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33 articles for “Machine learning pipelines”
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Machine Learning Pipelines: A Survey on Automation, Scalability, and Deployment Strategies
Abstract: Machine learning (ML) has become a critical enabler of intelligent applications across domains, requiring robust, efficient, and scalable deployment workflows. This review paper provides an in-depth overview of machine learning pipelines, emphasizing three key dimensions: automation, scalability, and deployment methodologies. It begins by exploring automation techniques that reduce manual effort in data ingestion, preprocessing, model selection, and hyperparameter tuning. Tools such as AutoML, TFX, and workflow orchestration platforms are examined …
Published in Journal of Advances in Shell Programming · Vol. 12, Issue 2, 2025 · pp. 17–28 Read article
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IoT Sensors to Monitor Pipeline Pressure and Flow Rate Combined with ML-Algorithms to Detect Leakages
Abstract: In the field of fluid mechanics, pipelines are the lifeblood of industries, transporting everything from natural gas and oil to water and chemicals. Maintaining their integrity is paramount for safety, economic efficiency, and environmental protection. Traditional leak detection methods explained in fluid mechanics can be slow, expensive, and sometimes fail to identify small leaks early enough to prevent significant damage. However, the convergence of Internet of Things (IoT) and Machine …
Published in Recent Trends in Fluid Mechanics · Vol. 12, Issue 2, 2025 · pp. 40–48 Read article
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AI-Based Discovery of High-Performance Energy Storage Polymer Composites: A Comprehensive Review
Abstract: The accelerating global demand for high-performance energy storage systems has stimulated significant research into advanced polymer composites as next-generation electrolytes, electrode binders, and functional membranes for batteries, supercapacitors, and photovoltaic devices. However, the vast compositional and structural design space of polymer materials presents formidable challenges for conventional trial-and-error discovery strategies, which remain slow, costly, and biased by prior expert knowledge. Machine learning (ML) and artificial intelligence (AI) have emerged as …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 1083–1097 Read article
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Machine Learning-Driven Polymer Composite Smart Skin for Integrated Sensing in Soft Robotic Systems
Abstract: Soft robotics has grown rapidly, but its progress is still constrained by the limitations of current sensing skins. Most polymer-based sensors provide either flexibility or sensitivity, yet they struggle to deliver real-time communication and adaptive intelligence when deployed in complex robotic environments. This disconnect between material performance and system-level responsiveness forms a critical bottleneck for practical deployment. Existing approaches often treat tactile sensing and wireless communication as separate problems. As …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 121–136 Read article
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Automated Intelligence, Machine Learning, and Big Data in Education: A Practical Framework, Synthetic Demonstration, and Deployment Guidance
Abstract: Artificial intelligence (AI), machine learning (ML), and big-data methods are increasingly used to improve educational decision making through personalization, early-warning systems, scalable feedback, and operational analytics. This manuscript proposes a practical end-to-end framework for educational AI/ML projects, covering problem definition, data engineering, modeling, evaluation, intervention design, and responsible governance. To provide a complete and reproducible template without exposing sensitive student data, we present a synthetic demonstration study that mirrors typical …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 1, 2026 Read article
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Wear and Tribological Characteristics of Novel Metal Matrix Composites
Abstract: The development of advanced metal matrix composites (MMCs) with enhanced tribological performance has become increasingly important due to the premature failure of critical engineering components operating under severe wear conditions in automotive, aerospace, marine, defense, and power generation systems. Conventional composites such as Copper–Alumina and Aluminium–Silicon Carbide have demonstrated improved mechanical and wear characteristics; however, their widespread application is often limited by issues including particle agglomeration, non-uniform reinforcement distribution, porosity …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 1326–1346 Read article
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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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MAC Unit Implementation on FPGA
