8.473 hasil untuk machine learning
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Machine learning reveals microbiome differences by periodontitis severity
…defined severity groups and evaluated the performance of microbiome-based machine-learning models for classifying periodontitis severity. Subgingival plaque samples from 84 patients were analyzed using 16S rRNA gene sequencing. Microbial diversity showed a modest decreasing trend with increasing…
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Predicting amputation using machine learning: A systematic review
…of bias, most often due to small sample sizes. In conclusion, multiple machine learning models have been successfully developed that have the potential to be superior to traditional modeling techniques and prospective clinical judgment in predicting amputation. Further research is needed to…
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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.Individualized Clinical Assessment Recommendation System (iCARE) employs locally weighted…
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Machine learning driven biomarker selection for medical diagnosis
…we evaluate 4 different methods for biomarker selection and 5 different machine learning (ML) classifiers for identifying correlations—evaluating 20 approaches in all. We found that contemporary methods outperform previously reported logistic regression in cases where 3 and 10 biomarkers are…
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Retraction: Detecting phishing websites using machine learning technique
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Multi-contrast machine learning improves schistosomiasis diagnostic performance
…identification of eggs requires a skilled microscopist. Here we present a machine learning (ML)-based strategy for automated detection of S. haematobium that combines two imaging contrasts, brightfield (BF) and darkfield (DF), to improve diagnostic performance. We collected BF and DF images of…
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Machine learning-based analyzing earthquake-induced slope displacement
…to seismic activity. This study evaluates the capabilities of various machine learning models, including artificial neural network (ANN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) in analyzing earthquake-induced slope displacement. A dataset of 45…
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Determining graphlet explanations for machine learning on graphs
This study introduces a post-hoc approach for explainable machine learning on graph-structured data by identifying relevant subgraphs and their corresponding subgraph patterns, the graphlet motifs. Unlike traditional motif detection methods, which rely solely on statistical occurrence frequencies…
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Supervised Machine Learning and Physics Machine Learning approach for prediction of peak temperature distribution in Additive Friction Stir Deposition of Aluminium Alloy
…for properties. This work employs a framework combining supervised machine learning (SML) and physics-informed neural networks (PINNs) to predict peak temperature distribution in AFSD from process parameters. Eight regression algorithms were implemented for SML modeling, while four PINNs leveraged…
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Machine learning approach to student performance prediction of online learning
Student performance is crucial for addressing learning process problems and is also an important factor in measuring learning outcomes. The ability to improve educational systems using data knowledge has driven the development of the field of educational data mining research. Here, this paper…
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Improving the Caenorhabditis elegans Genome Annotation Using Machine Learning
For modern biology, precise genome annotations are of prime importance, as they allow the accurate definition of genic regions. We employ state-of-the-art machine learning methods to assay and improve the accuracy of the genome annotation of the nematode Caenorhabditis elegans. The proposed machine…
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Development of machine learning models for diagnosis of glaucoma
The study aimed to develop machine learning models that have strong prediction power and interpretability for diagnosis of glaucoma based on retinal nerve fiber layer (RNFL) thickness and visual field (VF). We collected various candidate features from the examination of retinal nerve fiber layer…
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Machine Learning of Protein Interactions in Fungal Secretory Pathways
In this paper we apply machine learning methods for predicting protein interactions in fungal secretion pathways. We assume an inter-species transfer setting, where training data is obtained from a single species and the objective is to predict protein interactions in other, related species. In our…
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Predicting Market Impact Costs Using Nonparametric Machine Learning Models
…directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of…
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Human and Machine Learning in Non-Markovian Decision Making
Humans can learn under a wide variety of feedback conditions. Reinforcement learning (RL), where a series of rewarded decisions must be made, is a particularly important type of learning. Computational and behavioral studies of RL have focused mainly on Markovian decision processes, where the next…
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Predictive models for charitable giving using machine learning techniques
…predictive models are developed. Multiple Linear Regression (MLR) and machine learning techniques, including Artificial Neural Networks (ANN) and Support Vector Regression (SVR) are used to develop the predictive models. The results suggest that population, education level, and the amount of…
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Machine Learning Algorithms for Automatic Classification of Marmoset Vocalizations
Automatic classification of vocalization type could potentially become a useful tool for acoustic the monitoring of captive colonies of highly vocal primates. However, for classification to be useful in practice, a reliable algorithm that can be successfully trained on small datasets is necessary…
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WORMHOLE: Novel Least Diverged Ortholog Prediction through Machine Learning
…we present WORMHOLE, a novel ortholog prediction meta-tool that applies machine learning to integrate 17 distinct ortholog prediction algorithms to identify novel least diverged orthologs (LDOs) between 6 eukaryotic species—humans, mice, zebrafish, fruit flies, nematodes, and budding yeast. Machine…
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SoilGrids250m: Global gridded soil information based on machine learning
…landform and lithology maps), which were used to fit an ensemble of machine learning methods—random forest and gradient boosting and/or multinomial logistic regression—as implemented in the R packages ranger, xgboost, nnet and caret. The results of 10–fold cross-validation show that the ensemble…
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Modeling second-order boundary perception: A machine learning approach
…by spatial differences in contrast or texture. We introduce a novel machine learning framework for modeling human perception of second-order visual stimuli, using image-computable hierarchical neural network models fit directly to psychophysical trial data. This framework is applied to modeling…
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