359.866 hasil untuk machine learning
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Artikel Jurnal Teks lengkap
Bias Preservation in Machine Learning: The Legality of Fairness Metrics Under EU Non-Discrimination Law
Western societies are marked by diverse and extensive biases and inequality that are unavoidably embedded in the data used to train machine learning. Algorithms trained on biased data will, without intervention, produce biased outcomes and increase the inequality experienced by historically…
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Wild Westworld: Section 230 of the CDA and Social Networks’ Use of Machine-Learning Algorithms
This Note argues that Facebook’s services—specifically the personalization of content through machine-learning algorithms—constitute the “development” of content and as such do not qualify for § 230 immunity. This Note analyzes the evolution of § 230 jurisprudence to help inform the development of…
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Artificial Intelligence in the Courtroom: The Delivery of Justice in the Age of Machine Learning
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Artificial Intelligence and Machine Learning Improving the Livelihood of Individuals with Disabilities in Various Capacities
Artificial Intelligence (“AI”) and Machine Learning (“ML”) are swiftly reshaping accessibility for individuals with disabilities, offering fresh opportunities for inclusion in education, the workplace, and daily life. This Note explores the integration of AI and ML into assistive technologies and…
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Machine Learning and Machine Vision Accelerate 3D Printed Orodispersible Film Development
…To address these shortcomings, both three-dimensional (3D) printing and machine learning (ML) were employed to provide on-demand manufacturing and quality control checks of ODFs. Direct ink writing (DIW) was able to fabricate complex ODF shapes, with thicknesses of less than 100 µm. ML algorithms…
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Machine Learning for Biomedical Literature Triage
This paper presents a machine learning system for supporting the first task of the biological literature manual curation process, called triage. We compare the performance of various classification models, by experimenting with dataset sampling factors and a set of features, as well as three…
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Transforming health policy through machine learning
In their Perspective, Ara Darzi and Hutan Ashrafian give us a tour of the future policymaker's machine learning toolkit.
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Machine learning for modeling animal movement
…effects on movement behavior and to fill in missing tracking data. Machine Learning and Deep learning algorithms are powerful and flexible predictive modeling tools but have rarely been applied to animal movement data. In this study we present a general framework for predicting animal movement that…
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Machine learning of the prime distribution
…posit that the Erdős–Kac law would very unlikely be discovered by current machine learning techniques. Numerical experiments that we perform corroborate our theoretical findings.
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Predicting hypertension control using machine learning
…will be controlled. This project endeavors to investigate whether machine learning can accurately predict the control of a patient’s hypertension within 12 months of a clinical encounter. To build the machine learning model, a retrospective review of the electronic medical records of 350,008…
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Artikel Jurnal
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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Artikel Jurnal
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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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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Using Machine Learning Algorithms to Predict People’s Intention to Use Mobile Learning Platforms During the COVID-19 Pandemic: Machine Learning Approach
BACKGROUND: Mobile learning has become an essential instruction platform in many schools, colleges, universities, and various other educational institutions across the globe, as a result of the COVID-19 pandemic crisis. The resulting severe, pandemic-related circumstances have disrupted physical…
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Machine Learning for HRI: Bridging the Gap between Action and Perception
International audience
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Models of Modals: From Pragmatics and Corpus Linguistics to Machine Learning
Modal verbs in English communicate delicate shades of meaning, there being a large range of verbs both on the necessity side (must, have to, should, ought to, need, need to) and the possibility side (can, may, could, might, be able to). They therefore constitute excellent test ground to apply and…
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Machine Learning Models for Slope Stability Classification of Circular Mode Failure: An Updated Database and Automated Machine Learning (AutoML) Approach
…and researcher. In this study, for the first time, an automated machine learning (AutoML) approach was proposed for model development and slope stability assessments of circular mode failure. An updated database with 627 cases consisting of the unit weight, cohesion, and friction angle of the slope…
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Enhancing Type 2 Diabetes Treatment Decisions With Interpretable Machine Learning Models for Predicting Hemoglobin A1c Changes: Machine Learning Model Development
…control among patients with T2D receiving usual care. METHODS: Our machine learning model predicts poor glycemic control (HbA(1c)≥8%) using the transformer architecture, incorporating an attention mechanism to process irregularly spaced HbA(1c) time series and quantify temporal relationships of…
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Integrating machine learning with pharmacokinetic models: Benefits of scientific machine learning in adding neural networks components to existing PK models
Recently, the use of machine‐learning (ML) models for pharmacokinetic (PK) modeling has grown significantly. Although most of the current approaches use ML techniques as black boxes, there are only a few that have proposed interpretable architectures which integrate mechanistic knowledge. In this…
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