8.473 hasil untuk machine learning
Menampilkan 221–240 · 1,31 detik · RSS
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Artikel Jurnal Teks lengkap
Bridging the gap between mechanistic biological models and machine learning surrogates
…many simulations or when real-time results are required. Surrogate machine learning (ML) models can be used to approximate the behaviour of complex mechanistic models, and once built, their computational demands are several orders of magnitude lower. This paper provides an overview of the relevant…
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Personalized prediction of early childhood asthma persistence: A machine learning approach
…individuals whose symptoms will persist. Our objective was to develop machine learning models to identify which individuals diagnosed with asthma before age 5 continue to experience asthma-related visits. We curated a retrospective dataset for 9,934 children derived from electronic health record…
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Robust and efficient COVID-19 detection techniques: A machine learning approach
…be infected by and closely related to Bats. However, over the periods of learning and critical development of experimental evidence, it is found to have some similarities with several gene clusters and virus proteins identified in animal-human transmission. Despite this substantial evidence and…
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Epidemiological breast cancer prediction by country: A novel machine learning approach
…cancer and newly identified risk factors. Additionally, we aim to utilize machine learning models to predict breast cancer incidence at a country level. Following an extensive review of the available literature, we have identified a range of recently studied risk factors associated with breast…
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Combination therapy synergism prediction for virus treatment using machine learning models
…known interactions. As far as we know, this is the first dataset and learning model on combination therapy for viruses. Our proposal includes using a random forest model, an SVM model, and a deep model to train viral combination therapy. The machine learning models showed the highest performance…
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Experimental economics for machine learning—a methodological contribution on lie detection
…contribute to technological progress in the future. We argue that with machine learning (ML), a new technology is at hand, where for the first time experimental economics can contribute to enabling substantial improvement of technology. At the same time, ML opens up new questions for experimental…
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Machine learning in internet financial risk management: A systematic literature review
…risks. Presently, internet finance enterprises are progressively adopting machine learning and other artificial intelligence methods for risk alertness. What is the current status of the application of various machine learning models and algorithms across different institutions? Is there an optimal…
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Variable importance analysis with interpretable machine learning for fair risk prediction
Machine learning (ML) methods are increasingly used to assess variable importance, but such black box models lack stability when limited in sample sizes, and do not formally indicate non-important factors. The Shapley variable importance cloud (ShapleyVIC) addresses these limitations by assessing…
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Explainable machine learning on baseline MRI predicts multiple sclerosis trajectory descriptors
…1) to propose new MS trajectory descriptors that could be employed in Machine Learning (ML) regressors and classifiers to predict patient evolution; 2) to explore the contribution of ML models in discerning MS trajectory descriptors using only baseline Magnetic Resonance Imaging (MRI) studies. This…
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Analysis and interpretability of machine learning models to classify thyroid disease
Thyroid disease classification plays a crucial role in early diagnosis and effective treatment of thyroid disorders. Machine learning (ML) techniques have demonstrated remarkable potential in this domain, offering accurate and efficient diagnostic tools. Most of the real-life datasets have…
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Automating detection of diagnostic error of infectious diseases using machine learning
…predicted diagnosis and documented diagnosis, weighted by mortality. Two machine learning models were trained for prediction of infectious disease and mortality using the first 24h of data. Charts were manually reviewed by clinicians to determine whether there could have been a more correct or…
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Machine learning prediction of malaria vaccine efficacy based on antibody profiles
…trial. For this purpose, we both utilized and adapted supervised machine learning methods to identify predictive antibody profiles at two different time points: after immunization and before CHMI. We developed an adapted multitask support vector machine (SVM) approach and compared it to standard…
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Correction: Machine learning-based e-commerce platform repurchase customer prediction model
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Intelligent personalized shopping recommendation using clustering and supervised machine learning algorithms
…the MyGroceryTour platform. Our online system uses different traditional machine learning (ML) and deep learning (DL) algorithms, and provides recommendations to users in a real-time manner. It aims to help Canadian customers create their personalized intelligent weekly grocery lists based on their…
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Classification of bacterial plasmid and chromosome derived sequences using machine learning
…and 10,654 plasmids from the PATRIC database, we evaluated several machine learning models including random forest, logistic regression, XGBoost, and a neural network for their ability to classify chromosomal and plasmid sequences using nucleotide k-mers as features. Based on the methods tested, a…
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Predicting oil contamination in water using machine learning on microbial compositions
We present a compact and generative machine-learning framework that predicts oil contamination based on microbial community compositions from experimental samples. Our method combines dimensionality reduction with data augmentation and generative modeling to address high-dimensional, non-linear…
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An inflammatory biomarker panel for prediabetes classification using interpretable machine learning
…biomarkers. In this study, our objective was to develop an interpretable machine learning model using biomarkers related to oxidative stress, inflammation, and lipid metabolism to classify prediabetes independently of traditional glycemic markers, such as HbA1c. We also compared multiple biomarker…
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Autism spectrum disorder detection using diffusion tensor imaging and machine learning
…be biomarkers for this disorder. Emerging advances in neuroimaging and machine learning can provide a fast and objective alternative for its early diagnosis. We propose and evaluate a machine-learning (ML)-powered computer-aided diagnosis (CAD) system for the detection of ASD from DTI. For the…
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Prediction of myeloid malignant cells in Fanconi anemia using machine learning
Fanconi anemia (FA) is an inherited bone marrow failure syndrome with cancer predisposition. Most FA patients develop aplastic anemia during childhood and have an extremely high cumulative risk to develop cancer during their lifespan. Myeloid malignancy is one of the main neoplastic risks for…
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Artikel Jurnal Teks lengkap
Climate predictors of child undernutrition: Insights from a machine learning model
…specific climatic variables and nutrition remain unclear. This study uses machine learning to identify and quantify key climatic predictors of undernutrition among children. Methods: In a mixed-method approach, a cross-sectional study assessed nutrition and child health outcomes in May 2024, while…
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