{"title":"Machine Learning Classification Algorithms for Sentiment Analysis in Arabic: Performance Evaluation and Comparison","authors":"Ruba Kharsa, S. Harous","doi":"10.1109/ICECTA57148.2022.9990108","DOIUrl":"https://doi.org/10.1109/ICECTA57148.2022.9990108","url":null,"abstract":"Researchers started utilizing and optimizing the state-of-the-art Machine Learning (ML) and Deep Learning (DL) models to benefit Arabic language tools and applications. They employed social media platforms such as Twitter to gather enormous datasets in the Modern Standard Arabic and Arabic dialects, then used the collected datasets to train their models. This noticeable development in the field needs a detailed comparison study to review the work done and highlight the openings for future contributions and improvements. Based on the conducted review, there exists a gap in the time-complexity evaluation of the used ML algorithms in the field of Arabic Sentiment Analysis. Thus, this study presents an experimental approach for determining the time complexity of seven popular ML algorithms in classifying positive and negative Arabic sentences. The results show that the Multi-Layer Perceptron (MLP) and the Support Vector Machine (SVM) have the highest complexity, whereas the Logistic Regression (LR) has the lowest.","PeriodicalId":337798,"journal":{"name":"2022 International Conference on Electrical and Computing Technologies and Applications (ICECTA)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126740087","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}
{"title":"6G Decentralization","authors":"Steven A. Wright","doi":"10.1109/ICECTA57148.2022.9990106","DOIUrl":"https://doi.org/10.1109/ICECTA57148.2022.9990106","url":null,"abstract":"Decentralization was a key design objective underlying the design of the Internet, with independent networks collaborating to deliver packet network services. As web services become more central to daily activities, Web 3.0 seeks to use decentralization to disrupt the digital sovereignty of proprietary Web 2.0 platforms. Decentralization remains a tool for economic regulation to deter market dominance or anti-trust monopoly influences that can distort online services While public network infrastructures like 5G have been evolving native support for a greater variety of services decentralization has not been a major focus. With 6G requirements gathering in process, will decentralization become a key dimension of 6G performance?","PeriodicalId":337798,"journal":{"name":"2022 International Conference on Electrical and Computing Technologies and Applications (ICECTA)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115449461","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}
{"title":"Different Techniques And Algorithms To Combat The Issue Of Money Laundering In Bitcoin","authors":"Mariam Alnaqbi, Mariam Mohamed Al-Ali, Mahra Alremeithi, Maryam Yaqoub Al Ali, Deepa Pavithran","doi":"10.1109/ICECTA57148.2022.9990475","DOIUrl":"https://doi.org/10.1109/ICECTA57148.2022.9990475","url":null,"abstract":"The emergence of Bitcoin has continued to grow both in value and fame, as it was introduced as the first decentralized cryptocurrency. Many studies have shown that criminals are exploiting bitcoin by using it to launder their money, which originates from illegal activities or cybercrime. This paper aims at providing a comparison of detection techniques for preventing money laundering in bitcoin through various methods including graph theory, prevention of mixing services, and machine learning techniques including Random Forest, Shallow Neural Networks, optimizable Decision Trees, Bagging, Boosting algorithms, and Ensemble learning combining Random Forest, Bagging and Extra Tree.","PeriodicalId":337798,"journal":{"name":"2022 International Conference on Electrical and Computing Technologies and Applications (ICECTA)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121556570","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}
{"title":"Martian Ionosphere Electron Density Prediction Using Bagged Trees","authors":"A. Darya, N. Alameri, M. Shaikh, I. Fernini","doi":"10.1109/ICECTA57148.2022.9990500","DOIUrl":"https://doi.org/10.1109/ICECTA57148.2022.9990500","url":null,"abstract":"The availability of Martian atmospheric data provided by several Martian missions broadened the opportunity to investigate and study the conditions of the Martian ionosphere. As such, ionospheric models play a crucial part in improving our understanding of ionospheric behavior in response to different spatial, temporal, and space weather conditions. This work represents an initial attempt to construct an electron density prediction model of the Martian ionosphere using machine learning. The model targets the ionosphere at solar zenith ranging from 70 to 90 degrees, and as such only utilizes observations from the Mars Global Surveyor mission. The performance of different machine learning methods was compared in terms of root mean square error, coefficient of determination, and mean absolute error. The bagged regression trees method performed best out of all the evaluated methods. Furthermore, the optimized bagged regression trees model outperformed other Martian ionosphere models from the literature (MIRI and NeMars) in finding the peak electron density value, and the peak density height in terms of root-mean-square error and mean absolute error.","PeriodicalId":337798,"journal":{"name":"2022 International Conference on Electrical and Computing Technologies and Applications (ICECTA)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116595266","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}