2023 Intelligent Methods, Systems, and Applications (IMSA)最新文献_第7页

A Robust Ensemble Deep Learning Approach for Breast Cancer Diagnosis 一种鲁棒集成深度学习方法用于乳腺癌诊断

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217501

Nadeen Amgad, Mariam Ahmed, Haidy Haitham, Moamen Zaher, Ammar Mohammed

{"title":"A Robust Ensemble Deep Learning Approach for Breast Cancer Diagnosis","authors":"Nadeen Amgad, Mariam Ahmed, Haidy Haitham, Moamen Zaher, Ammar Mohammed","doi":"10.1109/IMSA58542.2023.10217501","DOIUrl":"https://doi.org/10.1109/IMSA58542.2023.10217501","url":null,"abstract":"Globally, the number one cause of death for women is breast cancer, which is a serious public health concern. Deep learning has shown promising results in detecting and diagnosing breast cancer from medical images. While deep learning models have great potential, identifying the most effective deep learning architecture presents a significant challenge. This paper proposes a two-stage ensemble classification approach for breast cancer detection. The first stage involves training multiple baseline models based on CNN architectures, including VGG16-SVM, ResNet50, DenseNet169, MobileNetV2, and InceptionV3. In the second stage, three different ensemble fusion methods, including voting, weighted voting, and meta-learning, were applied to combine the predictions of the baseline models. The second stage was introduced to overcome the challenge of selecting the best deep learning model by fusing a set of ensemble techniques. In the second stage, four different ensemble fusion methods are used, including hard voting, soft voting, weighted voting, and meta-learning. The findings of the experimentation on IHC images demonstrate that the fusion techniques have led to a boost in performance in comparison to the baseline CNN-based models. Furthermore, implementing an ensemble approach utilizing meta-learning has exhibited notable potential in augmenting the overall performance, Due to the imbalance in the dataset, the performance is evaluated in terms of F1-score. Its performance achieved F1-score of 89.2% surpassing the best baseline deep learning models by 22.2%.","PeriodicalId":110239,"journal":{"name":"2023 Intelligent Methods, Systems, and Applications (IMSA)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122969742","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}

引用次数: 0

The Effect of Using Tangible User Interfaces Compared to Traditional Learning for Teaching Programming in Higher Education: An Experimental Study 高等教育程序设计教学中使用有形用户界面与传统学习的效果:实验研究

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217780

Ashraf Hany, Enjy Ramadan, Amr Akl, Ayman Atia

{"title":"The Effect of Using Tangible User Interfaces Compared to Traditional Learning for Teaching Programming in Higher Education: An Experimental Study","authors":"Ashraf Hany, Enjy Ramadan, Amr Akl, Ayman Atia","doi":"10.1109/IMSA58542.2023.10217780","DOIUrl":"https://doi.org/10.1109/IMSA58542.2023.10217780","url":null,"abstract":"There is a lot of interest in employing tangible user interfaces (TUIs) to enhance teaching programming, but there is conflicting evidence for their advantages since implementing them in educational settings offers more difficulties than traditional learning. This paper examines the effect of using TUIs in learning and compares it to traditional learning methods. The study measures the difference in learning rate on university students majoring in computer science. The study was conducted on 20 university students aged 17 to 22 who participated in a 45-minute learning activity. The activity used a Tangible User Interface (TUI) and paper notes from a textbook. Pre-test and post-test results were collected as part of the study's between-groups design, and the TUI group outperformed the traditional learning group in terms of learning gains. The TUI group had an increase of 25.4% in their scores following the three learning sessions and a 1.5% decrease when the retention was measured two weeks later. The traditional paper learning group had increase of 9.1% in scores and a decrease of 7.2% in retention.","PeriodicalId":110239,"journal":{"name":"2023 Intelligent Methods, Systems, and Applications (IMSA)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115420344","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}

