machinelearning (ML) is an advanced branch of Artificial Intelligence (AI) focused on creating algorithms and statistical models that empower computer systems to learn from data and autonomously make informed decisio...
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The affective computing field usually concerns data that is difficult, expensive or time-consuming to label. One way to overcome this limitation is the application of Semi-Supervised machinelearning, that typically w...
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ISBN:
(纸本)9798350304367;9798350304374
The affective computing field usually concerns data that is difficult, expensive or time-consuming to label. One way to overcome this limitation is the application of Semi-Supervised machinelearning, that typically works with a small set of labeled data and a larger one of unlabeled data. This paper assesses the suitability of these techniques on the prediction of affective state, by analyzing the physiological and emotional response data of 30 different subjects while watching several emotion-eliciting videos. Three Semi-Supervised learning algorithms are compared with their Supervised base classifiers in both a subject-independent and subject-dependent analyses, across a widely extended dataset. In view of the results, it can be concluded that Semi-Supervised learning did not outperform their respective Supervised base classifiers for this specific dataset as it was expected. Subject-dependent classification resulted in accuracy rates between 68% and 85%, whereas the accuracy rates were between 38% and 72% for subject-independent classification.
This paper surveys the latest unsupervised anomaly detection methodologies applied to health insurance fraud, covering studies from 2017 to 2024. Our review includes a variety of machine-learning approaches, evaluatin...
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ISBN:
(纸本)9798350366396;9798350366389
This paper surveys the latest unsupervised anomaly detection methodologies applied to health insurance fraud, covering studies from 2017 to 2024. Our review includes a variety of machine-learning approaches, evaluating their effectiveness in handling complex, high-dimensional, and imbalanced healthcare datasets. Techniques such as Isolation Forest, Bayesian hierarchical models, and deep autoencoders demonstrate superior performance compared to traditional methods. Despite significant advancements, gaps remain with regard to transfer learning, interpretability and explainability of models, and the development of real-time, incremental learning algorithms. Future research should focus on these areas to enhance fraud detection accuracy and trust. Our work aims to provide a valuable resource for researchers and practitioners, supporting the development of more robust and adaptive fraud detection systems to protect healthcare integrity and reduce financial losses.
Meta Automated machinelearning (Meta AutoML) is a concept that allows data scientists and domain experts to generate effective machinelearning models automatically. To achieve this, Meta AutoML merges existing AutoM...
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Digital Twin technology, which involves creating a virtual replica using data collected from the real world, including 3D CAD and CG modeling, is an essential tool for energy conservation. The 3D direct drawing system...
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ISBN:
(纸本)9798350375596;9798350375589
Digital Twin technology, which involves creating a virtual replica using data collected from the real world, including 3D CAD and CG modeling, is an essential tool for energy conservation. The 3D direct drawing system using AR technology can create many shape models for pictograms. However, the model-creating command is difficult for a user because it is multiple and complicated. In this paper, the pictogram drawing trajectory data are investigated and accumulated. After that, their shapes are predicted for automatically converted to the desired shape using the Neural Network method, k-nearest Neighbor method, and Support Vector machine method of machinelearning, and the accuracy rates of the two methods are compared.
The data-driven approach for creep life prediction typically integrates numerous characteristics, including material compositions, manufacturing details, and service conditions, into machinelearning models. In this s...
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The data-driven approach for creep life prediction typically integrates numerous characteristics, including material compositions, manufacturing details, and service conditions, into machinelearning models. In this study, a machinelearning-based creep life prediction approach with optimal feature subset selection is established for 2.25Cr1Mo pressure vessel steel. Before model training and testing, six critical features that significantly impact the creep life of 2.25Cr1Mo steel are selected, specifically the applied stress, temperature, and chemical compositions consisting of Cr, Ni, Mn, and Mo. Various machinelearning algorithms, along with the traditional L-M method, are utilized for model training and performance evaluation. Additionally, the developed models undergo validation using experimental data independent of the training and testing datasets to assess their generalization abilities. The results reveal that, among all tested models, the support vector regression (SVR) model, coupled with the optimal feature subset, demonstrates superior prediction accuracy and generalization capability. Finally, the creep life prediction model exhibiting optimal performance is deployed into a software application, leveraging the Python programming language. This predictor tool facilitates rapid and precise creep life predictions for 2.25Cr1Mo pressure vessel steel, relying solely on a limited amount of input information, and provides a clear and visual presentation of the prediction results.
