PREMKUMAR N

@kongunadu.ac.in

Associate Professor and Information Technology
kongunadu College of Engineering and Technology



              

https://researchid.co/prem4research

EDUCATION

M.E. , Computer Science and Engineering

RESEARCH INTERESTS

Fog Computing, Edge Computing, Cloud Computing, Software Engineering, Wireless Networks

10

Scopus Publications

30

Scholar Citations

4

Scholar h-index

Scopus Publications

  • Lightweight Secure Authentication Scheme to thwart Unauthorized Edge Datacenters in Fog Computing
    N. Premkumar and B. Santhosh Kumar
    Springer Science and Business Media LLC

  • Snake swarm optimization-based deep reinforcement learning for resource allocation in edge computing environment
    S. Kaliraj, V. Sivakumar, N. Premkumar, and S. Vatchala
    Wiley
    SummaryMobile edge computing has become popular in the past few years as a means of creating computing resources close to end‐user nodes at the network edge. Nodes—end users—demand work offloading to improve service utilization. Furthermore, when the number of users in mobile edge computing increases, the minimal resources deployed at the edge become a problem. Develop the idea of reinforcement learning using a metaheuristic technique intended to achieve effective resource allocation and resolve offloading issues to handle this issue. The ideal way to manage the implementation of mobile edge computing with a cognitive agent's help is to request compensation for all client necessities. To complete the infrastructure type for the Internet of Things (IoT), the operator information is combined with its distinctive methodology. Neural caching during task execution is provided by reinforcement learning based on snake swarm optimization (SSO). Neural caching during task execution is provided by reinforcement learning based on SSO. In the process of creating the cost mapping table and incentive factor‐based optimal resource allocation, this suggested method is applied to a contract with effective resource allocation among the end manipulators. Using performance metrics like delivery ratio, energy consumption, throughput, and delay, the suggested approach is put into practice and examined. It is also contrasted with traditional methods like Gray Wolf Optimization (GWO) ant colony optimization (ACO) and genetic algorithms (GA).



  • Pelican optimization algorithm with blockchain for secure load balancing in fog computing
    N. Premkumar and R. Santhosh
    Springer Science and Business Media LLC

  • Enhanced subtraction-average-based optimizer and blockchain for security and load balancing in fog computing
    N. Premkumar, S. Sridharan, R. V. Viswanathan, and N. Magendiran
    Springer Science and Business Media LLC

  • Automatic software bug prediction using adaptive golden eagle optimizer with deep learning
    R. Siva, Kaliraj S, B. Hariharan, and N. Premkumar
    Springer Science and Business Media LLC
    AbstractIn the software maintenance and development process, the software bug detection is an essential problem because it related with the complete software successes. So, the earlier software bug detection is essential to enhance the software efficiency, reliability, software quality and software cost. Moreover, the efficient software bug prediction is a critical as well as challenging operation. Hence, the efficient software bug prediction model is developed in this article. To achieve this objective, optimized long short-term memory is developed. The important stages of the proposed model is preprocessing, feature selection and bug detection. At first the input bug dataset is preprocessed. In preprocessing, the duplicate data instances are removed from the dataset. After the preprocessing, the feature selection is done by Adaptive Golden Eagle Optimizer (AGEO). Here the traditional GEO algorithm is altered by means of opposition-based learning (OBL). Finally, the proposed approach utilizes a long short-term memory (LSTM) based recurrent neural network (RNN) for bug prediction. Long Short-Term Memory (LSTM) network is a type of recurrent neural network. The promise and NASA dataset are considered as the input for bug prediction. the performance of proposed approach is analysed based on various metrics namely, accuracy, F- measure, G-measure and Matthews Correlation Coefficient (MCC).

