MARANCO MURUGAIYAN

@srmist.edu.in

ASSISTANT PROFESSOR, DEPARTMENT OF NETWORKING AND COMMUNICATIONS
Department of Networking and Communications, SRM Institute of Science and Technology, Kattankulathur

MARANCO MURUGAIYAN


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https://researchid.co/marancom

EDUCATION

Ph.D in Cyber Security (2023)
M.E in Network Engineering (2014)

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Networks and Communications, Computer Science Applications, Artificial Intelligence, Information Systems
18

Scopus Publications

54

Scholar Citations

4

Scholar h-index

1

Scholar i10-index

Scopus Publications

  • Robot Co-Workers: Human-Robot Collaboration in Smart Manufacturing
    M. Sivakumar, N. Krishnaraj, P. Savaridassan, M. Maranco
    Industrial Robotics in Smart Manufacturing, 2026
    Through smart manufacturing, cobots introduced an integration that led to a transformation of industrial processes by enabling HRC. Cobots under Industry 4.0 improve workplace safety, productivity, and efficiency through their ability to work with human operators instead of replacing them. Artificial intelligence (AI) and machine learning platform, together with sensor-based automation, enables cobots to work alongside human operators at industrial sites because they differ from classic industrial robotics equipment. The chapter investigates HRC evolution while examining its manufacturing effects and reporting its industrial deployment within the automotive, electronics, and pharmaceutical manufacturing segments. The research demonstrates how cobots deliver three main advantages: better flexibility, lower operational mistakes, and better human worker ergonomics. The analysis examines the implementation difficulties of HRC, which include trust-related issues together with safety matters and technical integration requirements. Through the examination of practical industrial applications, this section demonstrates the ways businesses use cobots to enhance both operational speed and flexible design. The chapter evaluates forthcoming HRC developments, which include AI-controlled adaptability together with digital twin systems and regulatory improvements that will define advanced collaborative automation systems. The research manifests the need for combining human labor with robotics to achieve sustainable intelligent manufacturing operations that harmonize robotic systems with human creativity.
  • Deep convolutional neural network based archimedes optimization algorithm for heart disease prediction based on secured IoT enabled health care monitoring system
    Sureshkumar S, Santhosh Babu A. V, Joseph James S, Maranco M
    Scientific Reports, 2025
    The Internet of Things (IoT) is a rapidly evolving and user-friendly technology that connects everything and enables effective communication between linked things. In hospitals and other healthcare centers, healthcare monitoring systems have exploded in popularity over the last decade, and wireless healthcare monitoring devices using diverse technologies have a huge interest in several countries worldwide. The existing studies in healthcare IoT met a few shortcomings in terms of privacy, security, higher data dimensionality, higher cost, larger execution time, and so on. To tackle these issues, we proposed a novel IoT-enabled and secured healthcare monitoring framework (IoT-SHMF) for heart disease prediction. The data are taken from the Cleveland Heart Disease database. First, authentication is performed through registration, login, and patient data verification. The Matrix-based RSA encryption technology and a blockchain-based data storage concept provide safe data transmission and authorization. Subsequently, the secured data is downloaded by the hospital management (HM) system. The HM system scrutinizes the decrypted data. Finally, the Deep Convolutional Neural Network-based Archimedes Optimization (DCNN-AO) algorithm classifies the normal and abnormal classes of heart disease. The implementation work of the proposed model is simulated using JAVA software with different performance measures. Various performance metrics with state-of-art methods validate the effectiveness of the proposed model. The proposed IoT-based system ensures better security by about 98%. The decryption time of our proposed approach, when the sensor nodes are equal to 25, is 37 seconds.
  • Data-driven strategies for energy forecasting in sustainable supply chain management
    Sivakumar Murugaiyan, Krishnaraj Nagappan, P. Savaridassan, M. Maranco
    Urban Mobility and Challenges of Intelligent Transportation Systems, 2025
