204.674 hasil untuk machine learning
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Artikel Jurnal Teks lengkap
Machine Learning in Gamification and Gamification in Machine Learning: A Systematic Literature Mapping
Albeit in different ways, both machine learning and gamification have transfigured the user experience of information systems. Although both are hot research topics, so far, little attention has been paid to how these two technologies converge with each other. This relation is not obvious as while…
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Predicting ACL Reconstruction Failure with Machine Learning: Development of Machine Learning Prediction Models
…Purpose: To (1) evaluate the predictive performance of different machine learning algorithms for the occurrence of failure in ACLR and (2) identify the most relevant predictors associated with this outcome. Study Design: Cohort study; Level of evidence, 3. Methods: A total of 680 patients who…
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Peningkatan Performa Klasifikasi Machine Learning Melalui Perbandingan Metode Machine Learning dan Peningkatan Dataset
Classification using machine learning is an alternative that is widely used to classify data. There are various classification methods or also known as machine learning classification algorithms that can be used. However, to get the best classification results, we need a classifier that fits the…
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Is Machine Learning Real Learning?
The question of whether machine learning is real learning is ambiguous, because the term “real learning” can be understood in two different ways. Firstly, it can be understood as learning that actually exists and is, as such, opposed to something that only appears to be learning, or is misleadingly…
Juga tersedia di: DOAJ
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Advancing Gastrointestinal Cancer Risk Prediction With Patient-Centered Machine Learning: Machine Learning Modeling Study
Abstract BackgroundGastrointestinal (GI) cancers are a significant health concern in South Korea. Recently, machine learning (ML) models have emerged as powerful tools to support early screening efforts and identify people at risk before disease onset. However, the low incidence of GI malignancies…
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Securing Machine Learning in the Cloud: A Systematic Review of Cloud Machine Learning Security
With the advances in machine learning (ML) and deep learning (DL) techniques, and the potency of cloud computing in offering services efficiently and cost-effectively, Machine Learning as a Service (MLaaS) cloud platforms have become popular. In addition, there is increasing adoption of third-party…
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Artikel Jurnal Teks lengkap
Applications of machine learning for hemodialysis nursing cares based on a machine learning algorithm
…to enhance hemodialysis adequacy and prevent complications; however, machine learning (ML) emerged as a methodological approach for evaluating hemodialysis adequacy and complications. The current study aims to analyze ML approach in predicting and managing hemodialysis by R programming language…
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Novices’ conceptions of machine learning
With machine learning becoming more and more prevalent in society, it also becomes a topic that is relevant for general K12 education. At the same time, modern machine learning is relying heavily on mathematics, requiring novel approaches to teaching it in settings where a full coverage is not…
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Machine-learning the string landscape
We propose a paradigm to apply machine learning various databases which have emerged in the study of the string landscape. In particular, we establish neural networks as both classifiers and predictors and train them with a host of available data ranging from Calabi–Yau manifolds and vector…
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Photovoltaic sizing using machine learning
…where the photovoltaic system is to be installed. This study introduces machine learning algorithms into the photovoltaic systems’ sizing problem for the first time. The proposed case-independent model automates the sizing process and is generalized to be applied at any location. Linear regression…
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Microbiome Preprocessing Machine Learning Pipeline
Background16S sequencing results are often used for Machine Learning (ML) tasks. 16S gene sequences are represented as feature counts, which are associated with taxonomic representation. Raw feature counts may not be the optimal representation for ML.MethodsWe checked multiple preprocessing steps…
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Stellar Classification by Machine Learning
As an emerging subject with strong comprehensiveness, machine learning has made varying degrees of progress in various fields. In the field of astronomy, it has also been generally used, and there have been quantities of research using machine learning for data processing and model prediction. The…
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Machine Learning Photovoltaic String Analyzer
Photovoltaic (PV) system energy production is non-linear because it is influenced by the random nature of weather conditions. The use of machine learning techniques to model the PV system energy production is recommended since there is no known way to deal well with non-linear data. In order to…
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Machine Learning in Systems Biology
Abstract This supplement contains extended versions of a selected subset of papers presented at the workshop MLSB 2007, Machine Learning in Systems Biology, Evry, France, from September 24 to 25, 2007.
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Machine learning in dental photography
…communication. Recent advancements in artificial intelligence (AI) and machine learning (ML) hold potential to further revolutionize dental photography, particularly through automated image analysis. This review aimed to explore the application of ML in dental photography across various dental…
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Quantum Chemistry Meets Machine Learning
In this account, we demonstrate how statistical learning approaches can be leveraged across a range of different quantum chemical areas to transform the scaling, nature, and complexity of the problems that we are tackling. Selected examples illustrate the power brought by kernel-based approaches in…
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Artikel Jurnal Teks lengkap
Machine learning methods in finance
This article focuses on supervised learning and reinforcement learning. These areas overlap most with econometrics, predictive modelling, and optimal control in finance. We choose to focus on how to cast machine learning into various financial modelling and decision frameworks. This work introduces…
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