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165 articles for “hybrid machine”
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Early Heart Disease Prediction Using Hybrid Machine Learning Techniques
Abstract: In the contemporary era, cardiovascular disease is one in all the most causes of death within the world. Estimating Heart problems i.e cardiopathy is a crucial challenge within the area of clinical data analysis. Large volumes of data produced by the healthcare sector have been proved to be useful for helping with decision-making and speculation, thanks to machine learning (ML).. Various studies help us to review and supply glimpse into …
Published in Journal of Microcontroller Engineering and Applications · Vol. 9, Issue 2, 2022 · pp. 35–41 Read article
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Hybrid Machining Processes in Advanced Manufacturing: A Review of Mechanisms and Industrial Applications
Abstract: Hybrid machining processes (HMPs) have gained considerable attention in recent years as an effective approach to address the growing complexity and performance demands of modern manufacturing systems. These processes combine two or more machining techniques—such as mechanical, thermal, chemical, or electrical methods—into a single setup, enabling enhanced productivity, precision, and adaptability, particularly for hard-to-machine materials like ceramics, composites, and superalloys. The integration of distinct energy sources results in synergistic effects …
Published in Journal of Production Research & Management · Vol. 15, Issue 2, 2025 · pp. 19–24 Read article
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Hybrid Machine Learning and Finite Element Framework for Predicting Damage Behavior in Fiber-Reinforced Polymer Composites
Abstract: Fiber Reinforced Polymer (FRP) composites have broad spread use in aerospace, automotive, marine and structural applications due to its high specific strength, stiffness and corrosion resistance. The various damage mechanisms such as matrix cracking, fiber breakage, delamination and interfacial failure, however, make the forecasting of damage particularly complex. In this work, a hybrid machine learning (ML) and finite element (FE) system is proposed for predicting the damage behavior of FRP …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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A Hybrid Machine Learning Approach for Enhanced Patient Diagnosis and Health Outcome Prediction
Abstract: Rapid and accurate diagnosis is essential to present day practitioners of medicine, yet can be complicated by the enormous volume and complexity inherent in clinical data. To this end, we here propose a hybrid machine learning model in combination with Recursive Feature Elimination (RFE) and ensemble voting to enhance the diagnostic accuracy by integrating multiple models. Trained on real-world electronic health data, including lab results, demographics and medical history, the …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 13, Issue 2, 2026 Read article
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Multimodal Data Fusion with Hybrid Machine Learning for Enhanced Prediction of Li-Ion Battery Remaining Useful Life and State of Charge
Abstract: Lithium-ion battery materials used in modern energy storage systems are required to exhibit high reliability, safety, and long lifecycle performance under varying operational and environmental conditions. Accurate prediction of Remaining Useful Life (RUL) and State of Charge (SoC) is therefore essential for understanding material degradation behavior, improving manufacturing quality, and enabling effective lifecycle management. However, nonlinear electrochemical aging, load variability, and thermal uncertainty significantly complicate accurate estimation of these parameters. …
Published in International Journal of Electro-Mechanics and Material Behaviour · Vol. 4, Issue 1, 2026 · pp. 11–23 Read article
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A Hybrid Machine Learning Approach for Cardiovascular Disease Prediction
Abstract: Heart disease ranks among the top causes of death globally. Accurately predicting cardiovascular conditions has become a key challenge in the realm of clinical data analysis. It has been shown that machine learning is an effective means of assisting with predicting and decision-making based on the large volume of data produced by the medical industry. In this study, we describe a unique approach that increases the prediction accuracy of heart-related …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 1, 2025 · pp. 69–75 Read article
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Super Ni 718 Machinability Investigation on Electrical Discharge Machining Using Hybrid Al 7(075+178) Electrode Under Abrasive Assisted Dielectric
