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58 articles for “Gradient Boosting”
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Industrial Prognostics via Ensemble Machine Learning: An Uncertainty Aware Framework for RUL Estimation on NASA FD004 Telemetry
Abstract: Estimating the Remaining Useful Life (RUL) of industrial machinery in real-time is now vital for both operational safety and smart resource management. In the aviation industry, turbofan engines deal with constantly shifting flight conditions, making traditional, scheduled maintenance both expensive and prone to error. This paper addresses the flaws in common “point-prediction” AI models, which offer a single failure date without any margin for error, by introducing a new, uncertainty-aware …
Published in Journal of Aerospace Engineering & Technology · Vol. 16, Issue 2, 2026 Read article
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Prediction of Mechanical Properties for Advanced Engineering Applications utilizing Polymer Composite Materials by Machine Learning
Abstract: Polymer composites show great promise as engineering materials because of their mechanical performance, resistance to corrosion, lightweight nature, and adaptability in design. Aerospace, automotive, biomedical, maritime, and civil engineers all rely on mechanical property prediction to cut down on trial expenses, expedite product development, and optimize material selection. Speedy design optimization is not possible using traditional numerical and experimental methods due to the high costs associated with material characterisation, computational …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 1258–1284 Read article
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Genomic Selection for Grain Yield in Wheat Using Machine Learning on DArT Molecular Markers: A Comparative Evaluation Across Multi-Environment Trials
Abstract: Genomic selection (GS) predicts complex quantitative traits directly from genome-wide molecular markers, bypassing the need for extensive phenotypic trials and accelerating plant breeding cycles. We conducted a comparative evaluation of seven regression approaches — ridge regression (the machine-learning equivalent of RR-BLUP), Lasso, Elastic Net, Partial Least Squares, linear Support Vector Regression, Random Forest, and Gradient Boosting — for predicting grain yield from 1,279 Diversity Array Technology (DArT) molecular markers genotyped …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 2, 2026 Read article
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Automated Machine Learning System for Model Selection and Hyperparameter Optimization
Abstract: The proliferation of machine learning applications in various scientific and industrial domains has given rise to an urgent need for developing principled, automated techniques for optimal architecture selection and hyperparameter tuning for machine learning models without human expert intervention. In this paper, we introduce the Automated Machine Learning System for Model selection and hyperparameter Optimization (AMLSMO)—a state-of-the-art, all-encompassing AutoML system that combines the power of meta-learning-based warm-starting, Bayesian Optimization with …
Published in Recent Trends in Mathematics · Vol. 3, Issue 2, 2026 · pp. 15–23 Read article
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A Threshold-Weighted Mathematical Fusion Model for Epidemic Outbreak Prediction Using SEIR Residual Dynamics and Cloud-Based Machine Learning
Abstract: Accurate prediction of epidemic outbreaks is critical for effective public health management, resource planning, early warning generation, and timely intervention by municipal authorities. Traditional compartmental models such as Susceptible–Exposed–Infectious–Recovered (SEIR) offer valuable epidemiological insights and mathematical interpretability; however, they may not adequately capture the complex nonlinear relationships present in real-world urban health systems. Conversely, data-driven machine learning techniques can identify hidden patterns in large datasets but often lack epidemiological structure …
Published in Research & Reviews : Journal of Statistics · Vol. 15, Issue 2, 2026 · pp. 12–19 Read article
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Finding Churn Trend Scenario Using ML Techniques
Abstract: Abstract Media transmission and in addition the network access suppliers assumes an essential part in the mobile communications and the data society. Telecom segment is consistently developing with its progression in highlights and innovation. Consumer loyalty is an imperative factor which chooses the execution of firms. Customer churn expectation is an essential technique in CRM (Customer Relationship Management), knowing the churn propensity appeared by the customers helps to take better …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 6, Issue 1, 2019 · pp. 1–8 Read article
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An Analysis of Machine Learning Models for Early Cardiac Risk Stratification
Abstract: The paper shows an in-depth study of machine learning and artificial intelligence solutions to early cardiac risk stratification which has a crucial necessity because cardiovascular disease (CVD) prediction remains a significant issue that needs to be improved beyond the conventional risk score. Since CVD is the most serious disease killer in the world, claiming 17.9 million deaths every year, there is a strong need to get the most sophisticated predictive …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 13, Issue 2, 2026 Read article
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Machine Learning-Based Structure–Property Quantification of Advanced Polymer Composites
Abstract: Advanced polymer composites are widely used in high-performance engineering due to their superior mechanical and multifunctional properties. Accurate structure–property quantification is essential for efficient material design and reducing experimental costs. Existing Machine Learning (ML) approaches often exhibit limited predictive generalization due to inadequate feature discrimination and suboptimal hyperparameter tuning. To address these limitations, the proposed method enhances the ability to capture the complex nonlinear interactions among composite structural descriptors. The …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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AI-Assisted Kinetic Modeling of PLA Hydrolysis Under Subcritical Water Conditions for Sustainable Polymer Recycling
Abstract: The accumulation of poly (lactic acid) (PLA) in terrestrial and marine ecosystems has intensified the demand for closed-loop, green recycling technologies. Subcritical water (SCW) hydrolysis offers a promising, catalyst-free pathway for the rapid depolymerization of PLA into its constituent monomer, lactic acid. However, the complex, highly non-linear kinetics governing macro-molecular degradation under variable hydrothermal conditions limit real-time process optimization and industrial scalability. This study develops a novel artificial intelligence (AI)-assisted …
