Fifth International Conference on Computer Information Science and Artificial Intelligence最新文献_第6页

Autonomous driving based on deep neural network 基于深度神经网络的自动驾驶

Fifth International Conference on Computer Information Science and Artificial Intelligence Pub Date : 2023-03-28 DOI: 10.1117/12.2668350

Zhuo Cheng

{"title":"Autonomous driving based on deep neural network","authors":"Zhuo Cheng","doi":"10.1117/12.2668350","DOIUrl":"https://doi.org/10.1117/12.2668350","url":null,"abstract":"Deep learning, the critical part of machine learning, has become influential in different fields, including natural language processing, computational biology, and computer vision. In the last decade, there has been a massive surge in computer vision research since its related application is so promising. Many have proposed various methods to fulfill the automation of driving based on deep learning, but, up until now, there is still a gap between the virtual and reality. This paper focuses on its application in autonomous driving. A new framework is proposed to fill that gap using a deep neural network. Specifically, instead of using the raw images captured by cameras to make decisions, semantic segmentation is applied first to get intermediate products that can better connect virtual and reality. Considering the road landscape needs to be mainly treated, the pre-trained model PSPNet is used to process the original image data. Then this data is provided as input to a deep CNN model for feature extraction and prediction. Compared to the traditional method, a semantic segmentation process is added to help extract useful information within an image and is expected to bring some positive effects.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123866573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adversarial attacks on cross-resolution person re-identification 对跨分辨人再识别的对抗性攻击

Fifth International Conference on Computer Information Science and Artificial Intelligence Pub Date : 2023-03-28 DOI: 10.1117/12.2667907

Zhikang Song

引用次数: 0

A hybrid optimized multi-population flower pollination algorithm for web service composition problem 面向web服务组合问题的混合优化多种群传粉算法

Fifth International Conference on Computer Information Science and Artificial Intelligence Pub Date : 2023-03-28 DOI: 10.1117/12.2667493

Danni Lv, Lijuan Zhou, Ning Luo

引用次数: 0

Intelligent classification and identification of radar jamming signals 雷达干扰信号的智能分类与识别

Fifth International Conference on Computer Information Science and Artificial Intelligence Pub Date : 2023-03-28 DOI: 10.1117/12.2667248

Dongxia Li, Yahui Shi, Yangdong Sun, Bin Zhang

引用次数: 0

Dialog history management for end-to-end task-oriented dialog systems 面向端到端任务的对话系统的对话历史管理

Fifth International Conference on Computer Information Science and Artificial Intelligence Pub Date : 2023-03-28 DOI: 10.1117/12.2667733

Shijia Nie, Guanjun Li, Xuesong Zhang, Dawei Zhang, Jianhua Tao, Zhao Lv

引用次数: 0

Study for CT to MRI translation based on cycle-GAN and UNet-GAN 基于循环gan和UNet-GAN的CT到MRI转换研究

Fifth International Conference on Computer Information Science and Artificial Intelligence Pub Date : 2023-03-28 DOI: 10.1117/12.2668052

Y. Lai

{"title":"Study for CT to MRI translation based on cycle-GAN and UNet-GAN","authors":"Y. Lai","doi":"10.1117/12.2668052","DOIUrl":"https://doi.org/10.1117/12.2668052","url":null,"abstract":"MRI and CT are both important medical imaging modalities, but MRI and CT imaging are done in different ways, each with its own advantages and disadvantages. Obtaining both images at the same time can help physicians make better decisions about treatment options. However, due to various limitations, some patients can only obtain one type of image. Therefore, it is necessary to find a well-performing GAN to transform MRI and CT images. In this paper, the effect of Cycle-GAN with different activation functions is compared, such as LeakyRELU, and different number of layers in MRI-CT conversion. Also, this article compares the effects of Cycle-GAN and UNet-GAN. The results indicate that the Cycle-GAN model using LeakyRELU as the activation function is better than the Cycle-GAN model using RELU as the activation function. Second, the effect of deepening the layers of the GAN model is worse than that of the base model. And the effect of UNet-GAN is similar to that of Cycle-GAN. This is not quite as expected, because Cycle-GAN has one more discriminator than UNet-GAN, and the effect should be better. But the experimental results do not confirm this conclusion.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117067233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Classic Augmentation based Classifier Generative Adversarial Network (CACGAN) for COVID-19 diagnosis 基于经典增强分类器生成对抗网络(CACGAN)的COVID-19诊断

