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645 articles for “traditional methods”
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Balancing Quality Assurance and Scalability in Modern Open-Source Software: A Review
Abstract: The Modern Open-Source software development has increased rapidly and now is being used as critical infrastructure across the world. As the software expands maintaining high quality while supporting large scale has become the major challenge and tension globally. Traditional quality assurance methods are no longer sufficient when open-source software(oss) projects are handling with millions of users, developers and downloads. This review discusses four main issues: sustainability of package registers when …
Published in Journal of Open Source Developments · Vol. 13, Issue 1, 2026 Read article
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Intelligent Electromagnetic Synthesis: An AI-Driven IoT Framework for Adaptive Antenna Design in Missile Navigation
Abstract: The rapid evolution of hypersonic and long-range tactical missile systems necessitates antenna architecture capable of maintaining robust communication links under extreme thermal, mechanical, and signal-jamming environments. Traditional antenna design methodologies often relying on iterative simulation cycles and static optimization are increasingly insufficient for the real-time requirements of modern aerospace navigation. This paper proposes an AI-driven, IoT- integrated framework that facilitates autonomous antenna design and performance optimization. By deploying a distributed …
Published in International Journal of Radio Frequency Innovations · Vol. 4, Issue 1, 2026 Read article
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Deep Learning-Based Thermal Prediction Models for Solid-State Electronic Devices
Abstract: The rapid advancement of solid-state electronic devices in high-performance computing, communication systems, automotive electronics, and renewable energy applications has significantly increased concerns related to thermal management and device reliability. Excessive heat generation in semiconductor devices adversely affects operational efficiency, switching performance, lifespan, and overall system stability. Traditional thermal prediction methods often require complex numerical computations and extensive simulation time, making them less suitable for real-time monitoring and adaptive control applications. …
Published in International Journal of Solid State Innovations & Research · Vol. 4, Issue 1, 2026 Read article
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QR Based Plant Care System
Abstract: The QR Based Plant Care System is an innovative digital solution developed to improve plant monitoring, maintenance, and information management through the integration of QR code technology with smart agricultural practices. Traditional plant care methods mainly depend on manual record keeping, handwritten labels, and human observation, which often result in data loss, inconsistency, and inefficient plant management. With the increasing demand for sustainable agriculture and efficient resource utilization, there is …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 15, Issue 2, 2026 · pp. 32–40 Read article
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Develop the Design of Sustainable Polymer Materials: Applying Reinforcement Learning, IoT-Enabled Monitoring, and Data-Driven Manufacturing Approaches
Abstract: Sustainable polymer materials development is a must due to resource constraints, environmental concerns, and the demand for designed materials with high performance. When it comes to material optimization, energy utilization, process unpredictability, and lifecycle sustainability, traditional polymer production methods have their challenges. Reinforcement Learning (RL), Internet of Things (IoT) monitoring, and data-driven production are utilized in the design and manufacturing of sustainable polymer materials. It is recommended to use Internet …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 Read article
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Entomo-Analytics: Insect Behavioral Intelligence for Climate-Smart Environmental Monitoring Systems
Abstract: Rapid environmental change driven by climate variability, urbanization, and ecological degradation has intensified the need for innovative monitoring systems capable of providing real-time ecological intelligence. Traditional environmental monitoring methods often rely on satellite imaging and stationary sensors, which may lack fine-scale biological sensitivity. In contrast, insects—due to their abundance, ecological diversity, and rapid responsiveness to environmental shifts—offer a powerful yet underutilized source of bio-sensing data. This paper introduces the concept …
Published in Research and Reviews : Journal of Crop science and Technology · Vol. 15, Issue 2, 2026 · pp. 17–26 Read article
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Garbage Classifier Using Arduino
