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All Journal IAES International Journal of Robotics and Automation (IJRA) Indonesian Journal of Electrical Engineering and Computer Science
Raja, D. R. Kumar
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Automotive Engineering Electrical & Electronics Engineering

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Efficiently tracking and recognition of human faces in real-time video stream with high accuracy and performance Khan, Imran Ulla; Raja, D. R. Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1261-1268

Abstract

Real time tracking and recognition of human faces in video streams is a critical challenge in computer vision. Existing systems often struggle to balance accuracy and performance, particularly in dynamic environments with varying lighting conditions, occlusions, and rapid movements. High computational overhead and latency further hinder their deployment in realworld applications. These limitations underscore the need for a robust solution capable of maintaining high accuracy and real-time efficiency under diverse conditions. This research addresses these challenges by developing a deep learning-based system that efficiently tracks and recognizes human faces in real-time video streams. Proposed system integrates advanced face detection models you only look once version 5 (YOLOv5) with state-of-theart tracking algorithms, such as deep simple online and real time tracking (SORT), to ensure consistency and robustness. By leveraging graphics processing unit (GPU) acceleration, the system achieves optimal performance while minimizing latency. Multi-frame analysis techniques are incorporated to enhance accuracy in detecting and recognizing faces, even under challenging conditions such as partial occlusions and motion blur. Developed system has broad applications across multiple domains, including surveillance and security, where it can enhance real-time monitoring in crowded environments for seamless face tracking in interactive systems. By focusing on efficiency, robustness, and adaptability this work offering a scalable and high-performance solution for real-time human face tracking and recognition.
Adaptive deep learning framework for multi-scale plant disease detection Gadag, Tejashwini C.; Raja, D. R. Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1976-1989

Abstract

Plant disease detection is a critical task in modern agriculture, directly impacting crop yield, food security, and sustainable farming practices. Traditional methods rely on expert visual inspection, which is time-consuming, inconsistent, and inaccessible in remote areas. This study introduces an advanced deep learning (DL) framework, the adaptive multi-scale convolutional network (AMS-ConvNet), optimized for accurate and efficient plant disease identification. hierarchical feature extraction network (HFEN) integrates the multi-domain attention framework (MDAF) and adaptive scale fusion module (ASFM) to enhance feature extraction and address challenges such as complex natural backgrounds, non-uniform leaf structures, and varying environmental conditions. The proposed framework employs pre-trained knowledge adaptation (PTKA) techniques to improve generalization and overcome data scarcity. Comprehensive evaluations on multiple datasets demonstrate the model's better performance, achieving state-of-the-art metrics in precision, recall, F1-score, and accuracy. Furthermore, this approach ensures scalability and adaptability, making it suitable for real-field conditions. The study emphasizes the importance of robust, automated solutions in minimizing crop losses, reducing labor costs, and enhancing agricultural sustainability through precision disease management.
IntelliDrive autonomous robot powered by large language model Khan, Imran Ulla; Raja, D. R. Kumar
IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v14i3.pp339-347

Abstract

The rapid advancements in artificial intelligence (AI) and robotics have paved the way for innovative autonomous systems capable of performing complex tasks. This project integrates robotics with Large Language Models (LLMs) to develop an intelligent, versatile and user-friendly robotic system. The robot is designed to interpret structured commands, make real-time decisions, and navigate autonomously in dynamic environments, addressing key challenges faced by traditional autonomous systems. Central to the system is a Raspberry Pi 4, which serves as the main processing unit, integrating components such as a webcam for visual data capture, an L298N motor driver for motor control, and a Bluetooth speaker for real-time feedback. The LLM API enables the robot to process natural language commands, providing context-aware task execution and adaptability to changing scenarios. Testing has demonstrated the system’s ability to perform autonomous navigation, detect obstacles, and execute tasks effectively. This research offers a foundation for various industries, including logistics, healthcare, education, and hazardous environment operations. By incorporating LLMs the robot overcomes limitations of traditional rule-based systems, enhancing dynamic decision-making and user interaction. With its modular design and scalability, it bridges the gap between human-like intelligence and mechanical precision, setting the stage for future advancements in AI-driven robotics.
Co-Authors Gadag, Tejashwini C. Khan, Imran Ulla