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1939 articles for “data” (ranking capped at the first 2,000 matches — narrow the search to see the rest)
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Intelligent Traffic Monitoring: YOLO v8 and CSV Data Integration
Abstract: The “Intelligent Traffic Monitoring: YOLO v8 and CSV Data Integration” project is a cutting-edge solution for intelligent traffic monitoring, with YOLO v8 (You Only Look Once) serving as the fundamental technology for real-time vehicle detection and traffic counting on roads. In addition to these features, the system interfaces effortlessly with data pipelines and machine learning projects by storing gathered traffic data in CSV (Comma-Separated Values) format. The major goal of …
Published in International Journal of Electronics Automation · Vol. 1, Issue 2, 2023 · pp. 20–24 Read article
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Low-cost Vehicle Data Acquisition System
Abstract: The Vehicle and Driver Data Acquisition System Kit is an integrated solution designed to capture, analyze, and store essential information related to both vehicles and their drivers. This comprehensive kit incorporates cutting-edge sensors, communication modules, and data processing capabilities to provide a robust platform for real-time monitoring and post-event analysis. In the domain of electric vehicles (EVs), the need for effective data collection systems has become increasingly crucial. The current …
Published in Journal of Microwave Engineering and Technologies · Vol. 11, Issue 2, 2024 · pp. 9–14 Read article
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Data-driven Approaches to Mineral Resource Management Using AI: A Brief Review
Abstract: The role of Artificial Intelligence (AI) in the mineral resource sector has become increasingly significant over the past few years, as industries seek to optimize and modernize their operations. AI encompasses a variety of technologies and techniques, such as machine learning, deep learning, and expert systems, that are now widely used in mineral exploration, resource estimation, and mine management. These AI-driven approaches have brought about a transformative shift, enhancing efficiency, …
Published in International Journal of Minerals · Vol. 2, Issue 1, 2025 · pp. 25–29 Read article
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Viscoelastic Behavior and Wrinkle Formation in Cotton- Polyester Garments: A Data-Driven Approach for Textile Care
Abstract: This study investigates the wrinkle behavior of cotton-polyester blended fabrics by analyzing data from over 1,200 store-handled garments. Integrating concepts from polymer chemistry and computer vision, it aims to establish a smart textile care framework based on fiber-specific wrinkle characteristics. The research identifies how cotton’s hydrophilic and non-elastic structure results in increased wrinkling, while polyester’s thermoplastic and crystalline properties enhance wrinkle resistance. Elastomeric fibers like Lycra contribute to wrinkle recovery …
Published in Journal of Polymer & Composites · Vol. 13, Issue 4, 2025 · pp. 50–60 Read article
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Semantics Analysis of Expected Goals in Soccer Data Using Machine Learning
Abstract: In recent years, the increasing availability of soccer data has greatly enhanced the accuracy and depth of player performance evaluation. Soccer, being one of the most popular sports worldwide, attracts millions of fans due to its simple rules, minimal equipment requirements, and high entertainment value. However, analyzing an entire match manually can be time-consuming, leading to a growing demand for automated methods that can summarize and interpret game data efficiently. …
Published in International Journal of Data Structure Studies · Vol. 3, Issue 2, 2025 · pp. 31–47 Read article
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Predictive Maintenance in Semiconductor Systems: Insights from Machine Intelligence and Data-Driven Methods
Abstract: With the fast-paced development of semiconductor technology comes the need to focus on device reliability, or how long devices will function and the likelihood of devices having operational issues. Predicting failures and avoiding downtime with the implementation of timely, actionable, and data-driven maintenance strategies are essential to insure devices function sustainably within predetermined performance levels. The implementation of predictive maintenance within artificial intelligence and machine learning technologies will provide the …
Published in Journal of Semiconductor Devices and Circuits · Vol. 13, Issue 1, 2026 Read article
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Sustainable Supply Chain Models for Polymer and Composite Manufacturing: A Data-Driven Assessment of Circular Material Flows
Abstract: Polymer and composite manufacturing is faced with growing demands in waste reduction, resource management, and making a shift towards circular economy principles. Although urgent, the adoption of data-driven tools in each step of a supply chain to facilitate efficient cyclic material flows is low. This paper designs and empirically analyzes sustainable supply chain design in polymer and composite production with a focus on digital traceability, closed-loop and material recovery, and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 54–71 Read article
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Study on Single-Slope Solar Still for Experimental and Data-Driven Analysis for Improving Productivity with Different Basin Materials.
