R. Gayathri, R. Sathesh Raaj, M. Balaanand, J. Arun Kumar, N Sasikala, K. Rajesh Proceedings of the 5th International Conference on Smart Electronics and Communication Icosec 2024, 2024 The Constant challenge faced by the farmers is to maintain their crops and enhance productivity. Despite various methods and solution evolving, still farmers can’t upgrade to it. Farmers are not aware of the modern agricultural methods. The major cause is that the software and solution developed are of higher complexity. To upgrade them, technology is the foremost important tool to stand up in this modern world. To help them with their problem and adapt to the modern world, the development of software with inbuilt AI and user-friendly features (in agriculture) is needed. This study proposes a novel Agri-tech app, which is a IoT based app used by the farmers for weather forecasting, soil moisture and nutrient content analysis. The developed application also recommends the crop type that can be cultivated in the soil by using the database of soil moisture and nutrient content. IoT sensors fixed in the field are used to detect the moisture level in the soil and notify the farmers to irrigate the land. It recommends the farmer to add manures and fertilizers to soil by calculating the amount of nitrogen, phosphorous, and oxygen contents present in the soil. Using these technical innovation farmers can monitor their fields over a system or smart phone. Moreover, the proposed approach can promote crop diversity and rotation for healthier soil, encourage sustainable practices like organic farming.
A State of the Art Technology with Machine Intelligence in Human Emotion Recognition Xiyan Bi, Liguo Chen, R. Gayathri, Renjith V. Ravi Journal of Interconnection Networks, 2022 Emotion plays a significant role in human understanding and generally connected with rational decision making, attitudes, human activity, and human intelligence. To create realistic emotional relations between human beings and machines, the research community’s increasing interests need reliable and deployable solutions to recognize human emotional conditions. Automatic emotion detection is one of the main obstacles in providing innovative methods for more comfortable and more objective diagnosis, communication and analysis. Hence in this paper, an Artificial intelligence-assisted emotion prediction model (AIEPM) has been proposed to evaluate the probability of digital representation, identification and estimation of feelings, their state-of-the-art methods and primary research guidance. The proposed AIEPM analyses the effect on multimodal detection of emotional models. This paper presents emerging works based on language, sound, image, film, and physiological signals using current methods such as machine intelligence for recognizing human emotion. The proposed emphasis on this cutting-edge analysis reflects elements like the form and presentation of emotional stimulation, the sample’s scale. The numerical outcome suggested AIEPM, according to age, behaviour classification according to age (95.2%), emotions state recognition high probability in satisfaction compared to the proposed method, accuracy ratio (89.6%), performance ratio (94.5%) and recognition outcomes of existing and proposed work (98.2%) compared to other existing approaches improve behaviour classification.
Eshopping Scam Identification using Machine Learning K. Anupriya, R. Gayathri, M. Balaanand, C.B. Sivaparthipan Icsns 2018 Proceedings of IEEE International Conference on Soft Computing and Network Security, 2018 Eshopping are an important new way to make shopping onthe internet and makes the product to reach the customer in easy way. Inspite of this, hacking of credit card details of the customer while purchasing have raised huge concerns. Inthis fact that their happens a major deal on making organizations through web, causes loses for all retailers. This leads tragedy the shop dealers in tragedy to for verifying whether there isgenuine client doing shopping on their own credit card or not. Using Machine learning in, a strategy for information investigation that iteratively gain from information and enables PCs to discover shrouded bits of knowledge without being any express customized. Calculation starts by extricating information and obscure intriguing examples essential on the grounds that as models are presented to new information, it can freely adjust. It gain knowledge from past cycles to create dependable choices and outcomes. This paper demonstrates how Supervised Learning helps for calculation and neural network system calculation and their consolidated calculation makes effectiveness to acquire a high misrepresentation scope and furthermore with a low negative alert rate. Very much prepared Artificial neural system can act as a human mind, rely upon their neurons, little practical unit in cerebrum and also ANN. Machine learning, is utilized for false discovery in view of the client's conduct from their value-based record.
Internet of things based smart health monitoring of industrial standard motors Gayathri R., Shriram K Vasudevan Indonesian Journal of Electrical Engineering and Informatics, 2018 The Industry 4.0 vision provides recommendations how companies can ease the challenges. In an industrial environment, it is beneficial to have a predictive approach to make smart industry using IoT. The Predictive approach includes automating the maintenance activities of machines which help to deliver safety, performance, customer experience, capacity, cost efficiency and sustainability of the key business assets. It helps to improve work force safety which reduces the need to access the infrastructure, develop technologies to enable activities to be remotely controlled from safe areas and automate processes to remove manual tasks and helps to increase infrastructure reliability. It also improves the precision and accuracy of data collection, introducing data analytics, removing human bias, improving reproducibility. This will improve information about asset condition, inform inspection and repair schedules based on asset risks. By implementing predictive and preventive maintenance, one can improve equipment life and avoid any unplanned maintenance activity and thus reducing unscheduled downtime. We in this work have an unit which could be easily attached to the motor units and this does not demand any wiring to carried out. The sensor monitor signals from the motor, accurately measuring key parameters at regular interval of time, as desired. And, the data is sent to the cloud, which in our case is adafruit. From there, the data is analysed and it produces meaningful information. The server then sends alert message to the users about critical data of machine. This will help in fixing any technical issue with ease without incurring much delay.
