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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 Lightweight Cost-Sensitive Explainable Ensemble Framework for Early Heart Disease Risk Prediction
Abstract: Cardiovascular disease is still one of the leading causes of death, and hence, the early prediction of risk is a very important task in preventive medicine. Although recent studies have shown encouraging results in the application of machine learning algorithms to the prediction of heart disease, it has been noticed that most of the algorithms are more concerned with accuracy-driven optimization than the concerns of safety and false negatives. In …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 4, Issue 2, 2026 Read article
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Analyzing and Predicting Academic Behavior from Peer Pressure Indicators Using Machine Learning
Abstract: The academic achievement of a student is determined by their capability, but also by the companions with whom they associate. Friends can have a positive impact on students' motivation for school, and at times friends are distractions leading to a lack of attention on their school assignments. This particular study focuses on the number and quality of companions students associate with and to what extent that could be used as …
Published in International Journal of Education Sciences · Vol. 3, Issue 2, 2026 Read article
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Analysis on Link Prediction Algorithm for Social Network
Abstract: AbstractThe paper presents the analysis on link prediction algorithm for social network. Link prediction is one of the essential problems in computational social science. A principally general means to predict subsistence of unnoticed links is via structural similarity metrics, such as the number of common neighbors; node pairs with higher similarity are thus deemed more expected to be linked. There have been many algorithms to solve for the link prediction …
Published in Current Trends in Signal Processing · Vol. 10, Issue 1, 2020 · pp. 1–7 Read article
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Jaccard Index Versus Preferential Attachment: A Comparative Study of Similarity Based Link Prediction Techniques in Complex Networks
Abstract: Link prediction is a critical task in network analysis that aims to forecast potential connections between nodes. Numerous methods have been developed to address this challenge, with similarity-based techniques gaining substantial attention due to their simplicity and effectiveness. This research work presents a comprehensive review of two prominent similarity-based link prediction techniques, namely the Jaccard Index and Preferential Attachment. The Jaccard Index measures the similarity between two nodes based on …
Published in Journal of Computer Technology & Applications · Vol. 14, Issue 2, 2023 · pp. 7–11 Read article
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Nt-Probnp and Nitrogen Species Measurement As Useful Biomarkers for Detection of Asymptomatic Heart Failure in Patients with Type 2 Diabetes
Abstract: Insidious heart failure is associated or complicated hypertension, coronary artery disease, diabetes, chronic lung disease, atrial fibrillation, renal failure, depression, and anemia. High plasma NT-proBNP level found to be a predictive value in detecting latent left ventricular diastolic dysfunction. The aim of the study is to detect asymptomatic heart failure in outpatient type 2 diabetic patients using plasma NT-proBNP as a diagnostic biomarker of heart failure and nitrogen species as …
Published in Research and Reviews: A Journal of Medicine · Vol. 5, Issue 1, 2015 · pp. 8–12 Read article
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Review on Machine Learning Techniques for Heart Failure Analysis in Health Industries
Abstract: There are few bodily components as crucial as the heart. It aids in the filtration and distribution of blood to every area of a body. The world's biggest cause of death is heart disease. It has been reported that symptoms include breathing difficulties, fast heartbeat, and chest discomfort. They analyze this data on a regular basis. This review begins with a brief introduction of cardiac disease and the present methods …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 13, Issue 1, 2024 · pp. 29–43 Read article
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Fertilizer Prediction Using Machine Learning
Abstract: Fertilizer prediction is a critical aspect of modern agriculture, aimed at optimizing resource utilization while maximizing crop yields. In recent years, machine learning (ML) techniques have emerged as powerful tools for addressing this challenge by leveraging data-driven approaches to predict the optimal type and quantity of fertilizer required for different crops and soil conditions. This research paper provides a comprehensive review of the existing literature and methodologies employed in fertilizer …
Published in Journal of Mechatronics and Automation · Vol. 11, Issue 2, 2024 · pp. 26–35 Read article
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A Web Application for Predicting Diabetes Using Machine Learning Methods
Abstract: Diabetes is a long-term disease caused by high glucose quantity in the blood. It has the potential to result in serious health complications like heart disease, hypertension, and ocular damage. It is good to identify any health issues as early as possible to get the right medical treatment and make necessary lifestyle adjustments. One makes use of machine learning techniques to predict diabetes and develop treatment options using actual cases. …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 3, 2024 · pp. 92–102 Read article
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Simulation and Experimental Analysis of Abuse Testing for Prediction of Life Cycle for Lithium Ion Battery Cell and Pack Level
Abstract: Lithium-ion batteries play a crucial role in contemporary technology, serving as the power source for everything from consumer gadgets to electric vehicles. However, their safety and longevity are significant influenced by the reperformance under extreme conditions, commonly referred to as ab use testing .This paper explores the simulation and analysis of ab use testing and life cycle prediction for lithium-ion batteries at both the cell and pack levels. Abuse testing …
Published in International Journal of Mechanical Dynamics and Systems Analysis · Vol. 2, Issue 2, 2024 · pp. 1–24 Read article
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Real-Time Cab Fare and ETA Prediction Using API Integration
