Pub Date : 2024-07-25DOI: 10.54254/2755-2721/69/20241493
Yun Luo
This paper explores the innovative application of artificial intelligence (AI) in the construction of teaching models for college English listening and speaking courses. By leveraging advanced AI technologies, educators can enhance the effectiveness of language instruction and provide personalized learning experiences. This study examines the theoretical foundations, practical implementations, and the impact of AI-assisted teaching on student engagement and performance. Through comprehensive analysis and discussion, we highlight the potential of AI to transform traditional language education, address challenges, and improve learning outcomes. The findings suggest that integrating AI into college English courses offers significant advantages in terms of adaptability, interactivity, and efficiency, paving the way for future educational innovations.
{"title":"Innovative research on AI-assisted teaching models for college English listening and speaking courses","authors":"Yun Luo","doi":"10.54254/2755-2721/69/20241493","DOIUrl":"https://doi.org/10.54254/2755-2721/69/20241493","url":null,"abstract":"This paper explores the innovative application of artificial intelligence (AI) in the construction of teaching models for college English listening and speaking courses. By leveraging advanced AI technologies, educators can enhance the effectiveness of language instruction and provide personalized learning experiences. This study examines the theoretical foundations, practical implementations, and the impact of AI-assisted teaching on student engagement and performance. Through comprehensive analysis and discussion, we highlight the potential of AI to transform traditional language education, address challenges, and improve learning outcomes. The findings suggest that integrating AI into college English courses offers significant advantages in terms of adaptability, interactivity, and efficiency, paving the way for future educational innovations.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"51 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141805553","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}
Pub Date : 2024-07-25DOI: 10.54254/2755-2721/67/2024ma0070
Dongting Cai
Our review explores the integration of Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs), which are pivotal in the realm of generative models. VAEs are renowned for their robust probabilistic foundations and capacity for complex data representation learning, while GANs are celebrated for generating high-fidelity images. Despite their strengths, both models have limitations: VAEs often produce less sharp outputs, and GANs face challenges with training stability. The hybrid VAE-GAN models harness the strengths of both architectures to overcome these limitations, enhancing output quality and diversity. We provide a comprehensive overview of VAEs and GANs technology developments, their integration strategies, and resultant performance improvements. Applications across various fields, such as artistic creation, medical imaging, e-commerce, and video gaming, highlight the transformative potential of these models. However, challenges in model robustness, ethical concerns, and computational demands persist, posing significant hurdles. Future research directions are poised to transform the VAE-GAN landscape significantly. Enhancing training stability remains a priority, with new approaches such as incorporating self-correcting mechanisms into GANs training being tested. Addressing ethical issues is also critical, as policymakers and technologists work together to develop standards that prevent misuse. Moreover, reducing computational costs is fundamental to democratizing access to these technologies. Projects such as the development of MobileNetV2 have made strides in creating more efficient neural network architectures that maintain performance while being less resource-intensive. Further, the exploration of VAE-GAN applications in fields like augmented reality and personalized medicine offers exciting opportunities for growth, as evidenced by recent pilot studies.
我们的综述探讨了变异自动编码器（VAE）和生成对抗网络（GAN）的整合，它们在生成模型领域举足轻重。变异自编码器以其强大的概率论基础和复杂数据表示学习能力而闻名，而生成对抗网络则以生成高保真图像而著称。尽管这两种模型各有优势，但也存在局限性：VAE 通常无法生成清晰的输出，而 GAN 则面临着训练稳定性的挑战。混合 VAE-GAN 模型利用了两种架构的优势，克服了这些局限性，提高了输出质量和多样性。我们全面概述了 VAE 和 GAN 的技术发展、整合策略以及由此带来的性能改进。艺术创作、医疗成像、电子商务和视频游戏等各个领域的应用凸显了这些模型的变革潜力。然而，模型的稳健性、伦理问题和计算需求等方面的挑战依然存在，构成了重大障碍。未来的研究方向将大大改变 VAE-GAN 的格局。提高训练的稳定性仍然是当务之急，新方法（如将自我纠正机制纳入 GANs 训练）正在接受测试。解决伦理问题也至关重要，政策制定者和技术专家将共同努力制定防止滥用的标准。此外，降低计算成本也是实现这些技术普及化的基础。MobileNetV2 等项目在创建更高效的神经网络架构方面取得了长足进步，这些架构既能保持性能，又能降低资源密集度。此外，VAE-GAN 在增强现实和个性化医疗等领域的应用探索也提供了令人兴奋的发展机遇，最近的试点研究就证明了这一点。
