Sustainable Fabrication of ZnO-Based Photocatalysts for Light-Driven Degradation of Organic Dyes N. S. Mohan, V. Vijayalakshmi, Sudharsan Gunasekaran, Anu Tonk, Parthasarathi Mishra, S. Karvendhan, K. Brahma Raju, R. Kamalakannan Nano, 2026 Pure ZnO and Ni-doped ZnO nanoparticles with Ni concentrations of 5[Formula: see text]wt.%, 10[Formula: see text]wt.% and 15[Formula: see text]wt.% were successfully synthesized and systematically investigated for their optical, photocatalytic and antibacterial properties. UV–Vis diffuse reflectance analysis revealed a gradual narrowing of the optical band gap with increasing Ni content, indicating modification of the electronic structure due to dopant induced defect states. The photocatalytic activity was evaluated through methylene blue degradation under light irradiation, where 15[Formula: see text]wt.% Ni@ZnO exhibited the highest degradation efficiency, showing a lower normalized concentration value of 0.53 after 80[Formula: see text]min compared with 0.56 for pure ZnO. Kinetic analysis confirmed pseudo-first-order behavior, with the apparent rate constant increasing from [Formula: see text][Formula: see text]min[Formula: see text] for pure ZnO to [Formula: see text][Formula: see text]min[Formula: see text] for 15[Formula: see text]wt.% Ni@ZnO, along with an improved correlation coefficient [Formula: see text] of 0.9707. Antibacterial studies conducted using the agar well diffusion method demonstrated a pronounced enhancement in bactericidal performance with Ni doping. The maximum zones of inhibition were observed for 15[Formula: see text]wt.% Ni@ZnO, measuring 16[Formula: see text]mm against Staphylococcus aureus and 15[Formula: see text]mm against Vibrio spp., compared with 9[Formula: see text]mm and 8[Formula: see text]mm for pure ZnO, respectively. The enhanced photocatalytic and antibacterial activities are attributed to improved charge separation, increased reactive oxygen species generation and stronger interactions with bacterial cell membranes induced by Ni incorporation. These findings establish Ni-doped ZnO as an efficient multifunctional material for environmental remediation and antimicrobial applications.
Federated Machine Learning for Smart Healthcare Sensor Calibration Jayakumar D, Sathiyavani V, Balamanikandan A, Sudharsan G, Ankala Satya Prabha, Venkata Karthika V 2025 Global Conference on Information Technology and Communication Networks Gitcon 2025, 2025 This article presents a federated machine learning-based calibration scheme for privacy-preserving edge biomedical sensor networks. Traditional centralized calibration methods are plagued by data communication bottlenecks, vulnerability to compromise in sensitive biopotential measurements, and poor scalability in heterogeneous sensor environments. By way of response, we propose a decentralized system where each node of an adaptive biasing and multimodal signal conditioning analog front-end performs local model training employing light-weight machine learning techniques. The models are federated via a federated learning protocol to enable collaborative calibration parameter improvement without exposure to raw data. It is based on PyVISA-integrated automation pipelines and Python-driven inference pipelines to facilitate efficient measurement orchestration in sensor arrays. Cryptographic primitives like secure multiparty computation and differential privacy mechanisms are added to protect parameter exchanges during federated aggregation. Experimental validation across synthetic and real-world biopotential signals exhibits improved calibration accuracy, reduced convergence latency, and efficient privacy compliance. This approach delivers a scalable and computationally efficient cognitive biomedical sensing paradigm that facilitates secure, interoperable, and patient-aware Internet of Healthcare Things (IoHT) deployments.
Advanced Machine Learning Techniques Revolutionize Credit Card Fraud Detection Balamanikandan A, Jayakumar D, Dhanalakshmi M, Sudharsan G, Ankala Satya Prabha, Venkata Karthika V 3rd International Conference on Networks and Advances in Computational Technologies Netact 2025, 2025 The research investigates the use of new machine learning techniques, including Decision Trees, Random Forests, and Extreme Gradient Boosting, to detect and prevent credit card fraud. The strength and performance of the models are compared through their validation using publicly available data as well as real credit card data provided by financial institutions. The robustness of the system is verified by adding noise to the data samples. Major findings indicate that such machinelearning algorithms are very effective in identifying fraud, with the CNN model showing to have an accuracy rate of almost 99 %. The report indicates the necessity for sophisticated fraud detection systems given heightened online payments and evolving patterns of fraud. The research finds that the deep learning techniques, particularly CNNs, are highly effective for the detection of credit card fraud, identifying fine patterns in transactional data, and enhancing the accuracy of predictions. The research highlights the importance of real-time monitoring and updating of fraud detection systems so that they can learn to adapt to new developing fraud patterns, making the systems effective and reliable in real-world applications.
