2023 Intelligent Methods, Systems, and Applications (IMSA)最新文献_第9页

From Gestures to Audio: A Dataset Building Approach for Egyptian Sign Language Translation to Arabic Speech 从手势到音频:埃及手语翻译到阿拉伯语语音的数据集构建方法

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217562

Youssef Ismail, Ali Tarek, Omar Zakaria, Salma Taha, Suhaila Masoud, Kareem Sobh, Ahmed Tamer Salah, Ghada Khoriba

{"title":"From Gestures to Audio: A Dataset Building Approach for Egyptian Sign Language Translation to Arabic Speech","authors":"Youssef Ismail, Ali Tarek, Omar Zakaria, Salma Taha, Suhaila Masoud, Kareem Sobh, Ahmed Tamer Salah, Ghada Khoriba","doi":"10.1109/IMSA58542.2023.10217562","DOIUrl":"https://doi.org/10.1109/IMSA58542.2023.10217562","url":null,"abstract":"The communication barriers faced by people with disabilities, particularly the deaf or hard of hearing, nonverbal, deaf-mute, and blind have a significant impact on their quality of life and social inclusion. Our research aims to provide real-time translation from sign language to speech and vice versa. The ability to provide real-time speech-to-text and text-to-sign language translation will help alleviate these barriers, improve communication, and increase social inclusivity for this community ensuring they are not left out in conversations and social interactions. A significant amount of data is required to develop a deep learning model that automatically translates Egyptian sign language to audio. Due to the unavailability of large-scale datasets for Egyptian sign language, we present a dataset-building approach to address this issue. In this paper, we discuss collecting and preprocessing the dataset, which includes extracting audio, video scrapping, cropping the required part of the video containing the sign language translator, and applying human and hand recognition models to identify the translators and their gestures, respectively. The resulting dataset will be used to train deep learning models to translate Egyptian sign language to audio as an end-to-end approach. The dataset will be available in our research GitHub.","PeriodicalId":110239,"journal":{"name":"2023 Intelligent Methods, Systems, and Applications (IMSA)","volume":"8 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":"129219795","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

Session (15) 会话(15)

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/imsa58542.2023.10217407

引用次数: 0

Rooted Android Devices Risk Assessment using Analytic Hierarchy Process 基于层次分析法的Android设备风险评估

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217716

Wael F. Elsersy, N. El-Fishawy, Ehab E. Zakaria

引用次数: 0

Trajectory Generation of Hexapod Robots Based on Genetic Algorithm 基于遗传算法的六足机器人轨迹生成

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217728

Sameh I. Beaber, M. A. Kamel, Abdelrahaman S. Zaghloul, W. Hussein

{"title":"Trajectory Generation of Hexapod Robots Based on Genetic Algorithm","authors":"Sameh I. Beaber, M. A. Kamel, Abdelrahaman S. Zaghloul, W. Hussein","doi":"10.1109/IMSA58542.2023.10217728","DOIUrl":"https://doi.org/10.1109/IMSA58542.2023.10217728","url":null,"abstract":"In this paper, a novel trajectory generation algorithm for a rectangular hexapod robot in a complex environment based on a genetic algorithm (GA) when the robot is moving in a tripod gait. The main objective is to find the shortest trajectory between the initial and target locations while considering the hexapod’s physical constraints, such as the maximum step length, as well as collision avoidance with surrounding obstacles. Since rectangular hexapod robots perform better in forward and side motion compared to angular motion, it is preferable to move either horizontally or vertically. Therefore, finding the desired direction of motion (horizontal or vertical) is also considered during the trajectory planning procedure. First, a kinematic model of the hexapod is presented. Then, the planned trajectory is divided into a number of segments. Subsequently, GA is applied to find the optimum design variables that yield the shortest and collision-free trajectory. These variables include the number of steps, the length of each step, and the corresponding direction of motion (horizontal or vertical) during each segment. Finally, different simulation scenarios are conducted to demonstrate the effectiveness, convergence, and stability of the proposed algorithm.","PeriodicalId":110239,"journal":{"name":"2023 Intelligent Methods, Systems, and Applications (IMSA)","volume":"129 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":"114492439","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

Session (13) 会话(13)

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/imsa58542.2023.10217652

引用次数: 0

End of Session (15) 会议结束(15)

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/imsa58542.2023.10217682

引用次数: 0

Early Breast Cancer Screening from Thermography via Deep Pre-Trained Edge Detection with Extreme Gradient Boosting 基于深度预训练边缘检测和极端梯度增强的热成像早期乳腺癌筛查

