113,116 results for machine learning
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Machine Learning Differentiation of Autism Spectrum Sub-Classifications
…make diagnosing a disorder on the autism spectrum complex. We evaluated machine learning to classify individuals as having one of three disorders of the autism spectrum under DSM-IV, or as non-spectrum. METHODS: We employed machine learning to analyze retrospective data from 38,560 individuals…
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Machine Learning to Predict Quasicrystals from Chemical Compositions
…that the discovery of new quasicrystals can be accelerated with a simple machine‐learning workflow. With a list of the chemical compositions of known stable quasicrystals, approximant crystals, and ordinary crystals, a prediction model is trained to solve the three‐class classification task and its…
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Machine learning-optimized targeted detection of alternative splicing
…events of interest. Primers are designed using Optimal Prime, a novel machine learning algorithm trained on the performance of thousands of primer sequences. In experimental benchmarks, LSV-seq achieves high on-target capture rates and concordance with RNA-seq, while requiring significantly lower…
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Precision at scale: Machine learning revolutionizing laparoscopic surgery
…ovarian cancer patients. This editorial discusses the integration of machine learning in laparoscopic surgery, emphasizing its transformative potential in improving patient outcomes and surgical precision. Machine learning algorithms analyze extensive datasets to optimize procedural techniques…
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Emotion Classification from Electroencephalographic Signals Using Machine Learning
Background: Emotions significantly influence decision-making, social interactions, and medical outcomes. Leveraging emotion recognition through Electroencephalography (EEG) signals offers potential advancements in personalized medicine, adaptive technologies, and mental health diagnostics. This…
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Flavor Wheel Development from a Machine Learning Perspective
…the industry. This study aims to study these relationships by employing machine learning techniques to analyze a comprehensive dataset with 3051 chemical compounds and their associated aroma descriptors for seven distilled spirit categories: Baijiu, cachaça, gin, mezcal, rum, tequila, and…
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Predicting maternal risk level using machine learning models
…health risks is challenging. Compared with traditional predictive models, machine learning algorithms have emerged as promising predictive modelling methods providing accurate predictive models. METHODS: This work aims to explore the potential of machine learning (ML) algorithms in maternal risk…
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Interpretable machine learning comprehensive human gait deterioration analysis
INTRODUCTION: Gait analysis, an expanding research area, employs non-invasive sensors and machine learning techniques for a range of applications. In this study, we investigate the impact of cognitive decline conditions on gait performance, drawing connections between gait deterioration in…
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Photonic probabilistic machine learning using quantum vacuum noise
Probabilistic machine learning utilizes controllable sources of randomness to encode uncertainty and enable statistical modeling. Harnessing the pure randomness of quantum vacuum noise, which stems from fluctuating electromagnetic fields, has shown promise for high speed and energy-efficient…
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Discovery of Crystallizable Organic Semiconductors with Machine Learning
…and crystallization driving force (ΔG(c)). In this work, we employed machine learning (ML) to identify candidate organic materials with the potential to crystallize into platelets by estimating the aforementioned thermal properties. Six organic molecules identified by the ML algorithm were…
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Individualized Machine-learning-based Clinical Assessment Recommendation System
…diagnosis could offer significant benefits. We aim to provide a machine-learning framework that addresses the individualized feature addition problem and enhances diagnostic accuracy for clinical assessments. METHODS: Individualized Clinical Assessment Recommendation System (iCARE) employs locally…
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Machine learning approach for the prediction of macrosomia
…develop a comprehensive predictive model for detecting macrosomia using machine learning (ML) algorithms. The accuracy of macrosomia prediction using logistic regression, k-nearest neighbors, support vector machine, random forest (RF), XGBoost, and LightGBM algorithms was explored. Each approach…
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Machine learning of brain-specific biomarkers from EEG
…to derive biomarkers for various applications is far from exhausted. Machine learning (ML) can guide future innovation by harnessing the wealth of complex EEG signals to isolate relevant brain activity. Yet, ML studies in EEG tend to ignore physiological artefacts, which may cause problems for…
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Machine learning: implications and applications for ambulatory anesthesia
PURPOSE OF REVIEW: This review explores the timely and relevant applications of machine learning in ambulatory anesthesia, focusing on its potential to optimize operational efficiency, personalize risk assessment, and enhance patient care. RECENT FINDINGS: Machine learning models have demonstrated…
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Predicting special forces dropout via explainable machine learning
…study, we introduce a novel approach to selection research, using various machine learning models. We examined 274 special forces recruits, of whom 196 dropped out, who performed a set of physical and psychological tests. On this data, we compared four machine learning models on their predictive…
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Machine learning-based optimization of photogrammetric JRC accuracy
…established, revealing their correlations and sensitivities. Using seven machine learning algorithms, optimization models for photogrammetric JRC accuracy were developed, with Linear Multidimensional Regression and Gaussian Process Regression models improving JRC accuracy by an average of 85.73%…
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Machine Learning in Spine Oncology: A Narrative Review
STUDY DESIGN: Narrative Review. OBJECTIVE: Machine learning (ML) is one of the latest advancements in artificial intelligence used in medicine and surgery with the potential to significantly impact the way physicians diagnose, prognose, and treat spine tumors. In the realm of spine oncology, ML is…
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Machine Learning to Predict Prostate Artery Embolization Outcomes
PURPOSE: This study leverages pre-procedural data and machine learning (ML) techniques to predict outcomes at one year following prostate artery embolization (PAE). MATERIALS AND METHODS: This retrospective analysis combines data from the UK-ROPE registry and patients that underwent PAE at our…
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Machine Learning-Based Tomato Fruit Shape Classification System
…prone to human error. This study presents a novel approach incorporating machine learning techniques to establish a robust fruit shape classification system. We trained and evaluated seven supervised machine learning algorithms by leveraging a public dataset derived from the Tomato Analyzer tool…
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