Applied and Computational Engineering最新文献_第7页

A review of path planning algorithms in automobile autonomous driving 汽车自动驾驶中的路径规划算法综述

Applied and Computational Engineering Pub Date : 2024-07-25 DOI: 10.54254/2755-2721/79/20241661

Shunqi Qin

引用次数: 0

The application progress of Convolutional Neural Networks (CNN) in lung nodule diagnosis 卷积神经网络（CNN）在肺结节诊断中的应用进展

Applied and Computational Engineering Pub Date : 2024-07-25 DOI: 10.54254/2755-2721/79/20241576

Jingxuan Wu, Jiahao Yang, Guanlin Peng

引用次数: 0

Performance analysis of k-Nearest Neighbors classification on Reuters news article datasets 路透社新闻文章数据集的 k 近邻分类性能分析

Applied and Computational Engineering Pub Date : 2024-07-25 DOI: 10.54254/2755-2721/55/20241444

Qian Yang

{"title":"Performance analysis of k-Nearest Neighbors classification on Reuters news article datasets","authors":"Qian Yang","doi":"10.54254/2755-2721/55/20241444","DOIUrl":"https://doi.org/10.54254/2755-2721/55/20241444","url":null,"abstract":"The k-Nearest Neighbors (k-NN) algorithm is a fundamental and widely-used classification technique that has found applications in various domains, including text classification. In this paper, we present a comprehensive analysis of the k-NN classification algorithm applied to the Reuters news article dataset. Our study includes the data, implementation k-NN classification with different parameters, performance evaluation, and statistical analysis to draw meaningful conclusions. In a comprehensive analysis of the k-NN classification algorithm used for the Reuters news article data-set. A variety of metrics is used to evaluate the performance of the k-NN algorithm, such as accuracy, precision, recall, and F1 scores. These metrics provide a comprehensive view of how well the algorithm classifies news articles. Our statistical analysis reveals significant performance differences between various k-NN configurations. This can help researchers and practitioners make informed decisions when choosing the best parameters for their specific text classification tasks. In conclusion, our study provides valuable insights into the application of k-NN classification algorithms to textual data, highlighting the importance of parameter tuning and rigorous evaluation. These findings can guide practitioners to effectively use k-NN for text classification tasks and inspire further research in the field.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141803184","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

Music genre classification: Machine Learning on GTZAN 音乐流派分类：基于 GTZAN 的机器学习

Applied and Computational Engineering Pub Date : 2024-07-25 DOI: 10.54254/2755-2721/79/20241639

Ziyan Zhao, Zixiao Xie, Jiaze Fu, Xintao Tian

{"title":"Music genre classification: Machine Learning on GTZAN","authors":"Ziyan Zhao, Zixiao Xie, Jiaze Fu, Xintao Tian","doi":"10.54254/2755-2721/79/20241639","DOIUrl":"https://doi.org/10.54254/2755-2721/79/20241639","url":null,"abstract":"This paper explores music genre classification, aiming to enhance existing methodologies. As a crucial aspect of music information retrieval, genre classification facilitates organization and recommendation in music databases and streaming services. Our research, inspired by a Kaggle project, examines the background of music genre classification and introduces improvements. The study focuses on data preparation techniques and a novel methodology using Support Vector Machines (SVM). Utilizing the GTZAN dataset, we applied data segmentation and feature extraction, employing machine learning algorithms like Logistic Regression, Random Forest, and SVM. A significant innovation is our segmentation technique based on music's beats per minute (BPM), designed to preserve rhythmic structure, believed to be essential for accurate classification. We explored various feature extraction methods to boost classifier performance. Experimental results showed the 3-second segmented dataset performed better with SVM's linear kernel. Additionally, a 4-beat segmentation experiment suggested that finer segmentation captures richer audio features, potentially improving classification accuracy. The paper concludes with findings and future research directions, including dataset expansion, advanced segmentation based on musical theory, deep learning applications, and developing real-time classification systems.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"24 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141803322","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

Analysis of classification algorithms: Insights from MNIST and WDBC datasets 分类算法分析：来自 MNIST 和 WDBC 数据集的启示

Applied and Computational Engineering Pub Date : 2024-07-25 DOI: 10.54254/2755-2721/79/20241622

Jiyue Zhao, Tony Yuxiang Pan, Weibo Yao, Hongwei Lu, Zihan Liu

引用次数: 0

A review of the development and application of RF technology and its sub-technologies 射频技术及其子技术的发展和应用回顾

Applied and Computational Engineering Pub Date : 2024-07-25 DOI: 10.54254/2755-2721/79/20241055

Bojun Wang, Jingyi Li, Tianqi Wu, Changjia Qu

{"title":"A review of the development and application of RF technology and its sub-technologies","authors":"Bojun Wang, Jingyi Li, Tianqi Wu, Changjia Qu","doi":"10.54254/2755-2721/79/20241055","DOIUrl":"https://doi.org/10.54254/2755-2721/79/20241055","url":null,"abstract":"With the acceleration of industry reform, new communication technologies are required to have larger bandwidth, faster transmission speed and more comprehensive applications. As a mature and effective high-frequency technology, RF band technology can meet many communication requirements in practical applications and plays an irreplaceable role in current production and life. At the same time, RF technology is also an important part of modern communication system, which has a good development prospect and has been widely used in many fields. In order to let more people understand the importance of radio frequency technology and promote the further development of radio frequency technology, this paper will introduce the important applications of radio frequency technology in practice from four aspects: the application of radio frequency in satellite, radio frequency identification, radio frequency ablation technology and radio frequency integrated circuit, and treat the application of radio frequency technology in practice from different angles, and illustrate the significance and development prospects of radio frequency technology.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"47 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141805691","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