    Sustainable supply chains are increasingly relevant in today's world. This chapter explores sustainability, supply chain management, and big data analytics with a focus on energy forecasting. Sustainable supply chain management balances social responsibility, environmental goals, and cost efficiency. Big data analytics enhances decision-making and operational performance. The chapter highlights the importance of sustainability initiatives and the triple bottom line, showing how big data enables efficiency and supports sustainable development. Energy management is a key element, allowing organizations to forecast costs and improve energy efficiency. Real-world case studies illustrate how analytics and energy forecasting enhance sustainability. Challenges such as data aggregation and scalability are discussed, along with emerging trends and actionable recommendations.
  • Leveraging big data to drive smarter and sustainable transportation policies
    Sivakumar Murugaiyan, Krishnaraj Nagappan, P. Savaridassan, M. Maranco
    Urban Mobility and Challenges of Intelligent Transportation Systems, 2025
    In an era marked by rapid technological advancement and increasing urbanization, transportation systems are under growing pressure to evolve and meet new demands for efficiency, sustainability, and user satisfaction. This chapter explores how big data analytics can transform transportation policies to address these challenges. By leveraging vast amounts of data collected from diverse sources such as traffic sensors, GPS devices, and social media, policymakers and planners can gain unprecedented insights into transportation patterns and behaviors. This chapter reviews current methodologies for analyzing transportation data, highlights successful case studies where data-driven policies have led to significant improvements in mobility and efficiency, and discusses the challenges associated with integrating big data into transportation planning. It concludes with recommendations for future research and policy development, emphasizing the potential of big data to foster smarter, more resilient transportation systems.
  • Enhancing V2X communication with edge computing for real-time intelligent transportation systems
    Ankit Vatsa, N. Krishnaraj, P. Savaridassan, M. Maranco, M. Sivakumar
    Urban Mobility and Challenges of Intelligent Transportation Systems, 2025
    This chapter explores the integration of Vehicle-to-Everything (V2X) communication with edge computing to enhance the capabilities of Intelligent Transportation Systems (ITS). It discusses the technical architecture and the advantages of combining these two technologies, with a focus on improving processing of real time data, reducing latency, & optimizing decision making. The chapter examines the role of edge computing in enabling faster response times and more efficient communication within V2X environments, while also addressing the challenges and potential solutions associated with this integration. Key ITS applications such as smart traffic management, autonomous driving, and emergency response systems are analyzed, highlighting how this integration can drive advancements in road safety, operational efficiency, and environmental sustainability in urban transportation.
  • Cyber security for intelligent transportation systems protecting critical infrastructure
    M. Maranco, M. Sivakumar, N. Krishnaraj, P. Savaridassan, Kashyapa AbhiramIvaturi, R. Logeshwari
    Urban Mobility and Challenges of Intelligent Transportation Systems, 2025
    In the era of smart cities, Intelligent Transportation Systems (ITS) play a crucial role in enhancing urban mobility, safety, and efficiency. However, as ITS becomes more interconnected and reliant on digital technology, it faces increasing cybersecurity threats. This chapter explores these challenges, starting with the ITS architecture, including connected vehicles, traffic management centers, communication networks, and data platforms. IoT integration and real-time data dependence introduce unique vulnerabilities, opening risks to ransomware, data breaches, denial-of-service attacks, and sensor tampering. Consequences of such attacks range from traffic disruption and accidents to data loss and decreased public trust. To address these issues, we propose a cybersecurity framework for ITS that encompasses risk assessment, threat detection, incident response, and ongoing monitoring, enhanced by AI and machine learning.
  • Cloud, Fog, and Edge Computing for Industry 5.0
    Krishnaraj Nagappan, Priyanga Subbiah, Kiran Bellam, Maranco Murugaiyan
    Next Generation Data Science and Blockchain Technology for Industry 5 0 Concepts and Paradigms, 2025
