S MURUGAN

@sathyabama.ac.in

Professor, Department of Computer Science and Engineering
Sathyabama Institute of Science and Technology

S MURUGAN


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https://researchid.co/snmurugan

RESEARCH INTERESTS

Machine Learning
Deep Learning
Web Technology
40

Scopus Publications

623

Scholar Citations

12

Scholar h-index

13

Scholar i10-index

Scopus Publications

  • SMART PRINTING LABS: AI-ENABLED MANAGEMENT SYSTEMS
    Vivek Saraswat, Mahi Singh, Amritpal Sidhu, Mohd Faisal, Satish Upadhyay, S. Murugan, Ila Shridhar Savant
    Shodhkosh Journal of Visual and Performing Arts, 2025
    With the development of the printing technology toward automation and smartness, there is the emergence of Smart Printing Labs, areas that involve artificial intelligence (AI), Internet of Things (IoT), and cloud computing to form self-optimizing, data-driven production environments. The evidence in this paper is a framework of AI-Enhanced Smart Printing Lab that can improve operational efficiency and predictive maintenance and managerial decision-making via built-in sensing, analytics, and control. The suggested system uses machine learning algorithms (convolutional neural networks (CNN), long short-term memory (LSTM), and reinforcement learning (RL)) to plan the workflow, identify defects, and control the process in a real-time manner. Data collection and cloud data synchronization with IoT guarantee the constant control of print parameters, allowing to predict faults and maximize energy consumption. Experimental evidence shows throughput increase by 24 percent, reduction of downtimes by 36 percent and 18 percent decrease in energy and 50 percent cut in defect rates respectively as compared to conventional configurations. The study brings in a modular scalable architecture in line with the principles of Industry 4.0 and sustainable manufacturing. The future work aims to develop this system further with the help of federated AI models and cross-facility learning networks, which facilitate joint intelligence in the distributed industrial setting.
  • Modeling a Novel Self-Optimized Wolf Optimizer for a Heterogeneous Network Model for Energy and Node Lifetime Analysis
    Narla Mahendra, S. Murugan
    Journal of Visualized Experiments, 2025
    The vital services of surveillance, information collection, and data transmission from high-risk environments to safer locations are still provided by Wireless Sensor Networks (WSNs). These services are improved by the majority of energy-efficient routing protocols structured for this purpose. A homogeneous routing protocol is applied to decrease the energy utilization of far-off hubs more efficiently; however, the energy utilization rate is higher for this protocol, poorer dependability, and more unfavorable information broadcast to the Wireless Router (WR) or base station (BS) when employed for a longer timeframe. To overcome these drawbacks, a modified Self-Optimized Wolf Optimizer (SOWO) is employed in this research. Incorporating heterogeneous nodes into the current approach, selecting the head based on remaining energy introduces a multi-level interaction strategy throughout the connections. Employing an energy hole elimination method is the foundation of the developed routing technique. Each approach aims to extend the network's lifetime and reduce energy consumption. Based on the findings, the proposed routing scheme demonstrates superior consistency periods, residual energy, throughputs, and network lifespan compared to existing ones. The research addresses the classical clustered-WSN problem of maximizing lifetime and sustained delivery under tight per-node energy budgets while keeping load/fairness balanced. The simulation results show a 3.4% and 32.22% improvement in network stability and residual energy, respectively, over existing algorithms.
  • Artificial intelligence in social sectors: Designing an innovative Climate-Resilient Road Maintenance System (CEEMS)
    Parul Bansal, S. Murugan, Amit Kumar, Jatin Khurana, Anantha Subramanya Iyer, Neha
    Multidisciplinary Science Journal, 2025