  • Automatic Software Bug Prediction Using Adaptive Artificial Jelly Optimization With Long Short-Term Memory
    R. Siva, Kaliraj S, B. Hariharan, and N. Premkumar
    Springer Science and Business Media LLC
    AbstractIn the software maintenance and development process, software bug detection is an essential problem because it is related to complete software success. It is recommended to begin anticipating defects at the early stages of creation rather than during the assessment process due to the high expense of fixing the found bugs. The early stage software bug detection is used to enhance software efficiency, reliability, and software quality. Nevertheless, creating a reliable bug-forecasting system is a difficult challenge. Therefore, in this paper, an efficient, software bug forecast is developed. The presented technique consists of three stages namely, pre-processing, feature selection, and bug prediction. At first, the input datasets are pre-processed to eliminate the identical data from the dataset. After the pre-processing, the important features are selected using an adaptive artificial jelly optimization algorithm (A2JO) to eliminate the possibility of overfitting and reduce the complexity. Finally, the selected features are given to the long short-term memory (LSTM) classifier to predict whether the given data is defective or non-defective. In this paper, investigations are shown on visibly obtainable bug prediction datasets namely, promise and NASA which is a repository for most open-source software. The efficiency of the presented approach is discussed based on various metrics namely, accuracy, F- measure, G-measure, and Matthews Correlation Coefficient (MCC). The experimental result shows our proposed method achieved the extreme accuracy of 93.41% for the Promise dataset and 92.8% for the NASA dataset.


  • Secured and efficient data transmission in manets against malicious attack using DSR routing and BCS clustering with hybrid AES-ECC cryptanalysis
    G. Murugesan*, , Dr.M. Padmaa, K. Nagarajan, N. Premkumar, , , and
    Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP
    Mobile Ad-Hoc Network (MANET) is a self-configuring network of movable nodes linked by wireless creating a random topology. The nodes are free to move randomly. Thus, the networks wireless topology may be haphazard and may alter rapidly. The efficient route is established using Dynamic source routing (DSR) Routing Protocol. The Binary crow search (BCS) algorithm is used for clustering of sensor nodes and maintaining load balancing in an efficient way. Efficient black hole detection using Malicious Node Detection Mechanism-TX/RX (MNS-TX/RX) with optimized routing algorithm is implemented in a secure environment by using Advanced Hybrid Advanced Encryption Standard (AES) cryptanalysis and Elliptic Curve Cryptosystems. Thus “DSR-BCS-HAES-EEC-MANETs” algorithm has precisely detected the black hole node and finds the proper solution for transmitting data for maintaining lifetime and Load- balancing by analyzing performance such as Through-put, routing overhead, packet delivery ratio(PDR), drop, delay and energy consumption in a secure environment.

RECENT SCHOLAR PUBLICATIONS

  • Enhanced subtraction-average-based optimizer and blockchain for security and load balancing in fog computing
    N Premkumar, S Sridharan, RV Viswanathan, N Magendiran
    Wireless Networks, 1-13 2024

  • Lightweight Secure Authentication Scheme to thwart Unauthorized Edge Datacenters in Fog Computing
    N Premkumar, BS Kumar
    Wireless Personal Communications 139 (1), 167-181 2024

  • Secure authentication scheme with Archimedes optimization algorithm for load balancing technique in fog computing
    N Premkumar, R Santhosh
    International Journal of Information Technology 16 (6), 3861-3869 2024

  • Snake swarm optimization-based deep reinforcement learning for resource allocation in edge computing environment
    SV S. Kaliraj, V. Sivakumar, N. Premkumar
    Concurrency and Computation: Practice and Experience 8, 1-15 2024

  • Secure Load Balancing in Fog Computing Using improved Tasmanian Devil Optimization Algorithm with Blockchain
    N Premkumar, R Santhosh
    Wireless Personal Communications 136 (1), 547-565 2024

  • Pelican optimization algorithm with blockchain for secure load balancing in fog computing
    N Premkumar, R Santhosh
    Multimedia Tools and Applications 83 (18), 53417-53439 2024

  • Automatic software bug prediction using adaptive golden eagle optimizer with deep learning
    R Siva, K S, B Hariharan, N Premkumar
    Multimedia Tools and Applications 83 (1), 1261-1281 2024