Abstract: This study investigates the single-slope solar still under the diurnal variation of water temperature and distillate yield under identical operating conditions. Experimental analysis was conducted to evaluate the performance enhancement through the incorporation of natural basin materials, namely hemp and sand. The water distillation process is focused on improving potable water productivity and thermal behaviour. The inclusion of hemp and sand in the basin leads to noticeable differences in productivity …
Published in Emerging Trends in Chemical Engineering · Vol. 13, Issue 2, 2026 · pp. 31–46 Read article
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Adaptive Drift Correction in Polymer-Based Wearable Biosensors via Data-Driven Signal Modeling
Abstract: Polymer-based wearable biosensors have emerged as a promising technology for continuous health monitoring due to their mechanical flexibility, biocompatibility, and suitability for long-term physiological interfacing. However, prolonged exposure to biofluids, environmental variability, and mechanical deformation introduces signal drift, which significantly degrades measurement accuracy and limits clinical reliability. This paper presents a data-driven methodology for compensating signal drift in polymer-based wearable biosensors using adaptive signal processing and machine learning techniques. The …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 131–139 Read article
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A Dynamic Text Compression Model for Big Data Applications Using Hadoop
Abstract: In today’s data-driven era, efficiently handling vast amounts of information has become increasingly important. Data compression plays a vital role in this regard — it is essentially a method of encoding information in such a way that significantly reduces the number of bits required to store or transmit a file. By shrinking data to its most compact form, compression techniques help save storage space, reduce bandwidth consumption, and improve the …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 4, Issue 2, 2026 Read article
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Energy performance analysis: An exploratory study of HVAC systems in efficient management of data centres
Abstract: Data center growth is being driven by the rapid proliferation of cloud services. Data centers are using an increasing amount of energy. The network is severely impacted by server workloads, cooling, and supporting equipment. The study aims to identify the energy consumption involved in building services and their operations and analyze the ways in efficient management of performance in data center. The objective of the study is to identify the …
Published in International Journal of Environmental Planning and Development Architecture · Vol. 1, Issue 2, 2023 · pp. 51–71 Read article
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Unravelling the Power of Avro and Hadoop: Revolutionising Big Data Processing and Serialization
Abstract: As the technology is in progress, the accumulation of data is also increasing. As a result of which a lot of organisations are constantly seeking new yet innovative solutions to process and analyse this huge accumulation of data. Well Hadoop proved a gamе-changеr in thе rеalm of big data procеssing whereas Avro proved a solution provider to data Serialization. In this review work, wе will dеlvе into thе world of …
Published in International Journal of Data Structure Studies · Vol. 2, Issue 1, 2024 · pp. 8–13 Read article
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Supply Chain Optimization Using Big Data
Abstract: In the references to add productivity and short the appendences, and increase customer value, supply chain optimization is a very crucial part of contemporary business operations. Supply chain management has been transformed by the emergence of big data, which can provide analysis and insight from many different sources of information. This content explores the importance of using big data analytics to improve the delivery process. Thanks to the evolution of …
Published in Journal of Production Research & Management · Vol. 14, Issue 2, 2024 · pp. 19–25 Read article
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Li-Fi Visible Light: Data Transfers via LED Technology
Abstract: Li-Fi (Light Fidelity) is a cutting-edge technology that uses visible light to communicate data wirelessly, like how Wi-Fi uses radio waves. Light Emitting Diodes (LEDs) are the principal source of light in this system for information transmission. To transmit data, the LED light blinks on and off very quickly, quicker than our eyes can detect. After then, a sensor records these light signals and transforms them back into data that …