    The safety and effectiveness of transport networks, critical to social and economic development, depend on Road Maintenance Systems (RMS). The resilience of road infrastructure is increasingly at risk as climate change enhances extreme weather events, especially in developing countries. To overcome this difficulty, this research suggests the Climate-Resilient Road Maintenance System (CRRMS). This cutting-edge method combines climate adaptation techniques with Artificial Intelligence (AI) to improve road maintenance. By capturing temporal relationships in road conditions, CRRMS integrates Adaptive Black-winged kite optimized-Self-Attentive Graph Neural Network (ABW-SAGNN) with sensor inputs and road weather data to estimate maintenance requirements. Preprocessing methods like z-score normalization and Fourier Transform are used on the raw sensor and meteorological data to guarantee high-quality input for the model; significant features are extracted to better understand how climate variables affect road conditions, such as road surface temperature, humidity levels, and precipitation rates. A specially designed simulation program that simulates real-world situations and tests different climate change projections is used to assess the system. It examines the effects of climatic factors and harsh weather on road infrastructure. In key performance measures like resilience to extreme weather events, resource allocation efficiency, and maintenance prediction accuracy, CRRMS performs better than standard models in Mean Absolute Error (MAE) (0.03), Mean Standard Error (MSE) (0.02), and (0.98). The findings show that CRRMS could accurately predict repair requirements, reduce costs, and lessen congestion by taking proactive measures. The strategy emphasizes that AI could be used to solve important issues with road infrastructure, create more climate-resilient transport systems, advance long-term sustainability, and enhance community safety in the face of climate evaluation.
  • Energy Efficient Power-Aware DSR-based Routing for Homogeneous and Heterogeneous WSNs
    Mahendra Narla, S. Murugan
    Engineering Technology and Applied Science Research, 2025
    The research on managing resources and bandwidth evaluation within ad-hoc networks has highlighted energy-efficient routing protocols as a critical mechanism for conserving energy and prolonging the network's operational lifespan. This study introduces the Energy-Efficient Power Aware DSR (EEPW-DSR) routing protocol, which enhances Cluster Head (CH) selection by incorporating a distance-aware approach and addresses energy consumption challenges. The proposed method optimizes CH selection based on proximity to the Base Station (BS) and the node energy levels, significantly reducing communication distance and balancing energy consumption. Conventional methods, such as Standard DSR and Multi-hop Routing (MR), were considered for comparative performance analysis with the EEPW-DSR. Simulation results demonstrate that the proposed method outperforms conventional methods in network lifetime, network stability, and period of active node operation. These improvements highlight the robustness of the proposed approach in ensuring network longevity in both homogeneous and heterogeneous Wireless Sensor Networks (WSNs).
  • Modelling An Efficient Routing Model For Heterogeneous Ad-Hoc Network Using Improved Vector Protocol
    Mahendra Narla, S. Murugan
    6th IEEE International Conference on Recent Advances in Information Technology Rait 2025, 2025
    Wireless Ad-hoc networks present a promising solution for the escalating demand for wireless sensor network applications across various domains. The inherent limitations in end-sensor nodes pose challenges, primarily concerning the quality of service provision. This article introduces an efficient resource allocation method, termed the Improved Ad hoc Ondemand Associative Distance Vector Protocol (IAHADV) aimed at addressing the multifaceted heterogeneity within ad-hoc networks. This heterogeneity exists on two levels: The variety of applications and the ever-changing radio environment. The suggested method addresses scheduling and radio channel distribution problems. It adopts a centralized approach for allocation decision-making while distributing spectrum sensing tasks, thereby enhancing efficiency and minimizing interference. Despite the constrained capabilities of ad-hoc networks, this method significantly improves the capacity to leverage a broader spectrum range. IAHADV is well-suited for critical communications, gaining prominence in the evolution of wireless networks like the forthcoming 5G era. Simulation outcomes and comparisons conducted between the proposed protocol and others underscore the robust performance of this scheme. The results highlight its considerable effectiveness, exhibiting only a minor trade-off in terms of complexity.
  • Automated Toll Collection: Integrating License Plate Recognition with Insurance and Vehicle Data Verification
    Sanku Veer Vikram Sai, S. Murugan, Sappa Aravind
    Proceedings of 3rd International Conference on Augmented Intelligence and Sustainable Systems Icaiss 2025, 2025
