3.327 hasil untuk machine learning
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Artikel Jurnal Teks lengkap
Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems: A Review
…systems. Recently, much effort has been given in combining DA, UQ and machine learning (ML) techniques. These research efforts seek to address some critical challenges in high-dimensional dynamical systems, including but not limited to dynamical system identification, reduced order surro-gate…
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Review of Artificial Intelligence and Machine Learning Technologies: Classification, Restrictions, Opportunities and Challenges
Artificial intelligence (AI) is an evolving set of technologies used for solving a wide range of applied issues. The core of AI is machine learning (ML)—a complex of algorithms and methods that address the problems of classification, clustering, and forecasting. The practical application of AI&ML…
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Performance of Machine Learning Algorithms with Different K Values in K-fold CrossValidation
The numerical value of k in a k-fold cross-validation training technique of machine learning predictive models is an essential element that impacts the model’s performance. A right choice of k results in better accuracy, while a poorly chosen value for k might affect the model’s performance. In…
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Prediction of Breast Cancer, Comparative Review of Machine Learning Techniques, and Their Analysis
…of deaths in women. This article presents the comparative analysis of machine learning, deep learning and data mining techniques being used for the prediction of breast cancer. Many researchers have put their efforts on breast cancer diagnoses and prognoses, every technique has different accuracy…
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Presenting machine learning model information to clinical end users with model facts labels
There is tremendous enthusiasm surrounding the potential for machine learning to improve medical prognosis and diagnosis. However, there are risks to translating a machine learning model into clinical care and clinical end users are often unaware of the potential harm to patients. This perspective…
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Machine learning-based seismic fragility and seismic vulnerability assessment of reinforced concrete structures
…purpose of retrofitting and potential design strategies of RC buildings. Machine Learning (ML) algorithms were developed in Python software by innovative methods of hyperparameter optimization, such as halving search, grid search, random search, fine-tuning method, and the k -fold cross-validation…
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The role of sensors, big data and machine learning in modern animal farming
…paper explores the role of sensors, big data, artificial intelligence and machine learning in helping animal farmers to lower production costs, increase efficiencies, enhance animal welfare and grow more animals per hectare. It also explores the challenges and limitations of technology. The paper…
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FuXi: a cascade machine learning forecasting system for 15-day global weather forecast
Abstract Over the past few years, the rapid development of machine learning (ML) models for weather forecasting has led to state-of-the-art ML models that have superior performance compared to the European Centre for Medium-Range Weather Forecasts (ECMWF)’s high-resolution forecast (HRES), which is…
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Progress and Opportunities for Machine Learning in Materials and Processes of Additive Manufacturing
In recent years, there has been widespread adoption of machine learning (ML) technologies to unravel intricate relationships among diverse parameters in various additive manufacturing (AM) techniques. These ML models excel at recognizing complex patterns from extensive, well-curated datasets…
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Interpretable machine learning for building energy management: A state-of-the-art review
Machine learning has been widely adopted for improving building energy efficiency and flexibility in the past decade owing to the ever-increasing availability of massive building operational data. However, it is challenging for end-users to understand and trust machine learning models because of…
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Condition Monitoring using Machine Learning: A Review of Theory, Applications, and Recent Advances
…the condition of the machinery, predictive maintenance can minimize machine downtime and potential losses. Throughout the field, machine learning (ML) methods have become noteworthy for predicting failures before they occur. However, the efficacy of the predictive maintenance strategy relies on…
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Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods
Abstract The notion of uncertainty is of major importance in machine learning and constitutes a key element of machine learning methodology. In line with the statistical tradition, uncertainty has long been perceived as almost synonymous with standard probability and probabilistic predictions. Yet…
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A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction
Machine learning has shown utility in detecting patterns within large, unstructured, and complex datasets. One of the promising applications of machine learning is in precision medicine, where disease risk is predicted using patient genetic data. However, creating an accurate prediction model based…
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Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning
…that is hitherto underused in battery diagnosis-with Gaussian process machine learning. Over 20,000 EIS spectra of commercial Li-ion batteries are collected at different states of health, states of charge and temperatures-the largest dataset to our knowledge of its kind. Our Gaussian process model…
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Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Directions
…of artificial intelligence (AI), a number of AI-based techniques, such as machine learning, have already been established in the industry to achieve sustainable manufacturing. Thus, the aim of the present research was to analyze, systematically, the scientific literature relating to the application…
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A Survey on Machine Learning Techniques for Cyber Security in the Last Decade
…in detecting previously unseen and polymorphic security attacks. Machine learning (ML) techniques are playing a vital role in numerous applications of cyber security. However, despite the ongoing success, there are significant challenges in ensuring the trustworthiness of ML systems. There are…
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Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-Air
We study federated machine learning (ML) at the wireless edge, where power- and bandwidth-limited wireless devices with local datasets carry out distributed stochastic gradient descent (DSGD) with the help of a parameter server (PS). Standard approaches assume separate computation and…
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Evaluating the Quality of Machine Learning Explanations: A Survey on Methods and Metrics
The most successful Machine Learning (ML) systems remain complex black boxes to end-users, and even experts are often unable to understand the rationale behind their decisions. The lack of transparency of such systems can have severe consequences or poor uses of limited valuable resources in…
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Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management
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