204,674 results for machine learning
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Machine-Learning-Enabled Foil Design Assistant
In this work, supervised Machine Learning (ML) techniques were employed to solve the forward and inverse problems of airfoil and hydrofoil design. The forward problem pertains to the prediction of a foil’s aerodynamic or hydrodynamic performance given its geometric description, whereas the inverse…
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Topological data analysis and machine learning
…review of applications of topological data analysis to physics and machine learning problems in physics including the unsupervised detection of phase transitions. We finish with a preview of anticipated directions for future research.
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Machine Learning-Enhanced Flexible Mechanical Sensing
Highlights The latest progress on the integration of flexible mechanical sensing platforms with machine learning (ML) is reviewed. The advantages, challenges, and future perspectives of the application of ML to intelligent flexible mechanical sensing technology are discussed. The fundamental…
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GNSS-Based Machine Learning Storm Nowcasting
…and tomography-based vertical profiles to predict 0–2 h of storms using a machine learning approach for Poland. Moreover, we present an analysis of the importance of features that take part in the prediction process. The accuracy of the model reached over 87%, and the precision of prediction was…
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Diabetes Classification Using Machine Learning Techniques
Machine learning techniques play an increasingly prominent role in medical diagnosis. With the use of these techniques, patients’ data can be analyzed to find patterns or facts that are difficult to explain, making diagnoses more reliable and convenient. The purpose of this research was to compare…
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Handwritten Digit Recognition Using Machine Learning
Technology is getting more and more involved in our lives, and so are algorithms. These algorithms speed up work and reduce workload. Especially machine learning algorithms are improving day by day by imitating human behaviours. Handwriting recognition systems are also stand out on this field. In…
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Detecting Phishing Domains Using Machine Learning
…a problem. However, recent advances in phishing detection, such as machine learning-based methods, have assisted in combatting these attacks. Therefore, this paper develops and compares four models for investigating the efficiency of using machine learning to detect phishing domains. It also…
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Machine learning techniques for flood forecasting
…events. This research examines different substantial practicalities of machine learning (ML) in performing high-performance and accurate FF. The case study was The Dungun River. IGISMAPs datasets of water level and rainfall were investigated (1986–2000). The Forecasting was implemented for current…
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Reservoir optimization and machine learning methods
Optimization of storage using neural networks is now commonly achieved by solving a single optimization problem. We first show that this approach allows solving high-dimensional storage problems, but is limited by memory issues. We propose a modification of this algorithm based on the dynamic…
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Machine-learning engineering of quantum currents
…circuit elements are integrated together. In this work, we show how machine learning can effectively address this challenge and outperform the current existing methods. To this end, we exploit deep reinforcement learning to prepare prescribed quantum current states in circuits composed of lumped…
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Overview of Machine Learning Process Modelling
Much research has been conducted in the area of machine learning algorithms; however, the question of a general description of an artificial learner’s (empirical) performance has mainly remained unanswered. A general, restrictions-free theory on its performance has not been developed yet. In this…
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Machine-learning-accelerated Bose-Einstein condensation
Machine learning is emerging as a technology that can enhance physics experiment execution and data analysis. Here, we apply machine learning to accelerate the production of a Bose-Einstein condensate (BEC) of ^{87}Rb atoms by Bayesian optimization of up to 55 control parameters. This approach…
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Oncological Applications of Quantum Machine Learning
Background: Cancer is a leading cause of death worldwide. Machine learning (ML) and quantum computers (QCs) have recently advanced significantly. Numerous studies have examined the application of quantum machine learning (QML) in healthcare and validated its superiority over classical ML…
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University Admission System using Machine Learning
…guide the studenttowards the branch best suited for him. Genetics Based Machine Learning (GBML) forms our choice, as it is more human like, speculative, seeking better alternatives through the juxtaposition of hunches, inductive, using deductive procedures. Apportionment of credits involved in the…
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Machine learning in classifying bitcoin addresses
…the Bitcoin network can be solved with sufficiently high accuracy using machine learning methods. The paper provides a comparative study of various machine learning methods to solve the mentioned problem: logistic regression, decision tree, random forest, gradient boosting. Selecting hyper…
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Evapotranspiration Estimation Using Machine Learning Methods
The study examined the performance of four machine learning algorithms (regression trees, boosted trees, random forests, and artificial neural networks) for estimating evapotranspiration (ETo) based on incomplete meteorological data. Meteorological variables (mean and maximum air temperature…
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Automated Machine Learning and Asset Pricing
We evaluate whether machine learning methods can better model excess portfolio returns compared to the standard regression-based strategies generally used in the finance and econometric literature. We examine 17 benchmark factor model specifications based on Expected Utility Theory and theory drawn…
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Machine learning in designing amorphous alloys
…composition design and property analysis are now often conducted using machine learning techniques because of their low experimental cost, short development cycle, strong data processing capability, and high predictive performance, among other advantages. They present new approaches and chances to…
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Adaptive dermascopy application using machine learning
Skin cancer is the most lethal because skin cells develop abnormally. Finding skin cancer early is very important and may help stop some kinds of skin cancer, like melanoma and focal cell carcinoma. Early detection and classification of skin cancer are difficult and costly. Recurrent networks and…
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