61,077 results for machine learning
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Explainable Machine Learning
Machine learning methods are widely used in commercial applications and in many scientific areas [...]
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Precision Machine Learning
We explore unique considerations involved in fitting machine learning (ML) models to data with very high precision, as is often required for science applications. We empirically compare various function approximation methods and study how they scale with increasing parameters and data. We find that…
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Machine learning and soil sciences: a review aided by machine learning tools
The application of machine learning (ML) techniques in various fields of science has increased rapidly, especially in the last 10 years. The increasing availability of soil data that can be efficiently acquired remotely and proximally, and freely available open-source algorithms, have led to an…
From your institution’s collections
- Machine learning dan computational intelligenceWidodo Budiharto · 2016 · UNILAK
- Machine learning (konsep dan implementasi)Yaya Heryadi · 2020 · UNILAK
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Machine Learning for Communications
Due to the proliferation of applications and services that run over communication networks, ranging from video streaming and data analytics to robotics and augmented reality, tomorrow’s networks will be faced with increasing challenges resulting from the explosive growth of data traffic demand with…
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Machine Learning in KM3NeT
…(ARCA) and of determining the neutrino mass hierarchy (ORCA). Various Machine Learning techniques, such as Random Forests, BDTs, Shallow and Deep Networks are being used for diverse tasks, such as event-type and particle identification, energy/direction estimation, source identification…
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Machine Learning in Education
This narrative review examines the historical evolution, current applications, and major challenges of machine learning (ML) in education, positioning ML as a transformative yet deeply contested force in contemporary teaching and learning. Tracing developments from early computer-assisted…
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Federated Quantum Machine Learning
…One of the potential schemes to achieve this property is the federated learning (FL), which consists of several clients or local nodes learning on their own data and a central node to aggregate the models collected from those local nodes. However, to the best of our knowledge, no work has been done…
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Machine Learning in Gamification and Gamification in Machine Learning: A Systematic Literature Mapping
Albeit in different ways, both machine learning and gamification have transfigured the user experience of information systems. Although both are hot research topics, so far, little attention has been paid to how these two technologies converge with each other. This relation is not obvious as while…
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Applications of machine learning for hemodialysis nursing cares based on a machine learning algorithm
…to enhance hemodialysis adequacy and prevent complications; however, machine learning (ML) emerged as a methodological approach for evaluating hemodialysis adequacy and complications. The current study aims to analyze ML approach in predicting and managing hemodialysis by R programming language…
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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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