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Titlebook: Urban Intelligence and Applications; Second International Xiaohui Yuan,Mohamed Elhoseny,Jianfang Shi Conference proceedings 2020 Springer N

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發(fā)表于 2025-3-21 18:41:08 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Urban Intelligence and Applications
副標題Second International
編輯Xiaohui Yuan,Mohamed Elhoseny,Jianfang Shi
視頻videohttp://file.papertrans.cn/944/943985/943985.mp4
叢書名稱Communications in Computer and Information Science
圖書封面Titlebook: Urban Intelligence and Applications; Second International Xiaohui Yuan,Mohamed Elhoseny,Jianfang Shi Conference proceedings 2020 Springer N
描述This book constitutes revised papers from the?Second International Conference on?Urban Intelligence and Applications,?ICUIA 2020, held in August 2020. Due to the COVID-19 pandemic the conference was held online.?.The 26 papers were thoroughly reviewed and selected from 122 submissions. They are organised in the topical sections on technology and infrastructure; community and wellbeing; mobility and transportation; security, safety, and emergency management..
出版日期Conference proceedings 2020
關鍵詞artificial intelligence; computer hardware; computer networks; computer systems; computer vision; databas
版次1
doihttps://doi.org/10.1007/978-981-33-4601-7
isbn_softcover978-981-33-4600-0
isbn_ebook978-981-33-4601-7Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightSpringer Nature Singapore Pte Ltd. 2020
The information of publication is updating

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A Task-Aware Network for Multi-task Learningthods by reducing the average error by as much as 25%. Our method also exhibits a greater consistency for different tasks. By training variations of our proposed MTN, we observed that MTN-3 achieved the best performance with a cumulative error rate of 1.87% and 5.7% reduction in average error.
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Recommendation Based on Attention Degree and Entropyon L-HUMCF recommended to improve, as a result, joined the user of information entropy, EL-HUMCF algorithm is proposed, and experimental verification algorithm is effective. Experimental results show that the proposed algorithm is better than other personalized recommendation algorithms.
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Point Set Registration of Large Deformation Using Auxiliary Landmarksdegree of deformations. In particular, quantitative results show our method is 49% better than the second best result (from the state-of-the-art methods). Finally, we demonstrate the importance of using correct landmark correspondences in registration by showing good registration results in large an
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Brain Tumor Segmentation Algorithm Based on Attention Mechanism and Hybrid Cascaded Networksegmentation results. Brain MRI images of 285 patients which were provided by the BraTS2017 Competition were used to segment brain tumors as well as their sub-regions. The accuracy of segmenting the whole tumor region, the tumor core region, and the enhancing tumor region were 0.907, 0.836, and 0.74
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