    New technologies made possible by Industry 4.0 have revolutionized manufacturing and automation. The use of cloud, fog, and edge computing plays a critical role in the transformation of the industrial sector as we enter into the age of Industry 5.0. In the context of Industry 5.0, the importance and makes use of cloud, fog, and facet computing are surveyed in this e-book bankruptcy. We pass into the underlying thoughts of these computing paradigms and inspect how they paint together to give Industry 5.0 superpowers no longer seen before. Data garage, processing, and analytics are nonetheless dependent on cloud computing. However, fog and facet computing have emerged in response to the call for low-latency and real-time choices by way of shifting compute closer to information sources. This chapter specializes the actual-global packages and use cases of cloud, fog, and facet computing in an extensive variety of manufacturing settings. These technologies are propelling productivity, adaptability, and innovation across a huge range of industries, from production and supply chain control to healthcare and independent structures. Security, privacy, and interoperability are a few of the troubles we talk about when it comes to the significant use of these computing architectures. In order to fully take the advantage of cloud, fog, and facet computing in the context of Industry 5.0, we stress the importance of taking a comprehensive strategy. With the facts furnished in this chapter, readers can be better prepared to take benefit of the sport-converting capacity of those computing paradigms inside the context of Industry 5.0.
  • Data Analytics and Visualization in Smart Manufacturing Using AI-Based Digital Twins
    M. Sivakumar, M. Maranco, N. Krishnaraj, U. Srinivasulu Reddy
    Artificial Intelligence Enabled Digital Twin for Smart Manufacturing, 2025
    Smart manufacturing encompasses the use of cutting-edge technologies, including IoT (Internet of Things), AI (Artificial Intelligence), and data analytics, to optimize production procedures, enhance effectiveness, minimize expenses, and elevate total productivity. A crucial element of smart manufacturing is the notion of digital twins, which refer to virtual duplicates of tangible assets, procedures, or systems. These digital replicas are generated by utilizing real-time data obtained from sensors integrated into tangible assets, and are employed for the purposes of monitoring, analysis, prediction, and optimization. The primary issue lies in integrating data from many sources throughout the production ecosystem while ensuring data quality. Scalable analytics and visualization solutions are necessary to manage the substantial amount of data produced by sensors and other devices in smart manufacturing environments. Manufacturing processes can be intricately intricate, encompassing a multitude of interconnected systems and variables. Scientists are utilizing AI algorithms to examine data from digital replicas for the purpose of predictive maintenance. This allows manufacturers to foresee equipment malfunctions and plan maintenance in advance, thus reducing the amount of time that operations are halted. Manufacturers are utilizing AI-based digital twins to simulate and enhance industrial processes, allowing them to discover areas of congestion, optimize the usage of resources, and enhance overall productivity. Artificial intelligence algorithms are being utilized to analyze data from digital replicas of physical objects, known as digital twins, in order to promptly identify any flaws or imperfections, thereby guaranteeing the quality of the product and minimizing unnecessary waste. Ongoing progress in AI algorithms, namely in deep learning and reinforcement learning, will provide more precise forecasts and enhancements using data derived from digital twins. The significance of edge computing technologies will grow as they enable immediate analysis and visualization of data produced by sensors located at the network's periphery, thereby diminishing the need for low latency and high bandwidth.
  • Digital Twin-Enabled Smart Manufacturing: Challenges and Future Directions
    M. Maranco, M. Sivakumar, N. Krishnaraj, Kashyapa Abhiram Ivaturi, R. Nidhya
    Artificial Intelligence Enabled Digital Twin for Smart Manufacturing, 2025
    Digital twin technology represents a revolutionary leap forward in smart manufacturing, fundamentally reshaping traditional methodologies with its transformative capabilities. Digital twins are virtual counterparts that accurately simulate physical assets and processes, acting as a dynamic bridge between the tangible world of machinery and the digital domain of data analytics and simulation. Through the amalgamation of cutting-edge technologies like IoT sensors, AI algorithms, and simulation software, digital twins empower manufacturers with unparalleled insights and control over their operations. A pivotal advantage of digital twins lies in their ability to facilitate predictive maintenance, a cornerstone of contemporary manufacturing practices. Through the ongoing monitoring of equipment performance and condition in real time, manufacturers are able to predict maintenance requirements, proactively resolve problems, and prevent expensive periods of inactivity. This proactive approach represents a notable departure from traditional