Kaouthar Touchanti, Imad Ezzazi, M. Bekkali, Said Maser
{"title":"A 2-stages feature selection framework for colon cancer classification using SVM","authors":"Kaouthar Touchanti, Imad Ezzazi, M. Bekkali, Said Maser","doi":"10.1109/ISCV54655.2022.9806115","DOIUrl":"https://doi.org/10.1109/ISCV54655.2022.9806115","url":null,"abstract":"As the colon cancer gene expression dataset is of high dimension, many irrelevant, redundant and noisy features might be included which may cause unprecedented challenges for data mining and machine learning algorithms. In this paper, we have proposed a new feature selection based method for colon cancer classification. First, we have used the ReliefF filter technique to provide a ranking in terms of the discriminatory ability of each feature. Second, since ReliefF cannot handle feature redundancy as well as feature interaction, another step is performed to select the best subset of gene expression profiles from the available 2K subsets. The proposed method has efficiently reduced the dimensionality of the colon dataset and increased the classification accuracy. The results from the Colon Cancer Gene Expression Data Set confirmed the effectiveness of the proposed method compared to advanced techniques.","PeriodicalId":426665,"journal":{"name":"2022 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":"39 22","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114043905","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":"A study and identification of COVID-19 viruses using N-grams with Naïve Bayes, K-Nearest Neighbors, Artificial Neural Networks, Decision tree and Support Vector Machine","authors":"Mohamed el Boujnouni","doi":"10.21203/rs.3.rs-40344/v1","DOIUrl":"https://doi.org/10.21203/rs.3.rs-40344/v1","url":null,"abstract":"Coronavirus disease 2019 or COVID-19 is a global health crisis caused by a virus officially named as severe acute respiratory syndrome coronavirus 2 and well known with the acronym (SARS-CoV-2). This very contagious illness has severely impacted people and business all over the world and scientists are trying so far to discover all useful information about it, including its potential origin(s) and inter-host(s). This study is a part of this scientific inquiry and it aims to identify precisely the origin(s) of a large set of genomes of SARS-COV-2 collected from different geographic locations in all over the world. This research is performed through the combination of five powerful techniques of machine learning (Naïve Bayes, K-Nearest Neighbors, Artificial Neural Networks, Decision tree and Support Vector Machine) and a widely known tool of language modeling (N-grams). The experimental results have shown that the majority of the aforementioned techniques gave the same global results concerning the origin(s) and inter-host(s) of SARS-COV-2. These results demonstrated that this virus has one zoonotic source which is Pangolin.","PeriodicalId":426665,"journal":{"name":"2022 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":"34 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113976272","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}