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Titlebook: Advances in Integrations of Intelligent Methods; Post-workshop volume Ioannis Hatzilygeroudis,Isidoros Perikos,Foteini G Book 2020 Springer

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期刊全称Advances in Integrations of Intelligent Methods
期刊简称Post-workshop volume
影响因子2023Ioannis Hatzilygeroudis,Isidoros Perikos,Foteini G
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发行地址Combines different approaches and techniques from artificial intelligence.Includes case studies related to the application of hybrid methods.Is useful for academicians, researchers and practitioners i
学科分类Smart Innovation, Systems and Technologies
图书封面Titlebook: Advances in Integrations of Intelligent Methods; Post-workshop volume Ioannis Hatzilygeroudis,Isidoros Perikos,Foteini G Book 2020 Springer
影响因子This book presents a number of research efforts in combining AI methods or techniques to solve complex problems in various areas. The combination of different intelligent methods is an active research area in artificial intelligence (AI), since it is believed that complex problems can be more easily solved with integrated or hybrid methods, such as combinations of different  soft computing methods (fuzzy logic, neural networks, and evolutionary algorithms) among themselves or with hard AI technologies like logic and rules; machine learning with soft computing and classical AI methods; and agent-based approaches with logic and non-symbolic approaches. Some of the combinations are already extensively used, including neuro-symbolic methods, neuro-fuzzy methods, and methods combining rule-based and case-based reasoning. However, other combinations are still being investigated, such as those related to the semantic web, deep learning and swarm intelligence algorithms. Most are connected with specific applications, while the rest are based on principles..
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2190-3018 er combinations are still being investigated, such as those related to the semantic web, deep learning and swarm intelligence algorithms. Most are connected with specific applications, while the rest are based on principles..978-981-15-1920-8978-981-15-1918-5Series ISSN 2190-3018 Series E-ISSN 2190-3026
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Bitcoin Price Prediction Combining Data and Text Mining,978-3-642-20823-2
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Toward New Evaluation Metrics for Relational Learning,978-1-137-52178-1
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Lecture Notes in Physics Monographsing queries, defined for producing knowledge, to a number of edge nodes. The aim is to further reduce the latency by allocating queries to nodes that exhibit low load (the current and the estimated); thus, they can provide the final response in the minimum time. However, before the allocation, we sh
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J.B. Ward-Perkins and the Marble Committeehe generation of data for the estimation of the Hardware Area and Firmware Metrics of a . (RI) component. As a consequence, the process of learning the RI area through a data-driven ML algorithm guarantees a still accurate (.) but 600. faster estimation.
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