8.473 hasil untuk machine learning
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Machine learning approaches in non-contact autofluorescence spectrum classification
…algorithms, including Artificial Neural Network, Support Vector Machine, Logistic Regression, and K-Nearest Neighbors, to diagnose freshly resected murine tissue as sarcoma or healthy. Classification accuracies of over 93% are achieved with Logistic Regression, and Area Under the Curve scores over…
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Machine learning-aided microRNA discovery for olive oil quality
…using a high quality reference genome and a combination of genomics and machine learning-based methods. By leveraging random forest and support vector machine algorithms, we successfully identified 56 novel miRNAs in olive, surpassing the limitations of conventional homology-based methods. Our…
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Ten quick tips for ensuring machine learning model validity
Author summary: Artificial Intelligence (AI) and Machine Learning (ML) models are increasingly deployed on biomedical and health data to shed insights on biological mechanism, predict disease outcomes, and support clinical decision-making. However, ensuring model validity is challenging. The 10…
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Machine learning mathematical models for incidence estimation during pandemics
…and systematic under-reporting of the real incidence. Here, we propose a machine learning approach to estimate the incidence of a pandemic in real-time, using reported cases and the overall test rate. In particular, we use Bayesian symbolic regression to automatically learn the closed-form…
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Machine learning of factors for improving oyster hatchery production
…between production yield and its potential predictors, we applied machine learning (random forest, and neural network) and statistical (generalized additive model) models to a comprehensive dataset of environmental, water quality, and operational parameters from a Maryland oyster hatchery, aiming…
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Chrysoprase color grading with machine learning: A systematic approach
…color, is no exception. To address these challenges, this study employs a machine learning-based approach to automate chrysoprase color grading. CIE (1976) L*a*b* data were measured for 51 chrysoprase samples and 676 green reference points generated using the GemDialogue Color Reference using an…
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Interspecific comparison of gene expression profiles using machine learning
…we report ISEEML (Interspecific Similarity of Expression Evaluated using Machine Learning)–a novel machine learning-based algorithm for interspecific gene classification. In contrast to previous studies focused on sequence similarity, our algorithm focuses on functional similarity inferred from the…
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Machine learning based canine posture estimation using inertial data
…Units (IMUs) that are commercially available, and a supervised learning algorithm which was developed for different behaviours. Three IMUs, each containing a 3-axis accelerometer, gyroscope, and magnetometer, were attached to the dogs’ chest, back, and neck. To build and test the model, data were…
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Interpretable machine learning for predicting isolated basal septal hypertrophy
…aortic valve implantation (TAVI). Compared to traditional algorithms, machine learning algorithms are more effective at capturing nonlinear relationships and developing more accurate diagnostic and predictive models. Objective: To evaluate the effectiveness of five machine learning algorithms in…
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Correction: Forecasting outbound student mobility: A machine learning approach
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Bayesian optimization and machine learning for vaccine formulation development
…approaches have emerged. This manuscript highlights the applications of machine learning through two cases in which Bayesian optimization was used to develop viral vaccine formulations. The two case studies monitored the critical quality attributes of virus A in liquid form by infectious titer loss…
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Detection of pediatric developmental delay with machine learning technologies
…at a hospital in Taiwan from 2012 to 2016. We then built 3 types of machine learning based prediction models, namely the deep neural network models (DNN), the support vector machine (SVM) models, and the decision tree (DT) models, to evaluate the effectiveness of the proposed approach. Results…
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Identifying dynamic regulation with machine learning using adversarial surrogates
Biological systems maintain stability of their function in spite of external and internal perturbations. An important challenge in studying biological regulation is to identify the control objectives based on empirical data. Very often these objectives are time-varying, and require the regulation…
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Machine learning prediction model for the sewing thread consumption
…sewing consumption for each stitch was computed using the Extra Trees machine learning method. Multiple machine learning models including Extra Trees, Support Vector Machine, CatBoost, Random Forest, Linear Regression, and Artificial Neural Networks were compared. The Extra Trees model achieved…
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Artificial intelligence for dysphagia screening: A machine learning approach
Early and reliable screening for oropharyngeal dysphagia symptoms in at-risk populations is a crucial first stage in effective management. This study aims to propose a machine learning model that can predict whether individuals are at risk of having confirmed dysphagia based on diagnostic accuracy…
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Naïve Bayes is an interpretable and predictive machine learning algorithm in predicting osteoporotic hip fracture in-hospital mortality compared to other machine learning algorithms
…those unavailable early in admission. Moreover, attempts to explain machine learning [ML]-based predictions are lacking. Seven ML prognostication models were developed to predict in-hospital mortality following minimal trauma HF in those aged ≥ 65 years of age, requiring only sociodemographic and…
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Teamwork: Improved eQTL Mapping Using Combinations of Machine Learning Methods
…whose originality resides in the use of a combination of existing machine learning algorithms (committee). Although it was not the best performer, this method was by far the most precise on average. After the competition, we continued in this direction by evaluating other committees using the…
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Identifying Hosts of Families of Viruses: A Machine Learning Approach
…viral hosts from protein sequence data using popular discriminative machine learning tools. Furthermore, the predictive motifs robustly selected by the learning algorithm are found to show strong host-specificity and occur in highly conserved regions of the viral proteome.
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An Information-Theoretic Machine Learning Approach to Expression QTL Analysis
…method by which to identify eQTL associations with information theory and machine learning approaches. Mutual Information (MI) is used to describe the association between genetic marker and gene expression. MI can detect both linear and non-linear associations. What’s more, it can capture the…
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