Abstract: In today's era, where prioritizing sustainable methods of waste disposal is crucial, a pioneering initiative titled "Garbage Collection using Arduino Nano " stands out as a revolutionary approach to refining the traditional, labor-intensive methods of household waste collection. Utilizing the Arduino Nano microcontroller, this project introduces a sophisticated waste management system equipped with diverse sensors and mechanisms. Through the automation of waste collection, this avant-garde system not only enhances efficiency …
Published in Journal of Mechatronics and Automation · Vol. 11, Issue 1, 2024 · pp. 18–23 Read article
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Enhancing Production Line Efficiency: Simulating and Optimizing Single and Parallel Line Processes
Abstract: During a time of fast-paced industrial growth, increasing production line effectiveness is a core issue for manufacturers looking to maximize output, reduce waste, and stay competitive. This study explores the use of simulation-based optimization methods to enhance single and parallel production line designs. Stepping beyond traditional trial-and-error methods, the research utilizes Siemens Tecnomatix Plant Simulation to simulate actual manufacturing scenarios, considering intricacies like buffer capacities, machine sequencing, and event-driven scheduling. …
Published in Journal of Production Research & Management · Vol. 15, Issue 1, 2025 · pp. 22–32 Read article
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From Differential Equations to Data Science: A Survey on Analytical Methods in Contemporary Problems
Abstract: The integration of differential equations and data science methods represents a dynamic and evolving approach to solving contemporary challenges across a wide range of disciplines, including engineering, physics, biology, economics, and finance. Differential equations have long served as fundamental tools for modeling continuous systems and processes, offering powerful insights into the behavior of natural and man-made phenomena. For example, they describe how heat diffuses through materials, how populations grow in …
Published in Recent Trends in Mathematics · Vol. 2, Issue 2, 2025 · pp. 1–6 Read article
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Analysis and Identification of Malicious Mobile Applications Using Machines Learning
Abstract: Over the past few years, malware attacks on the Android platform have surged, posing significant risks to users' financial security, personal information, and device integrity. In the first half of 2019 alone, approximately 25 million smartphones were infected, highlighting the severity of these threats. The model ranks manifest features based on their frequency in normal and malicious apps, identifying key components that distinguish benign from malicious applications. To improve detection …
Published in Journal of Microcontroller Engineering and Applications · Vol. 12, Issue 2, 2025 · pp. 17–24 Read article
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Application of Convolutional Neural Networks in Design of Efficient Pipe Flow System
Abstract: Convolutional Neural Networks exhibit remarkable capabilities in flow pattern recognition, pressure drop prediction, leak detection, and system optimization through their ability to process complex spatial and temporal data patterns. The study examines CNN architectures specifically adapted for fluid dynamics applications, including data preprocessing techniques, feature extraction methods, and performance optimization strategies. Key applications include real-time flow monitoring, predictive maintenance, design parameter optimization, and anomaly detection in pipe networks. Comparative analysis …
Published in Recent Trends in Fluid Mechanics · Vol. 12, Issue 3, 2025 · pp. 1–9 Read article
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Revolutionizing Optical Fibre Field Distribution with Linear Finite Element Method
Abstract: This study investigates the use of the linear Finite Element Method (FEM) for the analysis of the field distribution in optical fibres. Understanding the distribution of electric and magnetic fields is necessary to characterise fibre properties including mode profiles, propagation constants, and dispersion, all of which are essential for optimising fibre performance in a range of applications like sensing and telecommunications. This study emphasises on applying a linear FEM formulation …
Published in Trends in Opto-electro & Optical Communication · Vol. 15, Issue 3, 2025 · pp. 32–42 Read article
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Flame-Retardant Polymeric Materials: Recent Advances and Future Directions
Abstract: This review explores essential flame-retardant polymers that play a vital role in various industries such as electrical & electronics, automobile, manufacturing, and firefighting. This review also highlights the latest progress in the design and synthesis of flame-retardant polymers, highlighting novel approaches such as the incorporation of nanomaterials, bio-based flame retardants, and the use of intumescent systems. Recent advancements include high-throughput screening, computational design, bio-based and sustainable flame retardants, self-healing materials, …
Published in Journal of Polymer & Composites · Vol. 12, Issue 5, 2024 · pp. 125–132 Read article
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Recent Advances in Content-based Image Retrieval: Techniques and Applications