Abstract: Multiply–accumulate (MAC) computations account for a large part of machine learning accelerator operations. The pipelined structure is usually adopted to improve the performance by reducing the length of critical paths. An increase in the number of flip-flops due to pipelining, however, generally results in significant area and power increase. Using this method, we create and build a cutset-free feedforward MAC architecture that maximizes data propagation and removes superfluous pipeline registers. …
Published in Journal of Microcontroller Engineering and Applications · Vol. 13, Issue 1, 2026 · pp. 29–37 Read article
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Hybrid Quantum–Machine Learning Framework for Nonlinear Rheological Modeling of Polymer and Composite Materials
Abstract: In polymer and composite materials, a major challenge lies in predicting their nonlinear rheological response, owing to complex multiscale interactions that are not captured by traditional constitutive laws or conventional machine learning approaches. In this study, a hybrid Quantum Machine Learning (QML) model comprising Quantum Support Vector Machine (QSVM) and Quantum Neural Network (QNN) architectures is proposed for viscosity prediction without requiring any specific rheological equation. To train and test …
Published in Journal of Polymer & Composites · Vol. 14, Issue 5, 2026 Read article
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Polymer Composite Nodes for Smart IoT Environmental Monitoring: Applying ML-Calibrated RF Sensors for Exceptionally Low-Power, Flexible, and Dependable Performance
Abstract: The quick usage of the Internet of Things (IoT) for monitoring the environment needs sensor platforms that are accurate, responsive, adaptable, energy-efficient, and able to work in many different conditions. This work presents polymer composite nodes equipped with machine learning (ML)-calibrated radio-frequency (RF) sensors that adhere to stringent criteria. Sensor substrates built of flexible polymer matrices with conductive fillers are strong, light, and flexible enough to fit on surfaces that …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 1205–1221 Read article
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Machine Learning-Assisted Design and Optimization of Lightweight Polymer Composites for IoT-Enabled Automotive Applications
Abstract: This study aims to develop an integrated machine learning and optimization framework for the intelligent design of lightweight polymer composites suited for IoT-enabled automotive applications. The goal is to enhance material performance while satisfying multiple design constraints such as mechanical strength, thermal stability, and process compatibility. A curated dataset of polymer composite formulations was used to train a Random Forest Regression (RFR) model capable of predicting tensile strength, thermal conductivity, …
Published in Journal of Polymer & Composites · Vol. 13, Issue 5, 2025 · pp. 12–27 Read article
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Data-Driven Predictive Analytics and Decision- Making in FinTech Using MongoDB and High-Throughput Data Pipelines
Abstract: This paper examines the implementation of MongoDB and high-throughput data pipelines within the financial technology (FinTech) sector to drive data-informed predictive analytics and decision-making. The study focuses on the architectural components, scalability, and challenges of integrating NoSQL databases into real-time data ingestion and analytics pipelines. The transformative potential of these technologies in modern financial systems is highlighted through practical use cases such as fraud detection, credit scoring, and personalized financial …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 3, Issue 1, 2025 · pp. 1–15 Read article
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AI-Driven DevSecOps Automation: An Intelligent Framework for Continuous Cloud Security and Regulatory Compliance
Abstract: Cloud-native systems, microservices, and infrastructure-as-code (IaC)–oriented CI/CD pipelines have accelerated the pace of software delivery, yet they have also introduced new layers of operational complexity and widened the overall security exposure of modern applications. Traditional DevSecOps workflows still depend heavily on isolated scanners, manual reviews, and static governance processes that are not well-suited for the elasticity and constant change characteristic of multi-cloud environments. To address these limitations, this paper introduces …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 1, 2025 · pp. 01–15 Read article
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Modern Computer-aided Drug Design Methods: A Review
Abstract: Computer-aided drug design (CADD) has emerged as a crucial tool in the drug discovery process, offering a time-efficient and cost-effective approach to identifying potential drug candidates. This review aims to provide an overview of modern CADD methods, including high-throughput screening (HTS), structure-based drug design (SBDD), ligand-based drug design (LBDD), structure-based virtual screening (SBVS), and ligand-based virtual screening (LBVS). We discuss the basic principles, applicability, and limitations of each method, highlighting …