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217569

Shaimaa Gamal, Hanan Atef, Doaa Youssef, Tawfik Ismail, J. El-Azab

{"title":"Early Breast Cancer Screening from Thermography via Deep Pre-Trained Edge Detection with Extreme Gradient Boosting","authors":"Shaimaa Gamal, Hanan Atef, Doaa Youssef, Tawfik Ismail, J. El-Azab","doi":"10.1109/IMSA58542.2023.10217569","DOIUrl":"https://doi.org/10.1109/IMSA58542.2023.10217569","url":null,"abstract":"Breast cancer is the most common type of cancer affecting women worldwide. Early diagnosis is crucial for successful treatment, recovery, and mortality reduction. Therefore, various non-invasive, minimally invasive, and invasive approaches have been developed for breast cancer detection. Breast thermography is a promising non-invasive, low-cost, and radiation-free imaging technique that can provide information about the physiological variations of breast tissue. It can be considered as a supplement to X-ray mammography or even a primary early cancer detection tool, particularly for women with dense breast tissue or those who are not suitable candidates for mammography. This study proposes a fully automated system for thermography-based early breast cancer detection. The characteristic features of the breast are extracted based on the deep pre-trained holistically-nested edge detector. Such features are used to diagnostically classify breast tissue into normal or malignant classes using the machine learning algorithm extreme gradient boosting. The classification improved to some extent by incorporating the Canny edge detector. The developed system achieved an accuracy of 97.4%, with a precision of 95%, a recall of 100%, and an F1 score of 97%.","PeriodicalId":110239,"journal":{"name":"2023 Intelligent Methods, Systems, and Applications (IMSA)","volume":"110 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":"122047553","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: 1

Comparative Analysis of Pre-Trained CNN Architectures for Multi-Class Weather Classification: A Study on Deep Learning Techniques 基于深度学习技术的多类天气分类预训练CNN体系结构比较分析

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217616

Abdelrahman Ezzeldin Nagib, Habiba Mohamed, Abdelaziz Ashraf

引用次数: 1

Atherosclerotic Plaque Segmentation in Carotid Ultrasound Image using Hybrid-A-UNet Deep Learning Model 基于Hybrid-A-UNet深度学习模型的颈动脉超声图像粥样硬化斑块分割

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217453

Vinayak Sharma, Sonam Jain, Pankaj K. Jain, K. K. Shukla

{"title":"Atherosclerotic Plaque Segmentation in Carotid Ultrasound Image using Hybrid-A-UNet Deep Learning Model","authors":"Vinayak Sharma, Sonam Jain, Pankaj K. Jain, K. K. Shukla","doi":"10.1109/IMSA58542.2023.10217453","DOIUrl":"https://doi.org/10.1109/IMSA58542.2023.10217453","url":null,"abstract":"Cardiovascular diseases (CVD) and strokes are major health concerns worldwide, and early detection of these conditions can prevent mortality and expensive surgeries. However, conventional methods of severity detection and prediction of CVD and stroke in their early stages are not sufficiently accurate or automated. Therefore, there is a need for Artificial Intelligence (AI) based methods that can accurately and automatically detect these conditions.In this study, a deep learning (DL) model attention-channel-based UNet (A-UNet) was proposed to identify carotid plaques in images of the common (CCA). The accuracy of the model was determined using a cross-validation K5 protocol. The performance of the A-UNet model was compared with that of the UNet model.The plaque segmentation results showed that the A-UNet model outperformed the UNet model. The correlation coefficient (CC) value for A-UNet was 0.96, compared to 0.93 for the UNet model. Similarly, the area under the curve (AUC) value for A-UNet was 0.97, compared to 0.964 for the UNet model. The study concludes that the A-UNet model is useful for segmenting very high risk plaque images that are difficult to diagnose using other methods.","PeriodicalId":110239,"journal":{"name":"2023 Intelligent Methods, Systems, and Applications (IMSA)","volume":"74 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":"127669494","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: 1

A Framework for Assessing Physical Rehabilitation Exercises 评估身体康复训练的框架

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217392

Moamen Zaher, Ahmed Samir, A. Ghoneim, Laila Abdelhamid, Ayman Atia

{"title":"A Framework for Assessing Physical Rehabilitation Exercises","authors":"Moamen Zaher, Ahmed Samir, A. Ghoneim, Laila Abdelhamid, Ayman Atia","doi":"10.1109/IMSA58542.2023.10217392","DOIUrl":"https://doi.org/10.1109/IMSA58542.2023.10217392","url":null,"abstract":"Physical rehabilitation exercises effectively reduce the utilization of healthcare systems through exercises designed to restore and improve the level of functionality of each patient and track the recovery process. Thus, the rehabilitation exercises lower hospital admissions, length of stays, and readmissions. This study proposes a framework for evaluating physical rehabilitation exercises and monitoring patients' progress to reduce the costs associated with rehabilitation. The automated evaluation of the exercises also provides personalization options that enable clinicians to design personalized treatment plans. A fully-automated evaluation framework must identify the distinct skeletal parts, angles, and trajectories for each exercise to distinguish one exercise from another. The proposed framework starts by recording a patient's movements using an RGB camera and then extracts the different skeletal parts from the video for classification and monitoring purposes. Thus, the study also addresses the feasibility of employing an RGB rather than a Kinect camera. A feature ranking algorithm (the Fast Correlation Based Filter) was applied to select essential features. Then, the experiments investigated two classifiers using two sets of rehabilitation exercises (along with their respective mistakes) to classify the movements. The proposed approach achieved 99.64% and 90% accuracy using the Extra Tree Classifier and the One Dollar algorithm, respectively.","PeriodicalId":110239,"journal":{"name":"2023 Intelligent Methods, Systems, and Applications (IMSA)","volume":"46 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":"125526358","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