113.116 hasil untuk machine learning
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Machine Learning Approach for Application-Tailored Nanolubricants’ Design
…must be employed. One of the most powerful emerging numerical methods is machine learning (ML). Here, we propose to use this technique, in combination with molecular and supramolecular recognition, to understand the morphology and macro-assembly processing strategies for the targeted design of…
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A Maximal Correlation Framework for Fair Machine Learning
As machine learning algorithms grow in popularity and diversify to many industries, ethical and legal concerns regarding their fairness have become increasingly relevant. We explore the problem of algorithmic fairness, taking an information–theoretic view. The maximal correlation framework is…
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Machine Learning in Causal Inference: Application in Pharmacovigilance
…regarding drug safety issues. We aim to discuss the application of machine learning methods as well as causal inference paradigms in pharmacovigilance. We first reviewed data sources for pharmacovigilance. Then, we examined traditional causal inference paradigms, their applications in…
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Predicting Box-Office Markets with Machine Learning Methods
The accurate prediction of gross box-office markets is of great benefit for investment and management in the movie industry. In this work, we propose a machine learning-based method for predicting the movie box-office revenue of a country based on the empirical comparisons of eight methods with…
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Shapley variable importance cloud for interpretable machine learning
Interpretable machine learning has been focusing on explaining final models that optimize performance. The state-of-the-art Shapley additive explanations (SHAP) locally explains the variable impact on individual predictions and has recently been extended to provide global assessments across the…
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Using Machine Learning for Pharmacovigilance: A Systematic Review
Pharmacovigilance is a science that involves the ongoing monitoring of adverse drug reactions to existing medicines. Traditional approaches in this field can be expensive and time-consuming. The application of natural language processing (NLP) to analyze user-generated content is hypothesized as an…
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Imbalanced Seismic Event Discrimination Using Supervised Machine Learning
…distribution. To improve the discrimination accuracy, this paper proposes machine-learning-based seismic discrimination methods—support vector machine, naive Bayes, and logistic regression. Furthermore, to overcome the nonlinear separation problem, the kernel functions and regularized logistic…
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Predicting Chronic Wound Healing Time Using Machine Learning
…patients. By leveraging data within the electronic health record (EHR), machine learning models offer the opportunity to facilitate earlier identification of wounds at risk of not healing or healing after an abnormally long time, which may improve treatment decisions and patient outcomes. Machine…
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Machine Learning for Endometrial Cancer Prediction and Prognostication
…of rapid and cost-effective computer-aided cancer surveillance systems. Machine learning (ML), a subcategory of artificial intelligence, provides opportunities for drug discovery, early cancer diagnosis, effective treatment, and choice of treatment modalities. The application of ML approaches in EC…
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Proteome-Informed Machine Learning Studies of Cocaine Addiction
…for innovative strategies in the field. We propose a proteome-informed machine learning (ML) platform for discovering nearly optimal anti-cocaine addiction lead compounds. We analyze proteomic protein–protein interaction networks for cocaine dependence to identify 141 involved drug targets and…
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Machine learning in postgenomic biology and personalized medicine
In recent years Artificial Intelligence in the form of machine learning has been revolutionizing biology, biomedical sciences, and gene-based agricultural technology capabilities. Massive data generated in biological sciences by rapid and deep gene sequencing and protein or other molecular…
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Causal machine learning for healthcare and precision medicine
Causal machine learning (CML) has experienced increasing popularity in healthcare. Beyond the inherent capabilities of adding domain knowledge into learning systems, CML provides a complete toolset for investigating how a system would react to an intervention (e.g. outcome given a treatment)…
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Applications of machine learning in pine nuts classification
…(NIR) spectra were collected from the seven species of pine nuts, five machine learning methods (Decision Tree (DT), Random Forest (RF), Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Naive Bayes (NB)) were used to identify species of pine nuts. 303 images were used to collect…
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Molecular Similarity Perception Based on Machine-Learning Models
…submitted to a group of experts for the similarity judgment. The proposed machine-learning models can be useful to reduce or assist human efforts in future evaluations. For this reason, the new molecules dataset and an online tool for molecular similarity estimation have been made freely available.
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Plant Genotype to Phenotype Prediction Using Machine Learning
…dimension datasets such as imagery collected by unmanned aerial vehicles. Machine learning (ML) algorithms have the potential to surpass the prediction accuracy of current tools used for genotype to phenotype prediction, due to their capacity to autonomously extract data features and represent…
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Gamification for Machine Learning in Surgical Patient Engagement
…these risk factors are often modifiable and can alter the plan of care. Machine learning can perform real-time predictions of outcomes, though these technologies are limited by usability and interpretability. Gamification, or the use of game elements in non-game contexts, may be able to incorporate…
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Predicting cash holdings using supervised machine learning algorithms
This study predicts the cash holdings policy of Turkish firms, given the 20 selected features with machine learning algorithm methods. 211 listed firms in the Borsa Istanbul are analyzed over the period between 2006 and 2019. Multiple linear regression (MLR), k-nearest neighbors (KNN), support…
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Artificial Intelligence and Machine Learning for Safe Medicines
SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40264-022-01177-0.
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Artikel Jurnal Teks lengkap
Diagnosing Cancer Using IOT and Machine Learning Methods
…technology, large datasets can now be used. Methods. This study used machine learning algorithms and IOT to classify microarray data. They were created from two sets of data: one with 1919 protein types and one with 24481 protein types for 97 people, 46 of whom had a recurring disease and 51 of…
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