Abstract: The exponential proliferation of ride-hailing platforms has necessitated the formulation of sophisticated and highly responsive predictive models for cab fare estimation and estimated time of arrival (ETA) computation. This work elucidates a robust framework leveraging real-time application programming interface (API) integration from Uber and Ola within a Flutter-based ecosystem to enhance predictive analytics. By assimilating real-time geospatial data, dynamic pricing algorithms, and latency-optimized API responses, this study investigates the empirical …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 2, 2025 · pp. 08–15 Read article
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Comparative Study of AI-Driven Fashion Trend Prediction System Using AI and ML: A Review
Abstract: To overcome the challenges in fashion trend forecasting, researchers have introduced several advanced and data-driven approaches. One such method uses a long short-term memory (LSTM) model combined with an encoder-decoder architecture to extract meaningful fashion content and recognize styles from product images. This model achieves higher accuracy in predicting upcoming fashion trends by incorporating varying price intervals and has shown impressive results when evaluated on the Amazon fashion dataset. Another …
Published in E-Commerce for Future & Trends · Vol. 12, Issue 2, 2025 · pp. 35–41 Read article
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Application-Driven Rule-Based Framework for Lubrication Failure Modes in Industrial Systems
Abstract: Modern lubricants increasingly rely on polymer-based composites, integrating synthetic base oils, polymer thickeners and solid additives like MoS₂ and PTFE for high-performance applications. These formulations not only enhance thermal and mechanical stability but also enable low-friction operation across diverse industrial conditions. Lubrication-related failures represent a critical cause of unplanned downtime and reduced reliability in industrial machinery. This paper presents an application-driven, rule-based framework designed to assess and mitigate lubrication failure …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 522–531 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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Enhancement in Biomedical Polymer Nanocomposites: Biocompatibility and Mechanical Property Predictions using Machine Learning
Abstract: A machine learning (ML)-based framework is developed and validated through experimental analysis and comparative modeling to enhance system dependability and improve prediction performance. The proposed framework includes key stages such as data preprocessing, feature evaluation, model training, and performance benchmarking to determine the most effective prediction technique. Several machine learning models were evaluated, including Ensemble models, Artificial Neural Networks (ANN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 275–296 Read article
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Model to Predict a Ratio Control of Hydrocarbon Acid and Water in a Packed Bed Reactor
Abstract: Model development was carried out to examine the ratio of hydrochloric acid gas and water in a packed bed reactor. The research predicted increase in output with increase in time, revealing the effectiveness ratio control of hydrochloric acid separation from water using absorption column mechanism. The density of the products played an active role in the separation process as well as in control action function. The developed model can be …
Published in Emerging Trends in Chemical Engineering · Vol. 8, Issue 1, 2021 · pp. 45–52 Read article
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Reducing the Uncertainty of Marine Accidents Failure Rate in Harsh Environment; Using Bayesian Theory
Abstract: The interest in arctic activities is strong, but has not led to significant quantifying analysis over the fact of finding rescannable failure rate. To attract investors and promote the development of a marine industry, a clear concept of project risk is paramount; in particular, issues relating to human role in operation are critical. In the public domain, reliability of information is often scarce or inappropriate for such environment. Also, an …
Published in Journal of Industrial Safety Engineering · Vol. 3, Issue 2, 2016 · pp. 30–39 Read article
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Adsorption Isotherm Study for Activated Carbon Produced from Cassava Peel
Abstract: Adsorption isotherm study of Activated Carbon produced from cassava peel was carried out using phenol solution as the adsorbate. Langmuir and Freundlich isotherm models were employed for the study. Varying ratios of 1:1, 0.75:1, 0.5:1, and 0.25:1 activating agent to cassava peel were used for the formulation of the Activated Carbon. The Freundlich R2 value for the 1:1, 0.75:1, 0.5:1, and 0.25:1 formulations was 0.822, 0.979, 0.971 and 0.974, respectively. …
Published in Journal of Materials & Metallurgical Engineering · Vol. 4, Issue 3, 2014 · pp. 8–12 Read article
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Prediction of Excitation Current of Synchronous Machines Based on Neural Network Model
Abstract: There are several difficulties found to estimate the excitation current & and optimum input parameters of synchronous motors. Heuristic methods are frequently used to weightt the problem's parameters or optimum coefficients. As a result, a neural network model is modified in this study to explore the best parameters and estimate the excitation current of a synchronous motor with minimal prediction errors for both the testing dataset and cross validation. Excitation …
Published in Recent Trends in Electronics Communication Systems · Vol. 10, Issue 1, 2023 · pp. 28–33 Read article
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Predicting oxygen deficit effect on two dimensional Flow transport of pasteurella in eleme stream
Abstract: Pasteurella deposition in Eleme stream was investigated from point sources of waste discharge in different locations, predictive model application were used to monitor the transport process of the contaminant in the stream, the study observed the growth rates from the graphical trend in gradual increase to the optimum level, while that of experimental values experienced similar condition but with rapid increase on few figures, the system observed predominant parameter such …
Published in Recent Trends in Fluid Mechanics · Vol. 8, Issue 1, 2021 · pp. 1–14 Read article
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Prediction of Traffic using Economic Attributes
Abstract: AbstractIncrease in traffic flow in a region reflects the development of transport infrastructure. Transport infrastructure development arises due to new investment in the region as it is a significant factor for assessing the social and economic viability of any highway project. Impact of economics on traffic is difficult to analyze traffic flow projection. To comprehend the objective, economic attributes such as investment and employment generated in the region are considered …
Published in Trends in Transport Engineering and Applications · Vol. 5, Issue 1, 2018 · pp. 32–36 Read article