Article Per Year (5 Year)
p-Index From 2021 - 2026
1.554
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)
Srikantaswamy, Mallikarjunaswamy
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 9 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : IAES International Journal of Artificial Intelligence (IJ-AI)

 1 
An efficient load-balancing in machine learning-based DC-DC conversion using renewable energy resources Shankara, Kavitha Hosakote; Hosakote Shankara, Kavitha; Srikantaswamy, Mallikarjunaswamy; Nagaraju, Sharmila
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp307-316

Abstract

This paper introduces the machine learning-based DC-DC conversion algorithm (ML-DC2A), a pioneering machine learning (ML) approach designed to enhance load-balancing in DC-DC conversion systems powered by renewable energy sources. Traditional control strategies, such as pulse-width modulation (PWM), maximum power point tracking (MPPT), and basic voltage and current controls, are foundational yet often fall short in adapting to the rapid fluctuation’s characteristic of renewable energy supply. The ML-DC2A optimizes crucial performance indicators including conversion efficiency, reliability, adaptability to energy supply variability, and response time to changing loads. By leveraging predictive analytics and adaptive algorithms, it dynamically manages the conversion process, offering superior performance over traditional techniques. A notable drawback of conventional methods is their inability to anticipate and adjust to real-time changes in energy availability and demand, leading to inefficiencies and potential system instability. The proposed ML-DC2A addresses these challenges by incorporating a sophisticated ML framework that predicts future energy scenarios and adaptively adjusts system parameters to maintain optimal performance. Initial results highlight the transformative potential of integrating ML into renewable energy conversion systems, promising significantly enhanced efficiency and system resilience, thus marking a significant step forward in sustainable energy management.
Efficient reconfigurable parallel switching for low-density parity-check encoding and decoding Venkatesh, Divyashree Yamadur; Mallikarjunaiah, Komala; Srikantaswamy, Mallikarjunaswamy
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp260-269

Abstract

In the evolution of next-generation communication systems, the demand for higher data integrity and transmission efficiency has brought low-density parity-check (LDPC) codes into focus, particularly for their error-correcting prowess. Traditional LDPC encoding and decoding techniques, such as the belief propagation (BP), Min-Sum, and Sum-Product algorithms, are hampered by high computational complexity and latency. Our research introduces a groundbreaking approach: an efficient, reconfigurable highspeed parallel switching operation for a complexity-optimized low-density parity-check encoding and decoding model (CoLDPC-EC). This method leverages advanced parallel processing and reconfigurable computing to drastically enhance operational speed and efficiency. It significantly outperforms conventional algorithms by optimizing key parameters like decoding throughput and power consumption, ensuring swift, energy-efficient error correction ideal for cutting-edge communication technologies. Our comparison with traditional methods underscores our solution's superior speed, flexibility, and efficiency, promising a leap forward in reliable, highspeed data transmission for next-generation networks. As per the simulation analysis, the proposed system shows better performance compared to conventional methods by 10.35%, 3.56%, and 2.36% in terms of decoding throughput, power consumption, and energy efficiency error correction, respectively.
Efficient reduction of computational complexity in video surveillance using hybrid machine learning for event recognition Honnegowda, Jyothi; Mallikarjunaiah, Komala; Srikantaswamy, Mallikarjunaswamy
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp317-326

Abstract

This paper addresses the challenge of high computational complexity in video surveillance systems by proposing an efficient model that integrates hybrid machine learning algorithms (HML) for event recognition. Conventional surveillance methods struggle with processing vast amounts of video data in real-time, leading to scalability, and performance issues. Our proposed approach utilizes convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to enhance the accuracy and efficiency of detecting events. By comparing our model with conventional surveillance techniques motion detection, background subtraction, and frame differencing. We demonstrate significant improvements in frame processing time, object detection speed, energy efficiency, and anomaly detection accuracy. The integration of dynamic model scaling and edge computing further optimizes computational resource usage, making our method a scalable and effective solution for real-time surveillance needs. This research highlights the potential of machine learning to revolutionize video surveillance, offering insights into developing more intelligent and responsive security systems. The results of your simulation analysis, indicating performance improvements in accuracy by 0.25%, 0.35%, and 0.45% for the motion detection algorithm, background subtraction, and frame differencing respectively, and in real-time data processing by 5.65%, 4.45%, and 6.75% for the motion detection algorithm, background subtraction, and frame differencing respectively, highlight the potential of machine learning to transform video surveillance into a more intelligent and responsive system.
Co-Authors Chikkasiddaiah, Chandana Goravi Sukumar, Pavithra Govindaswamy, Parthasarathy Gowda Puttaswamy, Nandini Honnegowda, Jyothi Hosakote Shankara, Kavitha Kanathur Ramaswamy, Nataraj Kanathur Ramaswamy, Rekha Krishnaiah, Modugu Mallikarjunaiah, Komala Nagaraju, Sharmila Pandith, Mamatha M. Ramaswamy, Nataraj Kanathur Ramaswamy, Rekha Kanathur Sathyanarayana, Rekha Satish, Pooja Shankara, Kavitha Hosakote Thazeen, Sadiya Velluri, Rekha Venkatesh, Divyashree Yamadur