214.423 hasil untuk machine learning
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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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Predicting Breast Cancer Leveraging Supervised Machine Learning Techniques
…to improve the developed diagnostic systems. In this regard, machine learning has recently been playing a potential role in the early and precise detection of breast cancer. This paper presents a new machine learning-based framework that utilizes the Random Forest, Gradient Boosting, Support Vector…
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Machine Learning in Unmanned Systems for Chemical Synthesis
…The upgraded paradigm that incorporates automation technology and machine learning (ML) algorithms has recently been merged into almost every subdiscipline of chemical science, from material discovery to catalyst/reaction design to synthetic route planning, which often takes the form of unmanned…
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The development of machine learning in bariatric surgery
BACKGROUND: Machine learning (ML), is an approach to data analysis that makes the process of analytical model building automatic. The significance of ML stems from its potential to evaluate big data and achieve quicker and more accurate outcomes. ML has recently witnessed increased adoption in the…
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Combining DELs and Machine Learning for Toxicology Prediction
DNA encoded libraries (DELs) allow identifying starting chemical matter in drug discovery. The volume of experimental data generated also makes DELs an attractive resource for machine learning (ML). ML allows modeling complex relationships between compounds and numerical endpoints, like the binding…
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Prediction of Prednisolone Dose Correction Using Machine Learning
Wrong dose, a common prescription error, can cause serious patient harm, especially in the case of high-risk drugs like oral corticosteroids. This study aims to build a machine learning model to predict dose-related prescription modifications for oral prednisolone tablets (i.e., highly imbalanced…
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Leveraging explanations in interactive machine learning: An overview
Explanations have gained an increasing level of interest in the AI and Machine Learning (ML) communities in order to improve model transparency and allow users to form a mental model of a trained ML model. However, explanations can go beyond this one way communication as a mechanism to elicit user…
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Supervised Machine Learning in Oncology: A Clinician’s Guide
…insights from large-volume data. Among those techniques is supervised machine learning, the study of computer algorithms that use self-improving models that learn from labeled data to solve problems. One clinical area of application for supervised machine learning is within oncology, where machine…
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