Applied and Computational Engineering最新文献_第9页

Innovative research on AI-assisted teaching models for college English listening and speaking courses 大学英语听说课程人工智能辅助教学模式创新研究

Applied and Computational Engineering Pub Date : 2024-07-25 DOI: 10.54254/2755-2721/69/20241493

Yun Luo

引用次数: 0

Enhancing capabilities of generative models through VAE-GAN integration: A review 通过 VAE-GAN 集成增强生成模型的能力：综述

Applied and Computational Engineering Pub Date : 2024-07-25 DOI: 10.54254/2755-2721/67/2024ma0070

Dongting Cai

{"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}

引用次数: 0

A path planning generator based on the Chaos Game Optimization algorithm 基于混沌博弈优化算法的路径规划生成器

Applied and Computational Engineering Pub Date : 2024-07-25 DOI: 10.54254/2755-2721/55/20241526

Jialong Li

{"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}

引用次数: 0

Graph neural networks in recommender systems 推荐系统中的图神经网络

Applied and Computational Engineering Pub Date : 2024-07-25 DOI: 10.54254/2755-2721/79/20241646

Xingyang He

引用次数: 0

Uncertainty-aware motion planning for autonomous vehicle: A review 自动驾驶汽车的不确定性感知运动规划：综述

Applied and Computational Engineering Pub Date : 2024-07-25 DOI: 10.54254/2755-2721/55/20241527

Haodong Lu, Haoran Xu

引用次数: 0

Weight class prediction based on sparrow search algorithm optimised random forest model 基于麻雀搜索算法优化随机森林模型的权重等级预测

Applied and Computational Engineering Pub Date : 2024-07-25 DOI: 10.54254/2755-2721/69/20241624

Yuanming Sun

{"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}

引用次数: 0

Optimization and comparative analysis of maze generation algorithm hybrid 迷宫生成混合算法的优化与比较分析

Applied and Computational Engineering Pub Date : 2024-07-25 DOI: 10.54254/2755-2721/79/20241082

Kaicheng Yang, Sutong Lin, Yu Dai, Wentai Li

{"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}

引用次数: 0

Housing data visualization and analysis 住房数据可视化和分析

Applied and Computational Engineering Pub Date : 2024-07-25 DOI: 10.54254/2755-2721/69/20241518

Yuxuan Tong

引用次数: 0

Credit risk unveiled: Decision trees triumph in comparative machine learning study 信用风险揭幕：决策树在机器学习比较研究中大获全胜

Applied and Computational Engineering Pub Date : 2024-07-25 DOI: 10.54254/2755-2721/79/20241613

Chenxi Wu

引用次数: 0

Maze and navigation algorithms in game development 游戏开发中的迷宫和导航算法

Applied and Computational Engineering Pub Date : 2024-07-25 DOI: 10.54254/2755-2721/79/20241081

Jiachen Piao, Xinyuan Hu, Qixuan Zhou

引用次数: 0