3.327 hasil untuk machine learning
Menampilkan 181–200 · 0,34 detik · RSS
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Artikel Jurnal Teks lengkap
The language of proteins: NLP, machine learning & protein sequences
…In recent years, following a series of breakthroughs in deep and machine learning, NLP methods have shown overwhelming progress. Here, we review the success, promise and pitfalls of applying NLP algorithms to the study of proteins. Proteins, which can be represented as strings of amino-acid…
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Generalization in quantum machine learning from few training data
Modern quantum machine learning (QML) methods involve variationally optimizing a parameterized quantum circuit on a training data set, and subsequently making predictions on a testing data set (i.e., generalizing). In this work, we provide a comprehensive study of generalization performance in QML…
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Chronic kidney disease prediction based on machine learning algorithms
…happening. Throughout this research, the potential of several different machine learning approaches for providing an early diagnosis of CKD has been investigated. There has been a significant amount of research conducted on this topic. Nevertheless, we are bolstering our approach by making use of…
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FCHL revisited: Faster and more accurate quantum machine learning
We introduce the FCHL19 representation for atomic environments in molecules or condensed-phase systems. Machine learning models based on FCHL19 are able to yield predictions of atomic forces and energies of query compounds with chemical accuracy on the scale of milliseconds. FCHL19 is a revision of…
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Machine learning for multi-omics data integration in cancer
Multi-omics data analysis is an important aspect of cancer molecular biology studies and has led to ground-breaking discoveries. Many efforts have been made to develop machine learning methods that automatically integrate omics data. Here, we review machine learning tools categorized as either…
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Diabetes prediction using machine learning and explainable AI techniques
…using a private dataset of female patients in Bangladesh and various machine learning techniques. The authors used the Pima Indian diabetes dataset and collected additional samples from 203 individuals from a local textile factory in Bangladesh. Feature selection algorithm mutual information has…
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An overview of machine learning applications for smart buildings
…the other hand, the rapid evolution of artificial intelligence (AI) and machine learning (ML) has equipped buildings with an ability to learn. A lot of research has been dedicated to specific machine learning applications for specific phases of a building's life-cycle. The reviews commonly take a…
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Recommendations for Reporting Machine Learning Analyses in Clinical Research
Use of machine learning (ML) in clinical research is growing steadily given the increasing availability of complex clinical data sets. ML presents important advantages in terms of predictive performance and identifying undiscovered subpopulations of patients with specific physiology and prognoses…
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An accurate and transferable machine learning potential for carbon
We present an accurate machine learning (ML) model for atomistic simulations of carbon, constructed using the Gaussian approximation potential (GAP) methodology. The potential, named GAP-20, describes the properties of the bulk crystalline and amorphous phases, crystal surfaces, and defect…
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Machine learning approaches for the prediction of materials properties
We give here a brief overview of the use of machine learning (ML) in our field, for chemists and materials scientists with no experience with these techniques. We illustrate the workflow of ML for computational studies of materials, with a specific interest in the prediction of materials…
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A survey of visual analytics techniques for machine learning
Visual analytics for machine learning has recently evolved as one of the most exciting areas in the field of visualization. To better identify which research topics are promising and to learn how to apply relevant techniques in visual analytics, we systematically review 259 papers published in the…
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Explainable AI: A Review of Machine Learning Interpretability Methods
Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption, with machine learning systems demonstrating superhuman performance in a significant number of tasks. However, this surge in performance, has often been achieved through increased model complexity…
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A Review on Linear Regression Comprehensive in Machine Learning
Perhaps one of the most common and comprehensive statistical and machine learning algorithms are linear regression. Linear regression is used to find a linear relationship between one or more predictors. The linear regression has two types: simple regression and multiple regression (MLR). This…
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Classification Based on Decision Tree Algorithm for Machine Learning
…the problem of extending a decision tree from available data, such as machine study, pattern recognition, and statistics. In various fields such as medical disease analysis, text classification, user smartphone classification, images, and many more the employment of Decision tree classifiers has…
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Performance and Cost Assessment of Machine Learning Interatomic Potentials
Machine learning of the quantitative relationship between local environment descriptors and the potential energy surface of a system of atoms has emerged as a new frontier in the development of interatomic potentials (IAPs). Here, we present a comprehensive evaluation of machine learning IAPs…
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Interpretable machine learning: Fundamental principles and 10 grand challenges
Interpretability in machine learning (ML) is crucial for high stakes decisions and troubleshooting. In this work, we provide fundamental principles for interpretable ML, and dispel common misunderstandings that dilute the importance of this crucial topic. We also identify 10 technical challenge…
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Artikel Jurnal
Machine learning for combinatorial optimization: A methodological tour d'horizon
This paper surveys the recent attempts, both from the machine learning and operations research communities, at leveraging machine learning to solve combinatorial optimization problems. Given the hard nature of these problems, state-of-the-art algorithms rely on handcrafted heuristics for making…
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Machine Learning Applications for Precision Agriculture: A Comprehensive Review
…sustainability. The mechanism that drives this cutting edge technology is machine learning (ML). It gives the machine ability to learn without being explicitly programmed. ML together with IoT (Internet of Things) enabled farm machinery are key components of the next agriculture revolution. In this…
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A Survey on the Explainability of Supervised Machine Learning
…of the different principles and methodologies of explainable Supervised Machine Learning (SML). We conduct a state-of-the-art survey that reviews past and recent explainable SML approaches and classifies them according to the introduced definitions. Finally, we illustrate principles by means of an…
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