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1983 articles for “failure-prediction AUROC of 0.967” (ranking capped at the first 2,000 matches — narrow the search to see the rest)
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A Review of Failure Investigations and Its Techniques
Abstract: Valve failure poses a significant financial risk in the offshore oil and gas industry. Serious repercussions from this problem could include property damage and lost output from operational shutdowns. Additionally, it can lead to serious health, safety, and environmental (HSE) concerns such as oil and gas leaks, environmental contamination, and potential fatalities. In the Norwegian offshore industry, different kinds of valve failures have happened for a number of reasons, including …
Published in Journal of Thermal Engineering and Applications · Vol. 12, Issue 2, 2025 · pp. 33–41 Read article
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Tribological Performance and Wear Coefficient Prediction of AA2024–TiC Composites via Python-Based Machine Learning
Abstract: Determining wear coefficient accurately serves as a critical factor to maximize engineering materials' tribological characteristics. The experiment examines the wear characteristics of TiC-reinforced AA2024 aluminum alloy subjected to different tribological operating conditions. A pin-on-disc tribometer performed wear tests under different conditions of load and TiC weight fraction and sliding speed and duration. ANOVA statistical results show that load intensity and TiC reinforcement density stand out as principal variables that affect …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 1099–1112 Read article
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Self Esteem and Sociotropy - Autonomy as predictors of Workplace Burnout among Private Sector Employees
Abstract: Workplace burnout is a growing concern in the private sector, where employees face intense performance pressures, leading to stress and reduced productivity. While job-related factors are well-documented contributors, the role of personality traits in burnout remains an area of interest. This study examines the impact of Self-Esteem, Sociotropy, and Autonomy on Workplace Burnout among private sector employees. A sample of 120 employees was analyzed using ANOVA, Pearson correlation, and multiple …
Published in International Journal of Behavioral Sciences · Vol. 2, Issue 2, 2025 · pp. 81–89 Read article
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AI/ML-Based Approach to Solar Irradiance Prediction and Energy Suitability
Abstract: In this paper, due to challenges in precisely predicting solar irradiance, which is essential for solar power system optimization, we employed six diverse machine learning (ML) techniques: Linear Regression, Decision Tree, Random Forest, Gradient Boosting methods (including XGBoost), and Neural Networks—to analyze and predict outcomes using a dataset containing meteorological and temporal features. Key variables include wind speed, humidity, and temperature, which significantly influence the model’s predictive capability. Each method …
Published in Journal of Alternate Energy Sources & Technologies · Vol. 16, Issue 3, 2025 · pp. 36–48 Read article
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Safety braking system to avoid the failure in air brake Hoses
Abstract: The braking system represents one of its most important components of any vehicle, as a failure can result in devastating accidents. This study describes the design and development of an Air Brake Hose Failure Detection and Safety Braking System to address the problem of brake failure in big vehicles. The system monitors the condition of air brake hoses in real-time and provides audio-visual alerts to the driver in case of …
Published in Recent Trends in Fluid Mechanics · Vol. 12, Issue 3, 2025 · pp. 51–55 Read article
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Real-Time Air Quality Prediction Using IoT-Integrated Polymer Sensors and Recurrent Neural Networks
Abstract: Real-time air quality monitoring remains a critical challenge in urban environments, where traditional sensor infrastructures often suffer from limited responsiveness, poor scalability, and high deployment costs. The increasing prevalence of NO₂ pollution, a key contributor to respiratory and cardiovascular ailments, demands advanced sensing platforms capable of both accurate detection and predictive inference. Existing methods either rely on rigid electronic sensors lacking adaptability or on statistical forecasting models that fail to …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 332–347 Read article
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Enhanced Multimodal Disease Prediction Using Hybrid Ensemble Learning and AutoML Techniques
Abstract: The integration of hybrid ensemble learning and automated machine learning (AutoML) is revolutionizing disease prediction by addressing the complexity, imbalance, and high dimensionality inherent in medical datasets. This paper proposes an advanced pipeline that combines diverse ensemble learning models with AutoML-based optimization to predict chronic diseases such as kidney diseas-e, Parkinson’s disease, and lung cancer. Publicly available datasets from UCI and PhysioNet repositories were preprocessed using outlier removal, normalization, and …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 1, 2026 · pp. 10–20 Read article
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An Integrated Simulation Framework for Predicting Dielectric Breakdown and Electrical Aging in Epoxy-Silica Composite Insulation Systems
Abstract: This paper provides a combined computation approach in forecasting the dielectric breakdown and electrical aging within epoxy-silica composite of insulation system. The approach will consist of a three-complementary methodology (a combination of computing electric field using the finite element analysis, estimation of the probability of failures or breakdowns using Weibull statistics, and prediction of degradation tendencies using artificial neural networks). The epoxy-silica composites are of 10-40 volumes fillers. The simulations …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 339–376 Read article
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IOT and algorithmic intelligent motor health monitoring as well as maintenance prediction
Abstract: Manufacturing, transportation, and energy systems rely largely on industrial electric motors, and their untimely failure can result in expensive downtime, safety hazards, and decreased operational efficiency. The majority of traditional motor maintenance procedures rely on reactive methods or routine inspections, which frequently miss early-stage problems and lead to needless maintenance or unexpected breakdowns. This project offers an Intelligent Motor Health Monitoring and Predictive Maintenance System that combines Internet of Things …
Published in International Journal of Electrical Machine Analysis and Design · Vol. 4, Issue 1, 2026 · pp. 28–37 Read article
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SGLT2 Inhibitors in Heart Failure and Chronic Kidney Disease: Expanding Therapeutic Horizons beyond Glycemic Control
