{"title":"Sentiment Classification of Tourist's Opinion on Tourist Places of Interest in South India using Tweet Reviews","authors":"B. Gopal, Anandharaj Ganesan","doi":"10.1093/comjnl/bxab197","DOIUrl":"https://doi.org/10.1093/comjnl/bxab197","url":null,"abstract":"","PeriodicalId":21872,"journal":{"name":"South Afr. Comput. J.","volume":"301 1","pages":"815-825"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83444460","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}
Bishal Basak Papan, Protik Bose Pranto, M. S. Rahman
{"title":"On 2-Interval Pairwise Compatibility Properties of Two Classes of Grid Graphs","authors":"Bishal Basak Papan, Protik Bose Pranto, M. S. Rahman","doi":"10.1093/comjnl/bxac011","DOIUrl":"https://doi.org/10.1093/comjnl/bxac011","url":null,"abstract":"","PeriodicalId":21872,"journal":{"name":"South Afr. Comput. J.","volume":"212 1","pages":"1256-1267"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77278785","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 coronavirus disease 2019 (COVID-19) pandemic has been a globally dangerous crisis that causes an increasingly high death rate. Applying machine learning to the computed-tomography (CT)-based COVID-19 diagnosis is essential and attracts the attention of the research community. This paper introduces an approach for simultaneously identifying COVID-19 disease and segmenting its manifestations on lung images. The proposed method is an asymmetric U-Net-like model improved with skip connections. The experiment was conducted on a light-weighted feature extractor called CRNet with a feature enhancement technique called atrous spatial pyramid pooling. Classifying between COVID-19 and non-COVID-19 cases recorded the highest mean scores of 97.1, 94.4, and 97.0% for accuracy, dice similarity coefficient (DSC) and F1 score, respectively. Alternatively, the respective highest mean scores of the classification between COVID-19 and community-acquired pneumonia were 99.89, 99.79, and 99.97%. The lesion segmentation performance was with the highest mean of 99.6 and 84.7% for, respectively, accuracy and DSC.
{"title":"A Novel Approach For CT-Based COVID-19 Classification and Lesion Segmentation Based On Deep Learning","authors":"H. M. Truong, H. T. Huynh","doi":"10.1093/comjnl/bxac015","DOIUrl":"https://doi.org/10.1093/comjnl/bxac015","url":null,"abstract":"The coronavirus disease 2019 (COVID-19) pandemic has been a globally dangerous crisis that causes an increasingly high death rate. Applying machine learning to the computed-tomography (CT)-based COVID-19 diagnosis is essential and attracts the attention of the research community. This paper introduces an approach for simultaneously identifying COVID-19 disease and segmenting its manifestations on lung images. The proposed method is an asymmetric U-Net-like model improved with skip connections. The experiment was conducted on a light-weighted feature extractor called CRNet with a feature enhancement technique called atrous spatial pyramid pooling. Classifying between COVID-19 and non-COVID-19 cases recorded the highest mean scores of 97.1, 94.4, and 97.0% for accuracy, dice similarity coefficient (DSC) and F1 score, respectively. Alternatively, the respective highest mean scores of the classification between COVID-19 and community-acquired pneumonia were 99.89, 99.79, and 99.97%. The lesion segmentation performance was with the highest mean of 99.6 and 84.7% for, respectively, accuracy and DSC.","PeriodicalId":21872,"journal":{"name":"South Afr. Comput. J.","volume":"68 5 1","pages":"1366-1375"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76306345","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}
Diego Teijeiro, M. Amor, Ramón Doallo, David Deibe
{"title":"Interactive Visualization of Large Point Clouds Using an Autotuning Multiresolution Out-Of-Core Strategy","authors":"Diego Teijeiro, M. Amor, Ramón Doallo, David Deibe","doi":"10.1093/comjnl/bxac179","DOIUrl":"https://doi.org/10.1093/comjnl/bxac179","url":null,"abstract":"","PeriodicalId":21872,"journal":{"name":"South Afr. Comput. J.","volume":"20 1","pages":"1802-1816"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90702525","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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