Abstract: As aluminum alloy is the lightest metal and has the best mechanical qualities among metal, it has proven to be a perfect choice for strututal industry, particularly in the aerospace sector.7xxx alloys from the Al alloy family are widely used in aviation constructions due to their outstanding processing, welding efficiency, great specific strength & stiffness, and high toughness. The two aluminum alloys that are most frequently used in airplane structures …
Published in Journal of Polymer & Composites · Vol. 13, Issue 1, 2025 · pp. 1–17 Read article
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Fingerprint Recognition for Crime Scenes Using Deep Learning
Abstract: Crime-scene fingerprint photos are crucial hints for resolving ongoing cases. Using deep machine learning and convolutional neural networks, we provide a comprehensive crime scene fingerprint identification method in this research (CNN). Precision photography and sophisticated physical and chemical processing techniques are used to collect images from crime scenes, which are then kept as databases. It can be challenging to categorize the photographs taken from the crime scene because they are …
Published in Trends in Opto-electro & Optical Communication · Vol. 12, Issue 2, 2022 · pp. 13–18 Read article
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A Survey of Seasonal-based Movie Recommendations Using Machine Learning Through a Hybrid Approach with User Interest in Various OTT Platforms
Abstract: No matter their age, gender, race, color, or region, everyone enjoys watching films particularly during festival season. We are all, in the most basic sense, connected to one another through this beautiful medium, but what really grabs attention is the fact that, regardless of how unique our choices and combinations are in terms of picture show preference, one thing remains constant. Certain people have a preference for certain types of …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 11, Issue 1, 2024 · pp. 24–29 Read article
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Optimum Machining Parameters for Al 7075 Hybrid Metal Matrix Composites Using Multi-objective Optimization Technique and the Modified Taguchi Approach
Abstract: Lightweight composite materials with improved mechanical properties are widely used in industries. There is a need to obtain optimum machining parameters of such hybrid composites. This paper uses reliable multi-objective optimization technique and modified Taguchi approach to determine optimal machining parameters such as speed (NS) varying from 1000 rpm to 1500 rpm, feed rate (FR) from 0.10 mm/rev to 0.20 mm/rev, depth-of-cut (DC) varied from 0.5 mm to 1.5 mm …
Published in Journal of Polymer & Composites · Vol. 11, Issue 8, 2023 · pp. 269–278 Read article
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Enhancing Performance of Hybrid Natural Fiber Reinforced Polymer: Insights into Processing, Characterization and Machining
Abstract: Natural fibre reinforced polymer composites (N-FRP) have gained popularity in recent years as an alternative to regular polymer composites, owing to growing environmental concerns. Natural fibers are becoming increasingly popular due to their outstanding properties such as flexibility, strength, compatibility with living creatures, and impact resistance. A notable application of natural fibers is in the medical industry, with the goal of producing cost-effective, sustainable, and long-lasting products. Combining different fibers …
Published in Journal of Polymer & Composites · Vol. 13, Issue 2, 2025 · pp. 139–153 Read article
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A Hybrid Mathematical Model for Epidemic Outbreak Forecasting Using Machine Learning and Cloud Computing
Abstract: The increasing frequency of infectious disease outbreaks has emphasized the necessity for intelligent epidemic surveillance systems capable of predicting disease spread at an early stage. Conventional outbreak detection approaches rely heavily on delayed statistical reporting and manual monitoring techniques, resulting in reduced responsiveness during critical periods. This paper presents a mathematical predictive framework for epidemic outbreak detection using machine learning and cloud computing technologies. The proposed framework integrates the Susceptible–Infected–Recovered …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 13, Issue 2, 2026 · pp. 01–06 Read article
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Development of Electrochemical Discharge Machine for Machining Non-conductive Materials
Abstract: Electrochemical discharge machining (ECDM) is the hybrid machining process combination of electrodischarge machining and electrochemical machining process. ECDM is different machining process for micro-drilling and micro-grooving of variety of glasses, ceramics and composites. Electrochemical discharge machining also called as spark assisted chemical engraving (SACE). ECDM process is an effective and micro-machining process for non-conductive materials. It is used in micro-electromechanical system (MEMS) applications. ECDM parameters are material removing rate (MRR), …
