Frontiers in Computing and Intelligent Systems最新文献_第2页

Analysis of Application Scenarios of Cloud Computing and Internet of Things Technology in Smart Cities 云计算和物联网技术在智慧城市中的应用场景分析

Frontiers in Computing and Intelligent Systems Pub Date : 2023-12-01 DOI: 10.54097/fcis.v6i1.17

Weijie Cai

引用次数: 0

Public Place Crowd Transaction Monitoring System 公共场所人群交易监控系统

Frontiers in Computing and Intelligent Systems Pub Date : 2023-12-01 DOI: 10.54097/fcis.v6i1.21

Zhize Wang

{"title":"Public Place Crowd Transaction Monitoring System","authors":"Zhize Wang","doi":"10.54097/fcis.v6i1.21","DOIUrl":"https://doi.org/10.54097/fcis.v6i1.21","url":null,"abstract":"Currently, the phenomenon of abnormal movement in public spaces by groups is becoming increasingly prominent, leading to issues concerning public flow and safety. The escalating problems of high crowd density, the presence of controlled dangerous items, and unexpected group activities highlight the necessity for timely detection in public settings. Timely identification of such scenarios will facilitate prompt responses and assistance from relevant government departments. Exploring how artificial intelligence technology can aid urban management personnel in effectively detecting abnormal group behaviors is crucial. Having the ability to swiftly and efficiently evacuate crowds in emergency situations holds significant practical importance. This paper employs deep learning methodologies to assist urban management personnel in efficiently monitoring crowd density and detecting abnormal behaviors. The aim is to maintain crowd density within reasonable limits and enable rapid and effective crowd evacuation in emergency situations. Detection of abnormal group behaviors typically involves methods based on global features, extracting feature patterns like optical flow from entire video segments and constructing corresponding histograms. Given that automatic classification of crowd patterns involves sudden and abnormal changes, a novel method is proposed to extract motion \"textures\" from dynamic STV (Space-Time Volume) blocks formed from real-time video streams.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"34 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139016077","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

DFENet: Double Feature Enhanced Class Agnostic Counting Methods DFENet：双特征增强型类无关计数法

Frontiers in Computing and Intelligent Systems Pub Date : 2023-12-01 DOI: 10.54097/fcis.v6i1.14

Jiakang Liu, Hua Huo

{"title":"DFENet: Double Feature Enhanced Class Agnostic Counting Methods","authors":"Jiakang Liu, Hua Huo","doi":"10.54097/fcis.v6i1.14","DOIUrl":"https://doi.org/10.54097/fcis.v6i1.14","url":null,"abstract":"Object counting is a basic computer vision task, which can estimate the number of each object in an image, thus providing valuable information. In dense scenes, there are huge differences in target individual scale, and the different target individual scale leads to low accuracy of target count. In addition, most of the existing target count datasets in the field require a lot of manual creation and annotation, which increases the cost and difficulty of the dataset, lack of ease of use and portability. To solve these problems, this paper proposes a class agnostic counting method Double Feature Enhancement Net based on improved Bilinear Matching Network+ (BMNet+). By introducing the feature enhancement module based on the principle of conditional random field and the adaptively spatial feature fusion module, combined with the feature similarity measurement strategy of bilinear matching network, the method can effectively extract the target features of different scales, enhance the adaptability to the targets with large scale changes, and improve the counting performance of the network. Experiments were carried out on FSC-147 data set, and the experimental results show that the proposed model has been further improved in counting accuracy. The MAE and MSE of the verification set are 15.03 and 54.53 respectively. In the test set, MAE reaches 13.65, MSE reaches 89.54, and the counting performance is at the advanced level in the field.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"624 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139018819","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

"Algorithm Analysis and Design" Python Teaching Example of Greedy and Dynamic Programming 算法分析与设计 "Python "贪婪编程 "和 "动态编程 "教学示例

Frontiers in Computing and Intelligent Systems Pub Date : 2023-12-01 DOI: 10.54097/fcis.v6i2.11

Ying Zhang, Lele Xi, Yixia Wu, Canping Li, Zebin Ma

引用次数: 0

Improvement of Deep Learning Model for Gastrointestinal Tract Segmentation Surgery 改进用于胃肠道分割手术的深度学习模型

Frontiers in Computing and Intelligent Systems Pub Date : 2023-12-01 DOI: 10.54097/fcis.v6i1.19