reactive maintenance methods, heralding a significant shift towards more efficient and cost-effective operations. Furthermore, digital twins serve as invaluable instruments for optimizing production processes and enhancing overall operational efficiency. Through real-time data collection and analysis, manufacturers can pinpoint inefficiencies, streamline workflows, and allocate resources more judiciously. This not only enhances productivity but also contributes to higher-quality output and reduced waste, thereby fostering a competitive edge in an increasingly dynamic market landscape. Despite their immense potential, the widespread adoption of digital twins is not without its challenges. Issues such as data security, interoperability between disparate systems, and the intricacies of implementation pose formidable obstacles for manufacturers. However, through meticulous planning, investment in robust technology infrastructure, and collaborative efforts with industry partners, these challenges can be surmounted, unlocking the full potential of digital twin technology in the realm of smart manufacturing.
  • Data Analytics and Artificial Intelligence for Predictive Maintenance in Manufacturing
    M. Sivakumar, M. Maranco, N. Krishnaraj
    Data Analytics and Artificial Intelligence for Predictive Maintenance in Smart Manufacturing, 2024
    Predictive maintenance (PdM) in the industrial sector utilizes artificial intelligence (AI) and data analytics to forecast equipment malfunctions in advance, allowing for proactive maintenance planning, reducing downtime, and achieving cost savings. The fundamental obstacle is to guarantee the availability and reliability of data, considering the possible flaws and inconsistencies in sensor data and maintenance records. Feature selection and engineering pose significant difficulties, especially in intricate industrial settings. Performing real-time analysis of sensor data necessitates the ability to process data with little delay, a task that might be challenging to accomplish. Scientists are investigating deep learning methods, specifically recurrent neural networks (RNNs) and convolutional neural networks (CNNs), to enhance the precision and efficiency of predictive maintenance. Researchers are currently working on developing artificial intelligence (AI) algorithms that can detect abnormal patterns in sensor data, which can serve as early indicators of possible equipment failures. Integrating predictive maintenance with digital twin technology enables the modeling of equipment behavior and improves predictive capabilities. The development of explainable AI methods is essential for establishing trust among domain experts and regulatory agencies. Researchers are currently exploring hybrid models that combine physics-based models with data-driven machine learning approaches in order to achieve more accurate predictions. In general, predictive maintenance shows potential for enhancing industrial operations. However, it is crucial to tackle difficulties related to data quality, interpretability, and real-time processing in order to achieve success.
  • Improved Wild Horse Optimizer with Deep Learning Model for Skin Lesion Detection and Classification on Dermoscopic Images
    M. Maranco, Amit Kumar Tyagi, M. Sivakumar
    Lecture Notes in Networks and Systems, 2024
  • Improving Biometric Security Using PulsePrint: Real-Time Defense Against Fingerprint Spoofing
    Vigneshwaran P, Maranco M, Kashyapa Abhiram Ivaturi, Purab Takur
    Proceedings of the 2nd IEEE International Conference on Networking and Communications 2024 Icnwc 2024, 2024
  • Intense Triad Defender for End-User Security in Cyber Physical System
    Sheryl Sharon G, Maranco M, Nidhya R, Sivakumar M
    Proceedings of the 2nd IEEE International Conference on Networking and Communications 2024 Icnwc 2024, 2024
  • Improvised Multi-Factor Authentication for End-User Security in Cyber Physical System
    International Journal of Intelligent Systems and Applications in Engineering, 2024
  • Data Analytics and Visualization in Smart Manufacturing Using AI-based Digital Twins
    M. Sivakumar, M. Maranco, N. Krishnaraj, U. Srinivasulu Reddy
    Data Analytics and Artificial Intelligence for Predictive Maintenance in Smart Manufacturing, 2024
  • Smart System for Vechicle Number Plate Recognition Using Convolutional Neural Network(CNN)
    R Nidhya, R Kalpana, R Sudhakar, M Maranco, G Smilarubavathy
    2023 1st International Conference on Optimization Techniques for Learning Icotl 2023 Proceedings, 2023
  • NOVEL APPROACH FOR END USER SECURITY ACCESS IN CYBER PHYSICAL SYSTEM USING MULTILAYER AUTHENTICATION
    Maranco M., Dr. Manikandan V.
    Indian Journal of Computer Science and Engineering, 2022
  • ENVIRONMENTAL SECURITY DESIGN OF MULTILAYER AUTHENTICATION IN WATER RESOURCE CONTROL SYSTEM
    Journal of Environmental Protection and Ecology, 2021

