214,423 results for machine learning
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Machine Learning: Theory and Practice
The field of machine learning (ML) has rapidly evolved into one of the most impactful areas of computer science and artificial intelligence. Models once considered experimental are now powering applications we use every day—from recommendation systems and voice assistants to fraud detection and…
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Stellar Classification by Machine Learning
As an emerging subject with strong comprehensiveness, machine learning has made varying degrees of progress in various fields. In the field of astronomy, it has also been generally used, and there have been quantities of research using machine learning for data processing and model prediction. The…
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Machine Learning Photovoltaic String Analyzer
Photovoltaic (PV) system energy production is non-linear because it is influenced by the random nature of weather conditions. The use of machine learning techniques to model the PV system energy production is recommended since there is no known way to deal well with non-linear data. In order to…
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Machine learning methods in finance
This article focuses on supervised learning and reinforcement learning. These areas overlap most with econometrics, predictive modelling, and optimal control in finance. We choose to focus on how to cast machine learning into various financial modelling and decision frameworks. This work introduces…
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Machine learning robot polishing cell
…and a force sensor for measuring the polishing pressure. By means of a machine learning system, predictions of tool wear and the related surface quality shall be made. The aim is the detection of the critical influence factors during the polishing process and to have a predictive maintenance system…
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Machine Learning-Enhanced Pairs Trading
…one-minute granularity over a full year, we apply various statistical and machine learning algorithms, including Bidirectional Long Short-Term Memory (BiLSTM) with attention, Transformers, N-BEATS, N-HiTS, Convolutional Neural Networks (CNNs), and Temporal Convolutional Networks (TCNs) to predict…
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Machine Learning for Quantum Metrology
…number of probes is employed. Here, we discuss an asymptotically optimal, machine learning based, adaptive single-photon phase estimation protocol that allows us to reach the standard quantum limit when a very limited number of photons is employed.
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Machine Learning and Antibiotic Management
Machine learning and cluster analysis applied to the clinical setting of an intensive care unit can be a valuable aid for clinical management, especially with the increasing complexity of clinical monitoring. Providing a method to measure clinical experience, a proxy for that automatic gestalt…
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Differentiable programming in machine learning
…implementation methods. In the context of issues encountered in machine learning and deep learning, the forward mode is deemed more suitable as it efficiently differentiates functions with numerous inputs compared to outputs. Given Python's pivotal role in the ML landscape, the paper elaborates on…
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Earthquake prognosis using machine learning
…well as the environment. In this project, the successful application of machine learning techniques have been used for different elements of research which would be possible to use to make a more accurate short-term prognosis of upcoming earthquakes. Random Forest Classifier is the algorithm used…
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Machine Learning Approaches for Metalloproteins
…there has been an acceleration in the number of studies applying machine learning to predict metalloprotein properties, primarily driven by the advent of more sophisticated machine learning algorithms. This review covers how machine learning tools have consolidated and expanded our comprehension of…
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Machine Learning Techniques for Calorimetry
…the energies and positions of electrons and photons. Even though several Machine Learning (ML) algorithms have been already used for calorimetry, with the constant advancement of the field, more and more sophisticated techniques have become available, which can be beneficial for object…
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Machine Learning for Additive Manufacturing
…pushing for the maturation of design, process, and production techniques. Machine learning (ML) is a branch of artificial intelligence concerned with training programs to self-improve and has applications in a wide range of areas, such as computer vision, prediction, and information retrieval. Many…
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Machine Learning for Energy Systems
The objective of this editorial is to overview the content of the special issue “Machine Learning for Energy Systems”. This special issue collects innovative contributions addressing the top challenges in energy systems development, including electric power systems, heating and cooling systems, and…
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Forecasting with Machine Learning Techniques
The decision-maker is increasingly utilising machine learning (ML) techniques to find patterns in huge quantities of real-time data [...]
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Machine Learning of Spatial Data
Properties of spatially explicit data are often ignored or inadequately handled in machine learning for spatial domains of application. At the same time, resources that would identify these properties and investigate their influence and methods to handle them in machine learning applications are…
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Machine Learning in Big Data
Machine learning is an artificial intelligence method of discovering knowledge for making intelligent decisions. Big Data has great impacts on scientific discoveries and value creation. This paper introduces methods in machine learning, main technologies in Big Data, and some applications of…
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Machine learning for algorithmic trading
The purpose of the study is to confirm the feasibility of using machine learning methods to predict the behavior of the foreign exchange market. The article examines the theoretical and practical aspects of the implementation of artificial neural networks in the process of Internet trading. We…
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Machine Learning with ROOT/TMVA
ROOT provides, through TMVA, machine learning tools for data analysis at HEP experiments and beyond. We present recently included features in TMVA and the strategy for future developments in the diversified machine learning landscape. Focus is put on fast machine learning inference, which enables…
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