Abstract: Content-based image retrieval (CBIR) plays a vital role in computer vision, driven by the increasing need for fast and accurate image retrieval across fields like healthcare, e-commerce, and digital libraries. This study offers a detailed review of CBIR methodologies, charting their progression from traditional feature extraction techniques, such as Local Binary Patterns (LBP), to contemporary deep learning-driven methods. The transformative impact of convolution neural networks (CNNs) is highlighted, emphasizing their …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 1, 2025 · pp. 67–71 Read article
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Keyless Lock Based on Internet of Things
Abstract: In recent years, there has been a growing interest in smart home technologies, with smart door locks being one of the focal points due to their potential to enhance security and convenience. Smart door lock systems are transforming access control by offering a keyless alternative for both residential and commercial settings. These electronic locks replace traditional keys with secure methods such as emergency alarms, privacy modes, battery backup, cameras, voice …
Published in Journal Of Network security · Vol. 12, Issue 2, 2024 · pp. 18–21 Read article
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Use of Artificial Intelligence to Access and Ensure Safe Drinking Water Supply: A Review
Abstract: Ensuring access to safe drinking water is a critical public health challenge. Traditional water quality assessment methods are often labor-intensive and time-consuming. Artificial intelligence offers a promising alternative, providing rapid, accurate, and scalable solutions for monitoring and predicting water quality. This systematic review examines the application of AI. The review highlights various AI models, including artificial neural networks, support vector machines, decision trees, and ensemble methods, in predicting water quality …
Published in Journal of Water Resource Engineering and Management · Vol. 11, Issue 2, 2024 · pp. 21–28 Read article
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Biosynthesis of Zinc Oxide Nanoparticles and Their Revolutionary Impacts on Agroindustry: Review
Abstract: Nanotechnology is a new approach of science which provides alternative tool in many fields including agriculture. This comprehensive review focuses on the biosynthesis and characterization of zinc oxide nanoparticles (ZnO NPs), highlighting their vast potential in agroindustry applications. Unlike traditional physical and chemical methods, which are often costly, time-consuming, and involve hazardous materials, this review explores the promising biological synthesis routes, offering a more sustainable and eco-friendly alternative for producing …
Published in Nano Trends – A Journal of Nano Technology & Its Applications · Vol. 26, Issue 2, 2024 · pp. 33–50 Read article
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Resolving International Sports Disputes: An Exploration of Mediation and Conciliation
Abstract: The globalization of sports has led to an increase in cross-border disputes, necessitating effective and efficient dispute resolution mechanisms. Traditional litigation and arbitration methods often prove time-consuming, costly, and ineffective in preserving relationships. This research explores the role of Alternative Dispute Resolution (ADR) methods, specifically mediation and conciliation, in resolving international sports disputes. Through a comparative analysis of international sports organizations and dispute resolution mechanisms, this study examines the benefits …
Published in Recent Trends in Sports · Vol. 1, Issue 2, 2024 · pp. 14–27 Read article
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Adaptive Traffic Control Systems: Enhancing Urban Mobility through Real-Time Traffic Management
Abstract: Traffic congestion is a ubiquitous challenge in urban areas, necessitating innovative solutions to improve transportation efficiency and alleviate gridlock. Traditional traffic signal control methods often prove inadequate in dynamically adapting to fluctuating traffic conditions, leading to increased travel times, fuel consumption, and emissions. In response, adaptive traffic control systems have emerged as a promising approach to mitigate congestion and enhance traffic flow in urban environments. These devices dynamically modify signal …
Published in International Journal of Electrical and Communication Engineering Technology · Vol. 2, Issue 2, 2024 · pp. 37–45 Read article
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Timestamp Extraction and Log Classification Using Supervised Machine Learning: A Comparative Study
Abstract: In modern software systems, logs are vital for monitoring application behavior, diagnosing issues, and analyzing performance. Timestamps are especially important for sequencing events, identifying anomalies, and understanding system failures. However, detecting timestamps in logs is challenging due to inconsistent formatting across systems and the presence of timestamp-like strings in non-timestamp fields. Traditional rule-based methods often fail in such cases. This study proposes a supervised machine learning approach to accurately classify …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 12, Issue 3, 2025 · pp. 26–38 Read article