Published in Trends in Opto-electro & Optical Communication · Vol. 15, Issue 1, 2025 · pp. 1–7 Read article
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Unveiling Patterns in Complexity: The Role of Simple Statistics and Fuzzy Mathematics in Data Analysis
Abstract: In this study, statistical methods must be integrated with fuzzy mathematics to solve complex data. Statistical methods offer clear, unbiased, and computationally feasible tools for analysing numerical data. whereas fuzzy mathematics excels in describing the vagueness and ambiguity of human feeling by way of linguistic variables, membership functions, and inference systems. Giving it an apparent advantage when modelling complex conditions. Hybrid frameworks offer fine-grained decision-making and resilient adaptability to real-world …
Published in Research & Reviews : Journal of Statistics · Vol. 14, Issue 1, 2025 · pp. 01–10 Read article
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Data Mining for E-Commerce and Social Media: Insights and Future Research Directions
Abstract: The fast expansion of e-commerce and social media has heralded a new era of data-rich settings, with enormous quantities of user interactions, preferences, and transactions generated on a daily basis. Data mining has developed as a critical strategy for leveraging big datasets, allowing businesses to gain concrete knowledge and drive decision-making. Data mining in e-commerce improves operational efficiency and user pleasure by allowing for personalized recommendations, consumer segmentation, fraud detection, …
Published in E-Commerce for Future & Trends · Vol. 12, Issue 1, 2025 · pp. 14–23 Read article
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Data-Driven Life Prediction of Fiber-Reinforced Polymer Composites Using IoT Sensing and Machine Learning Algorithms
Abstract: The accurate prediction of fatigue life in fiber-reinforced polymer (FRP) composites remains a major challenge due to their nonlinear, multi-mechanism degradation behavior under variable loading conditions. This study presents a data-driven framework, H-LiProNet, which combines real-time IoT sensing with hybrid machine learning to estimate remaining useful life (RUL) in FRP composites. The proposed system integrates embedded Fiber Bragg Grating (FBG) and acoustic emission (AE) sensors to capture strain and damage …
Published in Journal of Polymer & Composites · Vol. 13, Issue 4, 2025 · pp. 116–130 Read article
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Entropy, Symmetry, and Data Fusion: Emerging Methods in Multi-Objective Decision- Making and Smart Systems
Abstract: In the era of intelligent technologies and data-driven systems, multi-objective decision-making (MODM) has become an essential aspect of managing complex environments such as smart cities, autonomous systems, and cyber-physical networks. As decision-making scenarios become increasingly dynamic and uncertain, there is a growing need for advanced methodologies that can handle diverse objectives, conflicting constraints, and incomplete information. This review highlights the emerging role of entropy, symmetry, and data fusion as foundational …
Published in Emerging Trends in Symmetry · Vol. 1, Issue 2, 2025 · pp. 44–49 Read article
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Data-Driven Energy Forecasting for Smart Homes: Ensemble Learning from IoT Meters and Relevance for Polymer-Composite Based Smart Infrastructure
Abstract: Reliable estimation of household electricity demand is relevant in creating efficiency in energy usage, optimization of the loads, and intelligent demand-side management in intelligent grid systems. This paper introduces a varied machine learning model that approaches residential electric consumption prediction using an assortment of ensemble regression boosts, including Linear Regression, Lasso Regression, Decision Tree Regressor, Random Forest, and Gradient Boosting, to predict residential electricity consumption environments on a time-series arrested …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 29–64 Read article
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A Review of Locking Protocols for Concurrency Control in Parallel Databases
Abstract: In modern database systems, many transactions may run at the same time. When several users try to access the same data or update the data simultaneously, then it creates a problem such as inconsistency data, lost updates or misinformation and may lead to conflicts between the transactions and deadlocks. To avoid these types of issues and maintain the correctness of the database, concurrency control protocols are mainly used. This paper …
Published in Journal of Advanced Database Management & Systems · Vol. 13, Issue 2, 2026 Read article