  • Automatic Software Bug Prediction Using Adaptive Artificial Jelly Optimization With Long Short-Term Memory
    R Siva, B Hariharan, N Premkumar
    Wireless Personal Communications, 1-24 2023

  • Picture Of Data Warehouse Portrayed With Thought For Challenges, Modernization, And Improvement Of A Conventional Data Warehouse, As Well As Its Possible Future Perspectives
    GD Saxena, DNRE Parmod, DS David, ST Kumbhar, N Premkumar
    Journal of Pharmaceutical Negative Results, 8244-8254 2022

  • Challenges and issues of E-health applications in cloud and fog computing environment
    N Premkumar, R Santhosh
    Mobile Computing and Sustainable Informatics: Proceedings of ICMCSI 2021 2022

  • Secured and Efficient Data Transmission in Manets Against Malicious Attack using DSR Routing and BCS Clustering with Hybrid AES-ECC Cryptanalysis
    G Murugesan, M Padmaa, K Nagarajan, N Premkumar
    International Journal of Innovative Technology and Exploring Engineering 8 2019

  • Maximizing the Network Lifetime by using Mobile Data Gathering in Wireless Sensor Networks
    MK S.Vanitha, N.PremKumar, A.Kanimozhi
    SSRG International Journal of Computer Science and Engineering 4 (10), 15 2017

  • Reality Exposure of Package Reducing Attacks in Networks
    NP E.Vinodha
    International Journal of Innovative Research in Computer and Communication 2016

  • Public auditing and user revocation in dynamic cloud environment
    RC Nandhini S, Premkumar N
    Discovery Engineering 4 (14), 500 2016

  • The Novel Life Cycle Model for Component Based Software System Based on Architecture Quality Using KCW Framework
    S Kaliraj, N Premkumar, A Bharathi
    International Journal of Information Technology and Computer Science (IJITCS 2014

  • MANETs Using Advance DSR Algorithm and Improve the Secure Transmission
    PN Sivasakthi. S, Seramannan. S, Rajesh. S, Thamilselvan. S
    International Journal of Emerging Technology and Advanced Engineering 3 (7), 213 2013

  • REALITY COVERAGE OF PACKET DROPPING ATTACKS IN NETWORKS
    E Vinodha, N Premkumar

MOST CITED SCHOLAR PUBLICATIONS

  • Automatic software bug prediction using adaptive golden eagle optimizer with deep learning
    R Siva, K S, B Hariharan, N Premkumar
    Multimedia Tools and Applications 83 (1), 1261-1281 2024
    Citations: 6

  • Automatic Software Bug Prediction Using Adaptive Artificial Jelly Optimization With Long Short-Term Memory
    R Siva, B Hariharan, N Premkumar
    Wireless Personal Communications, 1-24 2023
    Citations: 6

  • Pelican optimization algorithm with blockchain for secure load balancing in fog computing
    N Premkumar, R Santhosh
    Multimedia Tools and Applications 83 (18), 53417-53439 2024
    Citations: 5

  • The Novel Life Cycle Model for Component Based Software System Based on Architecture Quality Using KCW Framework
    S Kaliraj, N Premkumar, A Bharathi
    International Journal of Information Technology and Computer Science (IJITCS 2014
    Citations: 5

  • Secure authentication scheme with Archimedes optimization algorithm for load balancing technique in fog computing
    N Premkumar, R Santhosh
    International Journal of Information Technology 16 (6), 3861-3869 2024
    Citations: 3

  • Challenges and issues of E-health applications in cloud and fog computing environment
    N Premkumar, R Santhosh
    Mobile Computing and Sustainable Informatics: Proceedings of ICMCSI 2021 2022
    Citations: 3

  • Maximizing the Network Lifetime by using Mobile Data Gathering in Wireless Sensor Networks
    MK S.Vanitha, N.PremKumar, A.Kanimozhi
    SSRG International Journal of Computer Science and Engineering 4 (10), 15 2017
    Citations: 2