3.327 hasil untuk machine learning
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The role of machine learning in clinical research: transforming the future of evidence generation
BACKGROUND: Interest in the application of machine learning (ML) to the design, conduct, and analysis of clinical trials has grown, but the evidence base for such applications has not been surveyed. This manuscript reviews the proceedings of a multi-stakeholder conference to discuss the current and…
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Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran
Estimation of the soil organic carbon (SOC) content is of utmost importance in understanding the chemical, physical, and biological functions of the soil. This study proposes machine learning algorithms of support vector machines (SVM), artificial neural networks (ANN), regression tree, random…
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Perspectives on machine learning-augmented Reynolds-averaged and large eddy simulation models of turbulence
Perspectives are presented on the use of machine learning to augment models of turbulent flows. Particular emphasis is placed on techniques that promote consistency of the machine learning model with the underlying physical model in view of the possibility of using sparse computational and…
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Designing a Network Intrusion Detection System Based on Machine Learning for Software Defined Networks
…are essential to achieve this goal successfully. Extending intelligent machine learning algorithms in a network intrusion detection system (NIDS) through a software-defined network (SDN) has attracted considerable attention in the last decade. Big data availability, the diversity of data analysis…
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Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects
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Shifting machine learning for healthcare from development to deployment and from models to data
In the past decade, the application of machine learning (ML) to healthcare has helped drive the automation of physician tasks as well as enhancements in clinical capabilities and access to care. This progress has emphasized that, from model development to model deployment, data play central roles…
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Machine Learning and AI in Cancer Prognosis, Prediction, and Treatment Selection: A Critical Approach
…prognosis estimation, and treatment selection based on deep knowledge. Machine learning (ML), a subset of AI that enables computers to learn from training data, has been highly effective at predicting various types of cancer, including breast, brain, lung, liver, and prostate cancer. In fact, AI…
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Predictive Analytics and Machine Learning for Real-Time Supply Chain Risk Mitigation and Agility
…this research study proposes an innovative strategy that makes use of machine learning as well as predictive analytics approaches. Traditional supply chain risk management frequently uses post-event analysis as well as historical data, which restricts its ability to address real-time interruptions…
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ADMET-AI: a machine learning ADMET platform for evaluation of large-scale chemical libraries
…Excretion, and Toxicity (ADMET). RESULTS: We developed ADMET-AI, a machine learning platform that provides fast and accurate ADMET predictions both as a website and as a Python package. ADMET-AI has the highest average rank on the TDC ADMET Leaderboard, and it is currently the fastest web-based…
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A Comprehensive Review on Machine Learning in Healthcare Industry: Classification, Restrictions, Opportunities and Challenges
Recently, various sophisticated methods, including machine learning and artificial intelligence, have been employed to examine health-related data. Medical professionals are acquiring enhanced diagnostic and treatment abilities by utilizing machine learning applications in the healthcare domain…
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Integrated Multi-Omics Analyses in Oncology: A Review of Machine Learning Methods and Tools
…innovations regarding the integration of heterogeneous data, the machine learning methodologies that successfully tackled the complexity of multi-omics data, and the frameworks to deliver actionable results for clinical practice. The review is organized according to the applied methods: Deep…
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Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism
Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be combined to enable accurate genotype-to-phenotype…
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Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next
…and stochastic PDEs. This novel methodology has arisen as a multi-task learning framework in which a NN must fit observed data while reducing a PDE residual. This article provides a comprehensive review of the literature on PINNs: while the primary goal of the study was to characterize these…
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Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review
…for further applications of earth observations. In this article, six machine-learning algorithms, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), fuzzy adaptive resonance theory-supervised predictive mapping (Fuzzy ARTMAP), spectral angle mapper (SAM) and…
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Automated machine learning: Review of the state-of-the-art and opportunities for healthcare
OBJECTIVE: This work aims to provide a review of the existing literature in the field of automated machine learning (AutoML) to help healthcare professionals better utilize machine learning models "off-the-shelf" with limited data science expertise. We also identify the potential opportunities and…
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