This paper discusses the main scientific results of the XXV internationalconference on data Analytics and Management in data-Intensive DomainsDomains, held on October 24-27, 2022 in Moscow, Russia. The motivation and...
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This paper discusses the main scientific results of the XXV internationalconference on data Analytics and Management in data-Intensive DomainsDomains, held on October 24-27, 2022 in Moscow, Russia. The motivation and goals of the conference, main areas of focus, related conferences, and associated events are considered. The program of the current year's conference is described, including the topics of invited and sectional reports. Conclusions are drawn based on the results of the analysis of the scientific contribution of the conference. A list of papers selected by the conference program committee for the special issue of the journal is attached. The papers relate to the areas of image analysis and processing, the application of machinelearning methods in medicine, astronomy, materials science, and extracting information from texts.
In Lux AI Challenge Season 2, this project uses a turn-based strategic game called Soft Actor-Critic (SAC). In a dynamic environment, SAC, a cutting-edge deep reinforcement learning algorithm, maximizes lichen growth ...
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In Lux AI Challenge Season 2, this project uses a turn-based strategic game called Soft Actor-Critic (SAC). In a dynamic environment, SAC, a cutting-edge deep reinforcement learning algorithm, maximizes lichen growth and resource collec-tion. Building factories, cultivating lichen, and vying for resources in the 2D grid that forms the Lux universe requires strategic decision-making. A good option for handling the intricacies of the Lux game is the SAC algorithm because of its sample efficiency and stability. To provide insights into the application of sophisticated machinelearning in strategic gaming scenarios, this endeavor investigates the performance of SAC, compares it with traditional methods, and makes proposed improvements.
This article addresses the fundamental questions on machinelearning: what does it mean for machines to learn from experience, and what does it mean by machines in machinelearning? Despite recent popularity and growt...
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ISBN:
(纸本)9798350358513;9798350358520
This article addresses the fundamental questions on machinelearning: what does it mean for machines to learn from experience, and what does it mean by machines in machinelearning? Despite recent popularity and growth, significant challenges remain as the industry rapidly advances toward autonomous machines. In the context of autonomy, there is more to learning from experience than training machines to approximate big data. This brings the fundamental questions to the forefront of automation science and engineering as a critical area of exploration. This article examines the precise notion of autonomy in the context of machinelearning and provides a general framework for cyber-physical systems to become fully autonomous by learning from experience. The framework is derived from the principles of developmental autonomous behavior, which encapsulates broad classes of learning mechanisms. It offers a novel use case of machinelearning where sensorimotor systems build inference engines internally on their own by their own initiatives to develop new skills and behavior. The key contributions of this article are threefold. First on knowledge: it provides precise definitions of emergent and predefined knowledge and their roles in cognitive development of machines. Second on autonomy: it clarifies what a fully autonomous machine means by providing the precise definitions of autonomy and emergent behavior. Third on machinelearning: it unifies machinelearning as the ultimate-proximate causal drivers of emergent behavior. Ultimately, this article logically explains why and how a fully autonomous machine is possible by directly answering the fundamental questions.
As machinelearning has become more relevant for everyday applications, a natural requirement is the protection of the privacy of the training data. When the relevant learning questions are unknown in advance, or hype...
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As machinelearning has become more relevant for everyday applications, a natural requirement is the protection of the privacy of the training data. When the relevant learning questions are unknown in advance, or hyper-parameter tuning plays a central role, one solution is to release a differentially private synthetic data set that leads to similar conclusions as the original training data. In this work, we introduce an algorithm that enjoys fast rates for the utility loss for sparse Lipschitz queries. Furthermore, we show how to obtain a certificate for the utility loss for a large class of algorithms.
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