359.866 hasil untuk machine learning
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Artikel Jurnal Teks lengkap
Pediatric diabetes prediction using machine learning
…researchers are increasingly turning to big data analytics and machine learning (ML) methodologies. This study presents the development of an ML-based system aimed at predicting the likelihood of diabetes and classifying its various types. A novel dataset, termed Diabetes Types Dataset, was…
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Machine learning models predicting inpatient falls
…between January 2018 and July 2022, including 4245 fallers. Four dynamic machine learning models were developed using HD variables alone and in combination with socio-demographics, comorbidities, physiological measures, medications, and timeseries data updated at 8- and 24 h intervals. Among the…
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Task-Agnostic Machine-Learning-Assisted Inference
Machine learning (ML) is playing an increasingly important role in scientific research. In conjunction with classical statistical approaches, ML-assisted analytical strategies have shown great promise in accelerating research findings. This has also opened a whole field of methodological research…
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Interpretable Machine Learning for Survival Analysis
With the spread and rapid advancement of black box machine learning (ML) models, the field of interpretable machine learning (IML) or explainable artificial intelligence (XAI) has become increasingly important over the last decade. This is particularly relevant for survival analysis, where the…
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Mitigating Racial Bias in Machine Learning
When applied in the health sector, AI-based applications raise not only ethical but legal and safety concerns, where algorithms trained on data from majority populations can generate less accurate or reliable results for minorities and other disadvantaged groups.
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Trimodal machine learning based biometrics system
Biometrics-based authentication systems have recently been considered as one of the safest methods to secure our data or possessions. In the literature, the solutions based on one or two measurable traits are broadly described. There was also claim that more features than two can make the samples…
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SmartEM: machine-learning guided electron microscopy
…generate enough data for whole circuit or brain reconstruction. To date, machine-learning methods have been used after the collection of images by electron microscopy (EM) to accelerate and improve neuronal segmentation, synapse reconstruction and other data analysis. With the computational…
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Interpretable machine learning for shoreline forecasting
Machine learning has revolutionized scientific modeling, providing breakthroughs in fields ranging from weather prediction to protein folding. However, its adoption in physics-based domains remains limited due to the lack of interpretability in traditional black-box models. In environmental…
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What can machine learning do for antimicrobial peptides, and what can antimicrobial peptides do for machine learning?
…provide a historical overview of AMPs, summarize previous applications of machine learning to AMPs, and discuss the results of our studies in the context of the latest AMP literature. Much work has been recently done in leveraging computational tools to design new AMP candidates with high…
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Physically-constrained evapotranspiration models with machine learning parameterization outperform pure machine learning: Critical role of domain knowledge
Physics-informed machine learning techniques have emerged to tackle challenges inherent in pure machine learning (ML) approaches. One such technique, the hybrid approach, has been introduced to estimate terrestrial evapotranspiration (ET), a crucial variable linking water, energy, and carbon…
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Comparing Scientific Machine Learning With Population Pharmacokinetic and Classical Machine Learning Approaches for Prediction of Drug Concentrations
A variety of classical machine learning (ML) approaches has been developed over the past decade aiming to individualize drug dosages based on measured plasma concentrations. However, the interpretability of these models is challenging as they do not incorporate information on pharmacokinetic (PK)…
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Machine learning and soil sciences: a review aided by machine learning tools
The application of machine learning (ML) techniques in various fields of science has increased rapidly, especially in the last 10 years. The increasing availability of soil data that can be efficiently acquired remotely and proximally, and freely available open-source algorithms, have led to an…
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Artikel Jurnal
Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study
…sentiment on vaping that may be useful for public health surveillance. Machine learning classifiers that identify vaping-relevant tweets and characterize sentiments in them can underpin a Twitter-based vaping surveillance system. Compared with traditional machine learning classifiers that are…
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Artikel Jurnal
Bridging expertise with machine learning and automated machine learning in clinical medicine
In this issue of the Annals, Thirunavukarasu et al.’s systematic review on the clinical performance of automated machine learning (autoML) highlights its extensive applicability across 22 clinical specialties, showcasing its potential to redefine healthcare by making artificial intelligence (AI)…
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Buku Teks lengkap
Machine Learning by Design
Machine Learning by Design: From Problem Framing to Reliable Systems is a concept-first textbook in machine learning for IOAI students, computer science undergraduates, instructors, and serious self-learners. Instead of presenting machine learning as only a catalog of models or a software toolkit…
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