Rajkumar Kalimuthu

@gnithyd.ac.in

Associate Professor/ Department of CSE (Cyber Security)
Guru Nanak Institute of Technology

Rajkumar Kalimuthu


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https://researchid.co/rajkumarengg2020

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Computer Science, Computer Networks and Communications, Software
12

Scopus Publications

167

Scholar Citations

5

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • Detecting hidden communication threats in cloud systems using advanced pattern and threat propagation analysis
    Gavini Sreelatha, Tan Kuan Tak, Rajkumar Kalimuthu, Pravin R. Kshirsagar, Balajee Maram, T. Venkatakrishnamoorthy
    Journal of Cloud Computing, 2025
    Cloud environments enable scalable multi-tenant computing but introduce security risks like covert channels, making their detection and classification essential for maintaining cloud security. Initially, covert channels exploit shared resource dynamics to mimic normal workload behavior, allowing malicious data transmission to go unnoticed by standard security measures. In addition, covert channels embed signals in encrypted or obfuscated traffic through multi-layered encryption and protocol tunneling, creating inherent noise that attackers exploit to sustain covert communication channels quietly. Hence, to tackle theses drawbacks, a Fourier-Warp Entropic Reinforcement Graph Detector is introduced, combining the Fourier-Warp Convolutional IsoForest Graph Detector and Adaptive Entropic Reinforcement Graph Transformer. This integrated system analyzes temporal and spatial workload patterns, detects abnormal timing behaviors, and identifies hidden communications. It then adaptively refines decisions, ensuring reliable distinction between normal workload fluctuations and covert activity, even when communications are encrypted or obfuscated. Thus, the model learns and classifies diverse covert threats with high precision by mapping inter-tenant relationships, delivering robust and adaptive protection for multi-tenant cloud environments. This method improves cloud security by constantly learning and neutralizing covert threats, achieving high accuracy, recall, and low detection errors, while significantly reducing RMSE.
  • Design of A Multi-Constraint PSO for Resource Allocation and Task Scheduling
    International Journal of Intelligent Systems and Applications in Engineering, 2024
  • Enhance Energy Efficiency based Automobile Wheels by using Piezoelectric Energy Harvesting Technology and Sustainable Power Generation
    Pradeep Surya Dadi, Meera R, K. Rajkumar, Surendran R
    Proceedings of the 2nd IEEE International Conference on Networking and Communications 2024 Icnwc 2024, 2024
    This paper investigates the feasibility of integrating piezoelectric energy harvesting technology into automobile wheels to improve energy efficiency and promote sustainable power generation. By strategically placing piezoelectric sensors around the circumference of the wheel, the rotational motion and pressure from the road are utilized to generate electricity. Through comprehensive analysis and simulations, optimal sensor placement, resonant frequency, and impedance are examined to maximize energy harvesting performance. The study evaluates the power output, current, and voltage at common Indian road speeds of 60 km / h and 80 km / h, considering the efficiency of a charge pump energy harvesting system. The results demonstrate significant potential for power generation, contributing to the development of self-powered automotive systems, and advancing sustainable energy solutions in the automotive industry.
  • Comparative Analysis of Machine Learning Algorithms and Datasets for Detecting Cyberbullying on Social Media Platforms
    Anusha Palagati, Santhosh Kumar Balan, S Arun Joe Babulo, Laxmi Raja, K.K. Natarajan, Rajkumar Kalimuthu
