Based on information technology, Internet of Things technology, big data technology, and cloud computing technology, smart city achieves the integration of urban information, thus developing an all-round perception of the city. Moreover, according to the development status of the city, it develops dynamic and refined management, which is of great help to improve the convenience of life of urban residents. This paper analyzes the key technology of smart city construction. It takes intelligent lighting as a case study to analyze cloud computing and Internet of Things technology application scenarios in smart cities.
{"title":"Analysis of Application Scenarios of Cloud Computing and Internet of Things Technology in Smart Cities","authors":"Weijie Cai","doi":"10.54097/fcis.v6i1.17","DOIUrl":"https://doi.org/10.54097/fcis.v6i1.17","url":null,"abstract":"Based on information technology, Internet of Things technology, big data technology, and cloud computing technology, smart city achieves the integration of urban information, thus developing an all-round perception of the city. Moreover, according to the development status of the city, it develops dynamic and refined management, which is of great help to improve the convenience of life of urban residents. This paper analyzes the key technology of smart city construction. It takes intelligent lighting as a case study to analyze cloud computing and Internet of Things technology application scenarios in smart cities.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"120 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138988218","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}
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.
{"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}
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.
{"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}
Ying Zhang, Lele Xi, Yixia Wu, Canping Li, Zebin Ma
The greedy algorithm and dynamic programming algorithm have always been difficult for students to understand in the course of algorithm analysis and design. This article uses Python as a descriptive language and selects classic examples of greedy and dynamic programming algorithms to analyze these two algorithms in detail, providing effective references for learning the Python language.
{"title":"\"Algorithm Analysis and Design\" Python Teaching Example of Greedy and Dynamic Programming","authors":"Ying Zhang, Lele Xi, Yixia Wu, Canping Li, Zebin Ma","doi":"10.54097/fcis.v6i2.11","DOIUrl":"https://doi.org/10.54097/fcis.v6i2.11","url":null,"abstract":"The greedy algorithm and dynamic programming algorithm have always been difficult for students to understand in the course of algorithm analysis and design. This article uses Python as a descriptive language and selects classic examples of greedy and dynamic programming algorithms to analyze these two algorithms in detail, providing effective references for learning the Python language.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"85 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139020966","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}
Hong Zhou, Yan Lou, Jize Xiong, Yixu Wang, Yuxiang Liu
In 2019, approximately 5 million individuals were diagnosed with gastrointestinal tract cancer globally, with about half eligible for radiation therapy. This treatment, crucial for many patients, faces challenges due to the manual segmentation process required in newer technologies like MR-Linacs. This project, supported by the UW-Madison Carbone Cancer Center, aims to automate the segmentation of stomach and intestines in MRI scans using deep learning. The Unet2.5D model, specifically Unet2.5D(Se-ResNet50), has shown promising results, achieving a Dice Coefficient of 0.848. Successful implementation of this model could significantly expedite treatments, enabling higher radiation doses to tumors while minimizing exposure to healthy tissues, ultimately improving patient care and long-term cancer control.
{"title":"Improvement of Deep Learning Model for Gastrointestinal Tract Segmentation Surgery","authors":"Hong Zhou, Yan Lou, Jize Xiong, Yixu Wang, Yuxiang Liu","doi":"10.54097/fcis.v6i1.19","DOIUrl":"https://doi.org/10.54097/fcis.v6i1.19","url":null,"abstract":"In 2019, approximately 5 million individuals were diagnosed with gastrointestinal tract cancer globally, with about half eligible for radiation therapy. This treatment, crucial for many patients, faces challenges due to the manual segmentation process required in newer technologies like MR-Linacs. This project, supported by the UW-Madison Carbone Cancer Center, aims to automate the segmentation of stomach and intestines in MRI scans using deep learning. The Unet2.5D model, specifically Unet2.5D(Se-ResNet50), has shown promising results, achieving a Dice Coefficient of 0.848. Successful implementation of this model could significantly expedite treatments, enabling higher radiation doses to tumors while minimizing exposure to healthy tissues, ultimately improving patient care and long-term cancer control.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"77 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139025578","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}
"Fundamentals of Information Technology" is a fundamental course in vocational colleges, mainly aimed at cultivating students' information technology application skills, sustainable development professional literacy and abilities. Traditional classroom teaching is no longer able to meet the personalized learning needs of students, and its effectiveness in improving their practical skills is not significant. This article analyzes the current situation of traditional teaching in this course and proposes a curriculum teaching reform strategy based on blended learning mode. In response to the strong hands-on ability of vocational college students, a combination of online preview by students before class and offline Q&A by classroom teachers is mainly adopted to guide students in independent exploratory learning, and process evaluation and assessment are adopted to form a student-centered classroom teaching method, allowing students to participate in the entire process of classroom teaching. While enhancing students' learning enthusiasm and initiative, it effectively enhances their computer practical skills.
