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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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Proportion of Malnutrition in Children of 0–60 months Attending Well Baby Clinic of Tertiary Care Hospital of Gujarat
Abstract: Protein energy malnutrition is one of the leading causes of morbidity and mortality in children under the age of five in developing countries. India being one of these countries malnutrition is a major public health problem. A hospital based cross sectional study was conducted including 200 children of 0–60 months coming at Well baby clinic of tertiary care hospital to know the proportion of malnutrition. Anthropometric measurements were taken. The …
Published in Research and Reviews : A Journal of Immunology · Vol. 4, Issue 3, 2014 · pp. 1–5 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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Application of Resource Allocation Similarity Based Link Prediction in Wireless Networks
Abstract: Link prediction in wireless networks plays a crucial role in predicting missing connections within multiplex networks. This study focuses on the utilization of similarity-based link prediction methods in wireless networks. These methods assume that the likelihood of linkage between nodes is determined by their similarity, based on shared features. Several similarity measures, such as Common Neighbors (CN), Preferential Attachment (PA), Adamic-Adar (AA), and Resource Allocation (RA) indices, are commonly employed …
Published in International Journal of Wireless Security and Networks · Vol. 1, Issue 2, 2023 · pp. 37–42 Read article
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Heart Disease AI-based Prediction: A Comparative Analysis
Abstract: The present investigation looks at how well various machine learning algorithms predict cardiac disease. Since heart disease is one of the major causes of death worldwide, early detection and precise diagnosis are essential for managing and treating the condition. Our goal is to enhance diagnostic processes and improve patient outcomes by leveraging machine learning techniques. Six widely-used machine learning algorithms are evaluated in this research paper. These algorithms were selected …
Published in Trends in Mechanical Engineering & Technology · Vol. 14, Issue 2, 2024 · pp. 21–29 Read article
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Time Series Methods in Meteorology: A Review of Predictive Models and Applications
Abstract: The accurate prediction of time series data holds substantial significance in various fields, enabling informed decision-making and resource optimization. In this study, temperature variations over time are predicted using the Autoregressive Integrated Moving Average (ARIMA) model. Reliable temperature projections are more important now than ever because of climate change and its effects. For time series prediction problems, the ARIMA model—which is well-known for its ability to capture temporal dependencies in …
Published in Research & Reviews : Journal of Space Science & Technology · Vol. 13, Issue 2, 2024 · pp. 35–46 Read article
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Climate Change Including Forest Fire Prediction using Machine Learning and Deep Learning
Abstract: Climate change alludes to long haul shifts in temperatures and atmospheric conditions. These movements might be regular, for example, through varieties in the sun-oriented cycle. In any case, since the 1800s, human exercises have been the fundamental driver of climate change, basically because of consuming fossil fuels like coal, oil and gas. Many individuals think climate change mostly implies hotter temperatures. Be that as it may, the temperature climb is …
Published in Journal of Computer Technology & Applications · Vol. 15, Issue 3, 2024 Read article
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The Integrity of Eadic-Hofstee Plot Model to Predict the kinetic Parameters of Crude Oil Degradation using Vernonia amygdalina Stem
Abstract: The integrity of Eadic-Hofstee concept was tested for the determination of the functional coefficients and parameters of crude oil degradation kinetics. The techniques enhanced the relationship between the substrate divided by the specific rate of the substrate degradation against substrate concentration (TPH). The investigation reveals the values of the biokinetic parameters of maximum specific rate of substrate degradation (Vmax) and the equilibrium constant values of the substrate degradation (Ks). The …
Published in Journal of Petroleum Engineering & Technology · Vol. 14, Issue 2, 2024 · pp. 37–47 Read article
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Statistical Models for Predicting Genetic Variability and Disease Susceptibility
Abstract: Differences in genetics are key to understanding why some individuals are more prone to certain diseases than others. Recent advancements in genomic research, combined with statistical modeling techniques, have made significant strides in predicting disease risk based on genetic factors. This review explores the application of statistical models for predicting genetic variability and their role in disease susceptibility. We discuss traditional methods like linear regression and genome-wide association studies (GWAS), …
Published in Research and Reviews : Journal of Computational Biology · Vol. 14, Issue 1, 2025 · pp. 30–34 Read article
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ML-Based Predictive Modeling of Mechanical Properties in 3D-Printed Polymer Composites for IoT Applications
Abstract: This study aims to develop an interpretable and high-accuracy machine learning framework for predicting the mechanical properties of 3D-printed fiber-reinforced polymer composites, with a focus on structure–property correlations relevant to polymer processing and functional performance. Composite specimens based on PLA and ABS matrices were fabricated using FDM with varying weight fractions (5–20 wt%) of carbon and glass fibers. Standardized mechanical testing (ASTM D638, D256, D790) was performed to evaluate tensile …
Published in Journal of Polymer & Composites · Vol. 13, Issue 4, 2025 · pp. 61–78 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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Exploring Spirituality and Cognitive Styles as a Predictor of Suicidal Ideation in Urban Population
Abstract: Suicidal ideation, or the thought of suicide, is a complicated and multi-dimensional issue of public health that effects individuals globally at a individual level, in families and in society at large. It is one of the main causes of death in the world. Beliefs, practices and experiences pertaining to the transcendence, sacred or divine, are all included in the broad category of spirituality. In cognitive psychology, the term "cognitive style" …
