Engineering, Electrical and Electronic Engineering, Signal Processing
11
Scopus Publications
130
Scholar Citations
5
Scholar h-index
3
Scholar i10-index
Scopus Publications
Machine learning-enhanced surface plasmon resonance glucose biosensor using black phosphorus-strontium titanate multilayer architecture for non-invasive diabetes management S. Ponlatha, U.Arun Kumar, Habib Kraiem, N.A. Natraj Ain Shams Engineering Journal, 2026 Diabetes management requires precise and frequent glucose monitoring; however, existing techniques remain invasive, painful and insufficient for continuous long-term use, limiting patient compliance and accessibility. To address these limitations, a surface plasmon resonance (SPR)-based glucose biosensor incorporating machine-learning-optimized black phosphorus (BP) sensing layers is proposed. A detailed analysis of the sensor design has been conducted using Maxwell’s Equations and Transfer Matrix Method (TMM) in order to optimize the thickness of the Au (7-54 nm), BP (0.2-2.2 nm) and SrTiO 3 (0.3-2.3 nm) layers followed by numerical validation using COMSOL Multiphysics. The optimized structure exhibits minimum reflectance values ranging from 0.253 % to 0.955 % for glucose-induced refractive index variations, corresponding to resonance angle shifts from 74° to 76.2°. A maximum local sensitivity of 300°/RIU is achieved over a narrow refractive index interval, while the overall sensitivity across the full sensing range varies between 166°/RIU and 183°/RIU. This performance surpasses many existing SPR sensors while maintaining a figure of merit of 76 and a detection accuracy of 0.152. A strong linear correlation between resonance angle and refractive index (R 2 = 0.99734), expressed as θ(°) = 178.5714RI − 164.3405, confirms excellent sensing precision. Furthermore, machine learning regression models demonstrate robust predictive performance with R 2 values ranging from 0.92 to 1.00, significantly enhancing real-time glucose response estimation. Electric field distribution analysis reveals maximum field confinement at the metal-dielectric interface at a 75° incident angle, ensuring efficient analyte interaction. These results demonstrate that the proposed SPR biosensor is highly sensitive, accurate, and suitable for intelligent wearable sensing applications for next-generation non-invasive glucose monitoring and diagnostics.
An Enhanced Approach of Empowering Social Interaction Among Students with ASD S. Meena, P. Ramani, D. Sangeetha, S. Ponlatha, Premalatha 2025 IEEE Pune Section International Conference Punecon 2025, 2025 Students with ASD often find it difficult to express their emotions in a social environment. Sometimes even the caregivers, teachers are not able to get connected with the kid. This eventually leads to many challenges in social interactions. To overcome this, multimodal emotional identification system has been adapted here with the procedure of articulation based analysis. The articulation is then converted as words by means of DistilBERT and LSTM based model. The model also takes the input from the caregiver or teachers or anyone who is interacting with the kid. Through the combined analysis of input queries and the responses to them, the analysis has been strengthened. The proposed approach outshined the other traditional ML algorithms in the shorter inference latency and in improvised real time adaptability. Along with the analysis model efficiency has been calculated for the benchmark analysis
Secured Cyber-Internet Security in Intrusion Detection with Machine Learning Techniques Aarthi C, Saranya K, Naga Saranya N, Ponlatha S International Journal of Computational and Experimental Science and Engineering, 2024 The rapid proliferation of Internet-connected devices has elevated the significance of cybersecurity, making intrusion detection a critical aspect of maintaining network integrity. Traditional security measures often fail to provide adequate protection against sophisticated attacks, necessitating advanced and robust solutions. This paper introduces a comprehensive cyber-internet security framework that leverages machine learning techniques for real-time intrusion detection and prevention. The proposed methodology employs a hybrid approach, integrating supervised and unsupervised learning models to detect anomalies and classify intrusions effectively. Specifically, a combination of Support Vector Machine (SVM), Decision Trees (DT), and K-means clustering is used to enhance detection accuracy and reduce false-positive rates.The experimental results demonstrate that the proposed model achieved a detection accuracy of 97.8%, a precision of 96.5%, and a recall of 95.2% on the NSL-KDD dataset. The implementation also reduced the false-positive rate to 1.2% and the computational overhead by 15% compared to traditional detection systems. Additionally, the proposed system was tested on real-time traffic data, where it successfully identified and mitigated various cyber threats, including Distributed Denial of Service (DDoS) attacks and network infiltrations, with minimal latency and high reliability.In conclusion, the study presents an efficient and secured cyber-internet security framework that significantly enhances intrusion detection capabilities using machine learning techniques. The proposed system provides a scalable and adaptive solution for securing critical infrastructure and networks against evolving cyber threats, making it an ideal candidate for deployment in real-world cybersecurity applications.
