2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)最新文献

Development and Validation of an Explainable Deep Learning Model to Predict Adverse Event During Hospital Admission in Patients with Sepsis 开发和验证可解释的深度学习模型来预测败血症患者住院期间的不良事件

2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2022-12-07 DOI: 10.1109/SNPD54884.2022.10051794

I-Min Chiu, Yu-Ping Chuang, Chi-Yung Cheng, C. Lin

{"title":"Development and Validation of an Explainable Deep Learning Model to Predict Adverse Event During Hospital Admission in Patients with Sepsis","authors":"I-Min Chiu, Yu-Ping Chuang, Chi-Yung Cheng, C. Lin","doi":"10.1109/SNPD54884.2022.10051794","DOIUrl":"https://doi.org/10.1109/SNPD54884.2022.10051794","url":null,"abstract":"Sepsis is among the most common conditions requiring emergency hospitalization. The early and accurate identification of sepsis patients with a high risk of in-hospital adverse events can aid physicians in making optimal clinical decisions. This study aimed to develop an explainable neural network model to predict adverse events during hospital admission in patients with suspected sepsis. Patient data were collected from a single medical center in Taiwan for the period of 2018–2020. The adverse events considered during hospital admission were cardiac arrest, respiratory failure requiring mechanical ventilation, and transfer to intensive care unit during admission. This study included 9398 patients in the analysis, with 6794 and 2603 patients in the development and validation sets, respectively. The proposed model could predict adverse events with an area under the receiver operating curve of 0.88 and 0.85 in the development and validation sets, respectively. Of the 2603 patients in the test set, 523 (20.1%) were classified as having adverse events during hospital admission. Of these patients, 104 eventually experienced adverse events. Thus, the model can predict adverse events with good performance and therefore, can be regarded as a gatekeeper before patients with sepsis are admitted to the general ward.","PeriodicalId":425462,"journal":{"name":"2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122338610","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

Stepwise Regression Machine Learning Models for In-Hospital Mortality Prediction in Patients After ST-Segment Slevation Myocardial Infarction (STEMI) 逐步回归机器学习模型在st段袖断性心肌梗死(STEMI)患者住院死亡率预测中的应用

2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2022-12-07 DOI: 10.1109/SNPD54884.2022.10051815

Chi-Yung Cheng, I-Min Chiu, C. Lin, Xin-Hong Lin, Fu-Cheng Chen, Ting-Yu Hsu

{"title":"Stepwise Regression Machine Learning Models for In-Hospital Mortality Prediction in Patients After ST-Segment Slevation Myocardial Infarction (STEMI)","authors":"Chi-Yung Cheng, I-Min Chiu, C. Lin, Xin-Hong Lin, Fu-Cheng Chen, Ting-Yu Hsu","doi":"10.1109/SNPD54884.2022.10051815","DOIUrl":"https://doi.org/10.1109/SNPD54884.2022.10051815","url":null,"abstract":"Acute myocardial infarction is a leading cause of cardiogenic shock and mortality. The aim of current study is to identify factors and develop machine learning models that predicts in-hospital mortality of ST-segment elevation myocardial infarction (STEMI) patients in South-East Asian population. This is a single center, retrospective study, from patients presenedt to the emergency room at Kaohsiung Chang Gung Memorial Hospital, Taiwan. The study included non-trauma adults (≥20 years of age) who were diagnosed with acute STEMI. A total of 1567 patients who met the inclusion criteria were enrolled. The area under the receiver operating characteristic curve was 0.839 in logistic regression (LR) and 0.825 in random forest (RF). The accuracy was 0.821 in LR and 0.812 in RF. The sensitivity and specificity were 0.883 and 0.815 in LR, and 0.875 and 0.806 in RF. In conclusion, the predictive models developed using LR and RF algorithms can be used to predict the risk of in-hospital death for STEMI patients.","PeriodicalId":425462,"journal":{"name":"2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114628831","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

Detecting Design Patterns in UML Class Diagram Images using Deep Learning 使用深度学习检测UML类图图像中的设计模式

2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2022-12-07 DOI: 10.1109/SNPD54884.2022.10051795

