Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Sampathrajan, Rajeshkumar
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Wicked node detection in wireless ad-hoc network by applying supervised learning Ranganathan, Chitra Sabapathy; Sampathrajan, Rajeshkumar
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4120-4127

Abstract

A wireless ad-hoc network (WANET) is a decentralized network supported by wireless connections without a pre-existing architecture. However, the mobility of nodes is a defining characteristic of WANETs, and the speed with which nodes may act poses several security risks. As a result of these wicked nodes, more data packets are lost, which might cause a significant delay. Thus, it is very important to identify wicked nodes in WANET. This work provides a support vector machine approach for detecting (SVMD) wicked nodes in the internet of things. The number of characteristics is reduced using the linear correlation coefficient (LCC) technique. With the LCC technique, we can precisely measure the strength of the connection between any two nodes while clearing the field of irrelevant information. Further, the support vector machine (SVM) algorithm may identify the wicked nodes by analyzing metrics such as the packet received ratio, packet delay ratio, and remaining energy ratio. The next step is to reach a verdict in which the wicked nodes are punished by being rendered inoperable. The simulation results show that the network latency is minimized, and the chance of missing detection is decreased using this method in WANET.
Chaotic ant colony algorithm to control congestion and enhance opportunistic routing in multimedia network Ranganathan, Chitra Sabapathy; Sampathrajan, Rajeshkumar
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7448

Abstract

The creation of wireless multimedia networks imposed wireless devices that can retrieve multimedia material such as video and audio streams, still photos, and scalar sensor data from the environment is made possible by the availability of low-cost devices. This approach considers the issues of routing packets across a multi-hop network consisting of several traffic sources and links when ensuring bounded delay. The exits of an obstacle create several geographic routing issues, for example, congestion and delay. This article, chaotic ant colony algorithm (CACA) to control congestion and enhance opportunistic routing (CAOR) in multimedia network, is proposed to solve these issues. This mechanism uses the CACA algorithm to detect the obstacle and transmit the data packets on the obstacle edges optimal nodes. Moreover, an opportunistic routing (OR) selects the best forwarder by the forward aware factor (FAF) from the forwarder list (FL). The FAF measures node energy, node received signal strength indication (RSSI), available bandwidth (AB), and packet transmission rate for choosing the best forwarder. Experimental outcomes demonstrate that established delay, energy utilization, and throughput performances are greater than the conventional mechanism.
Convolutional neural network based encoder-decoder for efficient real-time object detection Rajasekaran, Mothiram; Sabapathy Ranganathan, Chitra; Mohankumar, Nagarajan; Sampathrajan, Rajeshkumar; Merlin Inbamalar, Thayalagaran; Nandhini, Nageshvaran; Sujatha, Shanmugam
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp1960-1967

Abstract

Convolutional neural networks (CNN) are applied to a variety of computer vision problems, such as object recognition, image classification, semantic segmentation, and many others. One of the most important and difficult issues in computer vision, object detection, has attracted a lot of attention lately. Object detection validating the occurrence of the object in the picture or video and then properly locating it for recognition. However, under certain circumstances, such as when an item has issues like occlusion, distortion, or small size, there may still be subpar detection performance. This work aims to propose an efficient deep learning model with CNN and encoder decoder for efficient object detection. The proposed model is experimented on Microsoft Common Objects in Context (MS-COCO) dataset and achieved mean average precision (mAP) of about 54.1% and accuracy of 99%. The investigational outcomes amply showed that the suggested mechanism could achieve a high detection efficiency compared with the existing techniques and needed little computational resources.
Co-Authors Merlin Inbamalar, Thayalagaran Mohankumar, Nagarajan Nandhini, Nageshvaran Rajasekaran, Mothiram Ranganathan, Chitra Sabapathy Sabapathy Ranganathan, Chitra Sujatha, Shanmugam