    This paper presents an automated toll collection system that integrates License Plate Recognition (LPR), real-time data verification, and payment automation to enhance the efficiency, security, and scalability of toll operations. The system leverages high-resolution cameras, Optical Character Recognition (OCR), and machine learning algorithms to accurately identify vehicles and verify compliance with legal requirements. Real-time data transmission and secure API connections facilitate the validation of vehicle registration and insurance information. The payment automation module deducts toll fees from registered accounts, providing real-time notifications to users and ensuring compliance enforcement. Experimental evaluation demonstrates a license plate recognition accuracy of 98.5% and real-time processing with a speed of 0.5 seconds per vehicle. The system reduces congestion by up to 70%, optimizes revenue collection, and enhances user experience by eliminating manual payment processes. Future enhancements include improving environmental robustness, cross-regional database interoperability, and dynamic pricing models to further optimize system performance.
  • Implement An AI-Based Approach For Effective Construction Dust Monitoring
    M N Nachappa, Trushna Parida, S. Murugan, Simranjeet Nanda, Galiveeti Poornima, Rajesh Kumar Samala
    2025 International Conference on Metaverse and Current Trends in Computing Icmctc 2025, 2025
    A significant amount of airborne particles that are detrimental to the health of people living nearby and the environment are caused by construction dust. Construction workers are usually uncovered to dust at high stages and bear severe health risks. While many dust control methods are applied in the construction sites, work-related health risks still exist. To develop a framework for automated dust monitoring in construction sites, this research suggests a novel approach to construction dust. Monitoring using Adam Optimizer Driven Advanced Random Forest (AO-ARF) used to detect dust. For this study, various construction dust image data were gathered. Global histogram equalization was used to preprocess the data to preserve edges and fine details while reducing noise in the image. Python software is used to implement the suggested approach. The recommended technique was compared to other existing algorithms in a comparative study. The result showed that the suggested method exhibited superior performance in terms of accuracy, and running Time (s).
  • Utilizing Deep Learning for Automated Detection of Endangered Species in Camera Trap Data
    Dr. Suhas Ballal, Ritika Mehra, Sachin Mittal, Aarsi Kumari, Dr.S. Murugan, Dr. Satya Narayan Satapathy
    Natural and Engineering Sciences, 2025
    Effective and efficient monitoring of endangered species is a vital part of biodiversity conservation; however, the process of analysing camera trap data is not without its difficulties, being very labour intensive, and vulnerable to the potential for human error. Traditional image analysis frameworks have drawbacks with regards to classification accuracy when assessing animals in low light, occluded, or other complex environments. This study outlines the design and development of an automated assessment process using deep learning techniques, specifically convolutional neural networks (CNN), using transfer learning from large-scale wildlife datasets. The framework processes raw camera trap images and assigns correct identities to species, identifying and scoring endangered species with confidence levels through proper temporal data augmentation, based on the environmental conditions the photos were taken in. The performance of the automated method is then assessed against a traditional classifier based on feature-engineering, as well as human expert annotations. The comparative metrics used include precision, recall,F1-score, and processing length for original images, assessed in a variety of ecologically-meaningful zones and lighting contexts. Results show a detection accuracy of 94.6%, with a more than 15% improvement on baseline measures, and substantial savings in manual review time also derived from the automated processing. The method outlined presents an opportunity for enhanced efficiencies in biodiversity studies involving large numbers of images and will facilitate timely action in regards to conservation where needed. This system represents a doorway between computational development and ecological fieldwork, supporting evidence-led animal management and policy decisions within the context of exploring nature-inspired design in the environmental engineering context.
  • Artificial Intelligence Driven Skin Cancer Detection Using R-FCN Enhanced Deep Convolutional Neural Networks with SMOTE Balancing
    A Ronald Doni, Chin-Shiuh Shieh, Siva Shankar S, Prasun Chakrabarti, G Nagarajan, S Murugan
