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2022 2nd International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC)最新文献

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Light-Weight Face Shape Classifier for Real-Time Applications 用于实时应用的轻量级脸型分类器
2022 2nd International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC) Pub Date : 2022-05-08 DOI: 10.1109/MIUCC55081.2022.9781653
Kareem Moussa, Mahmoud Wessam, Retaj Yousri, M. Darweesh
{"title":"Light-Weight Face Shape Classifier for Real-Time Applications","authors":"Kareem Moussa, Mahmoud Wessam, Retaj Yousri, M. Darweesh","doi":"10.1109/MIUCC55081.2022.9781653","DOIUrl":"https://doi.org/10.1109/MIUCC55081.2022.9781653","url":null,"abstract":"Deep neural networks (DNNs) are memory and computationally intensive; hence they are difficult to apply to real-time systems with limited resources. Therefore, the DNN models need to be carefully optimized. The solution was a model based on a convolutional neural network (CNN) called MobileNet that decreases the computational and space complexities with classification precision loss by utilizing depthwise separable convolutions. This study uses MobileNet vl architecture to improve image classification complexities to reach an acceptable complexity that can be used in real-time applications that require a hasty response from the model. In this study, the MobileNet model was trained on a dataset consisting of 5000 images to be classified into the 5 human face shapes oval, square, heart, oblong, and round. The model has an F1-score of 0.781, recall of 0.782, precision of 0.78, and achieved an accuracy of 98.8%. With this level of accuracy, a real-time application that is result-driven would benefit significantly from this model.","PeriodicalId":105666,"journal":{"name":"2022 2nd International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123426721","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: 3
RetinalNet-500: A newly developed CNN Model for Eye Disease Detection RetinalNet-500:一个新开发的CNN眼病检测模型
2022 2nd International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC) Pub Date : 2022-05-08 DOI: 10.1109/MIUCC55081.2022.9781785
Sadikul Alim Toki, Sohanoor Rahman, SM Mohtasim Billah Fahim, Abdullah Al Mostakim, Md. Khalilur Rhaman
{"title":"RetinalNet-500: A newly developed CNN Model for Eye Disease Detection","authors":"Sadikul Alim Toki, Sohanoor Rahman, SM Mohtasim Billah Fahim, Abdullah Al Mostakim, Md. Khalilur Rhaman","doi":"10.1109/MIUCC55081.2022.9781785","DOIUrl":"https://doi.org/10.1109/MIUCC55081.2022.9781785","url":null,"abstract":"Fundus images are commonly used by medical experts like ophthalmologists, which are very helpful in detecting various retinal disorders. They used this to diagnose the different types of eye diseases like Cataracts, Diabetic Retinopathy, Glaucoma etc. These fundus images can be also used for the prediction of the severity of the diseases and can provide early signs or warnings. Recently, different machine learning algorithms are playing a vital role in the field of medical science, and it is no different in Ophthalmology either. In this research, we aim to automatically classify healthy and diseased retinal fundus images using deep neural networks. Because deep learning is an excellent machine learning algorithm, which has proven to be very accurate in computer vision problems. In our research, we used convolutional neural networks(CNN) to classify the retinal images whether they are healthy or not.","PeriodicalId":105666,"journal":{"name":"2022 2nd International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114532674","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 New Model for Stock Market Predication Using a Three-Layer Long Short-Term Memory 基于三层长短期记忆的股票市场预测新模型
2022 2nd International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC) Pub Date : 2022-05-08 DOI: 10.1109/MIUCC55081.2022.9781741
Ayman M. Nabil, Nebal Magdi
{"title":"A New Model for Stock Market Predication Using a Three-Layer Long Short-Term Memory","authors":"Ayman M. Nabil, Nebal Magdi","doi":"10.1109/MIUCC55081.2022.9781741","DOIUrl":"https://doi.org/10.1109/MIUCC55081.2022.9781741","url":null,"abstract":"The stock market predication is full of uncertainty and is affected by many factors. such as historical data, sentimental analysis, and financial analysis. Studies have also shown that predicting direction taking only one factor or merging two factors only from three different factors (Trend analysis - Financial ratios - Sentiment Analysis). A new proposed model will be used, which combines three features of stock evaluation for the first time: trend analysis based on historical data, analysis of financial ratios, and online news analysis as an input to the deep learning algorithm using long short-term memory to get more accurate results. It can be shown from the results that this model can be used to predict the stock performance with a higher accuracy compared with other models.","PeriodicalId":105666,"journal":{"name":"2022 2nd International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130444860","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: 2
