Frontiers in Computing and Intelligent Systems最新文献_第6页

A Sensitivity-based Location Privacy Protection Scheme in Vehicular Networks 基于灵敏度的车载网络位置隐私保护方案

Frontiers in Computing and Intelligent Systems Pub Date : 2023-11-14 DOI: 10.54097/fcis.v5i3.13858

Han Jiang, Hequn Xian

引用次数: 0

The Advantages of Artificial Intelligence Application in Computer Technology 人工智能在计算机技术中应用的优势

Frontiers in Computing and Intelligent Systems Pub Date : 2023-11-14 DOI: 10.54097/fcis.v5i3.13859

Wendong Yang

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Research on Arrhythmia Classification and Risk Degree Prediction based on Deep Neural Network and Convolutional Neural Network 基于深度神经网络和卷积神经网络的心律失常分类与风险度预测研究

Frontiers in Computing and Intelligent Systems Pub Date : 2023-11-14 DOI: 10.54097/fcis.v5i3.13848

Songling Huang, Zhenji Wen, Hanling Li

{"title":"Research on Arrhythmia Classification and Risk Degree Prediction based on Deep Neural Network and Convolutional Neural Network","authors":"Songling Huang, Zhenji Wen, Hanling Li","doi":"10.54097/fcis.v5i3.13848","DOIUrl":"https://doi.org/10.54097/fcis.v5i3.13848","url":null,"abstract":"In this study, a method of arrhythmia classification and risk prediction based on deep neural network and convolutional neural network (CNN) is proposed for ECG data. Electrocardiogram data record the electrophysiological activity of the heart, including normal heart beats and various arrhythmias. In order to monitor and identify arrhythmia in real time and accurately, this study used CNN model for data analysis. The characteristics of CNN, such as local perception, parameter sharing and multi-level feature extraction, make it perform well in ECG data analysis. The data comes from the ' Certification Cup ' Mathematics China Mathematical Modeling Network Challenge in 2023 and is preprocessed to meet the needs of the model. In the process of establishing and solving the model, the cross-entropy loss function is used to optimize, and the effectiveness and robustness of the model are verified by various evaluation methods. The results show that the model can accurately classify and predict the risk of arrhythmia, providing a powerful diagnostic tool for doctors and a valuable reference for future arrhythmia research.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139277467","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 Design of the Automatic Control Intelligent Ashbin 自动控制智能灰宾的设计

Frontiers in Computing and Intelligent Systems Pub Date : 2023-11-14 DOI: 10.54097/fcis.v5i3.13862

Liming Zhou

引用次数: 0

Algorithm Optimization and Performance Improvement of Data Visualization Analysis Platform based on Artificial Intelligence 基于人工智能的数据可视化分析平台的算法优化与性能提升

Frontiers in Computing and Intelligent Systems Pub Date : 2023-11-14 DOI: 10.54097/fcis.v5i3.13836

Zepeng Shen

引用次数: 0

Design of an Intelligent Algorithm for Rural Antique Ceramic Bottom Pattern 农村仿古陶瓷底纹智能算法的设计

Frontiers in Computing and Intelligent Systems Pub Date : 2023-11-14 DOI: 10.54097/fcis.v5i3.13839

Ru Zhang, Zhenhua Guo, Yangfan Xu

引用次数: 0

Intelligent Diagnostic System Development based on Artificial Intelligence Technology 基于人工智能技术的智能诊断系统开发

Frontiers in Computing and Intelligent Systems Pub Date : 2023-11-14 DOI: 10.54097/fcis.v5i3.13814

Ying Feng, Yongqin Wang

{"title":"Intelligent Diagnostic System Development based on Artificial Intelligence Technology","authors":"Ying Feng, Yongqin Wang","doi":"10.54097/fcis.v5i3.13814","DOIUrl":"https://doi.org/10.54097/fcis.v5i3.13814","url":null,"abstract":"Intelligent diagnosis is an important scenario in smart healthcare, with conversational diagnostic scenarios being the most common. The process of collecting symptom information through conversations with users and inferring diseases based on symptoms. Through a dialogue based diagnostic system, it can meet some of the medical consultation needs of residents, thereby freeing doctors from some basic consultations and greatly alleviating the shortage of medical resources. In the actual diagnosis process, the symptoms reported by patients are often insufficient to support accurate diagnosis. It is necessary to ask the user if they have any other symptoms through dialogue to form a diagnostic conclusion. Existing research mainly adopts reinforcement learning methods, which gradually learn the dialogue process between traditional Chinese medicine students and patients in real medical scenarios, and obtain strategies for symptom inquiry and disease diagnosis. Despite the advantages of reinforcement learning in dealing with temporal decision problems, the diagnostic accuracy is still low and data dependency is strong. In this article, a medical dialogue robot architecture based on medical dialogue diagnosis technology, medical knowledge graph technology, and \"inference machine\" technology is proposed to build an intelligent diagnosis architecture. Secondly, in terms of algorithm, this article proposes a disease diagnosis algorithm based on Naive Bayes Classification and a symptom screening algorithm based on symptom set differences for symptom query process, This algorithm increases the interpretability of diagnostic results by simulating the questioning and diagnostic process of doctors, and combines it with the medical dialogue robot architecture to achieve intelligent diagnosis throughout the entire process.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"143 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139276571","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

