M.Arvindhan

@asssitant professor

Assistant Professor
Galgotias University



                 

https://researchid.co/arvindhan

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Artificial Intelligence, Computer Science Applications, Human-Computer Interaction

26

Scopus Publications

Scopus Publications

  • Potato plant leaf diseases detection and identification using convolutional neural networks
    Sriram Gurusamy, B. Natarajan, R. Bhuvaneswari, and M. Arvindhan
    CRC Press


  • Preface



  • Adaptive Resource Allocation in Cloud Data Centers using Actor-Critical Deep Reinforcement Learning for Optimized Load Balancing
    M. Arvindhan and D. Rajesh Kumar
    Auricle Technologies, Pvt., Ltd.
    This paper proposes a deep reinforcement learning-based actor-critic method for efficient resource allocation in cloud computing. The proposed method uses an actor network to generate the allocation strategy and a critic network to evaluate the quality of the allocation. The actor and critic networks are trained using a deep reinforcement learning algorithm to optimize the allocation strategy. The proposed method is evaluated using a simulation-based experimental study, and the results show that it outperforms several existing allocation methods in terms of resource utilization, energy efficiency and overall cost. Some algorithms for managing workloads or virtual machines have been developed in previous works in an effort to reduce energy consumption; however, these solutions often fail to take into account the high dynamic nature of server states and are not implemented at a sufficiently enough scale. In order to guarantee the QoS of workloads while simultaneously lowering the computational energy consumption of physical servers, this study proposes the Actor Critic based Compute-Intensive Workload Allocation Scheme (AC-CIWAS). AC-CIWAS captures the dynamic feature of server states in a continuous manner, and considers the influence of different workloads on energy consumption, to accomplish logical task allocation. In order to determine how best to allocate workloads in terms of energy efficiency, AC-CIWAS uses a Deep Reinforcement Learning (DRL)-based Actor Critic (AC) algorithm to calculate the projected cumulative return over time. Through simulation, we see that the proposed AC-CIWAS can reduce the workload of the job scheduled with QoS assurance by around 20% decrease compared to existing baseline allocation methods. The report also covers the ways in which the proposed technology could be used in cloud computing and offers suggestions for future study.

  • The Crucial Function that Clouds Access Security Brokers Play in Ensuring the Safety of Cloud Computing
    Vidhyasagar Bs, M. Arvindhan, and Sivakumar Kalimuthu
    IEEE
    The cloud is a vast network of interconnected servers and other devices that provide computing, communication, and storage services. The cloud market is dynamic, with ever-changing user needs and resource demands. Resource availability may be impacted by changes in the market, such as the addition or removal of service providers. When it comes to policies, standards, and preferences, cloud service providers and new line users are worlds apart. Even though several CSPs provide the same services, new line Access to and pricing for these services can seem different from one provider to the next. There are many advantages to using cloud computing, such as reduced overhead due to centralization, the ability to work remotely from anywhere with an Internet connection, and increased productiv-ity. More research is needed into the cloud's privacy and security risks. To manage the degree of dimensionality, heterogeneity, and ambiguity associated with cloud services, a Cloud Access Security Broker (CASB) can be deployed between consumers of cloud services and cloud applications in a cloud computing (CC) environment. They allow the company to expand its security measures beyond its structure, into third-party software and data storage. To better understand how service users deploy CASBs to provide acceptable security solutions for cloud systems, and to figure out the elements that support or hinder the adoption of CASBs by organizations using remote computing technologies, an analysis based on themes was performed. This paper will provide an in-depth analysis of the security challenges experienced by various stakeholders in cloud computing, including cloud service providers, data owners, and cloud environments.

  • The Crucial Function that Clouds Access Security Brokers Play in Ensuring the Safety of Cloud Computing
    Vidhyasagar BS, M. Arvindhan, Arulprakash A, SB.Bharathi Kannan, and Sivakumar Kalimuthu
    IEEE
    The cloud is a vast network of interconnected servers and other devices that provide computing, communication, and storage services. The cloud market is dynamic, with ever-changing user needs and resource demands. Resource availability may be impacted by changes in the market, such as the addition or removal of service providers. When it comes to policies, standards, and preferences, cloud service providers and new line users are worlds apart. Even though several CSPs provide the same services, new line Access to and pricing for these services can seem different from one provider to the next. There are many advantages to using cloud computing, such as reduced overhead due to centralization, the ability to work remotely from anywhere with an Internet connection, and increased productivity. More research is needed into the cloud's privacy and security risks. To manage the degree of dimensionality, heterogeneity, and ambiguity associated with cloud services, a Cloud Access Security Broker (CASB) can be deployed between consumers of cloud services and cloud applications in a cloud computing (CC) environment. They allow the company to expand its security measures beyond its structure, into third-party software and data storage. To better understand how service users deploy CASBs to provide acceptable security solutions for cloud systems, and to figure out the elements that support or hinder the adoption of CASBs by organizations using remote computing technologies, an analysis based on themes was performed. This paper will provide an in-depth analysis of the security challenges experienced by various stakeholders in cloud computing, including cloud service providers, data owners, and cloud environments.