Classification of Human Hand Grasping Force Using sEMG 基于肌电图的人手抓握力分类
2022 2nd International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC) Pub Date : 2022-05-08 DOI: 10.1109/MIUCC55081.2022.9781700
Muataz Ghanem, M. Atia, S. Maged
{"title":"Classification of Human Hand Grasping Force Using sEMG","authors":"Muataz Ghanem, M. Atia, S. Maged","doi":"10.1109/MIUCC55081.2022.9781700","DOIUrl":"https://doi.org/10.1109/MIUCC55081.2022.9781700","url":null,"abstract":"Aiming to classify different hand grasping force levels with the use of prosthetic hands, the electromyography (EMG) signals from the forearm muscles are collected using a commercial surface electromyography (sEMG) sensor. The hand grasping force is recorded using a loadcell. The RMS and the mean frequency (MNF) are used for feature extraction. Both features are extracted using non-overlapping and overlapping windowing techniques. They are applied at different window sizes. SVM, K-NN, and artificial neural networks (ANNs) are used to predict the grasping force levels. The classifiers' performances are evaluated using the classification accuracy and the execution time. The time domain feature obtained the highest accuracies. The K-NN classifier showed the highest classification accuracy compared to the other classifiers. The ANNs produced the shortest execution times among all classifiers. Analysis of Variance is used to show any significance between the classifiers' means accuracies.","PeriodicalId":105666,"journal":{"name":"2022 2nd International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134537406","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 11 会议11
2022 2nd International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC) Pub Date : 2022-05-08 DOI: 10.1109/miucc55081.2022.9781745
Sesión, ¿Quién puede darle apoyo
{"title":"Session 11","authors":"Sesión, ¿Quién puede darle apoyo","doi":"10.1109/miucc55081.2022.9781745","DOIUrl":"https://doi.org/10.1109/miucc55081.2022.9781745","url":null,"abstract":"","PeriodicalId":105666,"journal":{"name":"2022 2nd International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC)","volume":"363 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133202548","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
Fault Detection in Wind Turbines using Deep Learning 基于深度学习的风力发电机故障检测
2022 2nd International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC) Pub Date : 2022-05-08 DOI: 10.1109/MIUCC55081.2022.9781749
Mahi Ayman, Mariam Othman, N. Mahmoud, Zeina Tamer, Maha Sayed, Yomna M. I. Hassan
{"title":"Fault Detection in Wind Turbines using Deep Learning","authors":"Mahi Ayman, Mariam Othman, N. Mahmoud, Zeina Tamer, Maha Sayed, Yomna M. I. Hassan","doi":"10.1109/MIUCC55081.2022.9781749","DOIUrl":"https://doi.org/10.1109/MIUCC55081.2022.9781749","url":null,"abstract":"Institutions have been redirecting investments away from fossil fuels, creating a path for clean energy generation. The wind industry has seen an exponential increase in recent years. Early fault detection creates an alternative for operation and maintenance (OM), allowing costs to be avoided before they reach a catastrophic stage, and improving turbine reliability. Predictive maintenance was the solution that presented itself for this problem, in which faults are detected before they occur and fixed accordingly. LSTM-Autoencoder and time-series data collected from SCADA sensors installed in wind turbines are used to detect anomalies in several components of the wind turbines that insinuate a major fault might occur. The dataset is collected from a wind farm in the West African Gulf of Guinea in 2016. Results have shown how PCA can be productive in identifying the features with the most influence on the prediction process, with the ability to predict faults 17.5 days prior on average.","PeriodicalId":105666,"journal":{"name":"2022 2nd International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133846945","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
Jigsaw Self-Supervised Visual Representation Learning: An Applied Comparative Analysis Study 拼图自监督视觉表征学习:应用比较分析研究
2022 2nd International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC) Pub Date : 2022-05-08 DOI: 10.1109/MIUCC55081.2022.9781725
Yomna A. Kawashti, D. Khattab, M. Aref