Published in Trends in Machine design · Vol. 6, Issue 3, 2019 · pp. 18–22 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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Biopolymer–Cement Hybrid Panels from Recycled Paper Mill Reject: Experimental Characterisation and Machine Learning Optimization
Abstract: The increased rate of the accumulation of industrial residues in the developing countries is a major cause of concern for the environment. The current study brings forth the use of industrial residues in the form of the production of eco-friendly building materials as a sustainable approach to their valorization. The valorization of recycled paper mill reject, a cellulose-based biopolymeric industrial residue, is being addressed in this study as a reinforcement …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 67–90 Read article
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Machine Learning Optimization for VARTM Carbon Polymer Laminates
Abstract: Vacuum-assisted resin transfer moulding (VARTM) is a key low-cost, out-of-autoclave process for manufacturing large-scale carbon-fibre reinforced polymer (CFRP) laminates crucial to aerospace wings, wind-turbine blades, marine hulls, and automotive structures. Unpredictable resin flow often leads to voids, dry spots, and race-tracking defects, resulting in 27.9% scrap rates and lengthy, costly trial-and-error design cycles. Although surrogate models provide rapid impregnation predictions for simple flat-plate geometries, vision-based monitoring is limited to idealized …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 229–245 Read article
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Artificial Neural Network Modelling to Optimize Micro-Drilling Parameters of ECDM of Developed Novel Zn/(Ag+Fe)-MMC
Abstract: Several engineering fields have increased their use of metal matrix composites (MMCs) in the past few years. Due to the increase in composites, the demand for accurate machining has also become important. Specifically, pertaining to biomaterial applications, accuracy factor with desired surface finish is critical. While the near-net shape manufacturing process has advanced, MMCs frequently require post-mould machining to achieve surface quality, and dimensional tolerances. In the present study, a …
Published in Journal of Polymer & Composites · Vol. 11, Issue 1, 2023 · pp. 01–13 Read article
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Hybrid Techniques in Mango Leaf Disease Identification: Evaluating Neural Networks and Support Vector Machines
Abstract: Mango leaf diseases pose a significant threat to mango production, impacting both yield and fruit quality. Early and accurate detection of these diseases is crucial for effective management. This paper evaluates the use of hybrid techniques, specifically the integration of neural networks (NNs) and support vector machines (SVM), in the identification and classification of mango leaf diseases. NN excel in extracting complex features from images, while SVMs are robust classifiers, …
Published in Trends in Opto-electro & Optical Communication · Vol. 14, Issue 3, 2024 · pp. 19–27 Read article
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Optimizing Machinability in Wire EDM of AISI P20 Steel Employing Composite Material Wires with Hybrid Neural Network Approach
Abstract: AISI P20+Ni steel is extensively used for forging dies, plastic moulds, and automotive die components due to its excellent polishability, hardness, and homogeneity. This research utilizes Wire Electrical Discharge Machining (WEDM) to process pre-hardened AISI P20+Ni steel, focusing on minimizing both recast layer thickness (RLT) and kerf width (KW). The performance of wires made from composite materials, including zinc-coated brass wire (ZBW), cryogenically treated ZBW (CZBW), and ultrasonic vibration-assisted brass …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 1120–1133 Read article
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An Analysis of Machining Parameters for Metal Matrix Composite
Abstract: The electro-discharge machining analysis of hybrid Composite is presented in this work. Variables are chosen for the input process parameters. The material removal rate is acknowledged as an output parameter, together with the current, graphite and silicon carbide percentage and pulse on time. We used the Taguchi Method to conduct our experiments. To create the theoretical model and look into how process parameters affect the rate at which material is …
Published in Journal of Polymer & Composites · Vol. 13, Issue 2, 2025 · pp. 76–85 Read article