Published in Research and Reviews: A Journal of Dentistry · Vol. 15, Issue 2, 2024 · pp. 14–20 Read article
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ML-Enhanced Smart Sensing Framework for IoT- Based Structural Health Monitoring Using Conductive Polymer Composites
Abstract: The growing demand for intelligent structural health monitoring (SHM) in dynamic infrastructures necessitates flexible sensing systems that are not only mechanically robust but also capable of real-time interpretation. Conventional SHM frameworks often rely on brittle sensor configurations and cloud-dependent processing pipelines, which suffer from latency, limited durability, and poor adaptability under variable loading conditions. Despite recent advances in composite materials and machine learning, current approaches lack a unified framework that …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 348–369 Read article
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Unravelling Modern News Classification Methods: A Systematic Review
Abstract: Nowadays, the news is being generated each second from every corner of the world, with millions of news articles generated every day. Some assume that at least 1.8 million articles are published yearly, in about 28,000 journals. It has become difficult to recognize what's fake and what's genuine due to the overflow of millions of articles every day. Not every person reads every news, so the classification of news according …
Published in Journal of Computer Technology & Applications Read article
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AI-Optimized Itinerary Design: Transforming the Future of Travel Planning
Abstract: The travel industry is struggling to meet the rising demand for efficient and personalized trip planning. Traditional methods often lack real-time updates and fail to adapt to individual preferences, necessitating innovative solutions. This study presents an AI-powered travel planner utilizing the Gemini API to enhance itinerary creation. By analyzing user preferences, interests, and real-time data, the system delivers tailored travel recommendations. Leveraging advanced technologies such as cloud computing, machine learning, …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 2, 2025 · pp. 74–82 Read article
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Integrative Structural-Functional Genomics of Fc and Fab: Precision Models for Monoclonal Antibody Stability and Anti-Aggregation Engineering
Abstract: Monoclonal antibodies (mAbs) represent the cornerstone of biotherapeutics, yet aggregation propensity compromises up to 50% of candidates during development, driven by Fab hypervariability and Fc vulnerabilities.(1,2) This review integrates functional genomics from OAS (4B+ sequences)(5) and structural databases (SAbDab: 10K+ structures)(6) with machine learning models achieving R=0.97 for SAP prediction.(11) We dissect biophysical mechanisms, benchmark predictive tools (DeepSP, ESM2), and engineering strategies (YTE, FW mutations) that enhance Tm by 5-10°C …
Published in International Journal of Molecular Biotechnological Research · Vol. 4, Issue 1, 2026 Read article
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Revolutionizing Petrology and Mineralogy: The Study of AI and Advanced Sensor Technologies
Abstract: Petrology and mineralogy are fundamental to understanding Earth's intricate processes, from crustal evolution to economic resource formation. However, traditional methods, while precise, are often laborious, time-consuming, and occasionally subject to interpretive bias. This abstract explores the transformative potential of integrating cutting-edge Artificial Intelligence (AI) and advanced sensor technologies to revolutionize data acquisition, analysis, and interpretation in these critical geosciences. Advanced sensor technologies, including high-resolution spectral imaging (hyperspectral, Raman), automated X-ray …
Published in International Journal of Minerals · Vol. 2, Issue 2, 2025 · pp. 1–11 Read article
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SpecForesight: A Predictive Analytics Pipeline for Laptop Price Forecasting
Abstract: This paper frames laptop pricing as a supervised predictive analytics problem, transforming product specifications into feature-rich signals to forecast price with calibrated regression models and operational guardrails against drift. A structured pipeline ingests tabular listings, performs data cleaning, and engineers domain-informed features (e.g., central processing unit (CPU) family and clocks, graphics processing unit (GPU) tiering, memory/storage density, display, and touch capabilities), followed by encoding and normalization to optimize model learnability. …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 1, 2026 · pp. 61–71 Read article