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) Indonesian Journal of Electrical Engineering and Computer Science
Rajgopal, Manjunath
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 
Efficient network management and security in 5G enabled internet of things using deep learning algorithms Poojari Thippeswamy, Sowmya Naik; Raghavan, Ambika Padinjareveedu; Rajgopal, Manjunath; Sujith, Annie
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp1058-1070

Abstract

The rise of fifth generation (5G) networks and the proliferation of internet-of-things (IoT) devices have created new opportunities for innovation and increased connectivity. However, this growth has also brought forth several challenges related to network management and security. Based on the review of literature it has been identified that majority of existing research work are limited to either addressing the network management issue or security concerns. In this paper, the proposed work has presented an integrated framework to address both network management and security concerns in 5G internet-of-things (IoT) network using a deep learning algorithm. Firstly, a joint approach of attention mechanism and long short-term memory (LSTM) model is proposed to forecast network traffic and optimization of network resources in a, service-based and user-oriented manner. The second contribution is development of reliable network attack detection system using autoencoder mechanism. Finally, a contextual model of 5G-IoT is discussed to demonstrate the scope of the proposed models quantifying the network behavior to drive predictive decision making in network resources and attack detection with performance guarantees. The experiments are conducted with respect to various statistical error analysis and other performance indicators to assess prediction capability of both traffic forecasting and attack detection model.
Cluster based water leakage detection frame work for the improvement of water management using WSN Shivashankar, Shivashankar; Rajgopal, Manjunath; Karani, Krishna Prasad; Basavaraju, Nandeeswar Sampigehalli; Giddappa, Erappa; Swamy, Shivakumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp603-612

Abstract

Water is a vital resource that is essential for human survival and economic development. In any case, water shortage and wastage have become significant difficulties that undermine economical turn of distribution network. A critical reason for water wastage is water leakage in the distribution system, which prompts an extensive loss of water assets and energy. Conventional manual techniques for identifying water leakage are tedious, work serious, and frequently ineffectual. Hence, there is a need for an automated system that can efficiently detect, control and monitor water leakage to improve water management. In this paper, cluster-based water leakage detection (CBWLAD) algorithm for the improvement of water management using wireless sensor network (WSN) is proposed. The design system contains sensor nodes which are conveyed all through the water distribution networks and associated with central control unit. The sensor nodes can distinguish changes in the water pressure and flow rate, which are indicative of water leakage. The real time monitoring feature also enables timely maintenance and repair of the network of water distribution prolongs the lifespan of infrastructure. Further, the research and development are required to optimize the system’s performance and adapt it to real-world scenarios.
Blockchain and machine learning driven agricultural transformation framework to enhance efficiency, transparency, and sustainability Shivashankar, Shivashankar; Prasad Karani, Krishna; Rajgopal, Manjunath; Totad, Sarala; Giddappa, Erappa; Swamy, Shivakumar
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.pp1976-1988

Abstract

The agricultural sector is undergoing a transformative journey empowered by technological innovations. In this context, this research work endeavors to revolutionize the agricultural supply chain (ASC) by developing a comprehensive online platform that connects sellers, farmers, and customers. Through meticulous planning, design, and implementation, the system aims to streamline the process of buying and selling agricultural products, thereby fostering efficiency, transparency and accessibility. The key features include user registration, product management, order tracking, and blockchain-machine learning (ML) based transaction security. The proposed research work's success hinges on thorough testing and validation, ensuring its reliability and usability. By leveraging technology to bridge gaps in the agricultural ecosystem, this proposed work seeks to empower stakeholders and contribute to the sustainable growth of the agricultural industry. In the current agricultural landscape in India, traceability has been a significant challenge. The industry lacks a comprehensive system that provides visibility into the source and quality of produce. Our proposed system aims to address the shortcomings of the existing agricultural ecosystem by introducing a comprehensive solution powered by blockchain technology and advanced data processing techniques.
Co-Authors Basavaraju, Nandeeswar Sampigehalli Giddappa, Erappa Karani, Krishna Prasad Poojari Thippeswamy, Sowmya Naik Prasad Karani, Krishna Raghavan, Ambika Padinjareveedu Shivashankar, Shivashankar Sujith, Annie Swamy, Shivakumar Totad, Sarala