8.473 hasil untuk machine learning
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Machine learning driven optimization of compressive strength of 3D printed bio polymer composite material
…process, a widely used additive manufacturing technique. Six different machine learning models (ML) were utilized to estimate CS using key process parameters, namely print speed (PS), layer height (LH), and printing temperature (PT). Among six ML models, Polynomial Regression (PR) performed best…
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Automated software bug severity classification using ensemble machine learning scheme: A real case study
…there are a few studies on automatic bug report classification using machine learning techniques, they mainly focus on structured open-source datasets. This paper presents an ensemble learning approach utilizing various multiclass machine learning, text classification, and natural language…
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Machine learning-driven Diabetes Health Tracer (DHT): Optimizing prognosis using RaSK_GraDe and RaSK_GraDeL models
Diabetes mellitus presents a significant global health challenge, particularly in regions like Pakistan, India, and Bangladesh. Machine learning (ML) techniques offer promising solutions for diabetes prediction, surpassing traditional methods in reliability and efficiency. This research conducts a…
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Biased sampling driven by bacterial population structure confounds machine learning prediction of antimicrobial resistance
…surveillance of bacterial pathogens, producing a wealth of data to train machine learning (ML) applications to predict AMR and identify resistance determinants. However, bacterial populations are highly structured, and sampling is biased towards human disease isolates, violating ML assumptions of…
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Improved predictive formulae for wave overtopping at sloped breakwaters using interpretable machine learning models
…traditional empirical, physical, and numerical models remain important, Machine Learning (ML) has recently emerged as a powerful complementary tool. This study presents a ML–based framework to predict mean wave overtopping discharge at sloped breakwaters, with a focus on both predictive accuracy…
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Topological data analysis for predicting disease outbreaks in humanitarian settings: A machine learning approach
…represent complex system structure. Methods: We developed and evaluated a machine-learning framework incorporating topological data analysis to predict cholera and measles surge events (binary indicators per LGA-week) across 97 Local Government Areas in Nigeria between 2018 and 2023. These 97 LGAs…
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Leveraging nonlinear relationships and interactions to improve 30-day pneumonia readmission machine learning models
…rates. Published models tend to rely on traditional methods or advanced machine learning models that exclude continuous variables, overlooking opportunities to uncover nonlinear relationships and interactions. In response, we used electronic medical record (EMR) data, including continuous variables…
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Machine learning-driven identification of virulence determinants in Borrelia burgdorferi associated with human dissemination
…disease progression and optimize treatment strategies. Here, we applied machine learning (ML) approaches to identify specific amino acid residues within surface-exposed virulence factors that predict human dissemination phenotypes. Utilizing the published whole genome sequences from 299 clinical…
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Explainable machine learning for the early differentiation of pediatric bronchopneumonia using routine laboratory parameters
…using routine, cost-effective laboratory parameters analyzed with machine-learning algorithms. Methods: This retrospective study consecutively enrolled 532 pediatric patients who presented with mild respiratory symptoms at their initial visit, comprising 218 in the BP group and 314 in the Upper…
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Predicting mental health risk in adolescents using interpretable machine learning: Evidence from SHAP analysis
…remains incompletely understood. This study applied an interpretable machine learning framework to identify key predictors of elevated mental health risk among adolescents and characterize their relative and non-linear contributions. Methods: Data were drawn from the 2021 Youth Risk Behavior Survey…
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Comparative machine learning models for predicting the compressive strength of ultra-high-performance concrete
…models are commonly used to evaluate UHPC compressive strength, machine learning approaches offer an efficient alternative for capturing complex nonlinear relationships. This study develops a machine learning–based framework to predict the compressive strength of UHPC and compares the predictive…
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A data-driven approach to hepatitis C forecasting using machine learning and epidemiological models
…nonlinearity and inherent complexity. Furthermore, we proposed a deep learning-based technique to solve the nonlinear differential equations governing the model for transmission of the hepatitis C virus in six compartments. The proposed deep learning-based model is compared with other established…
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Optimizing football analytics: Dimensionality reduction meets machine learning for offensive and defensive player profiling
…workflow. Dimensionality reduction techniques (PCA) and explainable machine learning (Shapley Additive exPlanations [SHAP]) with predictive modeling were applied. Eight algorithms were trained and compared to predict PC1 Offensive and PC1 Defensive. Results: The First Offensive Principal Component…
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Distinguishing common respiratory pathogens using machine learning of symptom profiles to prioritize diagnostic testing
…age and sex groups. By combining these symptom-based categories with machine learning and contemporaneous epidemiological information, we developed an approach to prioritize likely pathogens and narrow the scope of diagnostic testing. Our approach is intended to support early-stage decision-making…
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Proteomic machine-learning signatures associated with carotid plaque: A population-based hypothesis-generating study
…the risk of stroke. The aim of this study was to develop advanced machine learning (ML) models to predict the presence of CP in the general population. The study involved 693 participants from the Polish Longitudinal University Study – Bialystok PLUS, a representative sample of the local…
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Machine learning and deep learning techniques to support clinical diagnosis of arboviral diseases: A systematic review
…of arboviral diseases to support clinical diagnosis based on Machine Learning (ML) and Deep Learning (DL) models. Method: We carried out a Systematic Literature Review (SLR) in which Google Scholar was searched to identify key papers on the topic. From an initial 963 records (956 from string-based…
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Evaluating Machine Learning and Deep Learning models for predicting Wind Turbine power output from environmental factors
This study presents a comprehensive comparative analysis of Machine Learning (ML) and Deep Learning (DL) models for predicting Wind Turbine (WT) power output based on environmental variables such as temperature, humidity, wind speed, and wind direction. Along with Artificial Neural Network (ANN)…
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Machine learning prediction model based on enhanced bat algorithm and support vector machine for slow employment prediction
…prominent, becoming an urgent problem to be solved. Data mining and machine learning methods are used to analyze and predict the employment prospects for graduates and provide effective employment guidance and services for universities, governments, and graduates. It is a feasible solution to…
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The Use of Machine Learning Methodologies to Analyse Antibiotic and Biocide Susceptibility in Staphylococcus aureus
…using the standard statistical approaches in phenotypic data. Therefore, machine learning techniques were applied to explore the data. Results: In this pioneer study, we demonstrated that reduced susceptibility to two common biocides, chlorhexidine and benzalkonium chloride, which belong to…
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A Machine Learning Method for the Prediction of Receptor Activation in the Simulation of Synapses
…may involve large numbers of synaptic connections. We have developed a machine-learning based method that can accurately predict relevant aspects of the behavior of synapses, such as the percentage of open synaptic receptors as a function of time since the release of the neurotransmitter, with…
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