    International Conference on Computing and Intelligent Reality Technologies Proceedings of Iccirt 2024, 2024
    Cyberbullying is defined as sharing of undesirable material in the cyberspace, primarily- the use of the social sites that instigate hostility and create feelings of hatred among the individuals. Another increasing threat due to increase use of internet-based communication is cyberbullying especially on the site such as Twitter. Different ML and NLP approaches including support vector machine (SVM), Naϊve bayes, Random Forest, and logistic regression is employed to detect cyberbul Bullivating. These methods for supervised learning mine patterns in text and image, allowing computers to promptly flag risky interactions. Some of these approaches also utilizes OCR for the identification of image-based bullying. Consequently, the objective of this research is to identify the correct algorithm and dataset for improving the identification of cyberbullying and solving the problem that manifests itself in the steady increase in cases of this type of bullying.
  • A Stochastic Weighted Model for Task Scheduling and Resource Utilization in the Cloud
    Rajkumar Kalimuthu, Brindha Thomas
    Lecture Notes in Networks and Systems, 2023
  • Deep Learning-Based Diabetic Retinopathy Screening System
    Rajkumar Kalimuthu, Limbika Zangazanga, S. Jayanthi, Ignatius A. Herman
    Lecture Notes in Networks and Systems, 2023
  • A Single-Layer S/X- Band Shared Aperture Antenna with MIMO Characteristics at X-Band for Airborne Synthetic Aperture Radar Applications
    Raja Babu Bandi, Venkata Kishore Kothapudi, Lakshman Pappula, Rajkumar Kalimuthu, Srinivasa Kiran Gottapu
    International Journal of Antennas and Propagation, 2023
    New frequencies can be supported with very effective space use by using the shared aperture antenna This work presents on designing a dual-banddual-polarized (DBDP) S/X-band shared aperture antenna (SAA) for synthetic aperture radar (SAR) applications operating at S-band frequency (3.2 GHz) and X-band frequency (9.65 GHz). The single-layer SAA DBDP S-band antenna is designed in a square-shaped patch with coaxial feeding in both vertical and horizontal polarization. The X-band antenna design is in 1 × 3 vertical series with microstrip feeding and arranged at four corners of the proposed antenna. The S-band antenna is mainly used for airborne applications such as air traffic control and surface ship radar. In contrast, the X-band antenna application is maritime vessel traffic control, defense tracking, and vehicle speed detection for law enforcement. To verify the antenna, a prototype is fabricated and measured with s-parameters. The proposed design exhibits that the gain of the S-band is 7.2 dB and for the X-band is 12.4 dB, and the isolation is achieved more than −35 dB, and for this antenna, we achieved a bandwidth of 0.12 GHz for S-band and 0.27 GHz for X-band. However, the X-band antenna is a multi-input and multioutput antenna that is to be validated by using MIMO characteristic parameters such as envelope correlation coefficient (ECC), diversity gain (DG), channel capacity loss (CCL), mean effective gain, and mutual coupling. The MIMO characteristic parameter of X-band antenna values is found to be in a similar manner to both simulated and measured values. For this X-Band antenna, ECC, DG, CCL, and mutual coupling were achieved as below 0.05, 9.5 dB, 0.5 bps/Hz, and −30 dB to −55 dB, respectively. The total size of the antenna is 100 mm × 100 mm × 1.6 mm.
  • An effective multi-objective task scheduling and resource optimization in cloud environment using hybridized metaheuristic algorithm
    Raj Kumar Kalimuthu, Brindha Thomas
    Journal of Intelligent and Fuzzy Systems, 2022