Published in Journal of Modern Chemistry & Chemical Technology · Vol. 17, Issue 2, 2026 · pp. 65–74 Read article
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Analyzing Potential Leads Data to Improve Marketing Strategies
Abstract: To develop a lead scoring model using multiple machine learning models and create a user-friendly web application, the first step is to clearly define the problem and collect relevant data from various sources such as CRM, website, social media, email campaigns, etc. After cleaning and pre-processing the data, identify the essential features that can affect the lead score and split the data into training and testing sets. Next, select a …
Published in E-Commerce for Future & Trends · Vol. 10, Issue 2, 2023 · pp. 18–25 Read article
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Hybrid Feature Selection Approach for Stability Prediction for Intracerebral Hemorrhage Patients
Abstract: Intra Cerebral Hemorrhage (ICH) stability estimation is useful for enhancing diagnosis accuracy, selecting the best course of treatment, and clinically evaluating variations with healthy individuals. Due to their low predictive value, the clinical application of several ICH progression scoring systems is constrained. The dataset includes clinical parameters including age, Glasgow Coma Scale (GCS), and CT Angiography (CTA) spot that have been retrieved using geometric features from the segmented bleeding volume …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 9, Issue 2, 2022 · pp. 9–17 Read article
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A Comparative Study on Various Machine Learning Techniques for the Prediction of Cardiac Ailment
Abstract: Over the last couple of decades, cardiovascular complexities have become the leading source of death in impoverished regions. With heart attack rates on the acceleration at a youthful age, it is necessary to put in place a process to recognize the symptoms of a heart attack early and thus limit it. It is impossible for a common man to often undergo expensive tests such as an ECG and thus there …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 10, Issue 3, 2023 · pp. 25–30 Read article
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Class Imbalance Reduction and Training Data Selection for Cross Project Defect Prediction
Abstract: The research aims to predict errors in a targeted project using data from other projects. This project is named as the Cross-Project Defect Prediction (CPDP). There are a number of ways available to improve the predictable performance of CPDP models. However, there is no comparison of modern methods. Predictability facilitates the rational distribution of testing resources by detecting software modules that may be problematic before releasing products. If a project …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 9, Issue 3, 2022 · pp. 12–20 Read article
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Machine Learning Based Optimization of Optical Fiber Parameters for Minimizing Dispersion and Attenuation
Abstract: Optical fibers provide high-bandwidth and low-loss transmission for modern communication networks; however, attenuation and chromatic dispersion remain two important physical limitations affecting transmission distance, pulse integrity, and system capacity. Conventional optical-fiber design generally relies on analytical calculations and repeated numerical simulations to determine suitable geometric and material parameters. Such approaches become computationally demanding when several fiber parameters are simultaneously optimized. This paper presents a machine-learning-based methodology for predicting and optimizing …
Published in Trends in Opto-electro & Optical Communication · Vol. 16, Issue 2, 2026 · pp. 18–28 Read article
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AI-Assisted Design of a Compact Bandpass Microwave Filter for 5G/6G Applications
Abstract: The rapid development of fifth generation (5G) and emerging sixth generation (6G) wireless communication systems require compact, low-loss, highly selective and frequency-stable radio- frequency front-end components. Microwave bandpass filters play an important role in selecting desired communication channels while suppressing out-of-band interference. However, conventional filter design generally requires repeated electromagnetic simulation and manual optimization of geometrical parameters, resulting in considerable computational time. This paper presents an artificial intelligence (AI)-assisted methodology …
Published in Journal of Microwave Engineering and Technologies · Vol. 13, Issue 2, 2026 Read article
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Design and Analysis of Thermoelectric Dc boost converter
Abstract: In today’s consumer-oriented market, researchers are increasingly focusing on harvesting energy from ambient and renewable sources to enable sustainable power generation and reduce dependency on conventional energy resources such as batteries and fossil-fuel-based electricity. The rapid growth of portable electronics, wireless sensor networks, and Internet of Things (IoT) devices has created a significant demand for low-power, long-life, and maintenance-free energy solutions. In many practical situations, frequent battery replacement is difficult, …
Published in International Journal of Electro-Mechanics and Material Behaviour · Vol. 4, Issue 1, 2026 · pp. 1–10 Read article
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Development and implementation of Solar-Thermoelectric Hybrid Energy Harvester
Abstract: In today’s consumer-oriented and technology-driven market, researchers are increasingly emphasizing the need to harvest energy from ambient and renewable sources to support sustainable power generation and to minimize dependence on conventional energy resources such as batteries and fossil-fuel-based electricity. The rising deployment of portable electronics, wireless sensor networks, and Internet of Things (IoT) devices has created an urgent demand for compact, low-power, long-life, and maintenance-free energy solutions. In many real-world …
Published in International Journal of Machine Systems and Manufacturing Technology · Vol. 4, Issue 1, 2026 · pp. 33–47 Read article
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Routing Protocols in FANETs with Future Enhancements
Abstract: Flying Ad Hoc Networks (FANETs), which are swarms of Unmanned Aerial Vehicles (UAVs), are an emerging solution which revolutionized the area of mission-critical and infrastructure-less communication systems. These networks provide real-time data transfer for use cases such as disaster relief, battlefield observation, environmental monitoring, and 6G-based smart cities. However, the dynamic profile of FANETs, which is defined by high 3D mobility, limited energy resources, unstable wireless links, and constant topology …
Published in Journal of Aerospace Engineering & Technology · Vol. 15, Issue 3, 2025 · pp. 8–13 Read article