Robots and cyborgs with artificial intelligence-based technologies in the healthcare sector: A review Robotics and Automation in Healthcare Advanced Applications, 2024
Development of a medical decision support system for early detection of cardiac disorders Robotics and Automation in Healthcare Advanced Applications, 2024
An IOT-based efficient energy management in smart grid using SMACA technique S. Ponlatha, P. Umasankar, P. Balashanmuga Vadivu, D. Chitra International Transactions on Electrical Energy Systems, 2021 Energy management system (EMS) for distribution system with internet of things (IoT) using hybrid method is proposed in this paper. The proposed hybrid system is joined implementation of slime mould optimization algorithm (SMA) and chimp optimization algorithm (CA) and thus it is known as SMACA technique. The key point of the proposed scheme is to optimally direct the power and resources of the distribution system through persistent display of data as IoT-based communication system. At proposed scheme, every home device is interconnected using data acquisition module with an internet protocol (IP) address, which generates an enormous wireless network of working devices. For encouraging improved demand response for the distribution system to take care of energy, IoT-based communication system is utilized. To simply treat energy, optimal load requirement forecast and energy control processes are deal with SMACA system. In addition, the optimal utilization of the available resources and flexibility of these networks is provided and prolonged with IoT-based distribution system. In addition, the proposed system is capable for satisfying the common supply and energy requirement. Finally, the proposed model is performed on MATLAB/Simulink platform, and the performance of proposed system is compared with different techniques.
An artificial neural network based lossless video compression using multi- level snapshots and wavelet transform using intensity measures International Journal of Engineering and Technology, 2014
Robust feature selection based lossless video compression of tiny video scenes using multi feature reduction technique and wavelet transform International Journal of Applied Engineering Research, 2014
RECENT SCHOLAR PUBLICATIONS
Machine learning-enhanced surface plasmon resonance glucose biosensor using black phosphorus-strontium titanate multilayer architecture for non-invasive diabetes management S Ponlatha, UA Kumar, H Kraiem, NA Natraj Ain Shams Engineering Journal 17 (6), 104145 , 2026 2026 Citations: 1
Brain tumor detection using HyGSNet and feature extraction with DWT-based GDP P Sambandham, S Perla, KV Rao, G Ramanjaiah Journal of Neuroimmunology, 578869 , 2026 2026
An Enhanced Approach of Empowering Social Interaction Among Students with ASD S Meena, P Ramani, D Sangeetha, S Ponlatha 2025 IEEE Pune Section International Conference (PuneCon), 1-5 , 2025 2025
Next-generation hybrid multi-material surface plasmon resonance biosensor for non-invasive glucose detection with machine learning optimization T Sheheryar Plasmonics 20 (12), 11119-11135 , 2025 2025 Citations: 12
Enhancing image segmentation performance through adaptive K-means clustering and intelligent centroid seeding RR Sharma, GA Sungheetha, S Ponlatha, G Chandru, GGS Pradeep Advances in Electrical and Computer Technologies, 60-68 , 2025 2025
