3.327 hasil untuk machine learning
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Machine Learning in Disaster Management: Recent Developments in Methods and Applications
…Recent developments in artificial intelligence (AI) and especially in machine learning (ML) and deep learning (DL) have been used to better cope with the severe and often catastrophic impacts of disasters. This paper aims to provide an overview of the research studies, presented since 2017…
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Interpretable Ensemble-Machine-Learning models for predicting creep behavior of concrete
This study aims to provide an efficient and accurate machine learning (ML) approach for predicting the creep behavior of concrete. Three ensemble machine learning (EML) models are selected in this study: Random Forest (RF), Extreme Gradient Boosting Machine (XGBoost) and Light Gradient Boosting…
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COVID-19 cough classification using machine learning and global smartphone recordings
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Artikel Jurnal Teks lengkap
Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data
…of these phenotypes are unclear, limiting treatment stratification. Machine learning can identify groups with similar features using multidimensional data. Here, to classify MS subtypes based on pathological features, we apply unsupervised machine learning to brain MRI scans acquired in previously…
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Using machine learning approaches for multi-omics data analysis: A review
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Human-in-the-loop machine learning: a state of the art
Abstract Researchers are defining new types of interactions between humans and machine learning algorithms generically called human-in-the-loop machine learning. Depending on who is in control of the learning process, we can identify: active learning, in which the system remains in control…
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Selecting critical features for data classification based on machine learning methods
Abstract Feature selection becomes prominent, especially in the data sets with many variables and features. It will eliminate unimportant variables and improve the accuracy as well as the performance of classification. Random Forest has emerged as a quite useful algorithm that can handle the…
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Machine learning for digital soil mapping: Applications, challenges and suggested solutions
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Machine learning in additive manufacturing: State-of-the-art and perspectives
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Artikel Jurnal Teks lengkap
A review of machine learning applications in wildfire science and management
…the field has rapidly progressed congruently with the wide adoption of machine learning (ML) methods in the environmental sciences. Here, we present a scoping review of ML applications in wildfire science and management. Our overall objective is to improve awareness of ML methods among wildfire…
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Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems
…traditional physics-based modeling approaches with state-of-the-art machine learning (ML) techniques. This article provides a structured overview of such techniques. Application-centric objective areas for which these approaches have been applied are summarized, and then classes of methodologies…
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Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare
…and accurate system to diagnosis heart disease and the system is based on machine learning techniques. The system is developed based on classification algorithms includes Support vector machine, Logistic regression, Artificial neural network, K-nearest neighbor, Naïve bays, and Decision tree while…
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Mitigating the Multicollinearity Problem and Its Machine Learning Approach: A Review
…modeling, while recent studies suggest that optimization approaches via machine learning handle data with multicollinearity better than statistical estimators. Therefore, this study details the chronological developments to mitigate the effects of multicollinearity and up-to-date recommendations to…
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Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices
This Methods/Protocols article is intended for materials scientists interested in performing machine learning-centered research. We cover broad guidelines and best practices regarding the obtaining and treatment of data, feature engineering, model training, validation, evaluation and comparison…
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Machine learning-based prediction of COVID-19 diagnosis based on symptoms
…in the context of limited healthcare resources. We established a machine-learning approach that trained on records from 51,831 tested individuals (of whom 4769 were confirmed to have COVID-19). The test set contained data from the subsequent week (47,401 tested individuals of whom 3624 were…
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Prediction of Soil Heavy Metal Immobilization by Biochar Using Machine Learning
…efficiency in biochar-amended soils is required. This study employs machine learning (ML) approaches to predict the HM immobilization efficiency of biochar in biochar-amended soils. The nitrogen content in the biochar (0.3-25.9%) and biochar application rate (0.5-10%) were the two most significant…
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A rigorous and robust quantum speed-up in supervised machine learning
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The promise of machine learning in predicting treatment outcomes in psychiatry
…treatment decisions using new statistical approaches from the field of machine learning and applying them to larger volumes of data. In this pursuit, there has been a paradigm shift away from experimental studies to confirm or refute specific hypotheses towards a focus on the overall explanatory…
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Plant leaf disease detection using computer vision and machine learning algorithms
…plant leaf. Since tomato is merely available vegetable, the approaches of machine learning and image processing with an accurate algorithm is identified to detect the leaf diseases in the tomato plant. In this investigation, the samples of tomato leaves having disorders are considered. With these…
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