{"title":"Scanning by multi-sensors to detect surface and internal defects","authors":"Aboura Abderrahmane, Abdou Abdelhak, Bouchaala Bouchaala, Aoukili Abdeslam, Khebal Merwane","doi":"10.1109/ICATEEE57445.2022.10093732","DOIUrl":"https://doi.org/10.1109/ICATEEE57445.2022.10093732","url":null,"abstract":"this research paper aims to detect of hidden defects on a metallic plate by collecting data by placing several eddy current sensors side by side. With alternating feeding in order to avoid mutual induction between different sensors. This technique saves a great deal of time when evaluating conductive elements with a small number of tests that allow us to obtain a signal from which we know the presence of an internal defect, and thus pave the way for us to determine their shape and dimensions in other works.","PeriodicalId":150519,"journal":{"name":"2022 International Conference of Advanced Technology in Electronic and Electrical Engineering (ICATEEE)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124356715","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":"Transfer learning approach for Alzheimer’s disease diagnosis using MRI images","authors":"Ahmed Rafik Zouaoui, Youcef Brik, Bilal Attallah, Mohamed Djeriuoi, Mourad Belkhelfa","doi":"10.1109/ICATEEE57445.2022.10093702","DOIUrl":"https://doi.org/10.1109/ICATEEE57445.2022.10093702","url":null,"abstract":"Alzheimer's disease is the most prevalent type of dementia and is defined as a slow-progressing neurological disorder. As a first step, early diagnosis of Alzheimer's disease is crucial, then classification is required as a second step for patients to be offered the most effective treatment available. For testing and analyzing this research, the Alzheimer's Disease Neuroimaging Initiative (ADNI) Baseline dataset is used. In this study, we suggested utilizing a convolutional neural network (CNN) algorithm to diagnose Alzheimer's disease from MRI images using a supervised deep learning approach based on transfer learning. The implemented system examines two different CNN architectures, including VGG-16 and MobileNet-V2. According to our results, this study achieved the highest accuracy and F1-score with 99.71% and 100%, respectively.","PeriodicalId":150519,"journal":{"name":"2022 International Conference of Advanced Technology in Electronic and Electrical Engineering (ICATEEE)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115476539","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}