Proceedings of the 12th International Conference on Information Communication and Management最新文献_第2页

Network intrusion detection using Machine Learning approach 利用机器学习方法进行网络入侵检测

Proceedings of the 12th International Conference on Information Communication and Management Pub Date : 2022-07-13 DOI: 10.1145/3551690.3551693

Zhour Rachidi, Khalid Chougdali, A. Kobbane, J. Ben-othman

引用次数: 0

Research on Developing Strategy Feasibility of Developing Country Naval Colleges Using SWOT-CLPV Methods 基于SWOT-CLPV方法的发展中国家海军院校发展战略可行性研究

Proceedings of the 12th International Conference on Information Communication and Management Pub Date : 2022-07-13 DOI: 10.1145/3551690.3551697

L. Qi, Xu-Wen Sun, Sijin Ma

引用次数: 0

Deep Learning-Based CAD System for COVID-19 Diagnosis via Spectral-Temporal Images 基于深度学习的COVID-19频谱-时间图像诊断CAD系统

Proceedings of the 12th International Conference on Information Communication and Management Pub Date : 2022-07-13 DOI: 10.1145/3551690.3551695

Omneya Attallah

{"title":"Deep Learning-Based CAD System for COVID-19 Diagnosis via Spectral-Temporal Images","authors":"Omneya Attallah","doi":"10.1145/3551690.3551695","DOIUrl":"https://doi.org/10.1145/3551690.3551695","url":null,"abstract":"The diagnosis of COVID-19 and understanding the condition of the patients who have critical responses is crucial to stop the rapid propagation of such disease. Consequently, diminishing adverse impacts that affected various industrial divisions, especially healthcare. Deep learning methods have proven their great capabilities in studying and analyzing computed tomography (CT) images containing COVID-19. Most related studies utilized the spatial information of CT images to train deep learning models. Nevertheless, training these models with spatial-temporal images could enhance diagnostic accuracy. This paper proposes a computer-assisted diagnostic (CAD) system for COVID-19 diagnosis using three deep learning models trained with spectral-temporal images. First, it uses the multilevel discrete wavelet transform (DWT) to analyze the original CT images and obtain the spectral-temporal images. Then, it uses these images from different DWT levels to train three ResNets deep learning models. Afterward, for each ResNet trained with images of each DWT level, it extracts deep features. Next, for each ResNet, it fuses these deep features and then uses a feature selection approach to reduce their dimension. Finally, support vector machine (SVM) classifiers are used to perform classification. The performance of the proposed CAD proves that training ResNets with spectral-temporal images is better than using CT images. Also, the fusion and feature selection steps have enhanced the diagnostic accuracy, thus the proposed CAD could be employed to help radiologists in COVID-19 inspection.","PeriodicalId":112679,"journal":{"name":"Proceedings of the 12th International Conference on Information Communication and Management","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123673121","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: 11

Student Physical Violence Detection using Convolutional Neural Networks 使用卷积神经网络检测学生身体暴力

Proceedings of the 12th International Conference on Information Communication and Management Pub Date : 2022-07-13 DOI: 10.1145/3551690.3551696

John Clement Suladay Escobanez, B. Comendador

引用次数: 2

Tax avoidance, strategic activism and enterprise risk 避税、战略行动主义与企业风险

Proceedings of the 12th International Conference on Information Communication and Management Pub Date : 2022-07-13 DOI: 10.1145/3551690.3551707

J. Fu, Yan Yuan

引用次数: 0

Research on the Allocation of the Highly Educated Labour Force in China Focusing on the Internet Industry 基于互联网产业的中国高学历劳动力配置研究

Proceedings of the 12th International Conference on Information Communication and Management Pub Date : 2022-07-13 DOI: 10.1145/3551690.3551703

Y. Zheng

{"title":"Research on the Allocation of the Highly Educated Labour Force in China Focusing on the Internet Industry","authors":"Y. Zheng","doi":"10.1145/3551690.3551703","DOIUrl":"https://doi.org/10.1145/3551690.3551703","url":null,"abstract":"This paper is a report of an in-depth analysis of the difficulties related to the use of highly educated labour resources in China's Internet industry and methods to improve the situation. Under the macro-background of the impact of COVID-19 and the turbulence of the economic market, the negative impacts on the job market of the rapid growth of China's population and the expansion of college enrolment have become increasingly serious. As a result, the phenomenon of low graduate employment rate and increase in graduate applicants has become the norm. In this paper, a desk research method is adopted to collect and sort out secondary data from authoritative research institutions and academic research results for analysis. The results of this study demonstrate the following. First, Internet enterprises are bound to major cities, causing life pressure for workers and forming ‘urban barriers’ for the whole industry. Second, COVID-19 intensifies the existing macro-risks, resulting in more serious intra-industry conflicts. Third, the rise in the number of graduates has led to a devaluation of higher education qualifications in the recruitment market and exacerbated the trend of qualification competition. Fourth, due to the imbalance between the current job demand and talent in Internet enterprises, it is difficult to guarantee the appropriate treatment of workers. For these reasons, this study suggests that students should improve their personal ability in a more targeted manner. At the same time, enterprises should take a longer-term view, use information technology to break the existing industry barriers and optimise their management efficiency.","PeriodicalId":112679,"journal":{"name":"Proceedings of the 12th International Conference on Information Communication and Management","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121608021","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