214.423 hasil untuk machine learning
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Creeping Bentgrass Yield Prediction With Machine Learning Models
…temperature into account, limiting its accuracy. This study developed machine-learning-based turf growth models using the random forest (RF) algorithm to estimate short-term turfgrass clipping yield. To build the RF model, a large set of variables were extracted as predictors including the 7-day…
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Facial Emotion Recognition Predicts Alexithymia Using Machine Learning
…of the present study was to propose a new screening tool that utilizes machine learning models based on the scores of facial emotion recognition task. METHOD: In a cross-sectional study, 55 students of the University of Tabriz were selected based on the inclusion and exclusion criteria and their…
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ECG-based machine-learning algorithms for heartbeat classification
…different peaks, and other ECG signal features were used to train a machine-learning model. Most of the available studies uses the MIT-BIH database (only 48 patients). However, in this work, the recently reported Shaoxing People’s Hospital (SPH) database, which consists of more than 10,000…
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Application of Machine Learning in Rheumatic Immune Diseases
…auxiliary diagnostic method for rheumatic immune disorders is critical. Machine learning overcomes the inefficiencies and volatility of human data processing, ushering in a revolution in artificial intelligence research. With the use of big data, machine learning-based application research on…
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Identifying essential genes across eukaryotes by machine learning
…from human cell line screens and human population studies. We employed machine learning across six model eukaryotes and 60 381 genes, using 41 635 features derived from the sequence, gene function information and network topology. Within a leave-one-organism-out cross-validation, the classifiers…
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A machine learning model for nowcasting epidemic incidence
Due to delay in reporting, the daily national and statewide COVID-19 incidence counts are often unreliable and need to be estimated from recent data. This process is known in economics as nowcasting. We describe in this paper a simple random forest statistical model for nowcasting the COVID-19…
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Predicting Central Serous Chorioretinopathy Recurrence Using Machine Learning
Purpose: To predict central serous chorioretinopathy (CSC) recurrence 3 and 6 months after laser treatment by using machine learning. Methods: Clinical and imaging features of 461 patients (480 eyes) with CSC were collected at Zhongshan Ophthalmic Center (ZOC) and Xiamen Eye Center (XEC). The ZOC…
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DermIA: Machine Learning to Improve Skin Cancer Screening
…of up to 98.2% on their premium version. As artificial intelligence and machine learning become a more integral part of our society, this mobile application has the potential to revolutionize skin cancer surveillance. Application Specs Application name: DermIA Analyze Skin Cancer with your camera…
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Drug delivery: Experiments, mathematical modelling and machine learning
…we propose to combine real experiments, numerical simulation, and Machine Learning (ML) based on Artificial Neural Networks (ANN), aiming at a reliable identification of the physical model factors, e.g. the killing action of the drug. To this purpose, we exploit the employed mathematical-numerical…
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Machine learning-aided protein identification from multidimensional signatures
…molecular biology, and in medical diagnostics. Here, we present a macine learning aided microfluidic protein characterisation strategy that within a few minutes generates a three-dimensional fingerprint of a protein sample indicative of its amino acid composition and size and, thereby, creates a…
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Machine Learning Strategies When Transitioning between Biological Assays
[Image: see text] Machine learning is widely used in drug development to predict activity in biological assays based on chemical structure. However, the process of transitioning from one experimental setup to another for the same biological endpoint has not been extensively studied. In a…
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Machine learning in the prediction of cancer therapy
…overview of recent advancements in therapeutic response prediction using machine learning, which is the most widely used branch of artificial intelligence. We describe the basics of machine learning algorithms, illustrate their use, and highlight the current challenges in therapy response…
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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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