DeepRetina: A Deep Learning Approach for Diabetic Retinopathy Detection and Stage Classification H. Nithishvikraman, Sudharsan. G, Anand Joseph Daniel D I 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems Adics 2024, 2024 Using color fundus imaging to identify diabetic retinopathy (DR) is a tough process that requires skilled doctors to comprehend the existence and significance of certain small characteristics. This effort is further complicated by a complex categorization system. This research aims to use convolution neural network (CNN) to consistently diagnose diabetic retinopathy and grade patients into five groups or stages. An automatically generated diagnostic may be provided by a data-enhanced CNN architecture network that can identify the complex components involved in the class task, as well as exudates, hemorrhages, and micro-aneurysms in the retina, without requiring human input. This work trained CNN using data that was made accessible to the public. When compared to other algorithms on the same dataset, it demonstrated an astounding performance. It was used to the global dataset and obtained a great accuracy of 97% on the validation image.
Deep Learning in the Detection of Chronic Kidney Disease A Balamanikandan, M Saravanakumar, Sudharsan Gunasekaran, Vasiha Anjum, P Gurusamy, N Ashokkumar 2024 4th International Conference on Intelligent Technologies Conit 2024, 2024 Kidney stones are the main kidney-related issue that has arisen in recent times. Small kidney stones are notoriously difficult to detect, thus this research—known as the Detection of Chronic Kidney Disease Using Deep Learning—helps identify kidney tumors and cysts in addition to stones in the kidney. The primary goal of this effort is to reduce time delays and boost accuracy while using less electricity. Two Deep Learning models derived from Machine Learning are included in the paper. Convolutional Neural Network (CNN) and Mobile Net are the two models. The study was able to examine the accuracy of these two models in addition to the outcomes.
Design and analysis of compliant microgripper-A review Sudharsan Gunasekaran, Suresh Periyagounder, Saravanan Annamalai, Aravindha Balaji Aip Conference Proceedings, 2020 Complaint micro grippers for medical applications need to be precise, automated in a smooth manner performing painless surgery to the patient. In this paper design of the grippers and force amplification are presented. First, the benefits of the design of compliant micro grippers by force amplification are listed out second, the flexure used grippers are presented third, the sensor-based complaint micro grippers are discussed fourth, actuator based compliant microgripper is presented. Finally, compliance of the gripper is validated by comparison of the various cross-sectional dimension of flexure objects and the current trend in the fabrication of complaint micro gripper’s technology is summarized.
Machining of Ti6Al4V under Cu particle mixed dielectric medium using aluminium composite tool for production of electric motors components G Radhakrishnan, JJ Moses, MFX Muthu, S Gunasekaran Journal of Ceramic Processing Research, 1069-1086 , 2024 2024.0
Performance Study on a High-Strength Extruded Magnesium Alloy Van Frame Using FEA A Saravanan, G Sudharsan, P Suresh, SP Salaisargunan, S Anandhi, ... Strength of Materials 55 (6), 1297-1309 , 2023 2023.0 Citations: 3
Enhancing micromachining precision with novel electrolyte combinations: an investigation S Gunasekaran, S Periyagounder, M Subramaniam Journal of Ceramic Processing Research 24 (4), 705-713 , 2023 2023.0 Citations: 2
Modelling and simulation of incremental conductance algorithm for solar maximum power point tracker P Ganesan, S Gunasekaran 2022 IEEE Delhi Section Conference (DELCON), 1-6 , 2022 2022.0 Citations: 10