{"title":"Enhancing capabilities of generative models through VAE-GAN integration: A review","authors":"Dongting Cai","doi":"10.54254/2755-2721/67/2024ma0070","DOIUrl":"https://doi.org/10.54254/2755-2721/67/2024ma0070","url":null,"abstract":"Our review explores the integration of Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs), which are pivotal in the realm of generative models. VAEs are renowned for their robust probabilistic foundations and capacity for complex data representation learning, while GANs are celebrated for generating high-fidelity images. Despite their strengths, both models have limitations: VAEs often produce less sharp outputs, and GANs face challenges with training stability. The hybrid VAE-GAN models harness the strengths of both architectures to overcome these limitations, enhancing output quality and diversity. We provide a comprehensive overview of VAEs and GANs technology developments, their integration strategies, and resultant performance improvements. Applications across various fields, such as artistic creation, medical imaging, e-commerce, and video gaming, highlight the transformative potential of these models. However, challenges in model robustness, ethical concerns, and computational demands persist, posing significant hurdles. Future research directions are poised to transform the VAE-GAN landscape significantly. Enhancing training stability remains a priority, with new approaches such as incorporating self-correcting mechanisms into GANs training being tested. Addressing ethical issues is also critical, as policymakers and technologists work together to develop standards that prevent misuse. Moreover, reducing computational costs is fundamental to democratizing access to these technologies. Projects such as the development of MobileNetV2 have made strides in creating more efficient neural network architectures that maintain performance while being less resource-intensive. Further, the exploration of VAE-GAN applications in fields like augmented reality and personalized medicine offers exciting opportunities for growth, as evidenced by recent pilot studies.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"47 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141805583","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}
Pub Date : 2024-07-25DOI: 10.54254/2755-2721/55/20241526
Jialong Li
This research paper explores a novel path planning generator that leverages the Chaos Game Optimization (CGO) algorithm, a mathematical technique inspired by the chaos game that creates fractals. The CGO algorithm is applied to analyze fractal configurations and self-similarity problems in path planning. The paper provides detailed information about the initialization of candidate solutions and the iterative process of updating their positions and fitness values. Through MATLAB simulations, the paper demonstrates the CGO algorithm's effectiveness in generating optimal paths in complex scenarios with randomly generated blocks or labyrinth environments. The approach shows great potential in enhancing the capabilities of autonomous robots in navigating dynamic and challenging environments. This paper also simulated the path planning generator using the CGO algorithm in MATLAB. By implementing chaos theory and randomness, the CGO algorithm provides a robust and efficient solution for path planning, enabling robotic systems to handle complex and nonlinear problems. The paper concludes that the application of chaos theory in robotics opens up exciting possibilities for advancing the capabilities of robotic systems and enhancing their performance in real-world scenarios.
{"title":"A path planning generator based on the Chaos Game Optimization algorithm","authors":"Jialong Li","doi":"10.54254/2755-2721/55/20241526","DOIUrl":"https://doi.org/10.54254/2755-2721/55/20241526","url":null,"abstract":"This research paper explores a novel path planning generator that leverages the Chaos Game Optimization (CGO) algorithm, a mathematical technique inspired by the chaos game that creates fractals. The CGO algorithm is applied to analyze fractal configurations and self-similarity problems in path planning. The paper provides detailed information about the initialization of candidate solutions and the iterative process of updating their positions and fitness values. Through MATLAB simulations, the paper demonstrates the CGO algorithm's effectiveness in generating optimal paths in complex scenarios with randomly generated blocks or labyrinth environments. The approach shows great potential in enhancing the capabilities of autonomous robots in navigating dynamic and challenging environments. This paper also simulated the path planning generator using the CGO algorithm in MATLAB. By implementing chaos theory and randomness, the CGO algorithm provides a robust and efficient solution for path planning, enabling robotic systems to handle complex and nonlinear problems. The paper concludes that the application of chaos theory in robotics opens up exciting possibilities for advancing the capabilities of robotic systems and enhancing their performance in real-world scenarios.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"53 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141805656","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}
Pub Date : 2024-07-25DOI: 10.54254/2755-2721/79/20241646
Xingyang He
As a way to alleviate the information overload problem arisen with the development of the internet, recommender systems receive a lot of attention from academia and industry. Due to its superiority in graph data, graph neural networks are widely adopted in recommender systems. This survey offers a comprehensive review of the latest research and innovative approaches in GNN-based recommender systems. This survey introduces a new taxonomy by the construction of GNN models and explores the challenges these models face. This paper also discusses new approaches, i.e., using social graphs and knowledge graphs as side information, and evaluates their strengths and limitations. Finally, this paper suggests some potential directions for future research in this field.
