Rajkumar Kalimuthu

@gnithyd.ac.in

Assistant Professor/ Department of Information Technology
Guru Nanak Institute of Technology

https://researchid.co/rajkumarengg2020

RESEARCH, TEACHING, or OTHER INTERESTS

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

Scopus Publications

106

Scholar Citations

Scholar h-index

Scholar i10-index

Scopus Publications

Enhance Energy Efficiency based Automobile Wheels by using Piezoelectric Energy Harvesting Technology and Sustainable Power Generation
Pradeep Surya Dadi, Meera R, K. Rajkumar, and Surendran R
IEEE
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.

Design of A Multi-Constraint PSO for Resource Allocation and Task Scheduling

A Stochastic Weighted Model for Task Scheduling and Resource Utilization in the Cloud
Rajkumar Kalimuthu and Brindha Thomas
Springer Nature Singapore

Deep Learning-Based Diabetic Retinopathy Screening System
Rajkumar Kalimuthu, Limbika Zangazanga, S. Jayanthi, and Ignatius A. Herman
Springer Nature Singapore

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, and Srinivasa Kiran Gottapu
Hindawi Limited
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.

Quantum Behaved Particle Swarm Optimization-Based Deep Transfer Learning Model for Sugarcane Leaf Disease Detection and Classification
T. Tamilvizhi, R. Surendran, K. Anbazhagan, and K. Rajkumar
Hindawi Limited
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, and Ignatius A. Herman
Springer Singapore

An effective multi-objective task scheduling and resource optimization in cloud environment using hybridized metaheuristic algorithm
Raj Kumar Kalimuthu and Brindha Thomas
IOS Press
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.

Task scheduling in cloud using resistive and ranking meta-heuristic optimization approach
Rajkumar Kalimuthu and Brindha Thomas
ENGG Journals Publications
Cloud computing (CC) is an emergent and revolutionary technological paradigm to facilitate data generation over the networking environment. It is determined as the computing edge where it provides cloud services over the networking point. It assists in handling the delay issues during the task scheduling process. With the advent analysis over the existing approaches, inappropriate task scheduling in cloud computing outcomes in huge delay than the other computing models. Therefore, the actual advantages of cloud computing are attained with the adoption of appropriate task scheduling strategies. However, it is an NP-hard issue and needs effectual and optimal approaches to dealing with resource utilization, response time, and latency at the networking model. This research concentrates on modelling an efficient hybrid optimization approach to overcome the drawbacks of single standard optimization approaches. This work hybridizes the resistive-based Particle swarm Optimization (cid:4666)ℎ𝑟𝑃𝑆𝑂 ) approach with Improved ranking-based Grey wolf optimization ( 𝑖𝑟 (cid:3398) 𝐺𝑊𝑂 ) approach to handle the exploration and exploitation issues over the standard methods. Here, ℎ𝑟 (cid:3398) 𝑃𝑆𝑂 is used to schedule the task among the connected devices, and 𝑖𝑟 (cid:3398) 𝐺𝑊𝑂 helps manage the resources at the device level. The resources are allocated and managed based on the demand generated with the incoming requests in the anticipated model. The ultimate target of this work is to diminish the delay, average response time, and optimal resource utilization by efficiently scheduling the tasks by managing the available resources. The simulation is done with the MATLAB 2016b simulator. The evaluation results show that the anticipated model provides promising outcomes with energy consumption, average response time, execution time, etc. The proposed model shows a better trade-off in contrast to prevailing approaches.

Design and development of machine learning model for osteoarthritis identification
Naidu Srinivas Kiran Babu, E. Madhusudhana Reddy, S. Jayanthi, and K. Rajkumar
Springer Singapore

RECENT SCHOLAR PUBLICATIONS

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

Design of a multi-constraint PSO for resource allocation and task scheduling
R Kalimuthu, B Thomas
Int. J. Intell. Syst. Appl. Eng. 12 (7s), 426-440 2024

Artificial intelligences with Algorithms
KRRRS Nandini
https://amzn.eu/d/07oKsof 1, 158 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

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

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

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

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

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

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

Artificial Intelligence, IoT and their Future on Aviation Safety
RKZ Njolomole
Journal of Information Technology & Software Engineering 2021

Crime Detection Analysis Using Public Camera
R Kalimuthu
Journal of Innovation Computer Science Engineering 10 (2) 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

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

Android Phone Based Smart Video Surveillance System
D Sauli, R Kalimuthu
International Journal of Advanced Technology & Science Research 2 (5) 2021

Blockchain Based Public Integrity Verification For Cloud Storage Against Procrastinating Auditors
S Phillimon, R Kalimuthu
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

A Cloud Based Android System for Reporting Crimes Against Child Sexual Abuse
VE Chinoko, R Kalimuthu, P Macheso
International Journal Of Computer Communication And Informatics 3 (2), 84-93 2021

Predicting user movement frequent patterns in mobile service environment
K Rajkumar
Journal of Innovation in Computer Science and Engineering 10 (1), 1-6 2020

MOST CITED SCHOLAR PUBLICATIONS

Predicting user movement frequent patterns in mobile service environment
K Rajkumar
Journal of Innovation in Computer Science and Engineering 10 (1), 1-6 2020
Citations: 4

Design of a multi-constraint PSO for resource allocation and task scheduling
R Kalimuthu, B Thomas
Int. J. Intell. Syst. Appl. Eng. 12 (7s), 426-440 2024
Citations: 3

A Cloud Based Android System for Reporting Crimes Against Child Sexual Abuse
VE Chinoko, R Kalimuthu, P Macheso
International Journal Of Computer Communication And Informatics 3 (2), 84-93 2021
Citations: 3

Artificial Intelligence, IoT and their Future on Aviation Safety
RKZ Njolomole
Journal of Information Technology & Software Engineering 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