    In today’s world, cloud computing plays a significant role in the development of an effective computing paradigm that adds more benefits to the modern Internet of Things (IoT) frameworks. However, cloud resources are considered to be dynamic and the demands necessitated for resource allocation for a certain task are different. These diverse factors may cause load and power imbalance which also affect the resource utilization and task scheduling in the cloud-based IoT environment. Recently, a bio-inspired algorithm can work effectually to solve task scheduling problems in the cloud-based IoT system. Therefore, this work focuses on efficient task scheduling and resource allocation through a novel Hybrid Bio-Inspired algorithm with the hybridized of Improvised Particle Swarm Optimization and Ant Colony Optimization. The vital objective of hybridizing these two approaches is to determine the nearest multiple sources to attain discrete and continuous solutions. Here, the task has been allocated to the virtual machine through a particle swarm and continuous resource management can be carried out by an ant colony. The performance of the proposed approach has been evaluated using the CloudSim simulator. The simulation results manifest that the proposed Hybridized algorithm efficiently scheduling the task in the cloud-based IoT environment with a lesser average response time of 2.18 sec and average waiting time of 3.6 sec as compared with existing state-of-the-art algorithms.
  • Quantum Behaved Particle Swarm Optimization-Based Deep Transfer Learning Model for Sugarcane Leaf Disease Detection and Classification
    T. Tamilvizhi, R. Surendran, K. Anbazhagan, K. Rajkumar
    Mathematical Problems in Engineering, 2022
    Plant diseases pose a major challenge in the agricultural sector, which affects plant development and crop productivity. Sugarcane farming is a highly organized part of farming. Owing to the desirable condition for sugarcane cultivation, India stands among the second largest producers of sugarcane over the globe. At the same time, sugarcane gets easily affected by multifarious diseases which significantly influence crop productivity. The recently developed computer vision (CV) and deep learning (DL) models with an effective design can be employed for the detection and classification of diseases in sugarcane plant. The disease detection in sugarcane plant is not accurate in the existing techniques. This paper presents a quantum behaved particle swarm optimization based deep transfer learning (QBPSO-DTL) model for sugarcane leaf disease detection and classification which produces high accuracy. The proposed QBPSO-DTL method is designed and trained for the prediction of diseased leaf images. The proposed QBPSO-DTL technique encompasses the design of optimal region growing segmentation to determine the affected regions in the leaf image. In addition, the SqueezeNet model is employed as a feature extractor and the deep stacked autoencoder (DSAE) model is applied as a classification model. Finally, the hyperparameter tuning of the DSAE model is carried out by using the QBPSO algorithm. For demonstrating the enhanced outcomes of the QBPSO-DTL approach, a wide range of experiments were implemented and the results ensured the improvements of the QBPSO-DTL model.
  • Design and Development of Framework for Big Data Based Smart Farming System
    S. Jayanthi, K. Rajkumar, Shaheen, Sanjeev Shrivastava, Ignatius A. Herman
    Lecture Notes in Networks and Systems, 2022
  • Task scheduling in cloud using resistive and ranking meta-heuristic optimization approach
    Rajkumar Kalimuthu, Brindha Thomas
    Indian Journal of Computer Science and Engineering, 2021
  • Design and development of machine learning model for osteoarthritis identification
    Naidu Srinivas Kiran Babu, E. Madhusudhana Reddy, S. Jayanthi, K. Rajkumar
    Lecture Notes in Networks and Systems, 2021

