2022 International Arab Conference on Information Technology (ACIT)最新文献_第10页

A Classifier to Detect Stream Applications Based on Network Traffic Statistical Metrics 基于网络流量统计度量的流应用检测分类器

2022 International Arab Conference on Information Technology (ACIT) Pub Date : 2022-11-22 DOI: 10.1109/ACIT57182.2022.9994184

Omar M. Darwish, Sarah Herzallah, Majdi Maabreh, Shorouq Al-Eidi, Mahmoud Al-Maani, Yahya M. Tashtoush

{"title":"A Classifier to Detect Stream Applications Based on Network Traffic Statistical Metrics","authors":"Omar M. Darwish, Sarah Herzallah, Majdi Maabreh, Shorouq Al-Eidi, Mahmoud Al-Maani, Yahya M. Tashtoush","doi":"10.1109/ACIT57182.2022.9994184","DOIUrl":"https://doi.org/10.1109/ACIT57182.2022.9994184","url":null,"abstract":"One of the most crucial considerations, when considering security vulnerabilities, is network traffic. There is still potential for more research on the inter-arrival time side, even though some studies concentrate on network traffic from the perspective of the packet fields such as packet length and packet number. Inter-arrival timings are crucial to investigate because there are numerous attacks, such as Covert Timing Channels attacks, that heavily rely on them. In this article, we conduct a statistical analysis of the TCP inter-arrival times of two major key streaming programs (Zoom and Skype), which are frequently used, particularly during and following the COVID-19 pandemic. Using two internet-connected devices and the statistical measures of TCP, a dataset of 18,371 instances is created for this use. Five machine learning algorithms are evaluated on balanced and imbalanced forms of the dataset. The results revealed that the traffic of Zoom and Skype calls can be identified by machine learning algorithms with an accuracy of up to 99% by random forest.","PeriodicalId":256713,"journal":{"name":"2022 International Arab Conference on Information Technology (ACIT)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131998475","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

Fuzzy Logic and Deep learning Techniques for Covid-19 Detection 新型冠状病毒检测的模糊逻辑和深度学习技术

2022 International Arab Conference on Information Technology (ACIT) Pub Date : 2022-11-22 DOI: 10.1109/ACIT57182.2022.9994125

Belkis Hassani, Mohamed Akram Khelili, O. Kazar, S. Slatnia, S. Harous, B. Athamena, Z. Houhamdi

引用次数: 0

COVID-19 Future Forecasting Using Machine Learning Models 使用机器学习模型进行COVID-19未来预测

2022 International Arab Conference on Information Technology (ACIT) Pub Date : 2022-11-22 DOI: 10.1109/ACIT57182.2022.9994129

H. Awada, Jamal Haydar, A. Mokdad, Ahmad Ghandour

{"title":"COVID-19 Future Forecasting Using Machine Learning Models","authors":"H. Awada, Jamal Haydar, A. Mokdad, Ahmad Ghandour","doi":"10.1109/ACIT57182.2022.9994129","DOIUrl":"https://doi.org/10.1109/ACIT57182.2022.9994129","url":null,"abstract":"Covid-19 is a very infectious virus. According to World Health Organization (WHO), millions of individuals have been diagnosed with Covid-19 since then, and at least a million have died as the virus has expanded dramatically. While most of the news on this front is scary, technology is helping to pave the path through this crisis. Manual forecasting is a difficult challenge for humans due to its large scale and complexity. Machine Learning (ML) techniques can effectively predict Covid-19 infected patients. There are a lot of study that have been developed to predict and forecast the future number of cases affected by Covid-19. In this area, our forecasting can be tackled as a problem of supervised learning. Supervised ML is very popular regression methods due to its simplicity to be interpreted by Humans. In this paper, we use two datasets to predict the symptoms through two different types of regression algorithms (single and multiple regression), the ML algorithms are LR, SVM, LASSO, ES and Polynomial regression, for the multiple regression we used LR, SVM and LASSO. The obtained results validate that for the single regression the Exponential Smoothing (ES) outperforms other machine learning approaches like Linear Regression (LR) and LASSO in terms of R-Square, Adjusted R-Square, Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). The same accuracy is observed for the models used in the multiple regression.","PeriodicalId":256713,"journal":{"name":"2022 International Arab Conference on Information Technology (ACIT)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133124717","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

Smart Assistive 3D Tool to Remedy Children's Learning Difficulties for Distance Education 智能辅助3D工具解决儿童远程教育学习困难

2022 International Arab Conference on Information Technology (ACIT) Pub Date : 2022-11-22 DOI: 10.1109/ACIT57182.2022.9994172