BAU FAM: Biometric-blacklisting anonymous users using fictitious and adroit manager Journal of Advanced Research in Dynamical and Control Systems, 2017
RECENT SCHOLAR PUBLICATIONS
Engineering bandgaps and charge dynamics: CNT-spinned LiZn 0.5 Al 2 O 4 spinel nanoarchitectures for high-efficiency dye-sensitized solar cells S Nandhini, R Gayathri Journal of Materials Science: Materials in Electronics 37 (14), 1117 , 2026 2026
Voting-based algorithm for governance in healthcare blockchain networks Buvana, Gayathri, Rajalakshmi AIP Conference Proceedings 3341 (1), 020012 , 2026 2026
Effective adaptive res-BiGRU network for pest classification performance based on regionViT-yolov8-aided pest detection technique SM Mehzabeen, R Gayathri Big Data Research 42, 100571 , 2025 2025
Object detection and classification using computer vision and deep learning on edge devices S Mary Joans, JS Jasmine, N Gomathi, R Gayathri International Journal of System Assurance Engineering and Management, 1-13 , 2025 2025
Remote Sensing Image Scene Classification Using Level-Based Attention of Inter-Intra Convolutional Features with Label Smoothing Regularization G Akila, R Gayathri Journal of the Indian Society of Remote Sensing, 1-16 , 2025 2025
Blockchain-driven privacy-preservation of healthcare insurance data using improved ECC based encryption and deep learning based key tuning J Buvana, R Gayathri Knowledge-Based Systems, 114078 , 2025 2025 Citations: 4
Enhancing Hepatitis C Diagnosis: The Impact of SMOTE, Optuna, and SHAP on Detection Methods Mehzabeen SM, Gayathri R, Pattunnarajam P, Ramya A Iranian Journal of Electrical and Electronic Engineering 21 (4), 1-16 , 2025 2025 Citations: 1
Enhancing Hepatitis C Diagnosis: The Impact of SMOTE, Optuna, and SHAP on Detection Methods SM Mehzabeen, R Gayathri, P Paramasaivam, A Ramya IRANIAN JOURNAL OF ELECTRICAL AND ELECTRONIC ENGINEERING 21 (4), 3418-3418 , 2025 2025
Heuristically Improvised rice disease classification framework based on adaptive segmentation with the fusion of LSTM layer into Multi-Scale Residual attention Network SM Mehzabeen, R Gayathri Biomedical Signal Processing and Control 99, 106875 , 2025 2025 Citations: 9
Utilizing Blockchain Technology for Health Insurance in Healthcare Sector J Buvana, R Gayathri 2024 IEEE International Conference on Smart Power Control and Renewable … , 2024 2024 Citations: 2
An enhanced ensemble machine learning classification method to detect attention deficit hyperactivity for various artificial intelligence and telecommunication applications … M Sheriff, R Gayathri COMPUTATIONAL INTELLIGENCE , 2024 2024
Epilepsy Disease Detection Using the Proposed CNN-FCM Approach R Srinath, R Gayathri, C Shalini, P Maragathavalli International Conference on Innovations in Data Analytics, 371-380 , 2023 2023 Citations: 2
Detection and Classification of Alzheimer’s disease from cognitive impairment with resting-state fMRI PR Buvaneswari, R Gayathri Neural Computing and Applications 35 (31), 22797-22812 , 2023 2023 Citations: 32
Enhanced Feature Fusion from Dual Attention Paths Using Feature Gating Mechanism for Scene Categorization of Aerial Images G Akila, R Gayathri International Conference on Image Processing and Capsule Networks, 563-579 , 2023 2023
Weighted multi-deep feature extraction for hybrid deep convolutional LSTM-based remote sensing image scene classification model G Akila, R Gayathri Geocarto International 37 (27), 18217-18253 , 2022 2022 Citations: 8
SRAM memory built in self-test using march algorithm J Kruthika, GR Nisha, R Gayathri, V Jeyalakshmi 2022 International Conference on Augmented Intelligence and Sustainable … , 2022 2022 Citations: 7
Optimized convolutional neural network for tamil handwritten character recognition RB Lincy, R Gayathri International Journal of Pattern Recognition and Artificial Intelligence 36 … , 2022 2022 Citations: 10
Epilepsy disorder detection and diagnosis using empirical mode decomposition and deep learning architecture R Srinath, R Gayathri Concurrency and Computation: Practice and Experience 34 (11), e6903 , 2022 2022 Citations: 7
Transfer learning based handwritten character recognition of tamil script using inception-V3 Model R Gayathri, R Babitha Lincy Journal of Intelligent & Fuzzy Systems 42 (6), 6091-6102 , 2022 2022 Citations: 12
