Journal of Theory and Practice of Engineering Science最新文献

Enhanced Heart Attack Prediction Using eXtreme Gradient Boosting 利用梯度提升技术增强心脏病发作预测能力

Journal of Theory and Practice of Engineering Science Pub Date : 2024-04-25 DOI: 10.53469/jtpes.2024.04(04).02

Mingyang Feng, Xiaosong Wang, Zhiming Zhao, Chufeng Jiang, Jize Xiong, Ning Zhang

{"title":"Enhanced Heart Attack Prediction Using eXtreme Gradient Boosting","authors":"Mingyang Feng, Xiaosong Wang, Zhiming Zhao, Chufeng Jiang, Jize Xiong, Ning Zhang","doi":"10.53469/jtpes.2024.04(04).02","DOIUrl":"https://doi.org/10.53469/jtpes.2024.04(04).02","url":null,"abstract":"Heart attack prediction is a vital component of cardiovascular healthcare, aiming to identify individuals at risk for timely intervention and improved patient outcomes. Despite significant advancements in predictive modeling techniques, several challenges persist, including algorithmic limitations, interpretability issues, data dependence, and scalability concerns. These challenges underscore the need for robust, interpretable, and generalizable predictive models capable of handling the complexities of medical data effectively. In this study, we propose a novel approach leveraging the eXtreme Gradient Boosting (XGBoost) algorithm for heart attack analysis and prediction. We conducted a comprehensive analysis of heart disease datasets, employing rigorous data preprocessing, feature selection, and hyperparameter optimization techniques to develop a highly accurate and interpretable predictive model. Our results demonstrate the efficacy of the XGBoost algorithm in capturing intricate patterns from medical data, achieving superior predictive performance across various metrics. The proposed model addresses the existing challenges in heart attack prediction, offering a promising solution for enhancing cardiovascular healthcare outcomes.","PeriodicalId":489516,"journal":{"name":"Journal of Theory and Practice of Engineering Science","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140654681","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

Review of Research on Nuclear Signal Pulse Shaping 核信号脉冲整形研究综述

Journal of Theory and Practice of Engineering Science Pub Date : 2024-04-25 DOI: 10.53469/jtpes.2024.04(04).03

Zhihe Tang, Zewei Liu

引用次数: 0

A Review on Mechanical Automation Control System 机械自动化控制系统综述

Journal of Theory and Practice of Engineering Science Pub Date : 2024-04-25 DOI: 10.53469/jtpes.2024.04(04).06

Hongjie Ji, Hongbo Ji

引用次数: 0

Feasibility Study of UHPC Reinforced Masonry Structure 超高性能混凝土加固砌体结构可行性研究

Journal of Theory and Practice of Engineering Science Pub Date : 2024-04-25 DOI: 10.53469/jtpes.2024.04(04).04

Yang Chen, Shaojie Wang

引用次数: 0

Analysis on Machining Precision Control of Mechanical Die 机械模具加工精度控制分析

Journal of Theory and Practice of Engineering Science Pub Date : 2024-04-25 DOI: 10.53469/jtpes.2024.04(04).05

Bo Fu

引用次数: 0

Deep Reinforcement Learning for Mobile Robot Path Planning 移动机器人路径规划的深度强化学习

Journal of Theory and Practice of Engineering Science Pub Date : 2024-04-10 DOI: 10.53469/jtpes.2024.04(04).07

Hao Liu, Yi Shen, Shuangjiang Yu, Zijun Gao, Tong Wu

引用次数: 2

Research on the Application of Artificial Intelligence and Big Data Technology in Financial Fraud Detection 人工智能和大数据技术在金融欺诈检测中的应用研究

Journal of Theory and Practice of Engineering Science Pub Date : 2024-04-02 DOI: 10.53469/jtpes.2024.04(03).21

Ziyue Wang

引用次数: 0

Advancing Legal Citation Text Classification A Conv1D-Based Approach for Multi-Class Classification 推进法律引文文本分类基于 Conv1D 的多类分类方法

Journal of Theory and Practice of Engineering Science Pub Date : 2024-02-28 DOI: 10.53469/jtpes.2024.04(02).03

Ying Xie, Zhengning Li, Yibo Yin, Zibu Wei, Guokun Xu, Yang Luo

引用次数: 4

Utilizing AI-Enhanced Multi-Omics Integration for Predictive Modeling of Disease Susceptibility in Functional Phenotypes 利用人工智能增强型多指标集成，对功能表型中的疾病易感性进行预测建模