RECENT SCHOLAR PUBLICATIONS

  • Detecting hidden communication threats in cloud systems using advanced pattern and threat propagation analysis
    G Sreelatha, TK Tak, R Kalimuthu, PR Kshirsagar, B Maram, ...
    Journal of Cloud Computing 14 (1), 55 , 2025
    2025
    Citations: 2
  • Comparative Analysis of Machine Learning Algorithms and Datasets for Detecting Cyberbullying on Social Media Platforms
    A Palagati, SK Balan, SAJ Babulo, L Raja, KK Natarajan, R Kalimuthu
    2024 International Conference on Computing and Intelligent Reality … , 2024
    2024
  • Enhance Energy Efficiency based Automobile Wheels by using Piezoelectric Energy Harvesting Technology and Sustainable Power Generation
    PS Dadi, R Meera, K Rajkumar, R Surendran
    2024 2nd International Conference on Networking and Communications (ICNWC), 1-9 , 2024
    2024
    Citations: 9
  • Design of a multi-constraint PSO for resource allocation and task scheduling
    R Kalimuthu, B Thomas
    International Journal of Intelligent Systems and Applications in Engineering … , 2024
    2024
    Citations: 6
  • Artificial intelligences with Algorithms
    KRRRS Nandini
    https://amzn.eu/d/07oKsof 1, 158 , 2023
    2023
  • A Single‐Layer S/X‐Band Shared Aperture Antenna with MIMO Characteristics at X‐Band for Airborne Synthetic Aperture Radar Applications
    RB Bandi, VK Kothapudi, L Pappula, R Kalimuthu, SK Gottapu
    International Journal of Antennas and Propagation 2023 (1), 1384388 , 2023
    2023
    Citations: 8
  • Deep Learning-Based Diabetic Retinopathy Screening System
    R Kalimuthu, L Zangazanga, S Jayanthi, IA Herman
    International Conference on Innovations in Computer Science and Engineering … , 2022
    2022
  • A Stochastic Weighted Model for Task Scheduling and Resource Utilization in the Cloud
    R Kalimuthu, B Thomas
    International Conference on Innovations in Computer Science and Engineering … , 2022
    2022
  • Optimal Capacity Utilization in Mobile Ad hoc Network with Adaptive Contention Window Management Scheme
    B Surjeet, N Gupta, AS Teles, A Alkhayyat, R Kalimuthu
    2022
    Citations: 1
  • Design and development of framework for big data based smart farming system
    S Jayanthi, K Rajkumar, Shaheen, S Shrivastava, IA Herman
    Innovations in Computer Science and Engineering: Proceedings of the Ninth … , 2022
    2022
    Citations: 3
  • An effective multi-objective task scheduling and resource optimization in cloud environment using hybridized metaheuristic algorithm
    RK Kalimuthu, B Thomas
    Journal of Intelligent & Fuzzy Systems 42 (4), 4051-4063 , 2022
    2022
    Citations: 11
  • Quantum Behaved Particle Swarm Optimization‐Based Deep Transfer Learning Model for Sugarcane Leaf Disease Detection and Classification
    T Tamilvizhi, R Surendran, K Anbazhagan, K Rajkumar
    Mathematical Problems in Engineering 2022 (1), 3452413 , 2022
    2022
    Citations: 121
  • Artificial Intelligence, IoT and their Future on Aviation Safety
    RKZ Njolomole
    Journal of Information Technology & Software Engineering , 2021
    2021
    Citations: 2
  • Crime Detection Analysis Using Public Camera
    R Kalimuthu
    Journal of Innovation Computer Science Engineering 10 (2) , 2021
    2021
  • E-Crypto Learning and Applying Data Mining in Education Sector to Improve Learner’s Performance
    EC Mwakamo, R Kalimuthu
    Int. J. Sci. Res. in Multidisciplinary Studies Vol 7 (5) , 2021
    2021
  • Resource Renting for Periodic Workflow Application
    R Maganga, RK Kalimuthu
    2021
  • Realtime Online Product Bidding System using decentralized Peer to Peer network
    PB Nkhanie, R Kalimuthu
    2021
    Citations: 1
  • Android Phone Based Smart Video Surveillance System
    D Sauli, R Kalimuthu
    International Journal of Advanced Technology & Science Research 2 (5) , 2021
    2021
  • Design and Development of Machine Learning Model for Osteoarthritis Identification
    NSK Babu, EM Reddy, S Jayanthi, K Rajkumar
    Innovations in Computer Science and Engineering: Proceedings of 8th ICICSE … , 2021
    2021
  • Blockchain Based Public Integrity Verification For Cloud Storage Against Procrastinating Auditors
    S Phillimon, R Kalimuthu
    2021

