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Titlebook: Quantitative Evaluation of Systems; 19th International C Erika ábrahám,Marco Paolieri Conference proceedings 2022 Springer Nature Switzerla

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發(fā)表于 2025-3-21 19:23:35 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Quantitative Evaluation of Systems
副標(biāo)題19th International C
編輯Erika ábrahám,Marco Paolieri
視頻videohttp://file.papertrans.cn/781/780846/780846.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Quantitative Evaluation of Systems; 19th International C Erika ábrahám,Marco Paolieri Conference proceedings 2022 Springer Nature Switzerla
描述This book constitutes the proceedings of the 19th International Conference on Quantitative Evaluation Systems, QEST 2022, held in Warsaw, Poland, in September 2022..The 19 full papers presented together with 1 keynote paper were carefully reviewed and selected from 44 submissions. The papers are organized in the following topics: program analysis; parameter synthesis; markovian agents and population models; dynamical systems; tools; applications and automata theory; and applications..
出版日期Conference proceedings 2022
關(guān)鍵詞architecture verification and validation; artificial intelligence; automata theory; computer networks; c
版次1
doihttps://doi.org/10.1007/978-3-031-16336-4
isbn_softcover978-3-031-16335-7
isbn_ebook978-3-031-16336-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2022
The information of publication is updating

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Ahmad Karimi,Marcel Moosbrugger,Miroslav Stankovi?,Laura Kovács,Ezio Bartocci,Efstathia Bura. The use of technology has significantly enhanced the accuracy of disease detection in both plants and animals, initiating a series of activities to combat and curb their spread. Despite years of research, there are still gaps in the detection and discovery process, leading to delayed responses and
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ions, 2D and 3D Convolutional Neural Networks for spatial and temporal feature learning. For action recognition, the authors introduce classical frameworks for image classification, and then elaborate both image-based and clip-based 2D/3D CNN networks for action recognition. For action detection, th
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Jip Spel,Svenja Stein,Joost-Pieter Katoenal actions. This is also known as the .. This chapter shifts focus and looks at .—that is, setups where the model dynamics (aka transition dynamics) are not known. You will learn to calculate/learn state values and state-action values by .—that is, collecting experience by following some policy in t
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vers a wealth of useful information about the road. Throughout the past few decades, traffic sign detection has received significant research interest. This kind of mechanism is crucial to the advancement of driverless vehicles. It’s been a challenging task for self-driving cars to understand variou
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Michele Loreti,Aniqa Rehmann of ML that can be utilized for mental illness treatment has been prompted by the upsurge in mental health diseases and the directive for efficient medical sustenance. The quality of life for patients is improved and treatment outcomes are increased when mental health issues are identified early. T
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