Dheya Ghazi Mustafa, Intisar Ghazi Mustafa, Samah Zriqat, Q. Althebyan

引用次数: 1

Plagiarism Detection in Arabic Documents using word2vector and Arabic WordNet 基于word2vector和阿拉伯语WordNet的阿拉伯语文档剽窃检测

2022 International Arab Conference on Information Technology (ACIT) Pub Date : 2022-11-22 DOI: 10.1109/ACIT57182.2022.9994090

Khaleda Omar, A. Hilal

引用次数: 0

Using Multinomial Naive Bayes Machine Learning Method To Classify, Detect, And Recognize Programming Language Source Code 使用多项朴素贝叶斯机器学习方法分类，检测和识别编程语言源代码

2022 International Arab Conference on Information Technology (ACIT) Pub Date : 2022-11-22 DOI: 10.1109/ACIT57182.2022.9994117

A. Odeh, Munther Odeh, Nada Odeh

{"title":"Using Multinomial Naive Bayes Machine Learning Method To Classify, Detect, And Recognize Programming Language Source Code","authors":"A. Odeh, Munther Odeh, Nada Odeh","doi":"10.1109/ACIT57182.2022.9994117","DOIUrl":"https://doi.org/10.1109/ACIT57182.2022.9994117","url":null,"abstract":"Processing programming languages are very similar to processing natural languages, especially high-level languages such as Python, Java, C#, C, C++, and others. Therefore, the natural language processing concepts can be applied as one of the most important branches of artificial intelligence in detecting, recognizing, and classification scripts written in different programming languages. The programming language script classification can be counted as a classical machine learning problem. This research aims to present a model using Multinomial Naïve Bayes (MNB) artificial intelligence algorithm to identify and classify the programming language used in writing the source code file provided as an input for the proposed model. A set of categorized files containing source codes will be used in training the proposed model, and then the model will be able to automatically detect and classify a new script into one of the already trained categories. The machine learning method called NB Multinomial will be used to implement this matter. This work is very important for Mufti-programming language editors such as Visual Studio Code, Notepad+, and others, where the user can paste the source code, and the editor will recognize the programming language automatically.","PeriodicalId":256713,"journal":{"name":"2022 International Arab Conference on Information Technology (ACIT)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123972760","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: 2

Detection of Covid-19 Virus using Supervised Machine Learning Algorithms 使用监督机器学习算法检测Covid-19病毒

2022 International Arab Conference on Information Technology (ACIT) Pub Date : 2022-11-22 DOI: 10.1109/ACIT57182.2022.9994087

Ahmad T. Al-Taani, Batool Al-Rababaah

{"title":"Detection of Covid-19 Virus using Supervised Machine Learning Algorithms","authors":"Ahmad T. Al-Taani, Batool Al-Rababaah","doi":"10.1109/ACIT57182.2022.9994087","DOIUrl":"https://doi.org/10.1109/ACIT57182.2022.9994087","url":null,"abstract":"Due to the continuous increase of Covid-19 infections as a global pandemic, it became necessary to detect it to avoid the damage caused by the spread of the infection. Artificial Intelligence (AI) techniques such as machine learning and deep learning have an important and effective role in the medical field applications like the classification of medical images and the detection of many diseases. In this article, we propose the use of several supervised machine learning classifiers for Covid-19 virus detection using chest x-ray (CXR) images. Five supervised classifiers are used: Support Vector Machines (SVM), Naive Bayes (NB), K-Nearest Neighbors (KNN), Logistic Regression (LR) and Artificial Neural Network (ANN). A standard dataset of 1824 CXR images are used for training and testing; 70% for training and 30% for testing. Four image embedders including Vgg16, Vgg19, SqueezeNet, and Inception-v3 are used in the experiments. Experiment results showed that most of these models achieved promising accuracy, precision, recall, and F1-scores. KNN, ANN, and LR classifiers have achieved highest classification accuracies using SqueezeNet image embedder.","PeriodicalId":256713,"journal":{"name":"2022 International Arab Conference on Information Technology (ACIT)","volume":"143 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120818083","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

Critical Analysis of Palliative Homecare Using the $mathrm{i}^{ast}$ Framework's Strategic and Social Requirements Modelling Applied to a Cancer Care Organisation 使用$math {i}^{ast}$框架的战略和社会需求模型应用于癌症护理组织的姑息性家庭护理的批判性分析

2022 International Arab Conference on Information Technology (ACIT) Pub Date : 2022-11-22 DOI: 10.1109/ACIT57182.2022.9994182