{"title":"Graph neural networks in recommender systems","authors":"Xingyang He","doi":"10.54254/2755-2721/79/20241646","DOIUrl":"https://doi.org/10.54254/2755-2721/79/20241646","url":null,"abstract":"As a way to alleviate the information overload problem arisen with the development of the internet, recommender systems receive a lot of attention from academia and industry. Due to its superiority in graph data, graph neural networks are widely adopted in recommender systems. This survey offers a comprehensive review of the latest research and innovative approaches in GNN-based recommender systems. This survey introduces a new taxonomy by the construction of GNN models and explores the challenges these models face. This paper also discusses new approaches, i.e., using social graphs and knowledge graphs as side information, and evaluates their strengths and limitations. Finally, this paper suggests some potential directions for future research in this field.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"38 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141805955","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}
Pub Date : 2024-07-25DOI: 10.54254/2755-2721/55/20241527
Haodong Lu, Haoran Xu
This paper reviews a recently developed uncertainty-aware motion planning algorithm vastly applied to autonomous vehicles. Many vehicle manufacturers shifted their focus from improving vehicle energy conversion efficiency to autonomous driving, aiming to bring a better and more relaxed driving experience to drivers. However, many past motion planning algorithms used for autonomous driving were immature, so many errors were reported. These errors may put human drivers in life-threatening danger. Consisting of two connected systems supported by a well-trained graph neural network, the uncertainty-aware motion planning algorithm uses two related sub-systems to predict the motion of surrounding object and make necessary maneuvers accordingly. Using evidence from many research papers, an uncertainty-aware motion algorithm is an efficient and safe solution to insufficient consideration of the surrounding environment of vehicles. Even though its ability is primarily limited by the accuracy of sensors and the complexity of background, the unique advantage of this algorithm gives an alternative direction to the development of algorithms in autonomous vehicles.
{"title":"Uncertainty-aware motion planning for autonomous vehicle: A review","authors":"Haodong Lu, Haoran Xu","doi":"10.54254/2755-2721/55/20241527","DOIUrl":"https://doi.org/10.54254/2755-2721/55/20241527","url":null,"abstract":"This paper reviews a recently developed uncertainty-aware motion planning algorithm vastly applied to autonomous vehicles. Many vehicle manufacturers shifted their focus from improving vehicle energy conversion efficiency to autonomous driving, aiming to bring a better and more relaxed driving experience to drivers. However, many past motion planning algorithms used for autonomous driving were immature, so many errors were reported. These errors may put human drivers in life-threatening danger. Consisting of two connected systems supported by a well-trained graph neural network, the uncertainty-aware motion planning algorithm uses two related sub-systems to predict the motion of surrounding object and make necessary maneuvers accordingly. Using evidence from many research papers, an uncertainty-aware motion algorithm is an efficient and safe solution to insufficient consideration of the surrounding environment of vehicles. Even though its ability is primarily limited by the accuracy of sensors and the complexity of background, the unique advantage of this algorithm gives an alternative direction to the development of algorithms in autonomous vehicles.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"88 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141802353","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}
Pub Date : 2024-07-25DOI: 10.54254/2755-2721/69/20241624
Yuanming Sun
In this paper, we improve the traditional random forest model by optimising the random forest algorithm based on sparrow search algorithm and compare the effectiveness of the two models for weight class prediction. Initial exploration of the data revealed that age, height, weight and BMI play an important role in weight class prediction. Correlation analyses showed a strong correlation between age and BMI and weight class. The experimental results show that the random forest model optimised based on the sparrow search algorithm achieves 100% in prediction accuracy, which improves the accuracy by 1.2% compared with the traditional random forest algorithm, and has better prediction effect. The significance of this paper is that a random forest algorithm optimised based on the sparrow search algorithm is proposed and experimentally demonstrated to have better performance in weight class prediction. This is of great significance in the fields of weight management, health assessment, and disease risk assessment. In addition, this study demonstrates the value of data analysis and machine learning methods in solving real-world problems. In conclusion, this paper provides new ideas for further improvement and application of machine learning algorithms, and provides references and lessons for researchers in related fields.
