Signal Processing, Electrical and Electronic Engineering, Computer Networks and Communications, Information Systems
21
Scopus Publications
Scopus Publications
Advances in Nanostructured Electrodes for Next-Generation Bioelectronic Interfaces Umapathi Krishnamoorthy, Jayasheela M Chemphyschem, 2026 Nanostructured electrodes (NSEs) have emerged as pivotal components in the development of advanced bioelectronic bidirectional interfaces, enabling precise sensing and stimulation in neural, muscular, and cardiac systems. Their unique physicochemical properties such as high surface area, tuneable conductivity, and mechanical compliance, allow seamless integration with biological tissues, offering enhanced signal fidelity and reduced impedance. This review provides a comprehensive analysis of the latest materials and design strategies employed in fabricating NSEs, including carbon‐based nanomaterials, metallic nanostructures, and conductive polymers. Emphasis is placed on the application of such bioelectronic interfaces for neural interfacing, cardiac mapping and pacing, muscle stimulation, and organ‐on‐chip modelling devices. Furthermore, critical challenges related to biocompatibility, mechanical durability, and long‐term functionality of the interfaces are addressed, and the emerging trends in the development of soft, wireless, and adaptive electrode systems that promise to redefine clinical neuro‐engineering and personalized medicine are presented. By synthesizing the current advancements, limitations, and future directions that drive research and development at the interjunction of NSEs and bidirectional bio‐interfaces, this review serves as a foundational resource for researchers developing the next generation of intelligent, minimally invasive, and multifunctional bio‐interfaces.
Melanoma Detection Using Deep Convolutional Neural Networks: A High-Resolution Image-Based Approach B Anitha, K Umapathi, M Jayasheela Sensor Data Analytics for Intelligent Healthcare Delivery, 2025 Melanoma, a type of skin cancer, could be life-threatening to patients if it becomes malignant. Thus, detecting melanoma in its early stages becomes important. In addition, cancer diagnosis relies on biopsies, which are painful. Therefore, non-invasive early diagnostic tools are the need of the hour. Artificial Intelligence (AI)-based Machine Learning (ML) and Deep Learning (DL) tools have been a boon in biomedical diagnosis. Thus, this book chapter presents the results of a novel and efficient Deep Convolutional Neural Network (DCNN) model in classifying melanoma into benign and malignant classes. The proposed model is trained using a huge dataset of 10,605 dermoscopic images. The model addresses class imbalance and limited data issues by using class-weighted training and data augmentation methods. The model could achieve a classification accuracy of 91%. With its inherent capabilities, the model could aid a dermatologist when integrated into clinical practice.
Exploring the synthesis and biomedical potential of banana stem fiber for antimicrobial and wound healing applications K. Umapathi, M. Jayasheela, Manohar Ramya, J. K. Hari Sriram, G. Shibu, E. Gladson Paul, J. Yogitha, M. Sheik Abdullah, C. Sushma, M. Manjupriya, S. Jerone Samuvel Discover Applied Sciences, 2025 Need for non-toxic materials especially for biomedical application remains the most important driving force for the discovery of nature-derived biomaterials. In particular, fibers and extracts from plants with medicinal value are vastly investigated. One such source is the banana fiber. The lignocellulosic and protein-based structures of banana fibers contribute to their biodegradability and biocompatibility, which are essential attributes for medical use. Despite their promising biocompatibility, banana fibers face challenges such as variability in their mechanical properties and limited raw state performance. This necessitates precise fiber synthesis and advanced processing to obtain biocompatible fibers for biomedical use. This review discusses the chemical constituents, extraction methods, characterization of natural fiber derived from banana pseudo stem, along with the antimicrobial and tissue regeneration potential of banana fiber based biomedical materials recently developed. Further, steps involved in the conversion of banana fiber to useful biomedical material are presented along with fabrication techniques. In addition, challenges and limitations that hurdle research progress, and potential for future biomaterials through functionalization are discussed. Review findings support potential for research intervention at the integration of banana fibers, biopolymers and nanotechnology towards development of efficient wound healing materials. Inherent to the comprehensive coverage of topics, the authors assure that this review will potentially act as a valuable source of information that could boost research and development of banana fiber-based wound healing biomaterials. Graphical Abstract
Design of IoT based Thermometer M Jayasheela, R Sachin, M Sanjai Prasath, S Suriya 3rd International Conference on Emerging Applications of Material Science and Technology Iceamst 2025, 2025
Development of an Multimodal ACL Tear Analysis M. Jayasheela, S Uma, S Geetha, S Gowtham, R Jishnu Prasad 3rd International Conference on Emerging Applications of Material Science and Technology Iceamst 2025, 2025