    International Journal of Engineering Science and Information Technology, 2025
    Skin cancer is a serious worldwide health issue, and earlier diagnosis is crucial for patient outcomes and efficient treatment. However, due to the variety of skin cancer types and the complexity of medical imaging, making an accurate diagnosis can be challenging. This study tackles this issue by introducing a new deep learning (DL) algorithm that is specifically designed for skin tumor diagnosis and employs the Convolutional Neural Network (CNN) technology. This study offers a novel approach that makes use of Region-based Fully Convolutional Networks (R-FCN) to address the crucial problem of skin cancer lesion categorization. The suggested system seeks to increase classification efficiency by using region-based detection which improves classification accuracy and localization. The HAM10000 and ISIC-2020 datasets, which are difficult and unbalanced, were used to thoroughly evaluate the created Deep CNN (DCNN) architecture. The Synthetic Minority Over-sampling Technique (SMOTE) was purposefully used as the method of random sampling in order to lessen the imbalanced datasets. This greatly enhanced the suggested models generalization and robustness. The results demonstrate the remarkable efficacy of the research contribution, yielding performance metrics consistently above 98% for F1-score, specificity, sensitivity, recall, accuracy, precision, and the area under the ROC curve (AUC). In terms of balancing speed and accuracy the suggested approach also performs better than traditional methods like R-CNN and YOLOv8. The study demonstrates that a strong framework for automatic skin cancer detection and classification is provided by combining R-FCN with SMOTE and CNN techniques. This framework facilitates early diagnosis and aids dermatologists in clinical decision-making.
  • Spotted Hyena with Fire Hawk Optimization Algorithm Driven Cluster Based Routing for Wireless Sensor Networks
    Nirmal K, Murugan S
    International Journal of Engineering Trends and Technology, 2024
    A Wireless Sensor Network (WSN) collects data about the environment and transmits it to a central location via distributed Sensor Nodes (SNs). Advancements in sensor equipment, size, interfaces, and cost have led to many WSN applications. Energy effectualness is the most researchable topic of the energy-constrained WSN. Many models are used to handle energy consumption (ECON), and the most promising methods are clustering and routing. The WSN requires a routing protocol to transmit data to the sink via a cost-effective link. A primary issue is detecting the element's constrained energy so that the higher power is utilized consistently over time. Energy-efficient routing can extend lifespan by using less energy. This study develops a spotted hyena with fire hawk optimization algorithm-driven cluster-based routing (SHFHOA-CBR) technique in WSN. The SHFHOA-CBR technique follows two significant processes: energy-efficient clustering and routing. To accomplish this, the SHFHOA-CBR technique includes a spotted hyena optimizer-based clustering approach (SHO-CA) to choose an optimal set of CHs and generate clusters. The SHO-CA technique derives a fitness function (FF) comprised of distance to neighbouring nodes (DTN), residual energy (RE), and trust level (TL). For route selection, the SHFHOA-CBR technique encompasses the fire hawk optimizer-based routing (FHO-R) technique to choose optimal routes to BS. Finally, the FHO-R technique includes three input variables: node degree (ND), RE, and DTN. The investigational evaluation of the SHFHOA-CBR model is conducted using diverse measures, namely ECON, Latency, Packet Delivery Ratio (PDR), Throughput (THRO), Network Life-Time (NLT), End-to-End Delay (EED). The experimental outputs infer that the SHFHOA-CBR technique achieves promising performance over current techniques.
  • AI and machine learning in supply chain optimization: Mapping the territory
    R. Sethuraman, S. Murugan, M. Saravanan
    Blockchain Iot and AI Technologies for Supply Chain Management Apply Emerging Technologies to Address and Improve Supply Chain Management, 2024
  • Dynamic Arithmetic Optimization Algorithm with Deep Learning-based Intrusion Detection System in Wireless Sensor Networks
    K. Nirmal, S. Murugan
    Engineering Technology and Applied Science Research, 2024
  • Indoor Navigation using Augmented Reality for Mobile Application
    M S Ramesh, J Naveena Ramesh Vardhini, S Murugan, J Albert Mayan
    Proceedings of the 7th International Conference on Intelligent Computing and Control Systems Iciccs 2023, 2023
  • Implementation of Genetic Algorithm for Detecting and Eliminating Blackhole Attack in Vehicular Ad-Hoc Network