Comparative analysis on industrial iot communication protocols and its future directives P Ganesan, S Gunasekaran, AA Balaji, SJ Fathima, S Suryaprakash 2021 International Conference on Advancements in Electrical, Electronics … , 2021 2021.0 Citations: 10
Design and analysis of compliant microgripper–A review S Gunasekaran, S Periyagounder, S Annamalai, A Balaji AIP Conference Proceedings 2283 (1), 020100 , 2020 2020.0 Citations: 6
EXPERIMENTAL STUDY ON MICRO ELECTROCHEMICAL MACHINING OF SS 316L USING TEACHING LEARNING BASED OPTIMIZATION SA Sudharsan G, Suresh P International Journal of Mechanical and Production Engineering Research and … , 2020 2020.0
Experimental study on micro electrochemical machining of ss 316l using teaching learning based optimisation S Gunasekaran, S Periyagounder, A Sivalingam, S Annamalai Int J Mech Prod Eng Res Dev. 10, 1051-60 , 2020 2020.0 Citations: 2
MAGNESIUM ALLOYS: A REVIEW OF APPLICATIONS. S Annamalai, S Periyakgoundar, S Gunasekaran Materials & Technologies/Materiali in Tehnologije 53 (6) , 2019 2019.0 Citations: 35
Static analysis and weight reduction of aluminum casting alloy connecting rod using finite element method A Saravanan, P Suresh, G Sudharsan, V Suresh Int. J. Mech. Prod. Eng. Res. Dev 8 (3), 507-518 , 2018 2018.0 Citations: 10
Investigation of fatigue life and dynamic behavior of magnesium alloy of an automobile connecting rod A Saravanan, P Suresh, G Sudharsan, P Kumaravel
Optimization of Heavy Duty Three Wheeler Front Suspension Coil Spring Using FEA A Saravanan, P Suresh, SA Balaji, G Sudharsan
MOST CITED SCHOLAR PUBLICATIONS
MAGNESIUM ALLOYS: A REVIEW OF APPLICATIONS. S Annamalai, S Periyakgoundar, S Gunasekaran Materials & Technologies/Materiali in Tehnologije 53 (6) , 2019 2019.0 Citations: 35
Modelling and simulation of incremental conductance algorithm for solar maximum power point tracker P Ganesan, S Gunasekaran 2022 IEEE Delhi Section Conference (DELCON), 1-6 , 2022 2022.0 Citations: 10
Comparative analysis on industrial iot communication protocols and its future directives P Ganesan, S Gunasekaran, AA Balaji, SJ Fathima, S Suryaprakash 2021 International Conference on Advancements in Electrical, Electronics … , 2021 2021.0 Citations: 10
Static analysis and weight reduction of aluminum casting alloy connecting rod using finite element method A Saravanan, P Suresh, G Sudharsan, V Suresh Int. J. Mech. Prod. Eng. Res. Dev 8 (3), 507-518 , 2018 2018.0 Citations: 10
Design and analysis of compliant microgripper–A review S Gunasekaran, S Periyagounder, S Annamalai, A Balaji AIP Conference Proceedings 2283 (1), 020100 , 2020 2020.0 Citations: 6
Performance Study on a High-Strength Extruded Magnesium Alloy Van Frame Using FEA A Saravanan, G Sudharsan, P Suresh, SP Salaisargunan, S Anandhi, ... Strength of Materials 55 (6), 1297-1309 , 2023 2023.0 Citations: 3
Enhancing micromachining precision with novel electrolyte combinations: an investigation S Gunasekaran, S Periyagounder, M Subramaniam Journal of Ceramic Processing Research 24 (4), 705-713 , 2023 2023.0 Citations: 2
Experimental study on micro electrochemical machining of ss 316l using teaching learning based optimisation S Gunasekaran, S Periyagounder, A Sivalingam, S Annamalai Int J Mech Prod Eng Res Dev. 10, 1051-60 , 2020 2020.0 Citations: 2
Machining of Ti6Al4V under Cu particle mixed dielectric medium using aluminium composite tool for production of electric motors components G Radhakrishnan, JJ Moses, MFX Muthu, S Gunasekaran Journal of Ceramic Processing Research, 1069-1086 , 2024 2024.0
EXPERIMENTAL STUDY ON MICRO ELECTROCHEMICAL MACHINING OF SS 316L USING TEACHING LEARNING BASED OPTIMIZATION SA Sudharsan G, Suresh P International Journal of Mechanical and Production Engineering Research and … , 2020 2020.0
Investigation of fatigue life and dynamic behavior of magnesium alloy of an automobile connecting rod A Saravanan, P Suresh, G Sudharsan, P Kumaravel
Optimization of Heavy Duty Three Wheeler Front Suspension Coil Spring Using FEA A Saravanan, P Suresh, SA Balaji, G Sudharsan