RECENT SCHOLAR PUBLICATIONS

  • Robot Co-Workers
    M Sivakumar, N Krishnaraj, P Savaridassan, M Maranco
    Industrial Robotics in Smart Manufacturing , 2026
    2026
  • 13 Adaptive Multi-layer Authentication for Secure Internet of Medical Things (IoMT)
    M. Maranco, N. Krishnaraj, M. Sivakumar, P. Savaridassan, R Logeshwari, ...
    Healthcare 5.0 with Fuzzy Logic: Artificial Intelligence, Cyber-Physical … , 2025
    2025
  • 8 Securing AI and Machine Learning Models in Healthcare with Fuzzy
    M Sivakumar, N Krishnaraj, M Maranco, P Savaridassan, US Reddy
    Healthcare 5.0 with Fuzzy Logic: Artificial Intelligence, Cyber-Physical … , 2025
    2025
  • 15 Fuzzy-Based Multidimensional Risk Assessment Model for the Healthcare 5.0
    P Savaridassan, N Krishnaraj, M Maranco, M Sivakumar
    Healthcare 5.0 with Fuzzy Logic: Artificial Intelligence, Cyber-Physical … , 2025
    2025
  • 17 Case Studies on Securing Healthcare 5.0 with Fuzzy
    M Sivakumar, N Krishnaraj, M Maranco, P Savaridassan, S Jayaraja
    Healthcare 5.0 with Fuzzy Logic: Artificial Intelligence, Cyber-Physical … , 2025
    2025
    Citations: 1
  • Enhancing V2X Communication With Edge Computing for Real-Time Intelligent Transportation Systems
    A Vatsa, N Krishnaraj, P Savaridassan, M Maranco, M Sivakumar
    Urban Mobility and Challenges of Intelligent Transportation Systems, 209-228 , 2025
    2025
    Citations: 2
  • Data-Driven Strategies for Energy Forecasting in Sustainable Supply Chain Management
    S Murugaiyan, K Nagappan, P Savaridassan, M Maranco
    Urban Mobility and Challenges of Intelligent Transportation Systems, 443-460 , 2025
    2025
    Citations: 1
  • Cyber Security for Intelligent Transportation Systems Protecting Critical Infrastructure
    M Maranco, M Sivakumar, N Krishnaraj, P Savaridassan, K AbhiramIvaturi, ...
    Urban Mobility and Challenges of Intelligent Transportation Systems, 403-420 , 2025
    2025
    Citations: 3
  • Leveraging big data to drive smarter and sustainable transportation policies
    S Murugaiyan, K Nagappan, P Savaridassan, M Maranco
    Urban Mobility and Challenges of Intelligent Transportation Systems, 327-348 , 2025
    2025
    Citations: 2
  • Data analytics and artificial intelligence for predictive maintenance in manufacturing
    M Sivakumar, M Maranco, N Krishnaraj
    Data Analytics and Artificial Intelligence for Predictive Maintenance in … , 2024
    2024
    Citations: 16
  • 3 Data Analytics and
    M Sivakumar, M Maranco, N Krishnaraj
    Data Analytics and Artificial Intelligence for Predictive Maintenance in … , 2024
    2024
  • Digital Twin‐Enabled Smart Manufacturing: Challenges and Future Directions
    M Maranco, M Sivakumar, N Krishnaraj, KA Ivaturi, R Nidhya
    Artificial Intelligence‐Enabled Digital Twin for Smart Manufacturing, 479-504 , 2024
    2024
    Citations: 8
  • Data Analytics and Visualization in Smart Manufacturing Using AI‐Based Digital Twins
    M Sivakumar, M Maranco, N Krishnaraj, US Reddy
    Artificial Intelligence‐Enabled Digital Twin for Smart Manufacturing, 249-277 , 2024
    2024
    Citations: 9
  • Improving biometric security using pulseprint: real-time defense against fingerprint spoofing
    P Vigneshwaran, M Maranco
    2024 2nd International Conference on Networking and Communications (ICNWC), 1-7 , 2024
    2024
    Citations: 2
  • Intense Triad Defender for End-User Security in Cyber Physical System
    M Maranco, R Nidhya, M Sivakumar
    2024 2nd International Conference on Networking and Communications (ICNWC), 1-7 , 2024
    2024
  • Smart System for Vechicle Number Plate Recognition Using Convolutional Neural Network(CNN)
    R Nidhya, R Kalpana, R Sudhakar, M Maranco, G Smilarubavathy
    2023 1st International Conference on Optimization Techniques for Learning … , 2024
    2024
  • 240 Data Analytics and Visualization in Smart Manufacturing Using AI-based Digital Twins
    M Sivakumar, M Maranco, N Krishnaraj, U Srinivasulu Reddy
    Data Analytics and Artificial Intelligence for Predictive Maintenance in … , 2024
    2024
    Citations: 1
  • Improved Wild Horse Optimizer with Deep Learning Model for Skin Lesion Detection and Classification on Dermoscopic Images
    M Maranco, AK Tyagi, M Sivakumar
    International Conference on Intelligent Systems Design and Applications, 414-424 , 2023
    2023
    Citations: 1
  • Improvised multi-factor authentication for end-user security in cyber physical system
    M Maranco, V Manikandan
    2023
    Citations: 7
  • NOVEL APPROACH FOR END USER SECURITY ACCESS IN CYBER PHYSICAL SYSTEM USING MULTILAYER AUTHENTICATION
    M Maranco, V Manikandan
    Indian Journal of Computer Science and Engineering (IJCSE) 13 (4) , 2022
    2022
    Citations: 1