    Ganesh Dangat, S. Murugan
    Proceedings of International Conference on Computational Intelligence and Sustainable Engineering Solution Cises 2023, 2023
  • CLASSIFICATION OF GENDER BASED FOCUS MAPPING FOR EPILEPSY PATIENTS USING ROUGH SETS
    B. Muthukumar, S. Murugan, B. Bharathi
    Malaysian Journal of Computer Science, 2023
  • Fuzzy dragon deep belief neural network for activity recognition using hierarchical skeleton features
    Paul T. Sheeba, Sankaranarayanan Murugan
    Evolutionary Intelligence, 2022
  • Weather and population based forecasting of novel COVID-19 using deep learning approaches
    A. Ronald Doni, T. Sasi Praba, S. Murugan
    International Journal of System Assurance Engineering and Management, 2022
  • Localization in Underground Area Using Wireless Sensor Networks with Machine Learning
    P. Rama, S. Murugan
    Lecture Notes in Electrical Engineering, 2022
  • Application of Metamodeling in Parametric Optimization for Emission Reduction of Four-Stroke DI Diesel Engine with Refined Vegetable Oil as Fuel
    Janarthanam Hemanandh, Subbiah Ganesan, R. Devaraj, S. P. Venkatesan, S. Murugan, Soundararajan Hemanth
    Green Energy and Technology, 2022
  • A Text Based Sentiment Analysis Model using Bi-directional LSTM Networks
    J.S. Vimali, S. Murugan
    Proceedings of the 6th International Conference on Communication and Electronics Systems Icces 2021, 2021
  • Sentiment analysis on twitter social media product reviews
    Vimali J.S., Murugan S.
    Indian Journal of Computer Science and Engineering, 2021
  • Fuzzy DDBN: Fuzzy Dragon Deep Belief Neural Network and interesting features points for activity recognition
    Paul T Sheeba, S Murugan
    Sadhana Academy Proceedings in Engineering Sciences, 2020
  • Localization Approach for Tracking the Mobile Nodes Using FA Based ANN in Subterranean Wireless Sensor Networks
    P. Rama, S. Murugan
    Neural Processing Letters, 2020
  • Encapsulated Features with Multi-objective Deep Belief Networks for Action Classification
    Paul T. Sheeba, S. Murugan
    Advances in Intelligent Systems and Computing, 2020
  • Mucl-multi-hop revolutionary communication with localization in underground wireless sensor networks
    International Journal of Recent Technology and Engineering, 2019
  • Energy harvesting for lifetime maximization of the underground sensor networks via FFC in underground mining
    International Journal of Innovative Technology and Exploring Engineering, 2019
  • Smart car sharing
    S. Murugan, K. Madhu Varma, M. Y. N. Sai Prudhvi
    Journal of Computational and Theoretical Nanoscience, 2019
  • Selection of test case features using fuzzy entropy measure and random forest
    Sankaranarayanan Murugan, Govindarajan Kulanthaivel, Venugopal Ulagamuthalvi
    Ingenierie Des Systemes D Information, 2019
  • Classification and Prediction of Breast Cancer using Linear Regression, Decision Tree and Random Forest
    S. Murugan, B. Muthu Kumar, S. Amudha
    International Conference on Current Trends in Computer Electrical Electronics and Communication Ctceec 2017, 2018
  • Hybrid features-enabled dragon deep belief neural network for activity recognition
    Paul T. Sheeba, S. Murugan
    Imaging Science Journal, 2018
  • Text, images, and video analytics for fog computing
    A. Jayanthiladevi, S. Murugan, K. Manivel
    Handbook of Research on Cloud and Fog Computing Infrastructures for Data Science, 2018
  • Easy adaptation of customer reviews to generate ratings using a sentiment classifier
    Journal of Advanced Research in Dynamical and Control Systems, 2017
  • Synchronization of sticky notes using cloud
    P. Pirarthani, S. Murugan
    2015 International Conference on Communication and Signal Processing Iccsp 2015, 2015
  • Fuzzy decision making model for byzantine agreement
    Journal of Engineering Science and Technology, 2014
  • Performance behaviour of cryptography algorithms in aspect based web services
    International Journal of Applied Engineering Research, 2014
  • ETAG based assertions in performance testing: An overview
    International Journal of Applied Engineering Research, 2014
  • Rough set model for prediction of trustworthy web services
    Sankaranarayanan Murugan, Veilumuthu Ramachandran
    Advances in Intelligent Systems and Computing, 2013
  • Byzantine fault tolerance in soap communication services
    Malaysian Journal of Computer Science, 2012
  • Rough sets based trustworthiness evaluation of web services
    International Review on Computers and Software, 2012
  • Aspect oriented decision making model for byzantine agreement
    S. Murugan, V. Ramachandran
    Journal of Computer Science, 2012

