8.473 hasil untuk machine learning
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Beyond scores: A machine learning approach to comparing educational system effectiveness
…To address this issue, our paper provides a new approach based on machine learning models. The new approach is flexible, allows paired comparison, and is model-independent. An analysis conducted in Brazil verifies the suitability of the method to explore differences in effectiveness between…
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Enhancing economic competitiveness analysis through machine learning: Exploring complex urban features
…for comprehending cities as complex systems. The ability of deep learning neural networks to automatically construct models of nonlinear relationships among complex features provides a new approach to research in this issue. In this study, a complex urban feature dataset comprising 1008 features…
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Henry gas solubility optimization double machine learning classifier for neurosurgical patients
This study aims to predict head trauma outcome for Neurosurgical patients in children, adults, and elderly people. As Machine Learning (ML) algorithms are helpful in healthcare field, a comparative study of various ML techniques is developed. Several algorithms are utilized such as k-nearest…
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Identifying graft incompatible rootstocks for sweet cherry through machine learning algorithms
…demonstrate that combining traditional phenotypic evaluation methods with machine learning-based approaches offers a robust and comprehensive framework for addressing graft incompatibility, and contributes valuable insights for future breeding programs and rootstock selection strategies in sweet…
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Construction and application of machine learning models for predicting intradialytic hypotension
Introduction: Intradialytic hypotension (IDH) remains a prevalent complication of hemodialysis, which is associated with adverse outcomes for patients. This study seeks to harness machine learning to construct predictive models for IDH based on multiple definitions. Methods: In this study, a…
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Educational improvement through machine learning: Strategic models for better PISA scores
In this study, in addition to traditional variables such as economic wealth or the number of books read, on which many studies have already been conducted, variables that are thought to influence student achievement and better predict success are identified. Random Forest algorithm was used to…
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A practical approach for colorectal cancer diagnosis based on machine learning
In this paper, we present the results of applying machine learning models to build a Colorectal Cancer Diagnosis system. The methodology encompasses six key steps: collecting raw data from Electronic Medical Records (EMRs), revising feature attributes with expert input, data preprocessing, model…
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Combating trade in illegal wood and forest products with machine learning
…forest product shipments. We present a proof of concept framework using machine learning and big data approaches—combining domain expertise with automation—to achieve this objective. We formulated the underlying machine learning problem as an anomaly detection problem and collected and collated…
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Retraction: Machine learning-based e-commerce platform repurchase customer prediction model
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Microfinance institutions failure prediction in emerging countries, a machine learning approach
…comes here. It combines clever techniques, such as granular computing and machine learning, to handle messy and imbalanced data, ensuring that the model is not just a theoretical concept but a practical tool that can be used in the real world.Data from 56 financial institutions in Peru was analyzed…
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Adaptive lift chiller units fault diagnosis model based on machine learning
…model of Chiller is designed by combining least squares support vector machine (LSSVM) optimized by hybrid improved northern goshawk optimization algorithm (HINGO) and improved IAdaBoost ensemble learning algorithm. HINGO enhances the uniformity of the initial population distribution by means of…
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Predicting achievement of clinical goals using machine learning in myasthenia gravis
…model for MM in MG patients by leveraging their clinical scores and machine learning approaches. Methods: This study included 1,603 MG patients enrolled from the Japan MG Registry in the 2021 survey. We employed non-negative matrix factorization to decompose three MG clinical scores (MG composite…
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Correction: Sustainable visions: Unsupervised machine learning insights on global development goals
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Application of machine learning in predicting consumer behavior and precision marketing
…improve user conversion rate. This paper aims to study the application of machine learning in consumer behavior prediction and precision marketing. Four models, namely support vector machine (SVM), extreme gradient boosting (XGBoost), categorical boosting (CatBoost), and backpropagation artificial…
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Spatio-temporal risk prediction of leptospirosis: A machine-learning-based approach
…2011 to 2022. Our approach utilized a comprehensive strategy combining machine learning models trained on binarized incidences, along with descriptive techniques for identifying key factors. The analysis encompasses a broad spectrum of variables, including meteorological, topographic, and…
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Unraveling the drivers of leptospirosis risk in Thailand using machine learning
Leptospirosis poses a significant public health challenge in Thailand, driven by a complex mix of environmental and socioeconomic factors. This study develops an XGBoost machine learning model to predict leptospirosis outbreak risk at the provincial level in Thailand, integrating climatic…
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Landslide susceptibility assessment via the information value-coupled machine learning models
…with the combination of traditional statistical methods and machine learning models. Both the information value-maximum entropy coupled model (I-MaxEnt) and the information value-logistic regression coupled model (I-LR) were proposed to assess landslide susceptibility in the Tianshan northern slope…
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Machine learning-based analysis of drug resistance mutations in Mycobacterium tuberculosis
…in the bacterium and serve different purposes. Our study developed a machine learning prediction model to analyze mutations across multiple drug-resistance types. The proposed framework predicts drug-resistance mutations across four drug-resistance types, including Rifampicin Resistance, Isoniazid…
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In Vitro liposome release profile prediction using explainable machine learning approaches
Formulation features and test settings influence liposomal in vitro release (IVR) profiles, yet it is challenging to examine these multivariable associations across varied literature data. We created an explainable computational workflow in this proof-of-concept study for classifying liposomal…
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Correction: Flight delay prediction: Evaluating machine learning algorithms for enhanced accuracy
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