Abstract: Heart failure (HF) and chronic kidney disease (CKD) are closely related clinical diseases that significantly increase morbidity and death worldwide. The co-occurrence of both conditions is frequently referred to as the "cardio-renal syndrome," in which the failure of one organ hastens the decline of the other. The original purpose of sodium–glucose cotransporter-2 (SGLT2) inhibitors was to treat type 2 diabetic mellitus (T2DM) by reducing blood sugar levels. Nevertheless, new data …
Published in Research and Reviews: A Journal of Pharmacology · Vol. 16, Issue 2, 2026 Read article
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Machine Learning-Based Quantification of Polymer Structure Property Relationships for Predictive Material Design
Abstract: Polymer structures exhibit complex, hierarchical arrangements that strongly influence macroscopic properties, yet consistent quantification remains challenging due to nonlinear interactions and limited unified modeling strategies. Existing approaches inadequately capture generalized structure–property mappings across diverse polymer systems. This research aims to establish a machine learning-based quantification model for polymer structure–property relationships to support predictive material design. A Polymer Structure Property Dataset of 5,000 polymer samples includes structural descriptors and experimentally measured …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 737–754 Read article
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Mass Spectrometry–Based Phosphoproteomic Markers to Predict Kinase Inhibitor Response in Solid Tumors
Abstract: Mass spectrometry-based phosphoproteomics has emerged as a powerful tool for predicting kinase inhibitor responses in solid tumors, offering direct functional insights into signaling pathways that surpass traditional genomic profiling by capturing dynamic kinase activities and adaptive resistance mechanisms. Technological breakthroughs, including data- independent acquisition (DIA), trapped ion mobility spectrometry (timsTOF), and efficient enrichment methods like TiO2 or IMAC, now enable comprehensive profiling of over 40,000 phosphorylation sites from limited clinical …
Published in International Journal of Cell Biology and Cellular Functions · Vol. 4, Issue 2, 2026 Read article
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Leveraging AI and Machine Learning for Early Prediction and Prevention of Non- Communicable Diseases in Resource-Limited Settings
Abstract: Populations in these regions face persistent structural barriers, such as underdeveloped healthcare infrastructure, shortages of trained health professionals, and fragmented or incomplete health information systems. These limitations delay timely diagnosis, restrict access to preventive care, and compromise effective disease management. In recent years, rapid progress in artificial intelligence (AI) and machine learning (ML) has opened promising avenues to mitigate these challenges. Practical applications already emerging include mobile health platforms for …
Published in Journal of AYUSH: Ayurveda, Yoga, Unani, Siddha and Homeopathy · Vol. 15, Issue 1, 2026 · pp. 9–15 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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Adaptive Generalized Predictive Control of Anti Lock Braking System
Abstract: Anti lock braking system (ABS) is one of the most important topics in the field of car stability control system design. Among the various control strategies which are common in designing control systems, model predictive control (MPC) received great attention in industries. Independent generalized predictive control (IMGPC) is a new and effective predictive control strategy than can be easily implement on linear system. In this paper a novel adaptive control …
Published in Journal of Control & Instrumentation · Vol. 6, Issue 3, 2015 · pp. 1–12 Read article
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Soft Computing Based Prediction of Deviator Stress of Waste Plastic Reinforced Sand
Abstract: In the recent past, the soft computing techniques have received a significant attention for solution of the geotechnical stability problems. Following the trend, the present study tries to explore the use of different soft computing techniques such as random forest regression, support vector machines (SVM) RBF kernel, SVM poly kernel and M5P model tree for the prediction of deviator stress of sand reinforced with waste plastic strips. The deviator stress …
Published in Journal of Geotechnical Engineering · Vol. 6, Issue 3, 2019 · pp. 1–7 Read article
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Artificial Neural Network Based Defect Prediction in Casting
Abstract: The main problems which are facing by most of the casting industries is loss of productivity which is due to casting defects occurred during the production time. The main casting defects are cracks, misruns, blowholes scabs and airlocks. Most of the investigations made in this area is only discussing the defects occurred after a cast is made and no method has yet been developed to prevent the defects before casting. …
Published in Journal of Mechatronics and Automation · Vol. 2, Issue 2, 2015 · pp. 33–38 Read article
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Failure Analysis of Rotating Machine
Abstract: Most failures surveyed for electric motors are influenced by the particular industry, the geographic location and combination of applications in use. Rotating machine plays a vital role in industrial applications. Due to its variety of applications, it is difficult to diagnose the very cause of failure. Heavy duty rotating equipment are more prone to have failure with catastrophic consequences, if not rectified.Most failures surveyed for electric motors are influenced by …
Published in Journal of Power Electronics and Power Systems · Vol. 11, Issue 1, 2021 · pp. 1–5 Read article
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Investigation of DC link Capacitor Failures in DFIG based Wind Energy Conversion System
Abstract: This paper presents the effects of DC link electrolytic capacitor failure of DFIG based wind energy conversionsystem (WECS). The degradation of electrolytic capacitor can lead to the failure of DC link. As DFIG basedWECS utilize low power converter, so there is a need to explore the effects of capacitor failure. This failure(short circuit of capacitor or open circuit) leads to the power outages, high machine currents, high transientcurrents in rotor …
Published in Trends in Electrical Engineering · Vol. 1, Issue 1-2-3, 2011 · pp. 13–22 Read article
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Electric Vehicle Range Prediction
Abstract: The introduction of new energy vehicles has emerged as a new trend in the automotive industry inresponse to growing energy and environmental issues. The electric vehicle (EV) is the driving force behind newenergy vehicles. The one major problem electric vehicles have always been the distance(range) of the travel and mapto the nearby charging stations. For range prediction in the present study, four machine learningalgorithms—multiple linear regression, random forest regression, polynomial …
Published in Trends in Electrical Engineering · Vol. 13, Issue 3, 2023 · pp. 24–32 Read article