MOST CITED SCHOLAR PUBLICATIONS

  • Quantum Behaved Particle Swarm Optimization‐Based Deep Transfer Learning Model for Sugarcane Leaf Disease Detection and Classification
    T Tamilvizhi, R Surendran, K Anbazhagan, K Rajkumar
    Mathematical Problems in Engineering 2022 (1), 3452413 , 2022
    2022
    Citations: 121
  • An effective multi-objective task scheduling and resource optimization in cloud environment using hybridized metaheuristic algorithm
    RK Kalimuthu, B Thomas
    Journal of Intelligent & Fuzzy Systems 42 (4), 4051-4063 , 2022
    2022
    Citations: 11
  • Enhance Energy Efficiency based Automobile Wheels by using Piezoelectric Energy Harvesting Technology and Sustainable Power Generation
    PS Dadi, R Meera, K Rajkumar, R Surendran
    2024 2nd International Conference on Networking and Communications (ICNWC), 1-9 , 2024
    2024
    Citations: 9
  • A Single‐Layer S/X‐Band Shared Aperture Antenna with MIMO Characteristics at X‐Band for Airborne Synthetic Aperture Radar Applications
    RB Bandi, VK Kothapudi, L Pappula, R Kalimuthu, SK Gottapu
    International Journal of Antennas and Propagation 2023 (1), 1384388 , 2023
    2023
    Citations: 8
  • Design of a multi-constraint PSO for resource allocation and task scheduling
    R Kalimuthu, B Thomas
    International Journal of Intelligent Systems and Applications in Engineering … , 2024
    2024
    Citations: 6
  • Design and development of framework for big data based smart farming system
    S Jayanthi, K Rajkumar, Shaheen, S Shrivastava, IA Herman
    Innovations in Computer Science and Engineering: Proceedings of the Ninth … , 2022
    2022
    Citations: 3
  • A Cloud Based Android System for Reporting Crimes Against Child Sexual Abuse
    VE Chinoko
    International Journal Of Computer Communication And Informatics , 2021
    2021
    Citations: 3
  • Detecting hidden communication threats in cloud systems using advanced pattern and threat propagation analysis
    G Sreelatha, TK Tak, R Kalimuthu, PR Kshirsagar, B Maram, ...
    Journal of Cloud Computing 14 (1), 55 , 2025
    2025
    Citations: 2
  • Artificial Intelligence, IoT and their Future on Aviation Safety
    RKZ Njolomole
    Journal of Information Technology & Software Engineering , 2021
    2021
    Citations: 2
  • Optimal Capacity Utilization in Mobile Ad hoc Network with Adaptive Contention Window Management Scheme
    B Surjeet, N Gupta, AS Teles, A Alkhayyat, R Kalimuthu
    2022
    Citations: 1
  • Realtime Online Product Bidding System using decentralized Peer to Peer network
    PB Nkhanie, R Kalimuthu
    2021
    Citations: 1
  • Comparative Analysis of Machine Learning Algorithms and Datasets for Detecting Cyberbullying on Social Media Platforms
    A Palagati, SK Balan, SAJ Babulo, L Raja, KK Natarajan, R Kalimuthu
    2024 International Conference on Computing and Intelligent Reality … , 2024
    2024
  • Artificial intelligences with Algorithms
    KRRRS Nandini
    https://amzn.eu/d/07oKsof 1, 158 , 2023
    2023
  • Deep Learning-Based Diabetic Retinopathy Screening System
    R Kalimuthu, L Zangazanga, S Jayanthi, IA Herman
    International Conference on Innovations in Computer Science and Engineering … , 2022
    2022
  • A Stochastic Weighted Model for Task Scheduling and Resource Utilization in the Cloud
    R Kalimuthu, B Thomas
    International Conference on Innovations in Computer Science and Engineering … , 2022
    2022
  • Crime Detection Analysis Using Public Camera
    R Kalimuthu
    Journal of Innovation Computer Science Engineering 10 (2) , 2021
    2021
  • E-Crypto Learning and Applying Data Mining in Education Sector to Improve Learner’s Performance
    EC Mwakamo, R Kalimuthu
    Int. J. Sci. Res. in Multidisciplinary Studies Vol 7 (5) , 2021
    2021
  • Resource Renting for Periodic Workflow Application
    R Maganga, RK Kalimuthu
    2021
  • Android Phone Based Smart Video Surveillance System
    D Sauli, R Kalimuthu
    International Journal of Advanced Technology & Science Research 2 (5) , 2021
    2021
  • Design and Development of Machine Learning Model for Osteoarthritis Identification
    NSK Babu, EM Reddy, S Jayanthi, K Rajkumar
    Innovations in Computer Science and Engineering: Proceedings of 8th ICICSE … , 2021
    2021