MOST CITED SCHOLAR PUBLICATIONS

  • Data analytics and artificial intelligence for predictive maintenance in manufacturing
    M Sivakumar, M Maranco, N Krishnaraj
    Data Analytics and Artificial Intelligence for Predictive Maintenance in … , 2024
    2024
    Citations: 16
  • Data Analytics and Visualization in Smart Manufacturing Using AI‐Based Digital Twins
    M Sivakumar, M Maranco, N Krishnaraj, US Reddy
    Artificial Intelligence‐Enabled Digital Twin for Smart Manufacturing, 249-277 , 2024
    2024
    Citations: 9
  • Digital Twin‐Enabled Smart Manufacturing: Challenges and Future Directions
    M Maranco, M Sivakumar, N Krishnaraj, KA Ivaturi, R Nidhya
    Artificial Intelligence‐Enabled Digital Twin for Smart Manufacturing, 479-504 , 2024
    2024
    Citations: 8
  • Improvised multi-factor authentication for end-user security in cyber physical system
    M Maranco, V Manikandan
    2023
    Citations: 7
  • Cyber Security for Intelligent Transportation Systems Protecting Critical Infrastructure
    M Maranco, M Sivakumar, N Krishnaraj, P Savaridassan, K AbhiramIvaturi, ...
    Urban Mobility and Challenges of Intelligent Transportation Systems, 403-420 , 2025
    2025
    Citations: 3
  • Enhancing V2X Communication With Edge Computing for Real-Time Intelligent Transportation Systems
    A Vatsa, N Krishnaraj, P Savaridassan, M Maranco, M Sivakumar
    Urban Mobility and Challenges of Intelligent Transportation Systems, 209-228 , 2025
    2025
    Citations: 2
  • Leveraging big data to drive smarter and sustainable transportation policies
    S Murugaiyan, K Nagappan, P Savaridassan, M Maranco
    Urban Mobility and Challenges of Intelligent Transportation Systems, 327-348 , 2025
    2025
    Citations: 2
  • Improving biometric security using pulseprint: real-time defense against fingerprint spoofing
    P Vigneshwaran, M Maranco
    2024 2nd International Conference on Networking and Communications (ICNWC), 1-7 , 2024
    2024
    Citations: 2
  • 17 Case Studies on Securing Healthcare 5.0 with Fuzzy
    M Sivakumar, N Krishnaraj, M Maranco, P Savaridassan, S Jayaraja
    Healthcare 5.0 with Fuzzy Logic: Artificial Intelligence, Cyber-Physical … , 2025
    2025
    Citations: 1
  • Data-Driven Strategies for Energy Forecasting in Sustainable Supply Chain Management
    S Murugaiyan, K Nagappan, P Savaridassan, M Maranco
