3.327 hasil untuk machine learning
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Artikel Jurnal Teks lengkap
Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications
…between social workers or experts and the targeted individuals and machine learning techniques with feature engineering or deep learning for automatic detection based on online social contents. This article is the first survey that comprehensively introduces and discusses the methods from these…
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Rainfall forecasting model using machine learning methods: Case study Terengganu, Malaysia
…study was conducted focusing on developing and comparing several Machine Learning (ML) models, evaluating different scenarios and time horizon, and forecasting rainfall using two types of methods. Data involved for this research consist of taking the average rainfall from 10 stations around the…
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Overview: Computer Vision and Machine Learning for Microstructural Characterization and Analysis
…method for doing so. However, recent advances in computer vision (CV) and machine learning (ML) offer new approaches for extracting information from microstructural images. This overview surveys CV methods for numerically encoding the visual information contained in a microstructural image using…
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Predicting Type 2 Diabetes Using Logistic Regression and Machine Learning Approaches
…Indian women utilizing a logistic regression model and decision tree-a machine learning algorithm. Our analysis finds five main predictors of type 2 diabetes: glucose, pregnancy, body mass index (BMI), diabetes pedigree function, and age. We further explore a classification tree to complement and…
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Artikel Jurnal Teks lengkap
Reviewing machine learning of corrosion prediction in a data-oriented perspective
Abstract This work provides a data-oriented overview of the rapidly growing research field covering machine learning (ML) applied to predicting electrochemical corrosion. Our main aim was to determine which ML models have been applied and how well they performed depending on the corrosion topic…
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Completing density functional theory by machine learning hidden messages from molecules
…accurate density distributions and energies in reference molecules via machine learning. Surprisingly, a trial functional machine learned from only a few molecules is already applicable to hundreds of molecules comprising various first- and second-row elements with the same accuracy as the standard…
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Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma
…quick detection, low invasiveness, and high performance. Here, we conduct machine learning of serum metabolic patterns to detect early-stage LA. We extract direct metabolic patterns by the optimized ferric particle-assisted laser desorption/ionization mass spectrometry within 1 s using only 50 nL…
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Machine Learning and Novel Biomarkers for the Diagnosis of Alzheimer’s Disease
…and relatively invasive. Artificial intelligence techniques like machine learning tools have being increasingly used in precision diagnosis. METHODS: We conducted a meta-analysis to investigate the machine learning and novel biomarkers for the diagnosis of AD. METHODS: We searched PubMed, the…
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Physics-informed machine learning for reduced-order modeling of nonlinear problems
A reduced basis method based on a physics-informed machine learning framework is developed for efficient reduced-order modeling of parametrized partial differential equations (PDEs). A feedforward neural network is used to approximate the mapping from the time-parameter to the reduced coefficients…
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Artikel Jurnal Teks lengkap
Machine Learning Applications in Agriculture: Current Trends, Challenges, and Future Perspectives
…as the Internet of Things, sensors, robotics, Artificial Intelligence, Machine Learning, Big Data, and Cloud Computing are propelling the agricultural sector towards the transformative Agriculture 4.0 paradigm. The present systematic literature review employs the Preferred Reporting Items for…
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Forecasting and trading cryptocurrencies with machine learning under changing market conditions
This study examines the predictability of three major cryptocurrencies-bitcoin, ethereum, and litecoin-and the profitability of trading strategies devised upon machine learning techniques (e.g., linear models, random forests, and support vector machines). The models are validated in a period…
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Artikel Jurnal Teks lengkap
Hierarchical Incentive Mechanism Design for Federated Machine Learning in Mobile Networks
…a model owner may have insufficient data samples to build an effective machine learning model. To this end, we propose a federated learning (FL)-based privacy-preserving approach to facilitate collaborative machine learning among multiple model owners in mobile crowdsensing. Our system model allows…
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Current Trends and Applications of Machine Learning in Tribology—A Review
Machine learning (ML) and artificial intelligence (AI) are rising stars in many scientific disciplines and industries, and high hopes are being pinned upon them. Likewise, ML and AI approaches have also found their way into tribology, where they can support sorting through the complexity of…
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Machine Learning Based Solar Photovoltaic Power Forecasting: A Review and Comparison
…This paper presents a comprehensive and comparative review of existing Machine Learning (ML) based approaches used in PV power forecasting, focusing on short-term horizons. We provide an overview of factors affecting solar PV power forecasting and an overview of existing PV power forecasting…
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Machine learning enables design automation of microfluidic flow-focusing droplet generation
…design automation of flow-focusing droplet generators. We capitalize on machine learning algorithms to predict the droplet diameter and rate with a mean absolute error of less than 10 μm and 20 Hz. This tool delivers a user-specified performance within 4.2% and 11.5% of the desired diameter and…
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A Novel Diabetes Healthcare Disease Prediction Framework Using Machine Learning Techniques
…approaches, which pushes researchers to use big data analytics and machine learning (ML)-based methods. Applying four different machine learning methods, the research tries to overcome the problems and investigate healthcare predictive analytics. The study's primary goal was to see how big data…
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Machine learning in point-of-care testing: innovations, challenges, and opportunities
The landscape of diagnostic testing is undergoing a significant transformation, driven by the integration of artificial intelligence (AI) and machine learning (ML) into decentralized, rapid, and accessible sensor platforms for point-of-care testing (POCT). The COVID-19 pandemic has accelerated the…
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Artikel Jurnal
MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning**
Despite rapid progress in the field of metal-organic frameworks (MOFs), the potential of using machine learning (ML) methods to predict MOF synthesis parameters is still untapped. Here, we show how ML can be used for rationalization and acceleration of the MOF discovery process by directly…
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Artikel Jurnal Teks lengkap
Machine Learning in Wireless Sensor Networks for Smart Cities: A Survey
Artificial intelligence (AI) and machine learning (ML) techniques have huge potential to efficiently manage the automated operation of the internet of things (IoT) nodes deployed in smart cities. In smart cities, the major IoT applications are smart traffic monitoring, smart waste management, smart…
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