Comparison of decision tree and ensemble algorithms 决策树与集合算法的比较

Applied and Computational Engineering Pub Date : 2024-07-25 DOI: 10.54254/2755-2721/55/20241535

Yihang Chen, Shuoyu Chen, Yicheng Yang, Siming Lu

{"title":"Comparison of decision tree and ensemble algorithms","authors":"Yihang Chen, Shuoyu Chen, Yicheng Yang, Siming Lu","doi":"10.54254/2755-2721/55/20241535","DOIUrl":"https://doi.org/10.54254/2755-2721/55/20241535","url":null,"abstract":"This paper presents an in-depth exploration of the Adaboost algorithm in the context of machine learning, focusing on its application in classification tasks. Adaboost, known for its adaptive boosting approach, is examined for its ability to enhance weak learners, particularly decision tree classifiers. The study delves into the theoretical underpinnings of Adaboost, emphasizing its iterative process for minimizing the exponential loss function. The role of decision trees, as integral components of this algorithm, is analyzed in detail. These trees, with their hierarchical query structure, are pivotal in categorizing items based on relevant features. The paper further compares Adaboost with random forests, another prominent machine learning algorithm, highlighting the nuances in their methodologies and applications. Significantly, the research introduces improved methods for selecting and fine-tuning these algorithms to optimize performance in various data classification scenarios. Practical applications of Adaboost and decision trees in real-world data classification tasks are demonstrated, providing insights into their operational effectiveness. This study not only elucidates the strengths of these machine learning techniques but also offers a comparative analysis, guiding practitioners in choosing the most suitable algorithm for specific classification challenges. The findings contribute to the broader understanding of machine learning algorithms, particularly in the context of data classification, and propose innovative approaches for enhancing algorithmic efficiency and accuracy. This research serves as a valuable resource for both academic and practical applications in the field of machine learning.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"7 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141804224","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

Optimizing hospital outpatient services: A comparative study of backward induction and Q-learning techniques 优化医院门诊服务：逆向归纳和 Q-learning 技术的比较研究

Applied and Computational Engineering Pub Date : 2024-07-25 DOI: 10.54254/2755-2721/79/20241669

Shilin Zhang

{"title":"Optimizing hospital outpatient services: A comparative study of backward induction and Q-learning techniques","authors":"Shilin Zhang","doi":"10.54254/2755-2721/79/20241669","DOIUrl":"https://doi.org/10.54254/2755-2721/79/20241669","url":null,"abstract":"This study addresses the critical issue of optimizing outpatient services in high-capacity hospitals, focusing on developing cost-effective management strategies. Utilizing a simulated model of outpatient services, this research incorporates real data from the National Health Service (NHS) to tackle practical challenges in hospital management. The methodology encompasses the application of backward induction, Q-learning, and Deep Q-Network (DQN) algorithms to formulate solutions. The findings indicate that backward induction effectively resolves simpler scenarios within the assumed conditions. In contrast, Q-learning offers a viable approach, with DQN demonstrating superior performance in addressing more complex, realistic problems. The conclusion drawn from this study is that each algorithm exhibits unique strengths in its respective operational environment. While direct comparison between the models based on output analysis is not feasible due to the variation in environmental settings, it is evident that all three algorithms significantly contribute to resolving the targeted issues in outpatient service management. This research not only provides valuable insights into hospital outpatient service optimization but also opens avenues for further exploration in the application of advanced computational techniques in healthcare management.","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"36 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141805141","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

Label noise learning with the combination of CausalNL and CGAN models 结合 CausalNL 和 CGAN 模型进行标签噪声学习

Applied and Computational Engineering Pub Date : 2024-07-25 DOI: 10.54254/2755-2721/79/20241399

Zixing Gou, Yifan Sun, Zhebin Jin, Hanqiu Hu, Weiyi Xia

引用次数: 0

Design and optimization of multidimensional data models for enhanced OLAP query performance and data analysis 设计和优化多维数据模型，提高 OLAP 查询性能和数据分析能力

Applied and Computational Engineering Pub Date : 2024-07-25 DOI: 10.54254/2755-2721/69/20241503

Xu Li, Qi Shen, Tiancheng Yang

{"title":"Design and optimization of multidimensional data models for enhanced OLAP query performance and data analysis","authors":"Xu Li, Qi Shen, Tiancheng Yang","doi":"10.54254/2755-2721/69/20241503","DOIUrl":"https://doi.org/10.54254/2755-2721/69/20241503","url":null,"abstract":"This paper explores the design and optimization of multidimensional data models to enhance the query performance and data analysis capabilities of OLAP (Online Analytical Processing) systems. It delves into three prominent dimensional modeling techniques: Star Schema, Snowflake Schema, and Galaxy Schema, analyzing their impact on query complexity, data redundancy, storage requirements, and ease of maintenance. Additionally, it examines three aggregation strategiesPre-Aggregation, Dynamic Aggregation, and Hybrid Aggregationfocusing on their effectiveness in balancing query response time, storage efficiency, flexibility, and computational cost. The study further investigates performance optimization techniques, including query optimization, partitioning, and materialized views, providing case studies and experimental data to illustrate their benefits and challenges. The findings underscore the importance of tailored optimization strategies in OLAP systems to meet varying business needs and query patterns, highlighting the trade-offs between performance gains, storage requirements, and implementation complexity","PeriodicalId":502253,"journal":{"name":"Applied and Computational Engineering","volume":"45 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141805605","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