130 results for machine learning
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Wear Estimation of an Intelligent Tire Using Machine Learning
…the accelerometer and estimates tire wear by feature classification using machine learning. In particular, the proposed tire wear estimation method is designed to estimate tread depth in four types (7, 5.6, 4.2, and 1.4 mm) at speeds of 40, 50, and 60 kmph. Based on the data obtained during several…
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Machine Learning Application in Genomic, Exercise, and Vital Datasets
Abstracts PURPOSE Machine learning (ML) refers to newly developed computer algorithms that are improved through iterative experiences. ML applications are expected to assist humans in analyzing large amounts of data. This review has outlined the application of ML in analyzing variable vital data…
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Applications of Machine Learning in Bone and Mineral Research
In this unprecedented era of the overwhelming volume of medical data, machine learning can be a promising tool that may shed light on an individualized approach and a better understanding of the disease in the field of osteoporosis research, similar to that in other research fields. This review…
From your institution’s collections
- Machine learning dan computational intelligenceWidodo Budiharto · 2016 · UNILAK
- Machine learning (konsep dan implementasi)Yaya Heryadi · 2020 · UNILAK
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Artificial intelligence, machine learning, and deep learning in women’s health nursing
Artificial intelligence (AI), which includes machine learning and deep learning has been introduced to nursing care in recent years. The present study reviews the following topics: the concepts of AI, machine learning, and deep learning; examples of AI-based nursing research; the necessity of…
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Machine Learning Applications in Endocrinology and Metabolism Research: An Overview
Machine learning (ML) applications have received extensive attention in endocrinology research during the last decade. This review summarizes the basic concepts of ML and certain research topics in endocrinology and metabolism where ML principles have been actively deployed. Relevant studies are…
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Diagnostic Accuracy of Machine Learning Algorithms for Hepatitis A Antibody
Purpose: The objective of this study was to develop a model for predicting the positivity of hepatitis A antibody based on nationwide health information using a machine learning technique. Methods: We used a data set that included the records of 4,626 samples. the data was randomly divided into a…
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Optimization based machine learning algorithms for software reliability growth models
…many previous studies have relied on single optimization methods or deep learning approaches, which are prone to local optima and extrapolation issues, reducing prediction accuracy. To fill this gap, current study employs a broader range of optimization algorithms based on the Least Squares Method…
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How Can Clinicians Leverage Vibe Coding for Machine Learning and Deep Learning Research?
Research applying machine learning and deep learning has become increasingly common in medicine. However, for clinicians lacking Python programming skills, conducting such research has often been an intractable task—even when ample data were available. The emergence of ‘vibe coding’ in 2025 has…
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Machine Learning-Based Interpretation of Operating Conditions for Enriching Polyhydroxyalkanoate-Accumulating Microorganisms
…16S rRNA gene amplicon sequencing data from 48 samples. A leakage-free machine learning framework was applied to interpret the relationships among operating conditions, PHA accumulation, and microbial community dynamics. Under reactor-wise cross-validation, XGBoost explained PHA content from…
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Comparing the Performance of Machine Learning and Deep Learning Algorithms in Wastewater Treatment Process
This study assessed the performance of single and modified algorithms based on machine learning and deep learning for wastewater treatment process. More specifically, this study adopted support vector machine (SVM), random forest (RF), and artificial neural network (ANN) for machine learning as…
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Development of Diagnosis Algorithm for Cam Wear of Paper Container Using Machine Learning
Recently, improvement of productivity of the paper cup forming machine has being conducted by increasing manufacturing speed. However, rapid manufacturing speed imposes high load on cams and cam followers. It accelerates wear and cracking, and increases paper cup failure. In this study, a failure…
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Developing Machine Learning Based Models for Prediction of Pesticide Properties using Molecular Descriptors
This study assessed the performance of six machine learning (ML) models which were developed to predict various properties of 888 pesticides based on molecular descriptors. The log-transformed soil organic carbon partition coefficient values obtained from the Pesticide Properties Database (PPDB)…
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Machine Learning vs. Statistical Model for Prediction Modelling: Application in Medical Imaging Research
…model is developed based on the traditional statistical model, as well as machine learning, to account for the high-dimensional data. In this review, we investigated the statistical and machine learning methods used in clinical prediction model research, and briefly summarized each analytical…
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Prediction Models of Blood Glucose Change During Aerobic Exercise Using Machine Learning Techniques
PURPOSE This study aimed to explore the relationship between blood glucose level changes and body characteristics during exercise and to present six models for predicting changes in blood glucose levels during exercise. METHODS 148 healthy men and women (age: 31.9±9.7 year, fasting blood glucose…
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Machine Learning-based Classification of Acoustic Emission Signals in SiC Cathode Ultrasonic Machining Process
This study analyzed acoustic emission (AE) signals generated during ultrasonic machining of SiC cathodes and evaluated classification performances of various machine learning models. AE data were collected in both waveform and hit formats, enabling signal characterization through statistical…
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A Machine Learning-Based Signal Analytics Framework for Diagnosing the Anomalies of Centrifugal Pumps
…by collecting relevant multivariate data from several natures. Proposed machine learning models can be used for detecting and diagnosing pump faults via analytical processes containing signal preprocessing and feature engineering procedures. Simulation and case studies from rotating machinery have…
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A Study on 3D Printing Conditions Prediction Model of Bone Plates Using Machine Learning
…of the product in the industry. In this paper, a methodology using machine learning was proposed to improve the dimensional accuracy. The proposed methodology was evaluated through case studies. The results predicted by the machine learning methodology proposed in this paper and the experimental…
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Development of Forecasting Model for Machine Learning-Based Landfill Leachate Generation Using Linear Interpolation
Purpose : The purpose of this study is to compare single models according to the missing value handling techniques for predicting leachate generation. Method : Input factors such as leachate generation, landfill gas generation, and weather data (precipitation, wind speed, radiation, temperature…
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Diagnosis of invasive encapsulated follicular variant papillary thyroid carcinoma by protein-based machine learning
…investigated the potential value of a protein biomarker panel defined by machine learning for identifying the invasive encapsulated follicular variant of papillary thyroid carcinoma, initially using formalin-fixed paraffin-embedded samples. Methods We developed a supervised machine-learning model…
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Assessing the Impact of Temporal Resolution Using BSM1 on the Performance of Machine Learning
Objectives This study aims to establish efficient strategies for data-driven operational management by examining the variations in machine learning modeling outcomes and data characteristics based on data acquisition intervals and methods. Methods The BSM1 was used to simulate wastewater treatment…