Hong Zhou, Yan Lou, Jize Xiong, Yixu Wang, Yuxiang Liu

引用次数: 0

Exploration of Teaching Reform of Information Technology Fundamentals Course in Vocational Colleges based on Blended Teaching 基于混合式教学的职业院校信息技术基础课程教学改革探索

Frontiers in Computing and Intelligent Systems Pub Date : 2023-12-01 DOI: 10.54097/fcis.v6i2.15

Yanbin Chen

引用次数: 0

Optimization of Food Distribution System Based on Dynamic Planning Model 基于动态规划模型的食品配送系统优化

Frontiers in Computing and Intelligent Systems Pub Date : 2023-12-01 DOI: 10.54097/fcis.v6i2.14

Xunyang Li, Jin Chen

引用次数: 0

A Joint Fake News Detection Model based on Multi-Features 基于多特征的假新闻联合检测模型

Frontiers in Computing and Intelligent Systems Pub Date : 2023-12-01 DOI: 10.54097/fcis.v6i1.15

Shuxia Ren, Ning He, Xuanzheng Zhang

{"title":"A Joint Fake News Detection Model based on Multi-Features","authors":"Shuxia Ren, Ning He, Xuanzheng Zhang","doi":"10.54097/fcis.v6i1.15","DOIUrl":"https://doi.org/10.54097/fcis.v6i1.15","url":null,"abstract":"Text analysis-based models have achieved outstanding results in fake news detection tasks in recent years, which is closely linked to the quantity and quality enhancement of feature information extracted from the text. Drawing upon the existing semantic detection frameworks, studies in this field concentrate on extracting various textual information through a solitary auxiliary feature, text stance feature or sentiment feature. However, it is challenging to depict the general attributes of the text using a single auxiliary feature, which frequently results in missing essential details and leaves problems with stance distortion and emotional resonance. To tackle the problem, this study proposes a joint model for identifying fake news, incorporating numerous textual characteristics. By extracting and blending various aspects of text features, i.e., semantic, stance and sentiment features, a more detailed and effective joint analysis of textual information is attained, resulting in improved performance in fake news detection. On the RumourEval-17 datasets, our model attains the Macro F1 Score of 0.891, surpassing current models for detecting rumors. Additionally, our model obtains a Macro F1 Score of 0.904 on the latest COVID-19 dataset, demonstrating strong competitiveness and promising prospects for fake news detection.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"500 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139021822","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

Deep Learning Model Aids Breast Cancer Detection 深度学习模型辅助乳腺癌检测

Frontiers in Computing and Intelligent Systems Pub Date : 2023-12-01 DOI: 10.54097/fcis.v6i1.18

Quan Zhang, Guoqing Cai, Meiqing Cai, Jili Qian, Tianbo Song

引用次数: 0

Tower Defense Game Design based on Unity3D 基于 Unity3D 的塔防游戏设计

Frontiers in Computing and Intelligent Systems Pub Date : 2023-12-01 DOI: 10.54097/fcis.v6i1.16

Shuohan Chen

{"title":"Tower Defense Game Design based on Unity3D","authors":"Shuohan Chen","doi":"10.54097/fcis.v6i1.16","DOIUrl":"https://doi.org/10.54097/fcis.v6i1.16","url":null,"abstract":"In today's society, with the rapid development of computer technology, personal computers have been integrated into our lives, and computer games have become a way of entertainment for people. Among them, tower defense games, as a branch of strategy games, have also been loved by the majority of players. On the computer game download platform, such as Steam's download list, you can also see all kinds of excellent tower defense games listed, such as “Bloom TD” and “Plants vs. Zombies”. However, in recent years, most tower defense games lack innovation, and the level game play is monotonous. Players are beginning to get tired of the gameplay of classic tower defense games, and their popularity has begun to decline. In order to solve this problem, this article is based on the Unity3D game engine, combined with the characteristics of UGC and RTS games, and made some innovations to the classic tower defense games. According to the actual needs, a tower defense game called “The Rise of The Tribes” was developed. The main work of this paper is to analyze and design The game, realize The switching between The three scenes in The game and each interface, and solve how to build buildings, how to defend defensive buildings, and how to deploy and move soldiers and attack, how to generate, collect and consume resources, completed The debugging and testing of The game, summarized the development process and made an outlook on how to improve the game.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"22 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138992593","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