359.866 hasil untuk machine learning
Menampilkan 761–780 · 1,66 detik · RSS
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Artikel Jurnal Teks lengkap
Coverage bias in small molecule machine learning
Small molecule machine learning aims to predict chemical, biochemical, or biological properties from molecular structures, with applications such as toxicity prediction, ligand binding, and pharmacokinetics. A recent trend is developing end-to-end models that avoid explicit domain knowledge. These…
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Advances in machine learning for keratoconus diagnosis
PURPOSE: To review studies reporting the role of Machine Learning (ML) techniques in the diagnosis of keratoconus (KC) over the past decade, shedding light on recent developments while also highlighting the existing gaps between academic research and practical implementation in clinical settings…
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Predicting Asthma Exacerbations Using Machine Learning Models
…of prognosis are needed for real-world decision-making in asthma. Machine learning (ML) leverages mathematical and statistical methods to detect patterns for future disease events across large datasets from electronic health records (EHR). This study conducted training and fine-tuning of ML…
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Machine learning models for water safety enhancement
…the samples, thereby contributing to the overall safety of the water. The Machine Learning (ML) models employed in this study provided accurate predictions, ensuring reliability across various demographic groups and reinforcing the robustness of the findings. Overall, the results suggest that…
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Adapting hybrid density functionals with machine learning
…exact exchange admixture ratios on the fly using data-efficient quantum machine learning models with negligible overhead. The adaptive Perdew-Burke-Ernzerhof hybrid density functional (aPBE0) improves energetics, electron densities, and HOMO-LUMO gaps in QM9, QM7b, and GMTKN55 benchmark datasets. A…
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Harnessing machine learning in contemporary tobacco research
Machine learning (ML) has the potential to transform tobacco research and address the urgent public health crisis posed by tobacco use. Despite the well-documented health risks, cessation rates remain low. ML techniques offer innovative solutions by analyzing vast datasets to uncover patterns in…
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Correlation inference attacks against machine learning models
Despite machine learning models being widely used today, the relationship between a model and its training dataset is not well understood. We explore correlation inference attacks, whether and when a model leaks information about the correlations between the input variables of its training dataset…
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Decoding Glioblastoma Heterogeneity: Neuroimaging Meets Machine Learning
Recent advancements in neuroimaging and machine learning have significantly improved our ability to diagnose and categorize isocitrate dehydrogenase (IDH)-wildtype glioblastoma, a disease characterized by notable tumoral heterogeneity, which is crucial for effective treatment. Neuroimaging…
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Application of Machine Learning in Amorphous Alloys
…due to their long development cycles and low efficiency. In contrast, machine learning (ML) has gained widespread adoption in the design, analysis, and property prediction of amorphous alloys due to its advantages of low experimental cost, powerful performance, and short development cycles. This…
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Machine Learning‐Enabled Drug‐Induced Toxicity Prediction
…and time‐consuming. Big data and artificial intelligence (AI), especially machine learning (ML), are robustly contributing to innovation and progress in toxicology research. However, the optimal AI model for different types of toxicity usually varies, making it essential to conduct comparative…
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Machine Learning-Assisted Secure Random Communication System
Machine learning techniques have revolutionized physical layer security (PLS) and provided opportunities for optimizing the performance and security of modern communication systems. In this study, we propose the first machine learning-assisted random communication system (ML-RCS). It comprises a…
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Artikel Jurnal Teks lengkap
Exploring synergies: Advancing neuroscience with machine learning
Machine learning (ML) has transformed neuroscience research by providing powerful tools to analyze neural data, uncover brain connectivity, and guide therapeutic interventions. This paper presents core mathematical frameworks in ML that address critical challenges in neuroscience. We introduce…
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Predicting Hearing Aid Outcomes Using Machine Learning
…of intelligibility in quiet and noise. eXtreme Gradient Boosting machine learning method is used to identify predictive factors of HA outcome. SHapley Additive exPlanations Value analysis derived from the game theory is used to evaluate the individual impact of each factor. RESULTS: HA outcomes are…
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Advanced machine learning for innovative drug discovery
This editorial presents an analysis of the articles published in the Journal of Cheminformatics Special Issue “AI in Drug Discovery”. We review how novel machine learning developments are enhancing structural-based drug discovery; providing better forecasts of molecular properties while also…
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Machine Learning and Artificial Intelligence in Nanomedicine
…predict human efficacy. In recent years, artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools to address these hurdles at every stage of nanomedicine development. By rapidly screening extensive libraries and extracting structure–function relationships…
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Predicting hepatitis C infection via machine learning
…biochemical markers exhibit suboptimal diagnostic performance, whether machine learning can enhance their accuracy remains unclear. Methods: This study is a retrospective study, which included data from 179 patients whose HCV-Ab levels were greater than 1.00 S/CO to explore the relationship between…
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Artikel Jurnal Teks lengkap
Digitization of molecular complexity with machine learning
…currently remaining controversial. In this study, we introduce a novel machine learning-based framework employing a Learning to Rank (LTR) approach to quantify molecular complexity on the basis of labeled data. As a result, we developed a ranking model utilizing the dataset that comprizes…
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