GJETR JOURNAL

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The Global Journal of Engineering and Technology Research (GJETR) p-ISSN 3051-3774 e-ISSN 3051-3782 is a peer-reviewed international journal dedicated to the advancement of research and innovation across diverse domains of engineering and technology. The journal provides a dynamic platform for academicians, researchers, and industry professionals to present their original research findings, technical advancements, and practical applications that shape the future of technology.

GJETR promotes interdisciplinary research and aims to bridge the gap between theoretical development and practical implementation. By publishing high-quality, original papers, the journal fosters innovation, critical thinking, and knowledge dissemination in the field of engineering and applied technology.

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Dimitrios Zavantis

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Nilesh Uke

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Development of AI-Based Real-Time Traffic Monitoring System for Smart Cities

Global Journal of Engineering and Technology Research, 2025

Urban centers across the globe are grappling with the growing challenge of traffic congestion, ve... more Urban centers across the globe are grappling with the growing challenge of traffic congestion, vehicular emissions, and inefficient road usage. As cities expand and vehicle numbers continue to rise, conventional traffic monitoring systems are proving increasingly inadequate. They are often reactive, manually managed, and lack the dynamic adaptability to respond to real-time traffic fluctuations. In the context of the smart city framework, there is a pressing need for intelligent, autonomous systems that can actively monitor, analyze, and optimize traffic flows using real-time data. This paper presents a comprehensive approach to the development of an AI-based real-time traffic monitoring system tailored specifically for the requirements of modern smart cities. Using a combination of machine learning algorithms, deep learning object detection frameworks, edge computing, and IoT integration, this system can detect vehicle counts, classify vehicle types, identify congestion hotspots, and detect road anomalies such as stalled vehicles or accidents. The system architecture is designed for scalability, with an emphasis on low-latency processing and userfriendly dashboard visualization. Experimental deployments demonstrate a significant improvement in traffic management efficiency, situational awareness, and response coordination. The proposed system is not only technically robust but also aligned with sustainable urban mobility goals.

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Performance Analysis of Green Concrete Using Industrial Waste: A Sustainable Approach to Civil Engineering

Global Journal of Engineering and Technology Research, 2025

The global construction industry has long been a double-edged sword-on one side, it supports soci... more The global construction industry has long been a double-edged sword-on one side, it supports societal advancement and economic growth, and on the other, it contributes substantially to environmental degradation. Traditional concrete production methods consume vast quantities of natural resources and are responsible for a large percentage of global greenhouse gas emissions. This

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Optimizing Renewable Energy Integration in Microgrids Using Hybrid Energy Storage Systems

Global Journal of Engineering and Technology Research, 2025

The rapid acceleration of climate change and the global shift towards sustainable energy systems ... more The rapid acceleration of climate change and the global shift towards sustainable energy systems have catalyzed the integration of renewable energy sources (RES) into decentralized microgrid architectures. However, the inherent intermittency of solar, wind, and other green energy sources poses significant challenges to maintaining power quality, frequency stability, and load reliability in real-time operations. These limitations necessitate advanced storage strategies capable of adapting to the dynamic behavior of distributed generation. This research paper delves into the optimization of renewable energy integration using Hybrid Energy Storage Systems (HESS), a combination of two or more energy storage technologies that synergize to offset the weaknesses of individual systems. In this study, lithium-ion batteries and supercapacitors are integrated within a smart microgrid model to analyze their performance in various operational scenarios. Through simulation models developed in MATLAB/Simulink and an in-depth case study in a remote Indian village, the research demonstrates that HESS outperforms conventional single-source storage systems in terms of energy efficiency, cost-effectiveness, and grid stability. The paper concludes with recommendations for scaling HESS in future energy infrastructures.

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A Comparative Study of Machine Learning Algorithms for Predictive Maintenance in Manufacturing Systems

Global Journal of Engineering and Technology Research

The global manufacturing sector is rapidly transitioning towards smarter, datadriven operations t... more The global manufacturing sector is rapidly transitioning towards smarter, datadriven operations to enhance productivity, reduce operational costs, and improve overall equipment effectiveness. Predictive Maintenance (PdM) stands out as one of the most transformative approaches within this paradigm. By anticipating equipment failures before they occur, PdM ensures that maintenance is carried out only when required, leading to optimal utilization of resources. Machine learning (ML) techniques form the core of PdM, leveraging data to detect patterns and anomalies that precede system failures. This paper presents a comprehensive comparative study of leading ML algorithms-both traditional and deep learning-based-deployed for PdM in manufacturing systems. Algorithms such as Decision Trees, Random Forests, Support Vector Machines, K-Nearest Neighbors, Multilayer Perceptrons, Long Short-Term Memory (LSTM) networks, and Convolutional Neural Networks (CNNs) are rigorously evaluated on standardized datasets. Each model is analyzed in terms of prediction accuracy, interpretability, computational efficiency, and suitability for deployment in industrial settings. The results reveal key trade-offs and offer recommendations tailored to different types of manufacturing environments. The paper also discusses the current challenges in PdM adoption and offers insights into future research directions, including the integration of explainable AI and real-time edge computing solutions.

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Design and Implementation of IoT-Based Smart Water Management System for Urban Sustainability

Global Journal of Engineering and Technology Research, 2025

Urban areas today are grappling with the challenges of water scarcity, erratic distribution, and ... more Urban areas today are grappling with the challenges of water scarcity, erratic distribution, and deteriorating infrastructure, exacerbated by rapid population growth and the unpredictable impacts of climate change. In this context, sustainable water management has become a critical pillar of smart urban governance. Traditional systems, being largely reactive, suffer from inefficiencies due to delayed response times, manual monitoring, and lack of data-driven decision-making. The Internet of Things (IoT) introduces a transformative paradigm by enabling continuous, real-time sensing, analysis, and control of water infrastructure. This research paper proposes an IoT-based Smart Water Management System (SWMS) that integrates sensor networks, wireless data transmission, cloud computing, and intelligent automation to address the inefficiencies in urban water management. The design focuses on reducing water loss, detecting anomalies like leakages or unauthorized usage, ensuring water quality, and providing real-time data analytics to stakeholders. A prototype model was built and tested in a simulated urban setup to evaluate its performance. The results demonstrated a significant improvement in operational efficiency, achieving a 28% reduction in water wastage and faster response to anomalies. The study highlights the potential of the SWMS model to be scaled for large urban deployments and offers recommendations for municipal bodies seeking sustainable water governance.

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