{"title":"Weight class prediction based on sparrow search algorithm optimised random forest model","authors":"Yuanming Sun","doi":"10.54254/2755-2721/69/20241624","DOIUrl":"https://doi.org/10.54254/2755-2721/69/20241624","url":null,"abstract":"In this paper, we improve the traditional random forest model by optimising the random forest algorithm based on sparrow search algorithm and compare the effectiveness of the two models for weight class prediction. Initial exploration of the data revealed that age, height, weight and BMI play an important role in weight class prediction. Correlation analyses showed a strong correlation between age and BMI and weight class. The experimental results show that the random forest model optimised based on the sparrow search algorithm achieves 100% in prediction accuracy, which improves the accuracy by 1.2% compared with the traditional random forest algorithm, and has better prediction effect. The significance of this paper is that a random forest algorithm optimised based on the sparrow search algorithm is proposed and experimentally demonstrated to have better performance in weight class prediction. This is of great significance in the fields of weight management, health assessment, and disease risk assessment. In addition, this study demonstrates the value of data analysis and machine learning methods in solving real-world problems. In conclusion, this paper provides new ideas for further improvement and application of machine learning algorithms, and provides references and lessons for researchers in related fields.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"16 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141803009","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}
Pub Date : 2024-07-25DOI: 10.54254/2755-2721/79/20241082
Kaicheng Yang, Sutong Lin, Yu Dai, Wentai Li
The complexity of generating intricate and random mazes is a captivating challenge that finds applications in various fields, including computer science, mathematics, gaming, and simulations. This study presents an innovative approach by integrating two prominent perfect maze generation algorithms, Aldous-border (AB) and Wilson. Both are celebrated for their strong randomness and efficiency, yet their combination offers a novel way to optimize maze generation. Our research commenced with a detailed analysis of the relationship between the coverage rate, uniquely characterized by the AB algorithm, and map size. We then formulated a mechanism that transitions seamlessly into the Wilson algorithm, aiming to minimize time consumption. Through a series of carefully designed experimental trials, we hope to use a model to find the most suitable algorithm for switching to minimize the time it takes to generate a maze. These were subsequently evaluated and compared to identify the most fitting solution. Under the framework of our synthesized algorithm, an average time saving of 34.124% was achieved, demonstrating a promising enhancement in efficiency. Although still in the exploratory phase, the outcomes of this research provide foundational insights into maze generation's underlying principles and techniques. The outcomes of this research offer insights into maze generation and its applications and may serve as a useful reference for future studies and potential technological advancements.
生成错综复杂的随机迷宫是一项令人着迷的挑战，在计算机科学、数学、游戏和模拟等各个领域都有应用。本研究通过整合两种著名的完美迷宫生成算法（Aldous-border (AB) 和 Wilson），提出了一种创新方法。这两种算法都以随机性强和效率高而著称，但它们的结合提供了一种优化迷宫生成的新方法。我们的研究首先详细分析了 AB 算法特有的覆盖率与地图大小之间的关系。然后，我们制定了一种无缝过渡到威尔逊算法的机制，旨在最大限度地减少时间消耗。通过一系列精心设计的实验，我们希望利用模型找到最合适的切换算法，以最大限度地减少生成迷宫所需的时间。随后对这些算法进行评估和比较，以找出最合适的解决方案。在我们合成的算法框架下，平均节省了 34.124% 的时间，显示出有望提高效率。尽管仍处于探索阶段，但本研究成果为迷宫生成的基本原理和技术提供了基础性见解。本研究成果为迷宫生成及其应用提供了深入见解，可作为未来研究和潜在技术进步的有益参考。
{"title":"Optimization and comparative analysis of maze generation algorithm hybrid","authors":"Kaicheng Yang, Sutong Lin, Yu Dai, Wentai Li","doi":"10.54254/2755-2721/79/20241082","DOIUrl":"https://doi.org/10.54254/2755-2721/79/20241082","url":null,"abstract":"The complexity of generating intricate and random mazes is a captivating challenge that finds applications in various fields, including computer science, mathematics, gaming, and simulations. This study presents an innovative approach by integrating two prominent perfect maze generation algorithms, Aldous-border (AB) and Wilson. Both are celebrated for their strong randomness and efficiency, yet their combination offers a novel way to optimize maze generation. Our research commenced with a detailed analysis of the relationship between the coverage rate, uniquely characterized by the AB algorithm, and map size. We then formulated a mechanism that transitions seamlessly into the Wilson algorithm, aiming to minimize time consumption. Through a series of carefully designed experimental trials, we hope to use a model to find the most suitable algorithm for switching to minimize the time it takes to generate a maze. These were subsequently evaluated and compared to identify the most fitting solution. Under the framework of our synthesized algorithm, an average time saving of 34.124% was achieved, demonstrating a promising enhancement in efficiency. Although still in the exploratory phase, the outcomes of this research provide foundational insights into maze generation's underlying principles and techniques. The outcomes of this research offer insights into maze generation and its applications and may serve as a useful reference for future studies and potential technological advancements.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"50 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141803826","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}
Pub Date : 2024-07-25DOI: 10.54254/2755-2721/69/20241518
Yuxuan Tong
Data visualization is a powerful tool that can assist individuals and organisations in comprehending vast amounts of data and extracting valuable insights from it. The most significant function of data visualization is to make recommendations by figuring out the essence of the occurrence of the data. This paper will take housing data as an example, raise relevant questions, and reveal the logic behind the data and the relationship between variables through data visualization, linear regression, and other statistical methods.
