8.473 hasil untuk machine learning
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Machine learning for predicting Chagas disease infection in rural areas of Brazil
…and its associated life-threatening complications. In recent years, machine learning algorithms have emerged as powerful tools for disease prediction and diagnosis. Methods: In this study, we developed machine learning algorithms to predict the risk of Chagas disease based on five general factors…
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Probability density and information entropy of machine learning derived intracranial pressure predictions
Even with the powerful statistical parameters derived from the Extreme Gradient Boost (XGB) algorithm, it would be advantageous to define the predicted accuracy to the level of a specific case, particularly when the model output is used to guide clinical decision-making. The probability density…
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Correction: Machine learning in internet financial risk management: A systematic literature review
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Economic benefit analysis of lithium battery recycling based on machine learning algorithm
…restricts the development of the industry. To address the above issues, machine learning will be applied in the field of economic benefit analysis for lithium battery recycling, and backpropagation neural networks will be combined with stepwise regression. On the basis of considering social and…
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A hybrid human recognition framework using machine learning and deep neural networks
…large parts of their face, and a person based on their gait via deep and machine learning algorithms. The experimental results are excellent compared to the current face and gait detectors. We achieved success of between 97% and 100% in the detection of face and gait based on F1 score, precision…
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Prediction of future customer needs using machine learning across multiple product categories
…approach outperforms a baseline in the literature and through Multi-Task Learning can accurately predict needs for a category it wasn’t trained on e.g. train on toothpaste, cereal, and beer products yet still predict for shampoo products. The findings from this research could provide many…
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Profiling the AI speaker user: Machine learning insights into consumer adoption patterns
…machines, artificial neural networks, and XGboost, which are typical machine learning techniques for classification and leverages the 2019 Media & Consumer Research survey data from the Korea Broadcasting and Advertising Corporation (N = 3,922). The final XGboost model, which performed the best…
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Radiomics-based machine learning for automated detection of Pneumothorax in CT scans
…by developing an intelligent model that leverages radiomics features and machine learning techniques. By enhancing the detection of pneumothorax, this research aims to mitigate diagnostic errors and accelerate the process of image interpretation, ultimately improving patient outcomes. Data used in…
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A machine-learning model for prediction of Acinetobacter baumannii hospital acquired infection
…with improved outcomes. This study aimed to construct an innovative Machine Learning (ML) based prediction tool for Acinetobacter baumanni infection, among patients in the ICU, and to examine its robustness and predictive power. Methods: For model development and internal validation, we used The…
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Systematic evaluation of machine learning models for postoperative surgical site infection prediction
…surgical complication have been developed, with an increasing use of machine learning (ML) tools. Objective: The aim of this systematic review was to assess the performance as well as the methodological quality of validated ML models for the prediction of SSIs. Methods: A systematic search in…
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Machine learning models exploring characteristic single-nucleotide signatures in yellow fever virus
…areas where a large number of unvaccinated people live. We developed a machine learning framework combining three different algorithms (XGBoost, random forest and regularized logistic regression) to analyze YFV genomic sequences. This method was applied to 56 YFV sequences from human infections and…
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Enhancing flood prediction through physics-driven typhoon feature engineering and machine learning
Typhoon-induced extreme floods pose severe threats to subtropical watersheds, yet systematic integration of typhoon physics into machine learning flood prediction remains limited. This study developed a physics-informed machine learning framework for the Boluo watershed, South China, emphasizing…
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LHW-Net: An ensemble-based machine learning framework for brain tumor classification
…the LHW-Net framework, the extracted features are utilized in different machine learning classifiers, such as K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Classifier (SVC). The results of the individual classifiers are further combined using probabilistic score fusion approach…
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PoPI: A machine learning-based consensus mechanism for blockchain-enabled IoT systems
…this limitation, we propose Proof of Periodic Inference (PoPI), a machine learning model-based consensus mechanism tailored for blockchain-based IoT systems. PoPI uses a supervised machine learning model to periodically select a group of block producers and maintains security through random block…
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Automated counting of prostate cell types with image processing and machine learning
…particularly in the context of prostate cancer. Recent advances in machine learning have shown considerable promise in enhancing the accuracy and efficiency of cell enumeration. In this study, we present a novel software system for the automated counting of prostate cancer cells, integrating image…
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Forecasting auditor’s going concern opinion using with hybrid robust machine learning model
…researchers have recently turned to predicting GCO using various machine learning (ML) methods. The aim of this research is to propose a novel hybrid model that integrates ML models to enhance the prediction accuracy of the system. We use a combination of traditional (classical) and hybrid ML…
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Improving the composition of donor milk using machine learning and optimisation techniques
…quality of DHM by pooling milk from different donors by utilizing machine learning prediction and optimisation techniques. Methods: The current pooling strategy is compared with a new theoretical approach that pools milk batches from up to 5 donors. To predict the crude protein and energy content…
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Wild mushroom consumption susceptibility among Chinese university students: A machine learning study
Objectives: To investigate factors associated with susceptibility to wild mushroom consumption using machine learning approaches and identify key predictors for targeted intervention development. Methods: A cross-sectional survey of 216 Chinese university students employed three machine learning…
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Machine learning identifies novel signatures of antifungal drug resistance in Saccharomycotina yeasts
…but the genetic variants involved are poorly characterized. We employed machine learning, structural, and evolutionary approaches to identify genetic variants associated with drug resistance across an ancient yeast lineage. By focusing on a protein known to be involved in resistance to the…
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Enhancing cancer drug discovery: QSAR modeling with machine learning and chemical representations
…RDKFingerprint (RDF), Rdkit PhysChem (RDC), Torsion (TSN) combined with machine learning algorithms (Bayesian Ridge (BRG), Elastic Net (ENT), Extra Trees (ETT), Hist Gradient Boosting (HGT), K-Nearest Neighbors (kNN), Lasso (LSS), Multi-layer Perceptron (MLP), Partial least squares (PLS), Random…
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