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Titlebook: Cutting Edge Applications of Computational Intelligence Tools and Techniques; Kevin Daimi,Abeer Alsadoon,Luis Coelho Book 2023 The Editor(

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書目名稱Cutting Edge Applications of Computational Intelligence Tools and Techniques
編輯Kevin Daimi,Abeer Alsadoon,Luis Coelho
視頻videohttp://file.papertrans.cn/242/241680/241680.mp4
概述Introduces various techniques, methods, and approaches adopted by Computational Intelligence experts in the field.Provides an excellent professional development resource for educators and practitioner
叢書名稱Studies in Computational Intelligence
圖書封面Titlebook: Cutting Edge Applications of Computational Intelligence Tools and Techniques;  Kevin Daimi,Abeer Alsadoon,Luis Coelho Book 2023 The Editor(
描述.The book delivers an excellent professional development resource for educators and practitioners on the cutting-edge computational intelligence techniques and applications. It covers many areas and topics of computational intelligence techniques and applications proposed by computational intelligence experts and researchers and furthers the enhancement of the community outreach and engagement component of computational intelligence techniques and applications. Furthermore, it presents a rich collection of manuscripts in highly regarded computational intelligence techniques and applications topics that have been creatively compiled.. .Computers are capable of learning from data and observations and providing solutions to real-life complex problems, following the same reasoning approach of human experts in various fields. This book endows a rich collection of applications in widespread areas. Among the areas addressed in this book are Computational Intelligence Principles andTechniques; CI in Manufacturing, Engineering, and Industry; CI in Recognition and Processing; CI in Robotics and Automation; CI in Communications and Networking; CI in Traditional Vehicles, Electric Vehicles, an
出版日期Book 2023
關(guān)鍵詞Industry; Image Processing; Automation; Communications; Networking; Vehicles; Smart Cities; Smart Energy; Ma
版次1
doihttps://doi.org/10.1007/978-3-031-44127-1
isbn_softcover978-3-031-44129-5
isbn_ebook978-3-031-44127-1Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Using Artificial Neural Networks to Predict Critical Displacement and Stress Values in the Proximal d results (displacements and stresses) were organized as input and output data, respectively. The ANN was trained and then its accuracy was verified. It was observed that artificial NN can accurately forecast displacements and stresses while also saving a significant amount of computing time.
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Computational Intelligence Methods for User Matchingo fully leverage the interdependence between them. In contrast, our approach integrates spatial-temporal information to enhance user identification accuracy, through a three-step process. First, we use kernel density estimation to measure the similarity of users’ trajectories, taking both spatial an
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ATIAS: A Model for Understanding Intentions to Use AI Technologyrch on human trust in AI systems, which tends to focus on either ethical factors or technology acceptance factors. By combining both types of factors in a hybrid model, the approach aims to provide a more comprehensive understanding of why people use AI systems. ATIAS may prove valuable for policyma
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Feature Importance Study for Biogas Production from POME Treatment Plants Using Out-of-Bag Permutatid hydrogen sulfide emission. Temperature, pH and organic loading rate were found to be the most influential parameters for methane and carbon dioxide production, while pH was replaced by suspended solids the case of hydrogen sulfide. The final random forest machine learning model generated performan
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Towards an?Enhanced and?Lightweight Face Authentication Systemuirements. To achieve this, we explore two works with different model designs, research focuses, and potential solutions. In the first work, we try to enhance a usual face recognition model with additional task capability without any additional storage cost. Concretely, we first analyze the two task
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