Fifth International Conference on Computer Information Science and Artificial Intelligence Pub Date : 2023-03-28 DOI: 10.1117/12.2668115

Haodong Li

{"title":"Classic Augmentation based Classifier Generative Adversarial Network (CACGAN) for COVID-19 diagnosis","authors":"Haodong Li","doi":"10.1117/12.2668115","DOIUrl":"https://doi.org/10.1117/12.2668115","url":null,"abstract":"Computer-aided diagnosis of COVID-19 from lung medical images has received increasing attention in previous clinical practice and research. However, developing such automatic model is usually challenging due to the requirement of a large amount of data and sufficient computer power. With only 317 training images, this paper presents a Classic Augmentation based Classifier Generative Adversarial Network (CACGAN) for data synthetising. In order to take into account, the feature extraction ability and lightness of the model for lung CT images, the CACGAN network is mainly constructed by convolution blocks. During the training process, each iteration will update the discriminator's network parameters twice and the generator's network parameters once. For the evaluation of CACGAN, this paper organized multiple comparison between each pair from CACGAN synthetic data, classic augmented data, and original data. In this paper, seven classifiers are built, ranging from simple to complex, and are trained for the three sets of data respectively. To control the variable, the three sets of data use the exact same classifier structure and the exact same validation dataset. The result shows the CACGAN successfully learned how to synthesize new lung CT images with specific labels.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117100598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FL-Lightgbm prediction method of unbalanced small sample anti-breast cancer drugs 非平衡小样本抗乳腺癌药物的FL-Lightgbm预测方法

Fifth International Conference on Computer Information Science and Artificial Intelligence Pub Date : 2023-03-28 DOI: 10.1117/12.2667385

Chenxiao Zhou, Lianying Zou, Chuang Liu, Ziwei Song

{"title":"FL-Lightgbm prediction method of unbalanced small sample anti-breast cancer drugs","authors":"Chenxiao Zhou, Lianying Zou, Chuang Liu, Ziwei Song","doi":"10.1117/12.2667385","DOIUrl":"https://doi.org/10.1117/12.2667385","url":null,"abstract":"The problem of small amount data and sample imbalance exists in the machine learning prediction of the molecular properties of anti breast cancer candidate drugs. Proposing a FL-Lightgbm prediction model based on WGAN-GP data enchance model in order to solve this problem. Firstly, WGAN-GP model is used for data enhancement to increase the sample size of the training data set. Considering the small difference between positive and negative samples, the enhanced data of positive and negative samples are generated respectively, and then combined them according to the original order to ensure that the generated data and the original data maintain the same distribution; Then the Focal Loss function is introduced into the Lightgbm model to increase learning ability for unbalanced samples, the model constructed is called FL-Lightgbm prediction model. After the training of the enhanced data set, the proposed model shows excellent prediction accuracy for 178 randomly selected validation samples in the experiment, and its highest accuracy, AUC and F1 values reach 0.882, 0.851 and 0.7272 respectively. In these three indexes, the proposed model has better prediction ability than the original Lightgbm model with over sampling algorithms such as BorderlineSMOTE and ADASYN.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114337570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Application analysis of machine learning in cyberspace security research 机器学习在网络空间安全研究中的应用分析

Fifth International Conference on Computer Information Science and Artificial Intelligence Pub Date : 2023-03-28 DOI: 10.1117/12.2668873

Wenzhuo Du, Gerun Wang, Xuanbo Ma, Xintian Yu

引用次数: 0

Data collection and talent demand analysis of recruitment website based on principal component analysis 基于主成分分析的招聘网站数据收集与人才需求分析

Fifth International Conference on Computer Information Science and Artificial Intelligence Pub Date : 2023-03-28 DOI: 10.1117/12.2667708

Jihui Fan, Fengshan Yuan

引用次数: 0