RECENT SCHOLAR PUBLICATIONS

  • Human-in-the-loop machine learning system for collaborative defect analysis in industrial 3D printing operations
    R Arumuganainar, GV Londhe, M Vanitha, GLN Vanguri, S Murugan, ...
    Nondestructive Testing and Evaluation, 1-23 , 2026
    2026
  • Raspberry Pi-enabled wearable sensors for personal health tracking and analysis
    K Karthika, S Dhanalakshmi, SM Murthy, N Mishra, S Sasikala, ...
    2023 International Conference on Self Sustainable Artificial Intelligence … , 2023
    2023
    Citations: 65
  • Drunk Driving Detection and Automatic Car Ignition Locking System
    TS Kumar, R Raman, M Karthikeyan, CJ Rawandale, S Sasikala, ...
    2023 International Conference on Self Sustainable Artificial Intelligence … , 2023
    2023
    Citations: 53
  • CLASSIFICATION OF GENDER BASED FOCUS MAPPING FOR EPILEPSY PATIENTS USING ROUGH SETS
    B Muthukumar, S Murugan, B Bharathi
    Malaysian Journal of Computer Science 36 (1), 40-52 , 2023
    2023
  • Automatic skin tumour segmentation using prioritized patch based region–a novel comparative technique
    A Ashwini, S Murugan
    IETE Journal of Research 69 (1), 137-148 , 2023
    2023
    Citations: 30
  • Fuzzy dragon deep belief neural network for activity recognition using hierarchical skeleton features
    PT Sheeba, S Murugan
    Evolutionary Intelligence 15 (2), 907-924 , 2022
    2022
    Citations: 16
  • Auto-threshold dynamic memory efficient frequent pattern growth for data excavation
    G Gunasekaran, S Murugan, K Mani
    Int. J. Elect. Electron. Res. 10 (3), 614-619 , 2022
    2022
    Citations: 1
  • GNVDF: A GPU-accelerated Novel Algorithm for Finding Frequent Patterns Using Vertical Data Format Approach and Jagged Array
    P Sumathi, S Murugan
    International Journal of Modern Education and Computer Science (IJMECS) 13 … , 2021
    2021
    Citations: 2
  • A text based sentiment analysis model using bi-directional LSTM networks
    JS Vimali, S Murugan
    2021 6th International conference on communication and electronics systems … , 2021
    2021
    Citations: 50
  • Design and analysis of two-cylinder exhaust manifold with improved performance in CFD
    C Sadhasivam, S Murugan, J Vairamuthu, SM Priyadharshini
    Materials Today: Proceedings 37, 2141-2144 , 2021
    2021
    Citations: 9
  • Computational investigations on helical heat flow exchanger in automotive radiator tubes with computational fluid dynamics
    C Sadhasivam, S Murugan, P Manikandaprabu, SM Priyadharshini, ...
    Materials Today: Proceedings 37, 2352-2355 , 2021
    2021
    Citations: 6
  • Electro spray technique to enhance the physical property of Sulphur doped zinc oxide thin film
    S Murugan, A Manivasaham, RA Kumar
    Materials Today: Proceedings 47, 1717-1723 , 2021
    2021
    Citations: 8
  • Fabrication of thin film using insoluble sulphur doped on tin oxide through electro spray technique
    AV Babu, S Murugan, A Manivasaham
    Materials Today: Proceedings 47, 1960-1966 , 2021
    2021
    Citations: 2
  • A novel study on augmented physical parameters of nickel doped stannic oxide film
    AV Babu, S Murugan, DC BerniceVictoria, SJ Gnanamuthu, ...
    Materials Research Express 7 (12), 124001 , 2020
    2020
    Citations: 6
  • Fuzzy DDBN: Fuzzy Dragon Deep Belief Neural Network and interesting features points for activity recognition
    PT Sheeba, S Murugan
    Sādhanā 45 (1), 5 , 2020
    2020
    Citations: 3
  • Encapsulated Features with Multi-objective Deep Belief Networks for Action Classification
    PT Sheeba, S Murugan
    Cognitive Informatics and Soft Computing: Proceeding of CISC 2019, 205-214 , 2020
    2020
  • TSD-TSR: Traffic Sign Detection Using Various Technique Based on Different Condition
    R Karthik, S Murugan
    International Journal of Advanced Research in Engineering and Technology 11 (9) , 2020
    2020
  • Selection of Test Case Features Using Fuzzy Entropy Measure and Random Forest.
    S Murugan, G Kulanthaivel, V Ulagamuthalvi
    Ingénierie des Systèmes d'Information 24 (3) , 2019
    2019
    Citations: 7
  • Cloud Optimized Eclat Growth (COEG) Using Fuzzy Logic.
    P Vaithiyanathan, S Murugan
    International Journal of Distributed & Cloud Computing 7 (1) , 2019
    2019
  • A survey on traffic sign detection techniques using text mining
    S Murugan, R Karthika
    Asian Journal of Computer Science and Technology 8 (S1), 21-24 , 2019
    2019
    Citations: 1

