8.473 hasil untuk machine learning
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A machine learning computational approach for the mathematical anthrax disease system in animals
Objectives: The current research investigations present the numerical solutions of the anthrax disease system in animals by designing a machine learning stochastic procedure. The mathematical anthrax disease system in animals is classified into susceptible, infected, recovered and vaccinated…
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A machine learning-based model for predicting survival in patients with Rectosigmoid Cancer
…RSC patients. Subsequently, models were constructed using six different machine learning algorithms. Finally, the discrimination, calibration, and clinical applicability of each model were evaluated to determine the optimal model. Results: Through univariate and multivariate Cox regression…
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Construct prediction models for low muscle mass with metabolic syndrome using machine learning
…was then used to screen for important features. A total of nine Machine learning (ML) models were constructed in this study. Area under the curve (AUC), F1 Score, Recall, Precision, Accuracy, Specificity, PPV, and NPV were used to evaluate the model’s performance and explain important predictors…
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Optimising test intervals for individuals with type 2 diabetes: A machine learning approach
…excessive or insufficient support for patients at different risk levels. Machine learning (ML) developments offer new opportunities for personalised medicine in clinical practice. Objective: To demonstrate the potential of ML to guide resource allocation and tailored disease management, this study…
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Building models, building capacity: A review of participatory machine learning for HIV prevention
A growing number of researchers and practitioners are embracing a “participatory turn” in machine learning (ML) to improve model development, prevent harm, and provide communities more influence over systems that impact them. In this paper, we explore the intersection of participatory practices in…
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A descriptor-free machine learning framework to improve antigen discovery for bacterial pathogens
…screening of proteomes. Within RV, one prominent approach is to train Machine Learning (ML) models to classify PAs. These models can be used to predict unseen protein sequences and assist researchers in selecting promising candidates. Traditionally, proteins are fed into these models as vectors of…
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Machine learning techniques for continuous genetic assignment of geographic origin of forest trees
…applying the Matérn kernel function, genomic prediction (GP) and deep learning (DL), using two genome-wide single nucleotide polymorphism (SNP) datasets of trees from across Europe. The first dataset comprises 30,000 SNPs from 865 European beech (Fagus sylvatica) trees, the second dataset consists…
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Machine learning augmented diagnostic testing to identify sources of variability in test performance
…tests towards individuals who are more likely to be infected. We use machine learning to assess the surrounding risk landscape under which a diagnostic test is applied to augment its interpretation. We develop this to predict the occurrence of bovine tuberculosis incidents in cattle herds…
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Key personality and training factors influencing athletes’ mental health - based on machine learning
Athletes face a higher risk of mental health disorders compared to the general population, and prior theoretical and empirical work suggests that personality traits and training-related factors may play important roles in shaping athletes’ mental health. This study aimed to identify the key…
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Geospatial variation and machine learning approaches to predict open defecation practice in Zambia
…and hepatitis A. The purpose of this study was geospatial variation and machine learning approaches to predict open defecation in Zambia. Methods: This study used secondary data analysis from the cross-sectional Zambia Demographic and Health Survey (ZMDHS) 2024. Spatial distribution, spatial…
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Cross-dataset benchmarking of machine learning models for marine and atmospheric environmental prediction
…decision-making, yet practitioners still lack clear guidance on which machine-learning models are reliable across heterogeneous environmental tasks. We therefore developed a unified, leakage-aware benchmark across nine datasets, of which seven passed quality checks for modeling, spanning…
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Radiographic legal age estimation based on third molar development using Machine-learning algorithms
…procedure. The aim was to test the performance of supervised machine learning (SML) to classify individuals according to the threshold of 18 years using the third molar index (I3m). Methods: Panoramic radiographs (n = 597) of 52% males and 48% females between 13 and 26 years were used. Three…
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Unlocking precision diagnostics: A multimodal framework integrating metabolomics with advanced machine learning techniques
…spectrometry, and nuclear magnetic resonance. Deep Transfer Learning and Multiple Kernel Learning demonstrated superior performance, significantly improving classification accuracy, sensitivity, and robustness compared to single-modality analyses. Deep Transfer Learning employed a custom…
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Data subdivision approach enhances machine learning-based mortality prediction in pediatric ICU patients
Objective: To evaluate machine learning–based models for predicting all-cause mortality in pediatric ICU patients using comprehensive biochemical panels, with a focus on addressing missing data and class imbalance. Materials and methods: A retrospective analysis was performed on a publicly…
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Advancing outbreak detection: Hybridizing machine learning with wavelets for weekly dengue case forecasting
Background: Traditional surveillance systems often struggle with the volatility of weekly case data, limiting timely prevention and control efforts. In the Philippines, the standard method for setting outbreak thresholds relies on historical moving averages that are highly affected by extreme…
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Resource-efficient data transmission for WiFi-capable bio-loggers based on machine learning
…from even the simplest methods to reduce transmission costs. We employ machine learning techniques, specifically small decision trees, to enable a bio-logger to recognise a chosen behaviour based on its sensor readings. Based on the recognised behaviour, the logger filters which data to transmit…
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Use of machine learning to detect Escherichia coli in drinking water in Bangladesh
…and viruses. The aim of the study is to evaluate the performance of machine learning (ML) tools to detect E. coli in drinking water in Bangladesh using surveillance data under two scenarios: an imbalanced dataset and a balanced dataset. We utilized data from the 2019 Bangladesh Multiple Indicator…
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Machine learning-based quantitative structure–activity relationship model for antibiotic prediction and discovery
…and expensive. To accelerate antibiotic discovery, the integration of machine learning algorithms with Quantitative Structure–Activity Relationship (QSAR) calculations could provide a rapid solution. Thus, this study combines a QSAR model and machine learning algorithms to predict antibacterial…
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Integrating Quantum Machine Learning and Ontology Matching for Efficient Semantic Web Services discovery
Existing Semantic Web Service (SWS) discovery approaches based on classical machine learning suffer from limited representational capacity in high-dimensional semantic feature spaces and fail to fully exploit structured ontological knowledge for semantic alignment. This paper addresses these gaps…
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Machine learning–driven integration of 24-hour ambulatory blood pressure and its variability
…Therefore, we investigated if using a multivariable unsupervised machine learning model we could derive clinically meaningful groups integrating both the average BP values and the BPV into a single model. By combining these features, we aimed to facilitate the use and interpretation of this…
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