Journal of Theory and Practice of Engineering Science Pub Date : 2024-02-28 DOI: 10.53469/jtpes.2024.04(02).07

Yanlin Zhou, Xinyu She, Zheng He, Huiying Weng, Wangmei Chen

{"title":"Utilizing AI-Enhanced Multi-Omics Integration for Predictive Modeling of Disease Susceptibility in Functional Phenotypes","authors":"Yanlin Zhou, Xinyu She, Zheng He, Huiying Weng, Wangmei Chen","doi":"10.53469/jtpes.2024.04(02).07","DOIUrl":"https://doi.org/10.53469/jtpes.2024.04(02).07","url":null,"abstract":"With the continuous development of machine learning technology, the scientific research of biomedical materials is gradually shifting to a data-driven direction. The rise of this trend stems from the widespread use of Bio sequencing technology, which provides entirely new methods and insights for testing and evaluating the biological function of biomedical materials. The performance and performance of biomedical materials have a wide range of applications in medical applications, drug delivery, biosensors and other fields, so it is important to further optimize them. However, with the accumulation and increasing complexity of data, there is a need for more intelligent and efficient ways to process and analyze this heterogeneous scientific data. Therefore, the establishment of an open, shared infrastructure for storing heterogeneous scientific data from different research fields will be the cornerstone of cross-disciplinary joint analysis. This infrastructure will not only accelerate the collection and integration of data, but will also provide opportunities for collaboration and innovation across disciplines. This paper highlights a new trend in biomedical materials research, namely a data-driven approach, and the key role of Bio sequencing technology in this process. At the same time, we call for the establishment of an open data storage and sharing platform to promote multidisciplinary cooperation, accelerate the optimization and innovation of biomedical materials, and open up broader prospects for future biomedical applications. This effort is expected to push scientific research in the medical field to new heights, providing safer and more effective treatments and medical programs for patients.","PeriodicalId":489516,"journal":{"name":"Journal of Theory and Practice of Engineering Science","volume":"75 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140421209","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: 7

Deep Learning-Based Medical Image Registration Algorithm: Enhancing Accuracy with Dense Connections and Channel Attention Mechanisms 基于深度学习的医学图像配准算法：利用密集连接和通道注意机制提高准确性

Journal of Theory and Practice of Engineering Science Pub Date : 2024-02-28 DOI: 10.53469/jtpes.2024.04(02).01

Yulu Gong, Houze Liu, Lianwei Li, Jingxiao Tian, Hanzhe Li

{"title":"Deep Learning-Based Medical Image Registration Algorithm: Enhancing Accuracy with Dense Connections and Channel Attention Mechanisms","authors":"Yulu Gong, Houze Liu, Lianwei Li, Jingxiao Tian, Hanzhe Li","doi":"10.53469/jtpes.2024.04(02).01","DOIUrl":"https://doi.org/10.53469/jtpes.2024.04(02).01","url":null,"abstract":"In critical clinical medical image analysis applications, such as surgical navigation and tumor monitoring, image registration is crucial. Recognizing the potential for enhanced accuracy in existing unsupervised image registration techniques for single-modal imagery, this research introduces an innovative deep learning-based image registration algorithm. Its novelty resides in integrating short and long connections to create a densely connected structure, markedly refining the feature map interconnectivity within the U-Net architecture. This advancement addresses the significant semantic gap issues arising from disparities in feature map sampling depths. Moreover, the algorithm incorporates a channel attention mechanism within the U-shaped network's decoder, significantly mitigating image noise and facilitating the generation of smoother deformation fields. This enhancement not only boosts the model's detail sensitivity but also markedly increases image registration precision, particularly evident when processing single-modal brain MRI datasets, thereby proving the algorithm's efficacy and utility. Extensive clinical application-based training and testing have underscored this algorithm's substantial contributions to medical image registration accuracy enhancement. Overall, by leveraging deep learning technologies and innovative algorithmic structures, this study addresses pivotal challenges in medical image registration, offering more precise and dependable support for clinical applications like surgical navigation and tumor surveillance.","PeriodicalId":489516,"journal":{"name":"Journal of Theory and Practice of Engineering Science","volume":"578 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140417111","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