8.473 hasil untuk machine learning
Menampilkan 721–740 · 2,14 detik · RSS
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Economic development, weather shocks and child marriage in South Asia: A machine learning approach
…in South Asia, namely Bangladesh, India, Nepal and Pakistan. We apply machine learning techniques to child marriage data and develop a prediction model that relies largely on regional and local inputs such as droughts, floods, population growth and nightlight data to model the incidence of child…
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Combining metabolomics and machine learning to discover biomarkers for early-stage breast cancer diagnosis
…robust multivariate receiver operating characteristic analysis utilizing machine learning models, either linear support vector machines or random forest models, we successfully distinguished between cancerous and benign cases with promising outcomes. These results emphasize the potential of…
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Comparative study of machine learning approaches integrated with genetic algorithm for IVF success prediction
…aimed to develop a model for predicting IVF outcome by comparing five machine-learning techniques. Method: The research approached five prominent machine learning algorithms, including Random Forest, Artificial Neural Network (ANN), Support Vector Machine (SVM), Recursive Partitioning and…
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From data to decision: Machine learning determination of aerobic and anaerobic thresholds in athletes
…Thus, the aim of this study was to determine AeT and AnT thresholds using machine learning modelling (ML) and to compare ML-obtained results with the parameters’ values determined using conventional methods. ML seems to be highly useful due to its ability to handle complex, personalised data…
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Correction: The efficacy of machine learning models in lung cancer risk prediction with explainability
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Hybrid evolutionary machine learning model for advanced intrusion detection architecture for cyber threat identification
In response to the rapidly evolving threat landscape in network security, this paper proposes an Evolutionary Machine Learning Algorithm designed for robust intrusion detection. We specifically address challenges such as adaptability to new threats and scalability across diverse network…
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Use machine learning models to identify and assess risk factors for coronary artery disease
…personalized treatment options. This study attempts to construct a machine learning (ML) model for predicting CAD risk and further elucidate the complex nonlinear interactions between the disease and its risk factors. Employing the Z-Alizadeh Sani dataset, which includes records of 303 patients…
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Survival machine learning methods for mortality prediction after heart transplantation in the contemporary era
Although prediction models for heart transplantation outcomes have been developed previously, a comprehensive benchmarking of survival machine learning methods for mortality prognosis in the most contemporary era of heart transplants following the 2018 donor heart allocation policy change is…
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An efficient smart phone application for wheat crop diseases detection using advanced machine learning
…primary decision-making engine. The application utilizes sophisticated machine learning techniques, including Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and AdaBoost, combined with feature extraction methods such as Count Vectorization (CV) and Term Frequency-Inverse…
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Machine learning-based assessment of regional-scale variation of landslide susceptibility in central Vietnam
…events were developed using nine causative factors and a Random Forest machine learning algorithm. The results indicate a notable areal expansion of high to very high landslide susceptibility in the northern and eastern regions and a moderate reduction in the central and southern areas during the…
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Sex prediction through machine learning utilizing mandibular condyles, coronoid processes, and sigmoid notches features
…condyle, and sigmoid notch patterns in sex prediction through supervised machine learning algorithms. Cephalometric radiographs from 410 dental records of patients were screened to investigate the morphology of the coronoid process, condyle, and sigmoid notch and the Co-Gn distance. The following…
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Modeling the relationship between maternal health and infant behavioral characteristics based on machine learning
…on maternal indicators. (2) The development of a combined model using machine learning techniques, including random forest (RF) and multilayer perceptron (MLP) to establish the relationship between maternal health (physical and psychological health) and infant behavioral characteristics. The model…
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Quantitative drug susceptibility testing for Mycobacterium tuberculosis using unassembled sequencing data and machine learning
…even though other changes may be clinically relevant. We developed a machine learning system to predict minimum inhibitory concentration from unassembled whole-genome sequencing data for 13 anti-tuberculosis drugs. We trained, validated and tested the system on 10,859 isolates from the CRyPTIC…
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Application of machine learning in in vitro propagation of endemic Lilium akkusianum R. Gämperle
…the effectiveness of L. akkusianum propagation protocols by incorporating machine learning methodologies was evaluated. ML techniques enhance lily micropropagation by analyzing complex biological processes, merging with traditional methods. This collaborative approach validates current protocols…
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Carpal tunnel syndrome prediction with machine learning algorithms using anthropometric and strength-based measurement
…and pinch strength could serve as predictive indicators for CTS through machine learning techniques. Methods: Enrollment encompassed patients exhibiting CTS symptoms (n = 56) and asymptomatic healthy controls (n = 56), with confirmation via electrophysiological assessments. Anthropometric…
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Detection of disease-specific signatures in B cell repertoires of lymphomas using machine learning
…sequencing (NGS) of lymphoma-infiltrated tissues in conjunction with machine learning algorithms could have diagnostic utility in the subclassification of these cancers. We trained a random forest and a linear classifier via logistic regression based on patterns of clonal distribution, VDJ gene…
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Machine-learning based control of bi-modular multilevel PWM inverter for high power applications
This paper presents the topology and machine learning-based intelligent control of high-power PV inverter for maximum power extraction and optimal energy utilization. Modular converters with reduced components economic and reliable for high power applications. The proposed integrated intelligent…
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Leveraging machine learning to unravel the impact of cadmium stress on goji berry micropropagation
…across three distinct genotypes (ERU, NQ1, NQ7), employing an array of machine learning (ML) algorithms, including Multilayer Perceptron (MLP), Support Vector Machines (SVM), Random Forest (RF), Gaussian Process (GP), and Extreme Gradient Boosting (XGBoost). The primary motivation is to elucidate…
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Impact of economic indicators on rice production: A machine learning approach in Sri Lanka
…aiming to uncover correlations and build prediction models using machine learning techniques. The dataset, spanning from 1960 to 2020, includes key economic variables such as GDP, inflation rate, manufacturing output, population, population growth rate, imports, arable land area, military…
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Advancing food security: Rice yield estimation framework using time-series satellite data & machine learning
Timely and accurately estimating rice yields is crucial for supporting food security management, agricultural policy development, and climate change adaptation in rice-producing countries such as Bangladesh. To address this need, this study introduced a workflow to enable timely and precise rice…
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