Robust Spatial–Spectral Squeeze–Excitation AdaBound Dense Network (SE-AB-Densenet) for Hyperspectral Image Classification M kavitha, R Gayathri, DK Venkatesan, M Arif, D Vicoveanu, I Chiuchisan, ... Sensors 22 (9), 3229 , 2022 2022 Citations: 11
MOST CITED SCHOLAR PUBLICATIONS
Deep learning-based segmentation in classification of Alzheimer’s disease PR Buvaneswari, R Gayathri Arabian Journal for Science and Engineering 46 (6), 5373-5383 , 2021 2021 Citations: 109
Optimally configured convolutional neural network for Tamil Handwritten Character Recognition by improved lion optimization model R BabithaLincy, R Gayathri Multimedia Tools and Applications, 1-27 , 2020 2020 Citations: 60
Performance evaluation of deep e-CNN with integrated spatial-spectral features in hyperspectral image classification M Kavitha, R Gayathri, K Polat, A Alhudhaif, F Alenezi Measurement 191, 110760 , 2022 2022 Citations: 55
Feature level fusion of palmprint and iris R Gayathri, P Ramamoorthy International Journal of Computer Science Issues (IJCSI) 9 (4), 194 , 2012 2012 Citations: 51
Detection and classification of electroencephalogram signals for epilepsy disease using machine learning methods R Srinath, R Gayathri International Journal of Imaging Systems and Technology , 2020 2020 Citations: 39
Detection and Classification of Alzheimer’s disease from cognitive impairment with resting-state fMRI PR Buvaneswari, R Gayathri Neural Computing and Applications 35 (31), 22797-22812 , 2023 2023 Citations: 32
Automatic palmprint identification based on high order Zernike moment R Gayathri, P Ramamoorthy American Journal of Applied Sciences 9 (5), 759 , 2012 2012 Citations: 26
Fingerprint and palmprint Recognition Approach based on Multiple Feature extraction R Gayathri, P Ramamoorthy European Journal of scientific research 76 (4) , 2012 2012 Citations: 26
Palmprint recognition using feature level fusion R Gayathri, P Ramamoorthy Journal of Computer Science 8 (7), 1049 , 2012 2012 Citations: 20
Multifeature palmprint recognition using feature level fusion R Gayathri, P Ramamoorthy International Journal of Engineering Research and Application 2 (2), 1048-1054 , 2012 2012 Citations: 20
Performance Evaluation of Multimodal Multifeature Authentication System Using KNN Classification R Gayathri, P Ramamoorthy The Scientific World Journal 2015 , 2015 2015 Citations: 13
Transfer learning based handwritten character recognition of tamil script using inception-V3 Model R Gayathri, R Babitha Lincy Journal of Intelligent & Fuzzy Systems 42 (6), 6091-6102 , 2022 2022 Citations: 12
Onboard target detection in hyperspectral image based on deep learning with FPGA implementation R Gayathri Microprocessors and Microsystems 85, 104313 , 2021 2021 Citations: 12
Robust Spatial–Spectral Squeeze–Excitation AdaBound Dense Network (SE-AB-Densenet) for Hyperspectral Image Classification M kavitha, R Gayathri, DK Venkatesan, M Arif, D Vicoveanu, I Chiuchisan, ... Sensors 22 (9), 3229 , 2022 2022 Citations: 11
Optimized convolutional neural network for tamil handwritten character recognition RB Lincy, R Gayathri International Journal of Pattern Recognition and Artificial Intelligence 36 … , 2022 2022 Citations: 10
Heuristically Improvised rice disease classification framework based on adaptive segmentation with the fusion of LSTM layer into Multi-Scale Residual attention Network SM Mehzabeen, R Gayathri Biomedical Signal Processing and Control 99, 106875 , 2025 2025 Citations: 9
Off-Line Tamil handwritten character recognition based on convolutional neural network with VGG16 and VGG19 model RB Lincy, R Gayathri International Conference on Automation, signal processing, instrumentation … , 2020 2020 Citations: 9
Personal authentication using multifeatures multispectral palm print traits R Gayathri, M Senthil Kumar The Scientific World Journal 2015 , 2015 2015 Citations: 9
Feature fusion of palmprint and face biometrics R Gayathri, P Ramamoorthy European Journal of Scientific Research 77 (4), 457-470 , 2012 2012 Citations: 9
Weighted multi-deep feature extraction for hybrid deep convolutional LSTM-based remote sensing image scene classification model G Akila, R Gayathri Geocarto International 37 (27), 18217-18253 , 2022 2022 Citations: 8
