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      Optimization Design of Street Public Space Layout on Account of Internet of Things and Deep Learning

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      1 , 2 , 1 , 3 ,
      Computational Intelligence and Neuroscience
      Hindawi

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          Abstract

          With the gradual improvement of material living standards, people have higher and higher requirements for the livability of modern cities. As an important component of urban construction, the optimal layout of street public space has gradually received more and more attention. In the development stage of the new era, it is very important to improve the image of the city by transforming the street construction, optimizing the urban public space, and building a place full of vitality. Implementing the people-oriented connotation and improving the green travel components in the city, such as encouraging walking and increasing bicycles, are of great significance for optimizing the street public space. This article studies the relevant content of the optimization design of street public space layout based on the Internet of Things and deep learning and expounds the solutions for the optimization design of street public space layout based on the Internet of Things and deep learning. Design research provides cutting-edge scientific theories and evidence. This paper uses data to prove that based on the Internet of Things and deep learning technology, the optimized design of street public space layout has increased the latter's recognition among residents by an average of 21.7%. The designed model has both space utilization and environmental protection. Very good results have been obtained.

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          Sentence Representation Method Based on Multi-Layer Semantic Network

          With the development of artificial intelligence, more and more people hope that computers can understand human language through natural language technology, learn to think like human beings, and finally replace human beings to complete the highly difficult tasks with cognitive ability. As the key technology of natural language understanding, sentence representation reasoning technology mainly focuses on the sentence representation method and the reasoning model. Although the performance has been improved, there are still some problems such as incomplete sentence semantic expression, lack of depth of reasoning model, and lack of interpretability of the reasoning process. In this paper, a multi-layer semantic representation network is designed for sentence representation. The multi-attention mechanism obtains the semantic information of different levels of a sentence. The word order information of the sentence is also integrated by adding the relative position mask between words to reduce the uncertainty caused by word order. Finally, the method is verified on the task of text implication recognition and emotion classification. The experimental results show that the multi-layer semantic representation network can promote sentence representation’s accuracy and comprehensiveness.
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            Knowledge base graph embedding module design for Visual question answering model

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              Improving Visual Reasoning Through Semantic Representation

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                Author and article information

                Contributors
                Journal
                Comput Intell Neurosci
                Comput Intell Neurosci
                cin
                Computational Intelligence and Neuroscience
                Hindawi
                1687-5265
                1687-5273
                2022
                21 August 2022
                : 2022
                Affiliations
                1School of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, China
                2School of Architecture and Civil Engineering, Xiamen University, Xiamen 361005, China
                3School of Architecture and Urban Planning, Fuzhou University, Fuzhou 350116, China
                Author notes

                Academic Editor: Pradeep Tomar

                Article
                10.1155/2022/7274525
                9420577
                d83d5b46-fc6d-4c18-afb9-f2571b353bc5
                Copyright © 2022 Shanshan Yu et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Funding
                Funded by: Natural Science Foundation of Fujian Province
                Award ID: 2020J01510
                Categories
                Research Article

                Neurosciences
                Neurosciences

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