113.116 hasil untuk machine learning
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Dialysis adequacy predictions using a machine learning method
…to measuring dialysis adequacy by blood samples. This study used machine learning models to predict dialysis adequacy in chronic hemodialysis patients using repeatedly measured data during hemodialysis. This study included 1333 hemodialysis sessions corresponding to the monthly examination dates of…
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Nonparametric variable importance assessment using machine learning techniques
…variable importance measure and discuss how it facilitates the use of machine learning techniques to flexibly estimate the variable importance of a single feature or group of features. The importance of each feature or group of features in the data can then be described individually, using this…
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Mutual Information Scaling for Tensor Network Machine Learning
Tensor networks have emerged as promising tools for machine learning, inspired by their widespread use as variational ansatze in quantum many-body physics. It is well known that the success of a given tensor network ansatz depends in part on how well it can reproduce the underlying entanglement…
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Eye-Tracking Feature Extraction for Biometric Machine Learning
…prediction. The objective of this article is to review what specific machine learning features can be obtained from eye-tracking data for the classification task. METHODS: We performed a systematic literature review (SLR) covering the eye-tracking studies in classification published from 2016 to…
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Deploying Machine Learning Techniques for Human Emotion Detection
…using MediaPipe face mesh algorithm, which is based on real-time deep learning. In addition, the generated key points are encoded using a sequence of carefully designed mesh generator and angular encoding modules. Furthermore, feature decomposition is performed using Principal Component Analysis…
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Combining Fractional Derivatives and Machine Learning: A Review
…complex naturally occurring phenomena. Another popular topic nowadays is machine learning, i.e., learning behavior and patterns from historical data. In our ever-changing world with ever-increasing amounts of data, machine learning is a powerful tool for data analysis, problem-solving, modeling…
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Evaluation of Machine Learning Algorithms for Malware Detection
…analysis ineffective. Automatic behaviour-based malware detection using machine learning algorithms is thus considered a game-changing innovation. Threats are automatically evaluated based on their behaviours in a simulated environment, and reports are created. These records are converted into…
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Machine Learning-Based Crop Stress Detection in Greenhouses
…is an advantage that could aid in microclimate control. In this study, a Machine Learning (ML) model which takes into account microclimate and crop physiological data to detect different types of crop stress was developed and tested. For this purpose, a multi-sensor platform was used to record…
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Nucleotide augmentation for machine learning-guided protein engineering
SUMMARY: Machine learning-guided protein engineering is a rapidly advancing field. Despite major experimental and computational advances, collecting protein genotype (sequence) and phenotype (function) data remains time- and resource-intensive. As a result, the quality and quantity of training data…
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Spinal Cord Injury AIS Predictions Using Machine Learning
The study used machine learning to predict The American Spinal Injury Association Impairment Scale (AIS) scores for newly injured spinal cord injury patients at hospital discharge time from hospital admission data. Additionally, machine learning was used to analyze the best model for feature…
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Chinese diabetes datasets for data-driven machine learning
Data of the diabetes mellitus patients is essential in the study of diabetes management, especially when employing the data-driven machine learning methods into the management. To promote and facilitate the research in diabetes management, we have developed the ShanghaiT1DM and ShanghaiT2DM…
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Gradient-based enhancement attacks in biomedical machine learning
The prevalence of machine learning in biomedical research is rapidly growing, yet the trustworthiness of such research is often overlooked. While some previous works have investigated the ability of adversarial attacks to degrade model performance in medical imaging, the ability to falsely improve…
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Unsupervised machine learning discovers classes in aluminium alloys
…Al alloy classification. Iterative label spreading (ILS), an unsupervised machine learning approach, was used to identify the different classes of Al alloys, drawing from a specifically curated dataset of 1154 Al alloys (including alloy composition and processing conditions). Using ILS, eight…
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Anxiety onset in adolescents: a machine-learning prediction
…predictive value has not been established. Individual prediction through machine-learning algorithms might help bridge the gap to clinical relevance. A voting classifier with Random Forest, Support Vector Machine and Logistic Regression algorithms was used to evaluate the predictive pertinence of…
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Navigating with chemometrics and machine learning in chemistry
Chemometrics and machine learning are artificial intelligence-based methods stirring a transformative change in chemistry. Organic synthesis, drug discovery and analytical techniques are incorporating machine learning techniques at an accelerated pace. However, machine-assisted chemistry faces…
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Application of Machine Learning in Epileptic Seizure Detection
…can be provided for the patients. This research presents a novel machine learning-based approach for detecting epileptic seizures in EEG signals. A public EEG dataset from the University of Bonn was used to validate the approach. Meaningful statistical features were extracted from the original data…
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MagicalRsq: Machine-learning-based genotype imputation quality calibration
…calibrated for lower-frequency variants. Here, we propose MagicalRsq, a machine-learning-based method that integrates variant-level imputation and population genetics statistics, to provide a better calibrated imputation quality metric. Leveraging WGS data from the Cystic Fibrosis Genome Project…
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Machine learning of pair-contact process with diffusion
…phase transition. In this paper, both unsupervised and supervised learning are employed to study the PCPD with scrutiny. Firstly, two unsupervised learning methods, principal component analysis (PCA) and autoencoder, are taken. Our results show that both methods can cluster the original…
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Forecasting medical state transition using machine learning methods
Early circulatory failure detection is an effective way to reduce medical fatigue and improve state pre-warning ability. Instead of using 0-1 original state, a transformed state is proposed in this research, which reflects how the state is transformed. The performance of the proposed method is…
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