113,116 results for machine learning
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Modern views of machine learning for precision psychiatry
…precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide…
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Machine Learning for Microcontroller-Class Hardware: A Review
The advancements in machine learning opened a new opportunity to bring intelligence to the low-end Internet-of-Things nodes such as microcontrollers. Conventional machine learning deployment has high memory and compute footprint hindering their direct deployment on ultra resource-constrained…
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Medical Data Classification Assisted by Machine Learning Strategy
…speed and the lower training error when compared with conventional machine leaning methods, which has demonstrated the effectiveness of the new method in respect to medical data classification.
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A diagnosis model of dementia via machine learning
…to establish a diagnosis model to help doctors make a diagnosis through machine learning method, and use feature selection method to select important questions to reduce the number of questions in the questionnaire, so as to reduce medical and time costs. In this article, Clinical Dementia Rating…
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Cybercrime: Identification and Prediction Using Machine Learning Techniques
In the world of cyber age, cybercrime is spreading its root extensively. Supervised classification methods such as the support vector machine (SVM) and K-nearest neighbor (KNN) models are employed for the classification of cybercrime data. Likewise, the unsupervised mode of classification involves…
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Framework for Integrating Equity Into Machine Learning Models
Predictive analytic models leveraging machine learning methods increasingly have become vital to health care organizations hoping to improve clinical outcomes and the efficiency of care delivery for all patients. Unfortunately, predictive models could harm populations that have experienced…
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Interpreting tree ensemble machine learning models with endoR
Tree ensemble machine learning models are increasingly used in microbiome science as they are compatible with the compositional, high-dimensional, and sparse structure of sequence-based microbiome data. While such models are often good at predicting phenotypes based on microbiome data, they only…
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Machine learning application in soccer: a systematic review
…study was to systematically identify original studies that applied machine learning (ML) to soccer data, highlighting current possibilities in ML and future applications. A systematic review of PubMed, SPORTDiscus, and FECYT (Web of Sciences, CCC, DIIDW, KJD, MEDLINE, RSCI, and SCIELO) was…
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Comparative epigenomics by machine learning approach for neuroblastoma
…methylome data of NB from multiple databases. RESULTS: RF is a popular machine learning model owing to its simplicity, intuitiveness, and computational cost. RF analysis identified novel intermediate-risk patient groups with characteristic DNA methylation patterns within the low-risk group. Feature…
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Photonic machine learning with on-chip diffractive optics
Machine learning technologies have been extensively applied in high-performance information-processing fields. However, the computation rate of existing hardware is severely circumscribed by conventional Von Neumann architecture. Photonic approaches have demonstrated extraordinary potential for…
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Psychotherapists’ Acceptance of Telepsychotherapy: A Machine Learning Approach
…trauma, and telepsychotherapy acceptance. Regression decision trees machine learning analyses were used to build prediction models for each aspects of telepsychotherapy acceptance. RESULTS: Attitudes toward telepsychotherapy were most positive for therapists who reported neutral or strong online…
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Machine Learning for Risk Stratification in Kidney Disease
…and prevention measures. The goal of this review is to identify how machine learning tools contribute and facilitate risk stratification in the clinical setting. RECENT FINDINGS. The two key machine learning paradigms to predictively stratify kidney disease risk are genomics-based and electronic…
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Interpretable machine learning prediction of all-cause mortality
BACKGROUND: Unlike linear models which are traditionally used to study all-cause mortality, complex machine learning models can capture non-linear interrelations and provide opportunities to identify unexplored risk factors. Explainable artificial intelligence can improve prediction accuracy over…
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Botnet Attack Detection in IoT Using Machine Learning
…classifying botnet attacks in the IoT environment. This study proposes machine learning methods for classifying binary classes. This purpose is served by using the publicly available dataset UNSW-NB15. This dataset resolved a class imbalance problem using the SMOTE-OverSampling technique. A…
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Reconstructing Earth’s atmospheric oxygenation history using machine learning
…geochemical proxy data. Here, we propose an independent strategy—machine learning with global mafic igneous geochemistry big data to explore atmospheric oxygenation over the last 4.0 billion years. We observe an overall two-step rise of atmospheric O(2) similar to the published curves derived from…
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Applications of machine learning in tumor-associated macrophages
…in biology limit the extensive integrative analysis of cancer research. Machine learning algorithms are particularly suitable for oncology data analysis due to their flexibility and scalability to analyze diverse data types and strong computation power to learn underlying patterns from massive data…
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Identifying plastics with photoluminescence spectroscopy and machine learning
…be realized by a combination of photoluminescence spectroscopy and a machine learning-based theoretical analysis. We show that appropriate combinations of classifiers with dimensional reduction algorithms are able to identify specific material properties from the spectroscopic data. The best…
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Using machine-learning strategies to solve psychometric problems
…and criterion validity. XGBoost, Random Forest and Support-Vector machine learning algorithms were employed in order to make predictions based on the pattern of participants’ responses by systematically controlling computational experiments with artificial experiments whose results are guaranteed…
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Predicting radiocephalic arteriovenous fistula success with machine learning
…has become routine. Herein, we propose a point-of-care tool based on machine learning to enhance prediction of successful unassisted radiocephalic arteriovenous fistula (AVF) use. Our analysis includes pooled patient-level data from 704 patients undergoing new radiocephalic AVF creation, eligible…
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