Published in International Journal of Behavioral Sciences · Vol. 2, Issue 2, 2025 Read article
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Machine Learning Approach to Predict the Performability and Emissions of Diesel Engine Fueled with Doped Biodiesel Blend
Abstract: Enhancing the performability and emission characteristics of diesel engines has been a difficult task in light of growing concerns about global warming and other negative effects, as diesel accounts for 70% of global energy demand. In this study, engine performance and exhaust emissions for various fuel blends were thoroughly evaluated using machine learning techniques to predict engine emission and performance behavior. We focused on biodiesel blend and nanoparticle additive concentration …
Published in International Journal of Mechanical Dynamics and Systems Analysis · Vol. 3, Issue 1, 2025 · pp. 1–12 Read article
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ML-Enhanced Self-Healing Fiber-Reinforced Polymer Composites with Embedded IoT Sensors for Damage Prediction
Abstract: Fiber-reinforced polymer (FRP) composites are widely used in aerospace and structural systems; nevertheless, the potential for microcracking and fatigue-induced performance degradation remains an obstacle with respect to improved service life. Traditional self-healing methods, while performing well on a chemical level, often lack real-time diagnostic awareness and adaptive control. To circumvent this, we developed a machine-learning augmented self-healing FRP composite, in which a DCPD–Grubbs catalytic matrix was combined with IoT sensor …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 188–208 Read article
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Caries-Preventive Effects of Self-Applied Subacidic 0.5% NaF-HF Gel via Toothbrushing in 7–8-Year-Old Schoolchildren: A Randomized Controlled Clinical Trial
Abstract: To assess the caries-preventive effectiveness of self-applied subacidic 0.5% sodium fluoride–hydrofluoric acid (NaF-HF) gel used during toothbrushing in children aged 7–8 years Objective: To assess the caries-preventive effectiveness of self-applied subacidic 0.5% sodium fluoride–hydrofluoric acid (NaF-HF) gel used during toothbrushing in children aged 7–8 years. Methods: This 1-year, multi-arm, double-blind, placebo-controlled, parallel-group randomized study evaluated the caries-preventive efficacy of self-applied 0.5% NaF-HF gel among primary schoolchildren. A total of 1200 …
Published in Research and Reviews: A Journal of Dentistry · Vol. 17, Issue 1, 2026 · pp. 14–19 Read article
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Deep Learning-Based Thermal Prediction Models for Solid-State Electronic Devices
Abstract: The rapid advancement of solid-state electronic devices in high-performance computing, communication systems, automotive electronics, and renewable energy applications has significantly increased concerns related to thermal management and device reliability. Excessive heat generation in semiconductor devices adversely affects operational efficiency, switching performance, lifespan, and overall system stability. Traditional thermal prediction methods often require complex numerical computations and extensive simulation time, making them less suitable for real-time monitoring and adaptive control applications. …
Published in International Journal of Solid State Innovations & Research · Vol. 4, Issue 1, 2026 Read article
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Predictive Analytics for Student Well-Being and Occupational Success
Abstract: The integration of predictive analytics into higher education has significantly transformed institutional decision-making processes. However, prevailing implementations remain predominantly performance-centered, focusing on dropout prediction and grade forecasting rather than holistic developmental outcomes. Concurrently, higher education systems worldwide are confronting escalating concerns regarding student mental health, disengagement, career uncertainty, and labor market volatility. These intersecting challenges necessitate a broader theoretical reconceptualization of predictive analytics—one that integrates psychological well-being and long-term occupational …
Published in International Journal of Education Sciences · Vol. 3, Issue 1, 2026 · pp. 155–163 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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Prediction of Compressive Strength of Concrete Using Machine Learning Techniques
Abstract: Compressive strength of concrete is an important parameter for designing any concrete structure. Compressive strength of concrete is a complex nonlinear function of its ingredients. Prediction of Concrete compressive strength plays a vital role in pre design phases of the structure and quality control of construction. The conventional methods of compressive strength determination are time consuming, so the use of data mining methods to predict the strength beforehand is helpful. …
Published in Journal of Construction Engineering, Technology & Management · Vol. 5, Issue 3, 2015 · pp. 34–41 Read article
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Artificial Intelligence-based Chatbot for Disease Prediction
Abstract: Nowadays leading a healthy lifestyle is very important. Sometimes it is even difficult to find proper medical consultation in unsocialized and remote areas. One such solution to these issues is the development of an Artificial Intelligence based Chatbot for Disease Prediction. A Chatbot is a software program that simulates human conversations, through AI’s Natural Language Processing (NLP) capability. A text-to-text chatbot for disease prediction can involve patients into an interactive …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 10, Issue 1, 2023 · pp. 29–36 Read article
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Node Deployment Techniques for Link Prediction in Heterogeneous Social Networks
Abstract: AbstractThis research analyses the coverage problem in heterogeneous social network system with two types of sensor nodes having different sensing ranges. The Particle Swarm Optimization (PSO) algorithm is implemented for coverage optimization in heterogeneous network system. This algorithm is used for finding the optimal deployment of the sensor nodes by using specific fitness function. The performance of sensor nodes after running PSO algorithm is evaluated by using Euclidean distances for …
Published in Recent Trends in Sensor Research & Technology · Vol. 7, Issue 1, 2020 · pp. 16–22 Read article