{"title":"Exploration of Teaching Reform of Information Technology Fundamentals Course in Vocational Colleges based on Blended Teaching","authors":"Yanbin Chen","doi":"10.54097/fcis.v6i2.15","DOIUrl":"https://doi.org/10.54097/fcis.v6i2.15","url":null,"abstract":"\"Fundamentals of Information Technology\" is a fundamental course in vocational colleges, mainly aimed at cultivating students' information technology application skills, sustainable development professional literacy and abilities. Traditional classroom teaching is no longer able to meet the personalized learning needs of students, and its effectiveness in improving their practical skills is not significant. This article analyzes the current situation of traditional teaching in this course and proposes a curriculum teaching reform strategy based on blended learning mode. In response to the strong hands-on ability of vocational college students, a combination of online preview by students before class and offline Q&A by classroom teachers is mainly adopted to guide students in independent exploratory learning, and process evaluation and assessment are adopted to form a student-centered classroom teaching method, allowing students to participate in the entire process of classroom teaching. While enhancing students' learning enthusiasm and initiative, it effectively enhances their computer practical skills.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"406 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138991158","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}
Based on the data of per capita annual income, food production per hectare, and per capita consumption of consumers in different countries, this paper first establishes a dynamic planning model, starting from three indicators, namely, regional transportation time, purchasing capacity and actual demand, and using MATLAB, establishes a visual image to derive the changes of satisfaction, profitability, environment, and efficiency in the next ten years. In order to determine the priority of the four indicators, a hierarchical analysis model is established on the basis of the dynamic planning model to derive the calculated weights of each indicator. In order to verify the adaptability of the model, the model is applied to the developed country Britain and the developing country China, respectively, and the dynamic planning model is confirmed again according to the information returned by the visualization image, which illustrates the scalability of the model to the food distribution system in different countries and regions.
{"title":"Optimization of Food Distribution System Based on Dynamic Planning Model","authors":"Xunyang Li, Jin Chen","doi":"10.54097/fcis.v6i2.14","DOIUrl":"https://doi.org/10.54097/fcis.v6i2.14","url":null,"abstract":"Based on the data of per capita annual income, food production per hectare, and per capita consumption of consumers in different countries, this paper first establishes a dynamic planning model, starting from three indicators, namely, regional transportation time, purchasing capacity and actual demand, and using MATLAB, establishes a visual image to derive the changes of satisfaction, profitability, environment, and efficiency in the next ten years. In order to determine the priority of the four indicators, a hierarchical analysis model is established on the basis of the dynamic planning model to derive the calculated weights of each indicator. In order to verify the adaptability of the model, the model is applied to the developed country Britain and the developing country China, respectively, and the dynamic planning model is confirmed again according to the information returned by the visualization image, which illustrates the scalability of the model to the food distribution system in different countries and regions.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"20 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139016226","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}
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.
近年来，基于文本分析的模型在假新闻检测任务中取得了突出成果，这与从文本中提取特征信息的数量和质量提升密切相关。借鉴现有的语义检测框架，该领域的研究主要集中在通过单独的辅助特征、文本立场特征或情感特征来提取各种文本信息。然而，使用单一的辅助特征描述文本的一般属性具有挑战性，经常会导致遗漏重要细节，并留下立场失真和情感共鸣等问题。为解决这一问题，本研究提出了一种结合多种文本特征的假新闻识别联合模型。通过提取和融合各方面的文本特征，即语义特征、立场特征和情感特征，可以对文本信息进行更详细、更有效的联合分析，从而提高假新闻检测的性能。在 RumourEval-17 数据集上，我们的模型获得了 0.891 的宏观 F1 分数，超越了当前的谣言检测模型。此外，在最新的 COVID-19 数据集上，我们的模型获得了 0.904 的宏观 F1 分数，显示出在假新闻检测方面的强大竞争力和广阔前景。
{"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}
Quan Zhang, Guoqing Cai, Meiqing Cai, Jili Qian, Tianbo Song
Breast cancer, a lumpy nodule or granular calcified tissue caused by cancerous changes in chest tissue, has become one of the most prevalent cancers. Due to the location and structure of the tumor, it can be detected directly by ultrasound or X-ray and is less likely to spread to other parts of the body than tumors in other parts of the body. Considering the huge number of sick people, the resources required for a full census would be enormous, but thanks to the rapid development of medical image processing technology in recent years, assisted diagnosis through deep learning models has gradually become more widely accepted. For detection models, higher accuracy means lower misdiagnosis rates and timely treatment for patients. Therefore, in this paper, we first specify the diagnose as a binary classification problem and then introduce a new pooling scheme and training method to achieve better results compared to the traditional network backbone in the past.
乳腺癌是胸部组织癌变引起的肿块结节或颗粒状钙化组织，已成为发病率最高的癌症之一。由于肿瘤的位置和结构，它可以直接通过超声波或 X 光检查出来，而且与身体其他部位的肿瘤相比，不易扩散到身体其他部位。考虑到患病人数众多，全面普查所需的资源将十分庞大，但得益于近年来医学图像处理技术的飞速发展，通过深度学习模型进行辅助诊断已逐渐被更多人所接受。对于检测模型而言，更高的准确率意味着更低的误诊率和对患者的及时治疗。因此，本文首先将诊断明确为二元分类问题，然后引入新的池化方案和训练方法，与过去传统的网络骨干相比，取得了更好的效果。
{"title":"Deep Learning Model Aids Breast Cancer Detection","authors":"Quan Zhang, Guoqing Cai, Meiqing Cai, Jili Qian, Tianbo Song","doi":"10.54097/fcis.v6i1.18","DOIUrl":"https://doi.org/10.54097/fcis.v6i1.18","url":null,"abstract":"Breast cancer, a lumpy nodule or granular calcified tissue caused by cancerous changes in chest tissue, has become one of the most prevalent cancers. Due to the location and structure of the tumor, it can be detected directly by ultrasound or X-ray and is less likely to spread to other parts of the body than tumors in other parts of the body. Considering the huge number of sick people, the resources required for a full census would be enormous, but thanks to the rapid development of medical image processing technology in recent years, assisted diagnosis through deep learning models has gradually become more widely accepted. For detection models, higher accuracy means lower misdiagnosis rates and timely treatment for patients. Therefore, in this paper, we first specify the diagnose as a binary classification problem and then introduce a new pooling scheme and training method to achieve better results compared to the traditional network backbone in the past.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"51 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139013195","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}
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.
{"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}
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