Zhour Rachidi, Khalid Chougdali, A. Kobbane, J. Ben-othman
Abstract. Today, intrusion detection has become an active research area. Due to the rapidly increasing number of intrusion variants, intrusion detection system analyses and notifies the activities of users as normal (or) anomaly. In our paper, we built a model of intrusion detection system applied to the NSL-KDD data set using different supervised classifiers such as KNN and Naïve Bayes. We also proposed two algorithms for multi-classification based on the Random Forest (RF) which is an ensemble classifier and KNN. Then we used the K-folds method to evaluate and validate our model. To evaluate the performances, we realized experiments on NSL-KDD data set. The result shows that the second proposed algorithm is efficient with high accuracy and time optimization.
{"title":"Network intrusion detection using Machine Learning approach","authors":"Zhour Rachidi, Khalid Chougdali, A. Kobbane, J. Ben-othman","doi":"10.1145/3551690.3551693","DOIUrl":"https://doi.org/10.1145/3551690.3551693","url":null,"abstract":"Abstract. Today, intrusion detection has become an active research area. Due to the rapidly increasing number of intrusion variants, intrusion detection system analyses and notifies the activities of users as normal (or) anomaly. In our paper, we built a model of intrusion detection system applied to the NSL-KDD data set using different supervised classifiers such as KNN and Naïve Bayes. We also proposed two algorithms for multi-classification based on the Random Forest (RF) which is an ensemble classifier and KNN. Then we used the K-folds method to evaluate and validate our model. To evaluate the performances, we realized experiments on NSL-KDD data set. The result shows that the second proposed algorithm is efficient with high accuracy and time optimization.","PeriodicalId":112679,"journal":{"name":"Proceedings of the 12th International Conference on Information Communication and Management","volume":"1 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":"129850225","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}
In the 21st century, the construction and development of naval academies in developing countries are facing risks and challenges. How to scientifically determine the development strategy and realize the rapid development of colleges is a problem that every college administrator must seriously think about. Based on SWOT analysis, this paper discusses the feasibility of applying SWOT-CLPV model to study the development strategy of naval academies in developing countries. The research shows that this method is conducive for administrators to understand the internal and external factors affecting the development of academies and the cross influence between them from a deeper level. It is a strategy research method suitable for the development strategy research of naval academies in developing countries. It is of great value for college administrators to scientifically formulate college development strategy and strengthen strategic management.
{"title":"Research on Developing Strategy Feasibility of Developing Country Naval Colleges Using SWOT-CLPV Methods","authors":"L. Qi, Xu-Wen Sun, Sijin Ma","doi":"10.1145/3551690.3551697","DOIUrl":"https://doi.org/10.1145/3551690.3551697","url":null,"abstract":"In the 21st century, the construction and development of naval academies in developing countries are facing risks and challenges. How to scientifically determine the development strategy and realize the rapid development of colleges is a problem that every college administrator must seriously think about. Based on SWOT analysis, this paper discusses the feasibility of applying SWOT-CLPV model to study the development strategy of naval academies in developing countries. The research shows that this method is conducive for administrators to understand the internal and external factors affecting the development of academies and the cross influence between them from a deeper level. It is a strategy research method suitable for the development strategy research of naval academies in developing countries. It is of great value for college administrators to scientifically formulate college development strategy and strengthen strategic management.","PeriodicalId":112679,"journal":{"name":"Proceedings of the 12th International Conference on Information Communication and Management","volume":"31 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":"128292091","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}
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.
{"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}
Physical bullying has been an evident issue in the Philippines and Global Context. Thus, it was widely recognized as a major threat in the younger generation in almost every country in the world. It includes wide variety of acts and contexts. As worst reality, physical violence happens mostly in schools. As a result, it affects a person not only physically but also mentally that even leads to death. Nowadays, numerous research in action recognition has been done through Machine Learning to provide an assistance in predicting and recognizing actions using cameras. In this paper, the researcher utilized machine learning algorithms such as Convolutional Neural Networks to train and recognize actions of physical bullying such as Kicking, Punching, and Head Hitting. The findings revealed a positive result in detecting such actions using Convolutional Neural Networks. With such, it enables the prevention of further physical bullying occurrences in the future using CCTV Cameras.
{"title":"Student Physical Violence Detection using Convolutional Neural Networks","authors":"John Clement Suladay Escobanez, B. Comendador","doi":"10.1145/3551690.3551696","DOIUrl":"https://doi.org/10.1145/3551690.3551696","url":null,"abstract":"Physical bullying has been an evident issue in the Philippines and Global Context. Thus, it was widely recognized as a major threat in the younger generation in almost every country in the world. It includes wide variety of acts and contexts. As worst reality, physical violence happens mostly in schools. As a result, it affects a person not only physically but also mentally that even leads to death. Nowadays, numerous research in action recognition has been done through Machine Learning to provide an assistance in predicting and recognizing actions using cameras. In this paper, the researcher utilized machine learning algorithms such as Convolutional Neural Networks to train and recognize actions of physical bullying such as Kicking, Punching, and Head Hitting. The findings revealed a positive result in detecting such actions using Convolutional Neural Networks. With such, it enables the prevention of further physical bullying occurrences in the future using CCTV Cameras.","PeriodicalId":112679,"journal":{"name":"Proceedings of the 12th International Conference on Information Communication and Management","volume":"198 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":"116349566","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}
The paper uses China's A-share listed companies from 2011 to 2020 as a research sample to explore the relationship between strategic radicality and enterprise risk. It is found that strategic radicality is significantly positively correlated with enterprise risk. The more radical the strategy is, the greater the enterprise risk is. Tax avoidance has a negative regulated effect on strategic radicality and enterprise risk due to its effect of alleviating financing constraints. The research results of this paper have strong enlightenment for government regulators and enterprise management.
{"title":"Tax avoidance, strategic activism and enterprise risk","authors":"J. Fu, Yan Yuan","doi":"10.1145/3551690.3551707","DOIUrl":"https://doi.org/10.1145/3551690.3551707","url":null,"abstract":"The paper uses China's A-share listed companies from 2011 to 2020 as a research sample to explore the relationship between strategic radicality and enterprise risk. It is found that strategic radicality is significantly positively correlated with enterprise risk. The more radical the strategy is, the greater the enterprise risk is. Tax avoidance has a negative regulated effect on strategic radicality and enterprise risk due to its effect of alleviating financing constraints. The research results of this paper have strong enlightenment for government regulators and enterprise management.","PeriodicalId":112679,"journal":{"name":"Proceedings of the 12th International Conference on Information Communication and Management","volume":"36 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":"124731341","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}
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.
{"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}
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