A Survey on Impact of Internet of Medical Things Against Diabetic Foot Ulcer R. Athi Vaishnavi, P Jegathesh, M Jayasheela, K Mahalakshmi Eai Endorsed Transactions on Pervasive Health and Technology, 2024 INTRODUCTION: In this study, we explore the intricate domain of Diabetic Foot Ulcers (DFU) through the development of a comprehensive framework that encompasses diverse operational scenarios. The focus lies on the identification and classification assessment of diabetic foot ulcers, the implementation of smart health management strategies, and the collection, analysis, and intelligent interpretation of data related to diabetic foot ulcers. The framework introduces an innovative approach to predicting diabetic foot ulcers and their key characteristics, offering a technical solution for forecasting. The exploration delves into various computational strategies designed for intelligent health analysis tailored to patients with diabetic foot ulcers.OBJECTIVES: The primary objective of this paper is to present a technical solution for forecasting diabetic foot ulcers, utilizing computational strategies for intelligent health analysis.METHODS: Techniques derived from social network analysis are employed to conduct this research, focusing on diverse computational strategies geared towards intelligent health analysis for patients with diabetic foot ulcers. The study highlights methodologies addressing the unique challenges posed by diabetic foot ulcers, with a central emphasis on the integration of Internet of Medical Things (IoMT) in prediction strategies.RESULTS: The main results of this paper include the proposal of IoMT-based computing strategies covering the entire spectrum of DFU analysis, such as localization, classification assessment, intelligent health management, and detection. The study also acknowledges the challenges faced by previous research, including low classification rates and elevated false alarm rates, and proposes automatic recognition approaches leveraging advanced machine learning techniques to enhance accuracy and efficacy.CONCLUSION: The proposed IoMT-based computing strategies present a significant advancement in addressing the challenges associated with predicting diabetic foot ulcers. The integration of advanced machine learning techniques demonstrates promise in improving accuracy and efficiency in diabetic foot ulcer localization, marking a positive stride towards overcoming existing limitations in previous research.
Convolutional Neural Networks in Breast Cancer Diagnosis: An Integrated Model Dafnee, M. Jayasheela International Conference on Computing and Intelligent Reality Technologies Proceedings of Iccirt 2024, 2024 Breast cancer is always the top most reason for the increase in death rate of women. Therefore advancements in diagnosing technique is very much needed, so that the breast cancer can be detected without false positives or false negatives for clinicians to take necessary action at the earlier stage. Deep learning technique like Convolutional Neural Networks (CNNs) has become a very successful tool in medical imaging specifically for breast cancer diagnosis. Imaging modalities like mammogram, ultrasound and MRI has become the primary source of breast cancer diagnosis. This paper proposed the idea of integrating multiple CNN architectures with mammogram datasets for training the model and make use of ensemble technique to detect the breast cancer during the testing phase. This Integrated CNN model is expected to achieve higher detection accuracy with less false positive rate.
Interfacing the IoT in composite manufacturing: An overview Palanirajan Gowtham, Moses Jayasheela, Chinnaswamy Sivamani, Devarajan Balaji Reviews on Advanced Materials Science, 2024 It is a well-known fact that many sophisticated works consume a lot of human resources, leading to the need to find effective alternative. The manufacturing industry demands a lot of human resources, with around half of the global working population participating in this sector. Challenges such as sudden conflicts in the data, disasters, and loss of productivity are encountered by the manufacturing industries and can be overcome by monitoring machine performance data and automatically configuring the machines according to changing needs. This emphasizes the importance of the Internet of Things (IoT) in addressing niche areas of manufacturing. IoT is a buzzword heard everywhere around the globe. Implementing this technology makes most of the work more accessible than other conventional methods. This has created a lot of research interest on this topic. Among many manufacturing sectors, polymer composite material manufacturing is one of the most demanding. This review article purely focuses on polymer composite manufacturing and its allied processes. The consolidation of data is based on the influence of IoT on the extraction of fibers and manufacturing of polymer composite material using novel techniques, quality assessment of manufactured polymer composite material, challenges faced in exploring the use of IoT, and future scope. It can be stated from the survey that various researchers have minimally explored the incorporation of IoT, but its future looks very promising in terms of producing high-quality products at less time and lower cost by integrating this technique with conventional methods.
Pre-Cardiac arrythmia detection using Machine Learning Nagajothi S, Jayasheela M, Ishwarya T, Sabitha R, Keerthana G Proceedings 1st International Conference on Intelligent Technologies for Sustainable Electric and Communications Systems Itech Secom 2023, 2023