  • Comparing Techniques for Digital Handwritten Detection Using CNN and SVM Model
    M. Arvindhan, Shubham Upadhyay, Avdeep Malik, Sudeshna Chakraborty, and Kimmi Gupta
    Springer Nature Singapore

  • Potato plant leaf diseases detection and identification using convolutional neural networks
    Sriram Gurusamy, B. Natarajan, R. Bhuvaneswari, and M. Arvindhan
    CRC Press


  • A Hybrid Machine Learning Model for House Price Prediction
    B. Subbulakshmi, M. Nirmala Devi, Sriram, Srimadhi, and M. Arvindhan
    Springer Nature Singapore

  • An Innovation Development of Cost Wise Task Scheduling Model for Complex Transmission in Ultra Dense Cloud Networks
    M Arvindhan and Hitesh Singh
    IEEE
    Cost-wise task scheduling focuses on the cost associated with the completion of a task. It involves balancing the cost of completing a task against the potential benefit or value of completing the task. Cost-wise task scheduling involves determining which tasks should be completed first in order to maximize the value of the tasks in terms of cost-benefit ratio. This can involve analyzing the cost of materials and labor associated with each task, as well as considering the associated risks and rewards. Cost-wise task scheduling can help organizations to prioritize tasks and ensure that they are completed in an efficient and cost-effective manner. In this paper, a cost wise task scheduling model for complex transmission in ultra dense cloud networks. It is an model-based task scheduling model that is designed to use genetic models to set computation capacity and set task transmission time. The proposed model based on the task mapping, genetic model, and priority scheduling can effectively execute next task as soon as one task is finished.

  • Energy Minimization of Cloud Computing Data Center Strategies, Research Questions: A Survey
    Abhineet Anand, M. Arvindhan, Naresh Kumar Trivedi, Ajay Kumar, and Raj Gaurang Tiwari
    Springer Nature Singapore

  • Fog Computing & IoT Based Smart Healthcare System for Detecting Heart Related Problem
    Deep Mala, Abhineet Anand, Naresh Kumar Tiwari, and M. Arvindhan
    AIP Publishing


  • Cloud Infrastructure Fault Monitoring and Prediction System using LSTM based predictive maintenance
    Aditya Raj, Shivangi Jadon, Harsh Kulshrestha, Vipin Rai, M. Arvindhan, and Anurag Sinha
    IEEE
    By analysing the system logs, which provide us with useful information, we may identify the key information about the system or application that helps in better utilisation of system resources. It is difficult to simply look through every raw log message on a regular basis for large-scale systems, though. The two most difficult aspects of reading logs are the quantity of these unstructured log messages and linking the log messages to system activities. These restrictions limit the capacity to diagnose previously unknown system events that negatively impacted system performance as well as the ability to provide alerts or warnings on known system events. In this study, we provide a framework for a real-time system event analysis UI for log monitoring. Our UI framework offers a real-time, structured view of log events. The logs are examined on our UI interface after being processed and evaluated. Also, we have used several machine learning algorithm like LSTM for predictive maintenance in cloud fault monitoring system.


  • Early Prediction of Credit Card Transaction Using Local Outlier Factor and Isolation Forest Tree Machine Learning Algorithms
    K. P. Arjun, Godlin Atlas, N. M. Sreenarayanan, S. Janarthanan, and M. Arvindhan
    Springer Nature Singapore


  • Coronavirus Visual Dashboard and Data Repository: COVID-19
    M. Arvindhan, Mohit Bhatia, Nishant Sinha, and Tarun Kumar
    Springer Nature Singapore

  • Blockchain in Smart Healthcare Application
    Avnish Vishwakarma, Maniket Kumar, and M. Arvindhan
    Springer Nature Singapore

  • Estimation of Human Posture Using Convolutional Neural Network Using Web Architecture
    Dhruv Kumar, Abhay Kumar, M. Arvindhan, Ravi Sharma, Nalliyanna Goundar Veerappan Kousik, and S. Anbuchelian
    Springer International Publishing

  • Enabling technologies: A transforming action on healthcare with IoT a possible revolutionizing
    Prasanth Johri, M. Arvindhan, and A. Daniel
    Springer International Publishing


  • E-Construction Cart: An Online Construction Material Ordering Service
    Bishal Kumar, Kumar Sagar, M. Arvindhan, and Ankit Kr Tiwari
    IEEE
    With the recent advancement and growth in internet technology, it has become easier than before in constructing a rich ecosystem for building internet-based electronic commerce (e-commerce) applications. The development of online business has been critical when contrasted with ongoing years and it is drastically changing how organizations are working together in all areas, and the construction industry is no exception. [1] There are many web sites or portals available in the market, where we can find many vendors, selling construction materials, but there are a lot of unattested problems which need to be pulled up and rectified. This paper discusses the current problems in the online construction market ecosystem (especially considering the Indian e-construction market) and its viable solution, as well as providing the current work status and the future outcome of the related solution/project