{"title":"Jigsaw Self-Supervised Visual Representation Learning: An Applied Comparative Analysis Study","authors":"Yomna A. Kawashti, D. Khattab, M. Aref","doi":"10.1109/MIUCC55081.2022.9781725","DOIUrl":"https://doi.org/10.1109/MIUCC55081.2022.9781725","url":null,"abstract":"Self-supervised learning has been gaining momentum in the computer vision community as a hopeful contender to replace supervised learning. It aims to leverage unlabeled data by training a network on a proxy task and using transfer learning for a downstream task. Jigsaw is one of the proxy tasks used for learning better feature representations in self-supervised learning. In this work, we comparably evaluated the transferability of jigsaw using different architectures and a different dataset for jigsaw training. The features extracted from each convolutional block were evaluated using a unified downstream task. The best performance was achieved by the shallower architecture of AlexNet where the 2nd block achieved better transferability with a mean average precision of 36.17. We conclude that this behavior could be attributed to the smaller scale of our used dataset, so features extracted from earlier and shallower blocks had higher transferability to a dataset of a different domain.","PeriodicalId":105666,"journal":{"name":"2022 2nd International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114715415","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
Exploring Brain Tumor Classification Using Deep Learning 利用深度学习探索脑肿瘤分类
2022 2nd International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC) Pub Date : 2022-05-08 DOI: 10.1109/MIUCC55081.2022.9781767
Habiba Mohamed, Ayman Atia
{"title":"Exploring Brain Tumor Classification Using Deep Learning","authors":"Habiba Mohamed, Ayman Atia","doi":"10.1109/MIUCC55081.2022.9781767","DOIUrl":"https://doi.org/10.1109/MIUCC55081.2022.9781767","url":null,"abstract":"Diagnosis at a beginning period and recognition of the type of cancer can assist doctors and health experts in determining the most appropriate treatment. The target of this is to research is to build a reliable means and appropriate method for classifying human brain cancers that uses magnetic resonance imaging (MRI) to distinguish between the many forms of Glioblastoma, malignant tumors, and gland tumours are examples of brain tumors. In order to enhance and achieve accurate results can make preprocessing methods like resize MR images, cropping and data augmentation to avoid over fitting. By using deep learning pre-defined models as ResNet, VGG16, MobileNet and Inception. And transfer-based learning CNN that supported with calculation of dice, sensitivity and specificity we founded that by using dice with CNN model the achieved accuracy was 99.9%.","PeriodicalId":105666,"journal":{"name":"2022 2nd International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115088530","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
Blank page 空白页
2022 2nd International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC) Pub Date : 2022-05-08 DOI: 10.1109/miucc55081.2022.9781645
{"title":"Blank page","authors":"","doi":"10.1109/miucc55081.2022.9781645","DOIUrl":"https://doi.org/10.1109/miucc55081.2022.9781645","url":null,"abstract":"","PeriodicalId":105666,"journal":{"name":"2022 2nd International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC)","volume":"142 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124516925","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
Face analyzer 3D: Automatic facial profile detection and occlusion classification for dental purposes 面部分析仪3D:自动面部轮廓检测和牙合分类
2022 2nd International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC) Pub Date : 2022-05-08 DOI: 10.1109/MIUCC55081.2022.9781758
Abduallah Elmaraghy, Ganna Ayman, Mohamed Khaled, Sara Tarek, Maha Sayed, Mennat Allah Hassan, Yomna M. I. Hassan, Mostafa Hussin Kamel
{"title":"Face analyzer 3D: Automatic facial profile detection and occlusion classification for dental purposes","authors":"Abduallah Elmaraghy, Ganna Ayman, Mohamed Khaled, Sara Tarek, Maha Sayed, Mennat Allah Hassan, Yomna M. I. Hassan, Mostafa Hussin Kamel","doi":"10.1109/MIUCC55081.2022.9781758","DOIUrl":"https://doi.org/10.1109/MIUCC55081.2022.9781758","url":null,"abstract":"Dental pathology is a wide field of study as it passes through several stages of diagnosis and treatment for patients. This paper aims to assist orthodontists in classifying dental occlusion and measuring the asymmetry caused by it. The system takes a 2D facial image as input and uses it to reconstruct the 3D model. As 3D models have a lower error rate in information loss, they are more accurate than 2D images. Then, it uses a deep learning model to detect 3D facial landmarks on a 2D image to measure facial asymmetry. The challenges in this approach include achieving the highest possible accuracy in the reconstruction process and detecting 3D landmarks on the 3D facial model. The used technique in reconstruction reaches up to 90% accuracy compared to photogrammetry techniques. The proposed framework is expected to be time-efficient and to achieve up to 89% accuracy in the analysis and classification.","PeriodicalId":105666,"journal":{"name":"2022 2nd International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129675498","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
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