{"title":"Housing data visualization and analysis","authors":"Yuxuan Tong","doi":"10.54254/2755-2721/69/20241518","DOIUrl":"https://doi.org/10.54254/2755-2721/69/20241518","url":null,"abstract":"Data visualization is a powerful tool that can assist individuals and organisations in comprehending vast amounts of data and extracting valuable insights from it. The most significant function of data visualization is to make recommendations by figuring out the essence of the occurrence of the data. This paper will take housing data as an example, raise relevant questions, and reveal the logic behind the data and the relationship between variables through data visualization, linear regression, and other statistical methods.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"23 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141804177","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}
Pub Date : 2024-07-25DOI: 10.54254/2755-2721/79/20241613
Chenxi Wu
As times go on, credit risk has become a widespread issue across society, especially after the 2008 global financial crisis. However, the traditional financial technique could not determine the possibility of people defaulting, causing credit problems. With the rapid development of the Artificial Intelligence field, this could not be the problem. In this paper, several methods, including the Support Vector Machine model (SVM), K-Nearest Neighbors model (KNN) and Decision Tree model (DTs) are implemented using machine learning to try to predict the credit risk accurately and compare the accuracy of the three different methods. As a result, the Decision Trees show the highest result in these three methods.
{"title":"Credit risk unveiled: Decision trees triumph in comparative machine learning study","authors":"Chenxi Wu","doi":"10.54254/2755-2721/79/20241613","DOIUrl":"https://doi.org/10.54254/2755-2721/79/20241613","url":null,"abstract":"As times go on, credit risk has become a widespread issue across society, especially after the 2008 global financial crisis. However, the traditional financial technique could not determine the possibility of people defaulting, causing credit problems. With the rapid development of the Artificial Intelligence field, this could not be the problem. In this paper, several methods, including the Support Vector Machine model (SVM), K-Nearest Neighbors model (KNN) and Decision Tree model (DTs) are implemented using machine learning to try to predict the credit risk accurately and compare the accuracy of the three different methods. As a result, the Decision Trees show the highest result in these three methods.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"5 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141804383","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}
Pub Date : 2024-07-25DOI: 10.54254/2755-2721/79/20241081
Jiachen Piao, Xinyuan Hu, Qixuan Zhou
This paper introduces a small game that the authors plan on creating and discusses the code and algorithms implemented inside. The game is created using Pygame. Pygame is a set of python modules designed for writing 2D-games. The reason we use it is that Pygame is free and simple to operate for a new game designer. The game involves moving a character through a maze while eating coins along the path. The character is controlled using keyboard. The maze is randomly generated using various maze algorithms. Although the game is simple, the logics and algorithms included are useful for more complex games. This essay will introduce the maze algorithms and navigation algorithms needed for the game, as well as the code implemented.
{"title":"Maze and navigation algorithms in game development","authors":"Jiachen Piao, Xinyuan Hu, Qixuan Zhou","doi":"10.54254/2755-2721/79/20241081","DOIUrl":"https://doi.org/10.54254/2755-2721/79/20241081","url":null,"abstract":"This paper introduces a small game that the authors plan on creating and discusses the code and algorithms implemented inside. The game is created using Pygame. Pygame is a set of python modules designed for writing 2D-games. The reason we use it is that Pygame is free and simple to operate for a new game designer. The game involves moving a character through a maze while eating coins along the path. The character is controlled using keyboard. The maze is randomly generated using various maze algorithms. Although the game is simple, the logics and algorithms included are useful for more complex games. This essay will introduce the maze algorithms and navigation algorithms needed for the game, as well as the code implemented.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"58 26","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141804964","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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