2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)最新文献_第3页

Research on Multi-turn Dialogue Generation Strategy Guided by Topic 话题导向的多回合对话生成策略研究

2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00043

P. Zhang, Hongrong Wang, Jie Wang

引用次数: 0

Dynamic Detection Model of False Data Injection Attack Facing Power Network Security 面向电网安全的虚假数据注入攻击动态检测模型

2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00069

Fuhong Chang, Qi Li, Yuanyuan Wang, Wenfeng Zhang

引用次数: 0

Optimal transmission technology of transmission line data based on heterogeneous wireless network 基于异构无线网络的传输线数据优化传输技术

2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00109

Keqiang Wang, Zimeng Zhang, Xiaoning Tang, Xiaolong Liang, Xuanyi Wang, Henan Yin

{"title":"Optimal transmission technology of transmission line data based on heterogeneous wireless network","authors":"Keqiang Wang, Zimeng Zhang, Xiaoning Tang, Xiaolong Liang, Xuanyi Wang, Henan Yin","doi":"10.1109/AINIT54228.2021.00109","DOIUrl":"https://doi.org/10.1109/AINIT54228.2021.00109","url":null,"abstract":"Aiming at the low throughput of transmission line data optimization transmission in traditional technology, a transmission line data optimization transmission technology based on heterogeneous wireless network is designed. On the premise of clarifying the QoS requirements of transmission line data optimization transmission, the transmission line data transmission time series is obtained. The transmission line data transmission entrance parameters are obtained through calculation, and the IP interface signal is based on the heterogeneous wireless network wishbone. The status code of transmission line data transmission interface is designed, and the asynchronous refresh method is used to realize the optimization of transmission line data transmission. The experimental results show that the transmission throughput of the designed technology is significantly higher than that of the control group, which can solve the problem of low data transmission throughput of transmission line optimization with traditional technology.","PeriodicalId":326400,"journal":{"name":"2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116065852","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

The Application of Artificial Intelligence in Computer Network Technology 人工智能在计算机网络技术中的应用

2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00127

Z. Han

{"title":"The Application of Artificial Intelligence in Computer Network Technology","authors":"Z. Han","doi":"10.1109/AINIT54228.2021.00127","DOIUrl":"https://doi.org/10.1109/AINIT54228.2021.00127","url":null,"abstract":"Today’s era is an era of rapid development. With the continuous improvement of the national economy, China has higher requirements and standards for the development of Internet information technology. Based on this social context, \"artificial intelligence\" came into being. Artificial intelligence has also brought unprecedented changes to our lives, enriched the world we live in, and made the application of computer network technology more convenient. The needs of the social masses are also constantly improving, and people begin to pursue a higher quality of life. As a kind of high and new technology, computer network technology gradually appears in our life, closely related to the people’s daily life, and has become an advanced industry in the new era. This paper introduces the application of contemporary artificial intelligence technology in computer network technology, through the discussion of artificial intelligence, analyzes the application of artificial intelligence in classification and identification services, network security, computer network management, and can better understand the application strategy of computer network technology under today’s artificial intelligence.","PeriodicalId":326400,"journal":{"name":"2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)","volume":"61 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116270865","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: 2

Semantic based Cross-Language Clone Related Bug Detection 基于语义的跨语言克隆相关Bug检测

2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00101

Zeng Chen

引用次数: 0

Fake News Recognition in social media with Multi- level Attention Fusion 基于多层次注意力融合的社交媒体假新闻识别

2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00081

Bo Fu, Jie Sui

{"title":"Fake News Recognition in social media with Multi- level Attention Fusion","authors":"Bo Fu, Jie Sui","doi":"10.1109/AINIT54228.2021.00081","DOIUrl":"https://doi.org/10.1109/AINIT54228.2021.00081","url":null,"abstract":"With the rapid development of social media, fake news spreads long with real information with multimodal forms, which seriously damages the credibility of media and disrupts the social order. In this case, detecting rumours effectively combined with images and texts becomes a crucial challenge that needs to be confronted. This paper proposes an end-to-end fake news recognition framework (MLAF) based on the deep neural multimodal model and multi-level attention fusion. Specifically, to fit the multimodal form of fake news, this research introduces the extra user features based on text and visual modes. To explore the interactions between different data modalities and enhance cross-modal representation, this essay first fuses the text and user features by learning an adaptive attention matrix and then further updates the text and visual modes with a multi-level attention mechanism and multimodal affine fusion. Validated by extensive experiments on the Weibo data set extracted from the real world, the results demonstrate the accuracy of the proposed model is better than existing models, and it can enhance well the cross- modal representation of fake news.","PeriodicalId":326400,"journal":{"name":"2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125258620","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