Dina Tbaishat, Yousra Odeh, Faten F. Kharbat, O. Shamieh, M. Odeh

{"title":"Critical Analysis of Palliative Homecare Using the $mathrm{i}^{ast}$ Framework's Strategic and Social Requirements Modelling Applied to a Cancer Care Organisation","authors":"Dina Tbaishat, Yousra Odeh, Faten F. Kharbat, O. Shamieh, M. Odeh","doi":"10.1109/ACIT57182.2022.9994182","DOIUrl":"https://doi.org/10.1109/ACIT57182.2022.9994182","url":null,"abstract":"Home Health Care (HHC) is an essential and critical part of palliative care and especially for terminal cancer patients. This research is aimed as a first attempt to align with the research gap in modelling the social requirements of palliative care processes and the HHC process in particular. Consequently, this research is a first attempt at developing an $mathbf{i}^{ast}$ framework visual goal-oriented and social requirements models of the HHC process of the domain of palliative care with a reflected application using a case study from a leading regional cancer centre in the Middle East, namely KHCC. Furthermore, this research has made it possible for palliative care domain experts in the HHC process and using the associated $mathbf{i}^{ast}$ framework strategic dependency and strategic rationale models to visually trace the most critical and strategic actors in the HHC process along with the highly interacting dependers and dependees. Finally, the HHC $mathbf{i}^{ast}$ strategic models contribute to bridging the gap between the world of palliative care requirements and their reflective computer-based information systems and $mathbf{IoT}$ smart devices. Hence, this sheds light towards the realisation of the field of palliative care as being a “systems of systems” virtual organisation with the respective socio-technical systems involvement, for the best care of the palliative patient and especially terminal cancer patients. A further corollary of this research is the insufficiency and less representativeness of palliative care process models to utilise in guiding the development of the HHC $mathbf{i}^{ast}$ framework strategic models without linking to the full associated strategic and policy documents of palliative care.","PeriodicalId":256713,"journal":{"name":"2022 International Arab Conference on Information Technology (ACIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129442320","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

Design and Development of an IoT-based Solar Powered Camouflaged Robot for Military Applications 基于物联网的军用太阳能伪装机器人的设计与开发

2022 International Arab Conference on Information Technology (ACIT) Pub Date : 2022-11-22 DOI: 10.1109/ACIT57182.2022.9994141

Meftah Zouai, Abdelhak Merizig, Ichrak Boudjelkha, Houcine Belouaar, O. Kazar, Guadalupe Ortiz, Abderrahmane Lakas, Z. Houhamdi

{"title":"Design and Development of an IoT-based Solar Powered Camouflaged Robot for Military Applications","authors":"Meftah Zouai, Abdelhak Merizig, Ichrak Boudjelkha, Houcine Belouaar, O. Kazar, Guadalupe Ortiz, Abderrahmane Lakas, Z. Houhamdi","doi":"10.1109/ACIT57182.2022.9994141","DOIUrl":"https://doi.org/10.1109/ACIT57182.2022.9994141","url":null,"abstract":"Given Algeria's large area, estimated at 2,381,740 km, and its land borders, estimated at 6,385 km., which made unmanned border control mandatory to reduce the deployment of soldiers and protect the country. Robots play an important role in border protection, especially camouflaged robots, which are difficult for the enemy to detect. Camouflage becomes a survival skill for the robot and a defensive technology at the same time. In this paper, we designed a camouflaged robot in the form of the Fennec animal (Desert Fox), which is widely spread in the Algerian desert, based on the Internet of things and powered by solar energy, which is clean renewable energy available in the desert environment in which the robot will live. The features of the proposed model are that Camouflaged and mimics the funk in both its shape and movement, allowing it to move in this environment smoothly and detect intruders, bombs, mines and fires with its camera and sensors. Solar panels are used to provide power to the model with backup storage batteries, the IoT-based Fennec robot is connected by satellite to a military station. The robot sends information in real time and continuously and streaming video, and is equipped with a GPS sensor to determine its location.","PeriodicalId":256713,"journal":{"name":"2022 International Arab Conference on Information Technology (ACIT)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127746176","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

Engaging Online Learning at AAU and Its Impact on Students' Performance during COVID-19 新冠肺炎期间AAU参与在线学习及其对学生表现的影响

2022 International Arab Conference on Information Technology (ACIT) Pub Date : 2022-11-22 DOI: 10.1109/ACIT57182.2022.9994176

Bayan Abu Shawar, Tarik El Amsy, Nuha Hamada

引用次数: 0