MOST CITED SCHOLAR PUBLICATIONS

  • Performance analysis of Indian stock market index using neural network time series model
    DA Kumar, S Murugan
    2013 international conference on pattern recognition, informatics and mobile … , 2013
    2013
    Citations: 117
  • Raspberry Pi-enabled wearable sensors for personal health tracking and analysis
    K Karthika, S Dhanalakshmi, SM Murthy, N Mishra, S Sasikala, ...
    2023 International Conference on Self Sustainable Artificial Intelligence … , 2023
    2023
    Citations: 65
  • Classification and prediction of breast cancer using linear regression, decision tree and random forest
    S Murugan, BM Kumar, S Amudha
    2017 International Conference on Current Trends in Computer, Electrical … , 2017
    2017
    Citations: 63
  • Drunk Driving Detection and Automatic Car Ignition Locking System
    TS Kumar, R Raman, M Karthikeyan, CJ Rawandale, S Sasikala, ...
    2023 International Conference on Self Sustainable Artificial Intelligence … , 2023
    2023
    Citations: 53
  • A text based sentiment analysis model using bi-directional LSTM networks
    JS Vimali, S Murugan
    2021 6th International conference on communication and electronics systems … , 2021
    2021
    Citations: 50
  • Automatic skin tumour segmentation using prioritized patch based region–a novel comparative technique
    A Ashwini, S Murugan
    IETE Journal of Research 69 (1), 137-148 , 2023
    2023
    Citations: 30
  • Performance analysis of NARX neural network backpropagation algorithm by various training functions for time series data
    DA Kumar, S Murugan
    International Journal of Data Science 3 (4), 308-325 , 2018
    2018
    Citations: 28
  • Performance analysis of MLPFF neural network back propagation training algorithms for time series data
    DA Kumar, S Murugan
    2014 World Congress on Computing and Communication Technologies, 114-119 , 2014
    2014
    Citations: 20
  • Rule based classification of ischemic ecg beats using antminer
    S Murugan, S Radhakrishnan
    International Journal of Engineering Science and Technology 2 (8), 3929-3935 , 2010
    2010
    Citations: 18
  • Fuzzy dragon deep belief neural network for activity recognition using hierarchical skeleton features
    PT Sheeba, S Murugan
    Evolutionary Intelligence 15 (2), 907-924 , 2022
    2022
    Citations: 16
  • Hybrid features-enabled dragon deep belief neural network for activity recognition
    PT Sheeba, S Murugan
    The imaging science journal 66 (6), 355-371 , 2018
    2018
    Citations: 16
  • Automated ischemic beat classification using genetic algorithm based principal component analysis
    S Murugan, S Radhakrishnan
    International Journal of Healthcare Technology and Management 11 (3), 151-162 , 2010
    2010
    Citations: 13
  • A Study on Health Hazard of Salt Workers in Tamilnadu Coastal Areas
    D Durairaj, S Murugan, T Nadu, T Nadu
    Int. J. Pharm. Sci. Rev. Res 40 (29), 137-141 , 2016
    2016
    Citations: 10
  • Design and analysis of two-cylinder exhaust manifold with improved performance in CFD
    C Sadhasivam, S Murugan, J Vairamuthu, SM Priyadharshini
    Materials Today: Proceedings 37, 2141-2144 , 2021
    2021
    Citations: 9
  • Electro spray technique to enhance the physical property of Sulphur doped zinc oxide thin film
    S Murugan, A Manivasaham, RA Kumar
    Materials Today: Proceedings 47, 1717-1723 , 2021
    2021
    Citations: 8
  • Improving ischemic beat classification using fuzzy-genetic based PCA and ICA
    S Murugan, S Radhakrishnan
    Int J Comput Sci Eng 2 (5), 1532-1538 , 2010
    2010
    Citations: 8
  • Selection of Test Case Features Using Fuzzy Entropy Measure and Random Forest.
    S Murugan, G Kulanthaivel, V Ulagamuthalvi
    Ingénierie des Systèmes d'Information 24 (3) , 2019
    2019
    Citations: 7
  • Computational investigations on helical heat flow exchanger in automotive radiator tubes with computational fluid dynamics
    C Sadhasivam, S Murugan, P Manikandaprabu, SM Priyadharshini, ...
    Materials Today: Proceedings 37, 2352-2355 , 2021
    2021
    Citations: 6
  • A novel study on augmented physical parameters of nickel doped stannic oxide film
    AV Babu, S Murugan, DC BerniceVictoria, SJ Gnanamuthu, ...
    Materials Research Express 7 (12), 124001 , 2020
    2020
    Citations: 6
  • Selection of test case features using fuzzy entropy measure and random forest. Ingénierie des Systèmes d’Information, 24 (3), 261-268
    S Murugan, G Kulanthaivel, V Ulagamuthalvi
    2019
    Citations: 6