Xun Zhang, H. Washizaki, Nobukazu Yoshioka, Y. Fukazawa

{"title":"Detecting Design Patterns in UML Class Diagram Images using Deep Learning","authors":"Xun Zhang, H. Washizaki, Nobukazu Yoshioka, Y. Fukazawa","doi":"10.1109/SNPD54884.2022.10051795","DOIUrl":"https://doi.org/10.1109/SNPD54884.2022.10051795","url":null,"abstract":"Detecting software design pattern is an important part of software reverse engineering because design patterns can provide the most intuitive design idea of software products, which can be useful for maintenance engineers. In past studies, a lot of approaches have been proposed to detect design patterns, and the machine learning-based approach is a new trend in recent years. In this paper, we propose a preliminary idea of a deep learning-based approach to detect design patterns from UML class diagrams of software products, which can be used in some cases that traditional approaches may not work. We propose an overall process, which is divided into preparation phase and application phase. In preparation phase, we train a deep learning-based classifier to do the image classification task. In application phase, users may input the UML class diagram of a micro-architecture into the model and get the pattern it belongs to. We conduct a preliminary experiment to show the effectiveness of our approach, we train a Convolutional Neural Network (CNN) as the classifier and test it on our image dataset, which is constructed with UML images we collected from the Internet. We also use Gradient-weighted Class Activation Mapping (Grad-CAM) to do the visualization and use it to explain why our approach works. Lastly, we analyze the potential advantages and disadvantages of our approach.","PeriodicalId":425462,"journal":{"name":"2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132156760","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

Towards Cooperative Games for Developing Secure Software in Agile SDLC 基于敏捷SDLC的安全软件开发中的合作游戏

2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2022-12-07 DOI: 10.1109/SNPD54884.2022.10051798

M. Vaidhyanathan, Weisheng Si, Bahman Javadi, S. Çamtepe

{"title":"Towards Cooperative Games for Developing Secure Software in Agile SDLC","authors":"M. Vaidhyanathan, Weisheng Si, Bahman Javadi, S. Çamtepe","doi":"10.1109/SNPD54884.2022.10051798","DOIUrl":"https://doi.org/10.1109/SNPD54884.2022.10051798","url":null,"abstract":"This work applies Game Theory to developing secure software. With the perspective of Game Theory, one can see secure software development as a game between software developers and software security engineers, who play this game repeatedly in processes such as agile Software Development Life Cycle (SDLC). The problem we observe is that there can be conflicts between these two players regarding who should find and fix certain software vulnerabilities. To solve this problem, our approach uses Mechanism Design in Game Theory to design games that enforce cooperation between these two players. In doing so, we identify the source of the conflicts between them by looking at the components of the software. These components may be the methods or functions in the software, or individual modules, or similar building blocks. The novelty of our work is that our mechanism constructs a game which allocates software components between these two players such that they work cooperatively while trying to maximize their own payoffs.","PeriodicalId":425462,"journal":{"name":"2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128556430","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

Artificial Intelligence of Things Enabled Fungiculture in Shipping Container 物联网人工智能使集装箱中的真菌培养成为可能

2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2022-12-07 DOI: 10.1109/SNPD54884.2022.10051780

B. S. Wee, C. Chin, Anurag Sharma

引用次数: 0

Optimal Network Models for Reconstructing 3D Point Cloud from a Single 2D Image 从单张二维图像重建三维点云的最优网络模型

2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2022-12-07 DOI: 10.1109/SNPD54884.2022.10051796

Huang Chen, Chuen-Horng Lin, Yan-Yu Lin

引用次数: 0

Advanced Quantum Inspired Evolutionary Algorithm for Multivariate Optimization 多元优化的先进量子进化算法

2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2022-12-07 DOI: 10.1109/SNPD54884.2022.10051777

Sai Siddhartha Vivek Dhir Rangoju, O. Patel, Neha Bharill

引用次数: 0

The Study on Security Online Judge System Applied Sandbox Technology 应用沙盒技术的安全在线裁判系统研究

2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2022-12-07 DOI: 10.1109/SNPD54884.2022.10051768

J. Kuo, Zhi-Jia Wen, Han-Xuan Huang, I-Ting Guo

引用次数: 1

A Model of Integrating Bert and BiGRU+ Attention Dual-channel Mechanism for Investor Sentiment Analysis of Stock Price Forecast 基于Bert和BiGRU+注意力双通道机制的股票价格预测投资者情绪分析模型

2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2022-12-07 DOI: 10.1109/SNPD54884.2022.10051779

Huawei Ma, Jixin Ma, Shengbin Liang, W.-C. Du

引用次数: 2

Real-Time Traffic Sign Detection for Self-Driving and Energy-Saving Driving Based on YOLOv4 Neural Network 基于YOLOv4神经网络的自动驾驶与节能驾驶交通标志实时检测

2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2022-12-07 DOI: 10.1109/SNPD54884.2022.10051789

Chi-Chun Chen, Yuan-Hong Guan, Nabila Rizqia Novianda, Chung-Chen Teng, Meng-Hua Yen

引用次数: 0