Syed Mohd Zahid Syed Zainal Ariffin, Hazwani Hasnun Ali Musa, N. Jamil
{"title":"Performance Comparison of Convolutional Neural Network Models on Cervical Cell Classification","authors":"Syed Mohd Zahid Syed Zainal Ariffin, Hazwani Hasnun Ali Musa, N. Jamil","doi":"10.1109/IICAIET51634.2021.9574018","DOIUrl":"https://doi.org/10.1109/IICAIET51634.2021.9574018","url":null,"abstract":"Cervical cancer is regarded as one of the most common type of cancer suffered by women in the world. However, it can be treated if being detected early. The main method of diagnosing this type of cancer is through Papanicolaou test, in which cervical cells are collected and examined under a microscope. The process of examining the cervical cells is often done manually and is labourious. Several methods have been proposed to automate this process using machine learning. This study explores three Convolutional Neural Network models (i.e., AlexNet, Inception-V3 and ResNet50-V2) in classifying cervical cells. Transfer learning was implemented with different sets of varying number of images. Herlev dataset was used in this study. In the Herlev dataset, images were classified into seven classes (i.e. four abnormal and three normal). The classification done was based on seven-class classification. Performance of each model was measured based on accuracy, precision and recall. Based on the result obtained, ResNet50-V2 performed the best with the accuracy, precision and recall compared to the other two models.","PeriodicalId":234229,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121379134","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 on Multiword Expression Features in Emotion Detection of Code-Mixed Twitter Data","authors":"Kathleen Swee Neo Tan, T. Lim, Chi Wee Tan","doi":"10.1109/IICAIET51634.2021.9573850","DOIUrl":"https://doi.org/10.1109/IICAIET51634.2021.9573850","url":null,"abstract":"The need for automated detection of emotions in microblogs such as Twitter is growing as many organizations realize the need to take the emotions of their customers and the public in general into consideration in their problem solving and decision making. In multilingual countries such as Malaysia, tweets are typically written in code-mixed Malay-English with some informal Romanized dialect texts. These tweets are often embedded with multiword expressions. Although there have been studies on multiword expressions, there has not been substantial work on the use of multiword expressions as features to detect emotion of text. This paper studies the use of multiword expressions extracted from WordNet and WordNet Bahasa as features in the emotion detection of code-mixed Twitter data.","PeriodicalId":234229,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126968787","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}