Enhanced Image Segmentation in Medical Imaging Using Mini-Net S.Ponlatha, M.Sweetline Sonia, M.Iswarya, R.Kanagaraj Computer Science, Engineering and Technology 1 (1), 74-79 , 2025 2025
Multiscale wavelet-based compression schemes for preserving diagnostic information in medical imaging RR Sharma, GA Sungheetha, S Ponlatha, S Sabarish, R Murthy, ... Advances in Electrical and Computer Technologies, 555-561 , 2025 2025
Secured Cyber-Internet Security in Intrusion Detection with Machine Learning Techniques PS Aarthi C, Saranya K, Naga Saranya N International Journal of Computational and Experimental Science and … , 2024 2024 Citations: 2
SENSOR INTEGRATED WEARABLE AI-POWERED AIRBAG RIDER ACCIDENT RECOGNITION SYSTEM DC S.Ponlatha, P.Balashanmuga Vadivu, G.Neelvathi, M.Sweetline Sonia IN Patent 202,441,014,409 , 2024 2024
Development of A Medical Decision Support System For Early Detection Of Cardiac Disorders S Sumathi, S Ponlatha Robotics and Automation in Healthcare, 223-237 , 2024 2024 Citations: 1
Robots and cyborgs with artificial intelligence-based technologies in the healthcare sector: a review S Ponlatha, S Sumathi Robotics and Automation in Healthcare, 239-256 , 2024 2024 Citations: 1
Life Safety Air Bag System for Two-Wheeler PS Dr.S.Ponlatha, L.Shanmugasundaram , G.Vaideshwaran International Journal of New Innovations in Engineering and Technology 24 (1 … , 2024 2024
A Wearable Solar Powered Jacket for Health Monitoring System BM Dr. S. Ponlatha, Gowshik Kannan.C, Devarkonda Akash, Chemukula Murali Krishna International Journal of New Innovations in Engineering and Technology 24 (1 … , 2024 2024
Satellite Image Classification using Deep Learning Dr.S.Ponlatha, B.Sudhamathi, G.Sripathi, R.Priyanka Journal of Scientific Transactions in Environment and Technovation 18 (1), 29-36 , 2024 2024
Improving Robot Image Perception Design Using Artificial Intelligence RC R. Suresh, S. Ponlatha, K. Giri EST Journal on Emerging Trends in Modelling and Manufacturing 9 (4), 44-50 , 2023 2023
Highly Effective NB-LDPC Decoder Design on Space Telecommand Systems S. Ponlatha, C. Arunprasath, B. Prabakaran, S. Venkatesh Babu Journal on Electronic and Automation Engineering 2 (2), 86-92 , 2023 2023
SELECTION USING ENHANCED NORMAL FORM GAME THEORY BASED OPTIMIZATION APPROACH IN WSN DDC Dr. S. Ponlatha, P. Sowmiyaa ShodhKosh: Journal of Visual and Performing Arts 4 (2), 1189-1198 , 2023 2023
Patient Rescue and Condition Monitoring VGK S.Ponlatha, M.Gowthaman, K.Jayabalaji, A.Karthick International Journal of Innovative Research in Technology 9 (9), 226-231 , 2023 2023
Automatic Surveillance and Fire Fighting Robot Using IoT AS S.Ponlatha, R.Praveenraj, P.Santhosh Kumar, S.Saran International Journal of Innovative Research in Technology 9 (8), 821-826 , 2023 2023
MOST CITED SCHOLAR PUBLICATIONS
Comparison of video compression standards S Ponlatha, RS Sabeenian International Journal of Computer and Electrical Engineering 5 (6), 549 , 2013 2013 Citations: 55
An IOT‐based efficient energy management in smart grid using SMACA technique S Ponlatha, P Umasankar, P Balashanmuga Vadivu, D Chitra International Transactions on Electrical Energy Systems 31 (12), e12995 , 2021 2021 Citations: 39
Next-generation hybrid multi-material surface plasmon resonance biosensor for non-invasive glucose detection with machine learning optimization T Sheheryar Plasmonics 20 (12), 11119-11135 , 2025 2025 Citations: 12
Music genre classification using deep learning with KNN S Ponlatha, B Mathisalini, KA Deepthisri, M Kalaiyarasi, V Kowshika International Journal of Advanced Research in Science, Communication and … , 2021 2021 Citations: 7