    Urban Mobility and Challenges of Intelligent Transportation Systems, 443-460 , 2025
    2025
    Citations: 1
  • 240 Data Analytics and Visualization in Smart Manufacturing Using AI-based Digital Twins
    M Sivakumar, M Maranco, N Krishnaraj, U Srinivasulu Reddy
    Data Analytics and Artificial Intelligence for Predictive Maintenance in … , 2024
    2024
    Citations: 1
  • Improved Wild Horse Optimizer with Deep Learning Model for Skin Lesion Detection and Classification on Dermoscopic Images
    M Maranco, AK Tyagi, M Sivakumar
    International Conference on Intelligent Systems Design and Applications, 414-424 , 2023
    2023
    Citations: 1
  • NOVEL APPROACH FOR END USER SECURITY ACCESS IN CYBER PHYSICAL SYSTEM USING MULTILAYER AUTHENTICATION
    M Maranco, V Manikandan
    Indian Journal of Computer Science and Engineering (IJCSE) 13 (4) , 2022
    2022
    Citations: 1
  • Robot Co-Workers
    M Sivakumar, N Krishnaraj, P Savaridassan, M Maranco
    Industrial Robotics in Smart Manufacturing , 2026
    2026
  • 13 Adaptive Multi-layer Authentication for Secure Internet of Medical Things (IoMT)
    M. Maranco, N. Krishnaraj, M. Sivakumar, P. Savaridassan, R Logeshwari, ...
    Healthcare 5.0 with Fuzzy Logic: Artificial Intelligence, Cyber-Physical … , 2025
    2025
  • 8 Securing AI and Machine Learning Models in Healthcare with Fuzzy
    M Sivakumar, N Krishnaraj, M Maranco, P Savaridassan, US Reddy
    Healthcare 5.0 with Fuzzy Logic: Artificial Intelligence, Cyber-Physical … , 2025
    2025
  • 15 Fuzzy-Based Multidimensional Risk Assessment Model for the Healthcare 5.0
    P Savaridassan, N Krishnaraj, M Maranco, M Sivakumar
    Healthcare 5.0 with Fuzzy Logic: Artificial Intelligence, Cyber-Physical … , 2025
    2025
  • 3 Data Analytics and
    M Sivakumar, M Maranco, N Krishnaraj
    Data Analytics and Artificial Intelligence for Predictive Maintenance in … , 2024
    2024
  • Intense Triad Defender for End-User Security in Cyber Physical System
    M Maranco, R Nidhya, M Sivakumar
    2024 2nd International Conference on Networking and Communications (ICNWC), 1-7 , 2024
    2024
  • Smart System for Vechicle Number Plate Recognition Using Convolutional Neural Network(CNN)
    R Nidhya, R Kalpana, R Sudhakar, M Maranco, G Smilarubavathy
    2023 1st International Conference on Optimization Techniques for Learning … , 2024
    2024