Research on Early Warning Technology of Epilepsy Based on Deep Learning 基于深度学习的癫痫早期预警技术研究

2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00045

Yumo Wang, Yu Wang, X. Wang, Jingying Lv

{"title":"Research on Early Warning Technology of Epilepsy Based on Deep Learning","authors":"Yumo Wang, Yu Wang, X. Wang, Jingying Lv","doi":"10.1109/AINIT54228.2021.00045","DOIUrl":"https://doi.org/10.1109/AINIT54228.2021.00045","url":null,"abstract":"Epilepsy refers to the sudden abnormal discharge of brain neurons. As the second major neurological disease, epilepsy has brought trouble to many people’s lives. It is very important for patients to predict the onset of epilepsy in advance, because it can not only reduce the troubles in life, but also avoid physical side effects caused by excessive medication. This paper uses short-time Fourier transform on a 30-second EEG window to extract time-domain and frequency-domain information, and puts the generated time-frequency graph into the constructed network for training. The neural network designed in this paper uses feature extraction module and classification module to extract and classify time-frequency images. In addition, this paper constructs an attention module for electrode dimension to enhance the attention between electrodes. The experimental results on the CHB-MIT data set show that the accuracy of the algorithm in this paper has reached 90%, the false alarm rate is as low as 0.096/h, and it has high visibility, which meets the requirements of high accuracy and high robustness in the medical field.","PeriodicalId":326400,"journal":{"name":"2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)","volume":"693 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114003579","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

Design of Cancer Detection System Based on CNN Model and Virtual Reality with NLP Voice Output 基于CNN模型和虚拟现实的NLP语音输出癌症检测系统设计

2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00062

Zhuoran Xu

{"title":"Design of Cancer Detection System Based on CNN Model and Virtual Reality with NLP Voice Output","authors":"Zhuoran Xu","doi":"10.1109/AINIT54228.2021.00062","DOIUrl":"https://doi.org/10.1109/AINIT54228.2021.00062","url":null,"abstract":"Nowadays, online medical consultation has become very popular. However, online consultation can only solve some minor diseases such as colds and coughs, but the detection of cancers needs to be improved. This project implements the design of a cancer detection system based on the CNN model and virtual reality. This project uses the CNN and regression models for training a mature network to detect prostate cancer, lung cancer and skin cancer. The patient who comes to check uploads the corresponding picture or blood test report to determine whether the patient has the corresponding condition. Then use the NLP model to voice output diagnosis results. As a result, the voice broadcasts whether the patient has cancer, making the humancomputer interface more friendly. After long enough training time, two CNN models and one regression model showed high scores. The experiment results to detect whether the patient has the corresponding cancer are efficient, owing to the accuracy of the test of the three models is above 95%. And when inputting the patient’s lung CT and skin details, the location of tuberculosis can be found quite accurately and whether the patient has skin cancer. Combined with virtual reality technology, it depicts models including wards, CT rooms, diagnosis rooms and supermarkets, successfully creating a friendly online hospital.","PeriodicalId":326400,"journal":{"name":"2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122447253","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

Research on Massive Electric Power Big Data Transmission Encryption Based on Improved K-means Algorithm 基于改进K-means算法的海量电力大数据传输加密研究

2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00012

Lixia Wang, Wei Li, Dawei Wang

{"title":"Research on Massive Electric Power Big Data Transmission Encryption Based on Improved K-means Algorithm","authors":"Lixia Wang, Wei Li, Dawei Wang","doi":"10.1109/AINIT54228.2021.00012","DOIUrl":"https://doi.org/10.1109/AINIT54228.2021.00012","url":null,"abstract":"Since the massive electric power data is too big, the time required to transmit the electric power data transmission encryption is too long. To this end, a massive electric power big data transmission encryption method based on improved K-means algorithm is designed. Using the improved K-means algorithm to classify the electric power big data, for the classified data, filter it through attribute reduction and gene expression programming algorithms to obtain the data to be encrypted, ECC-AES hybrid encryption algorithm is used to encrypt electric power big data, which completes the design of electric power big data encryption. Through comparative experiments, it is compared with two electric power big data encryption methods. Experimental results show that, in terms of the time to obtain the data to be encrypted, the proposed electric power big data encryption method is 135ms less than the electric power big data transmission encryption method 1, and 271ms less than the electric power big data transmission encryption method 2. In terms of the time required for the entire encryption process, the proposed electric power big data encryption method is 225ms less than the electric power big data transmission encryption method 1, and 352ms less than the electric power big data transmission encryption method 2.","PeriodicalId":326400,"journal":{"name":"2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127645934","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

The Analysis of the Feasibility of Optimizing Elman Neural Network 优化Elman神经网络的可行性分析

2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI: 10.1109/AINIT54228.2021.00091

Manqi Liu

引用次数: 0