Deep learning based classification of bone tumors using image segmentation D Ponlatha, P Aravindhan, L Boovesh Periodico di Mineralogia 91 (3), 311-336 , 2022 2022 Citations: 5
An artificial neural network based lossless video compression using multilevel snapshots and wavelet transform using intensity measures S Ponlatha, R Sabeenian Int J Eng Technol 6 (4), 1900-1908 , 2014 2014 Citations: 3
Secured Cyber-Internet Security in Intrusion Detection with Machine Learning Techniques PS Aarthi C, Saranya K, Naga Saranya N International Journal of Computational and Experimental Science and … , 2024 2024 Citations: 2
Resource Dynamic Frequency Interference Mitigation Based on LTE-A and Neighboring Node Network Communication International Research DS Ponlatha Journal in Advanced Engineering and Technology 5 (2), 4189-41995 , 2019 2019 Citations: 2
Detection and prevention of elephant intrusion into crop fields near forest areas R Hemalathal, T Kanmani, C Keerthana, S Ponlatha, I Selvamani International Journal of Innovation Research in Technology, Science … , 2016 2016 Citations: 2
Machine learning-enhanced surface plasmon resonance glucose biosensor using black phosphorus-strontium titanate multilayer architecture for non-invasive diabetes management S Ponlatha, UA Kumar, H Kraiem, NA Natraj Ain Shams Engineering Journal 17 (6), 104145 , 2026 2026 Citations: 1
Development of A Medical Decision Support System For Early Detection Of Cardiac Disorders S Sumathi, S Ponlatha Robotics and Automation in Healthcare, 223-237 , 2024 2024 Citations: 1
Robots and cyborgs with artificial intelligence-based technologies in the healthcare sector: a review S Ponlatha, S Sumathi Robotics and Automation in Healthcare, 239-256 , 2024 2024 Citations: 1
Brain tumor detection using HyGSNet and feature extraction with DWT-based GDP P Sambandham, S Perla, KV Rao, G Ramanjaiah Journal of Neuroimmunology, 578869 , 2026 2026
An Enhanced Approach of Empowering Social Interaction Among Students with ASD S Meena, P Ramani, D Sangeetha, S Ponlatha 2025 IEEE Pune Section International Conference (PuneCon), 1-5 , 2025 2025
Enhancing image segmentation performance through adaptive K-means clustering and intelligent centroid seeding RR Sharma, GA Sungheetha, S Ponlatha, G Chandru, GGS Pradeep Advances in Electrical and Computer Technologies, 60-68 , 2025 2025
Enhanced Image Segmentation in Medical Imaging Using Mini-Net S.Ponlatha, M.Sweetline Sonia, M.Iswarya, R.Kanagaraj Computer Science, Engineering and Technology 1 (1), 74-79 , 2025 2025
Multiscale wavelet-based compression schemes for preserving diagnostic information in medical imaging RR Sharma, GA Sungheetha, S Ponlatha, S Sabarish, R Murthy, ... Advances in Electrical and Computer Technologies, 555-561 , 2025 2025
SENSOR INTEGRATED WEARABLE AI-POWERED AIRBAG RIDER ACCIDENT RECOGNITION SYSTEM DC S.Ponlatha, P.Balashanmuga Vadivu, G.Neelvathi, M.Sweetline Sonia IN Patent 202,441,014,409 , 2024 2024
Life Safety Air Bag System for Two-Wheeler PS Dr.S.Ponlatha, L.Shanmugasundaram , G.Vaideshwaran International Journal of New Innovations in Engineering and Technology 24 (1 … , 2024 2024
A Wearable Solar Powered Jacket for Health Monitoring System BM Dr. S. Ponlatha, Gowshik Kannan.C, Devarkonda Akash, Chemukula Murali Krishna International Journal of New Innovations in Engineering and Technology 24 (1 … , 2024 2024