引用次数: 0

COVID-19 Diagnosis From CT-images Using Transfer Learning 利用迁移学习从ct图像中诊断COVID-19

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217720

Nouran Waleed, L. Said, Sahar Selim, A. Radwan

{"title":"COVID-19 Diagnosis From CT-images Using Transfer Learning","authors":"Nouran Waleed, L. Said, Sahar Selim, A. Radwan","doi":"10.1109/IMSA58542.2023.10217720","DOIUrl":"https://doi.org/10.1109/IMSA58542.2023.10217720","url":null,"abstract":"In symptomatic patients, a positive COVID-19 test is critical for securing life-saving services such as ICU care and ventilator support; it may cause septic shock, septic pneumonia, respiratory failure, heart difficulties, liver issues, and even death. CAD systems help people in rural places and doctors in the early detection of COVID-19. A diagnostic and severity detection technique utilizing transfer learning and a backpropagation neural network has been developed with the aid of a computer for this purpose. This study aims to compare and analyze multiple deep learning-enhanced strategies for detecting COVID-19 in CT scan medical images. The COVID-19 CT scan binary classification challenge utilized two powerful pretrained CNN models: Inception ResNet V2 and ResNet50. To achieve higher accuracy in the diagnosis of COVID-19 using CT scan images, a new approach called Inception ResNet was employed, and it resulted in 97.3% accuracy and 97.38% specificity. Transfer learning techniques were employed to reduce the training time and get around the shortage of data. The proposed approaches outperformed more than other papers in the literature by 0.2%.","PeriodicalId":110239,"journal":{"name":"2023 Intelligent Methods, Systems, and Applications (IMSA)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116848140","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}

引用次数: 0

Deep Learning-Based Classification of Ocular Diseases Using Convolutional Neural Networks 基于深度学习的卷积神经网络眼部疾病分类

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217787

Khalid Mostafa, Mohamed Hany, Abdelaziz Ashraf, M. A. Mahmoud

{"title":"Deep Learning-Based Classification of Ocular Diseases Using Convolutional Neural Networks","authors":"Khalid Mostafa, Mohamed Hany, Abdelaziz Ashraf, M. A. Mahmoud","doi":"10.1109/IMSA58542.2023.10217787","DOIUrl":"https://doi.org/10.1109/IMSA58542.2023.10217787","url":null,"abstract":"Ocular diseases can range from mild discomfort to severe vision loss or even blindness, and can affect people of all ages. Early detection and treatment are crucial to prevent or delay vision loss and maintain eye health, especially for older adults or those with underlying medical conditions. Early detection and rapid treatment of ocular problems can be done using Computer vision / Deep learning tasks. The proposed method must be able to distinguish between six distinct diseases, including glaucoma, cataract, diabetes, age-related macular degeneration, hypertension, and pathological myopia, as well as other diseases that are not specifically mentioned, in the Ophthalmic Disease Recognition (ODIR) dataset. Due to the large degree of variation in picture quality, disease presentation, and patient demographics, the ODIR dataset presents a difficult job for multiple classification, so the accuracy was below 61%. In this study, the ODIR dataset is used to improve the suggested model, and perform extensive experiments to optimize the hyperparameters for training. The findings show that the suggested approach successfully completes the binary classification task on the ODIR dataset with excellent accuracy between 98% and 100%, recall from 97.99% to 100%, and precision between 96% and 100%.","PeriodicalId":110239,"journal":{"name":"2023 Intelligent Methods, Systems, and Applications (IMSA)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114413693","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}

引用次数: 0

End of Session (11) 会议结束(11)

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/imsa58542.2023.10217508

引用次数: 0

On Understanding Sports–HAR: Hierarchical, Mobile, Multi-Sensor based Classification of Table–Tennis Strokes 理解运动- har:乒乓球击球的分层、移动、多传感器分类

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217737

Mustafa Ezzeldin, A. Ghoneim, Laila Abdelhamid, Ayman Atia

引用次数: 1

End of Session (8) 会议结束(8)

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/imsa58542.2023.10217412

引用次数: 0

Development of a Visual Analysis Tool for Seabird Behavior 海鸟行为可视化分析工具的开发

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217617

Tomoya Onuki, Kazuo Misue

引用次数: 0

IMSA 2023 Cover Page IMSA 2023封面

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/imsa58542.2023.10217456

引用次数: 0

End of Session (17) 会议结束(17)

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/imsa58542.2023.10217384

引用次数: 0