Counterfeiting in Depth Synthesis based on Digital Watermarking 基于数字水印的深度合成防伪技术

Frontiers in Computing and Intelligent Systems Pub Date : 2023-11-14 DOI: 10.54097/fcis.v5i3.13998

Yu Liang, Yadong Yu, Yina Wang, Dunjun Li, Zejiong Zhou

{"title":"Counterfeiting in Depth Synthesis based on Digital Watermarking","authors":"Yu Liang, Yadong Yu, Yina Wang, Dunjun Li, Zejiong Zhou","doi":"10.54097/fcis.v5i3.13998","DOIUrl":"https://doi.org/10.54097/fcis.v5i3.13998","url":null,"abstract":"The purpose of this paper is to discuss and apply digital watermarking technology to solve the forgery problem in depth synthesis. With the rapid development of deep synthesis technology and its application in various fields, it is particularly important to protect the authenticity and integrity of digital content. Based on the understanding of digital watermarking, this paper explores an experimental design, which uses watermarking embedding and extraction algorithms and forgery detection technology to solve the problem of deep forgery, protect the copyright, integrity and anti-copy of digital products. In order to improve the robustness and reliability of the watermark, a suitable watermark embedding and extraction algorithm is designed by analyzing the characteristics of deep synthesis forged media in the experimental process. Then select the data set containing the original digital media and the deep synthetic forged samples, extract the features of the two, and find out the features that distinguish the differences between the two. Finally, the forgery detection technology is used to evaluate the performance of digital watermarking technology in depth forgery detection. In this paper, digital watermarking technology is used to provide an effective solution to the problem of forgery in depth synthesis, which can be applied to protect intellectual property rights, prevent tampering and forgery, and protect the authenticity and integrity of digital media content.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139276757","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 Source Code Comment Generation based on Abstract Syntax Tree 基于抽象语法树的源代码注释生成

Frontiers in Computing and Intelligent Systems Pub Date : 2023-11-14 DOI: 10.54097/fcis.v5i3.13837

Daoyang Ming, Weicheng Xiong

引用次数: 0

Object Detection of UAV Aerial Image based on YOLOv8 基于 YOLOv8 的无人机航空图像物体检测

Frontiers in Computing and Intelligent Systems Pub Date : 2023-11-14 DOI: 10.54097/fcis.v5i3.13852

Chen Liu, Fanrun Meng, Zhiren Zhu, Liming Zhou

{"title":"Object Detection of UAV Aerial Image based on YOLOv8","authors":"Chen Liu, Fanrun Meng, Zhiren Zhu, Liming Zhou","doi":"10.54097/fcis.v5i3.13852","DOIUrl":"https://doi.org/10.54097/fcis.v5i3.13852","url":null,"abstract":"With the development of technology, unmanned aerial vehicles (UAVs) have shed their military uses and gradually expanded to civilian and commercial fields. With the development of drone technology, object detection technology based on deep learning has become an important research topic in the field of drone applications. Apply object detection technology to unmanned aerial vehicles to achieve object detection and recognition of ground scenes from an aerial perspective. However, in aerial images taken by drones, the detection objects are mostly small targets, and the target scale changes greatly due to the influence of aerial perspective; The image background is complex, and the target object is easily occluded. It has brought many challenges to the target detection of unmanned aerial vehicles. Conventional object detection algorithms cannot guarantee detection accuracy when applied to drones, and optimizing the target detection performance of drones has become an important research topic in the field of drone applications. We improve the WIoUv3 loss function on the basis of YOLOv8s to reduce regression localization loss during training and improve the regression accuracy of the model. The experimental results indicate that the improved model mAP@0.5 It increased by 0.6 percentage points to 40.7%.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"11 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139276325","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