2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS)最新文献

Satellite Lithium-ion Battery Remaining Useful Life Estimation by Coyote Optimization Algorithm 基于Coyote优化算法的卫星锂离子电池剩余使用寿命估算

2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2019-12-01 DOI: 10.1109/ICICIS46948.2019.9014752

Sara Abdelghafar, Essam Goda, A. Darwish, A. Hassanien

{"title":"Satellite Lithium-ion Battery Remaining Useful Life Estimation by Coyote Optimization Algorithm","authors":"Sara Abdelghafar, Essam Goda, A. Darwish, A. Hassanien","doi":"10.1109/ICICIS46948.2019.9014752","DOIUrl":"https://doi.org/10.1109/ICICIS46948.2019.9014752","url":null,"abstract":"The estimation of batteries remaining useful life (RUL) is a critical task of the prognostic and health monitoring of satellites. RUL works as an effective decision-making tool for operators by quantifying how much time remains until it loses its functionality. As the capacity is an important indicator for estimating RUL, this paper proposes a novel optimized regression approach for predicting the capacity based on the Coyote Optimization Algorithm (COA) with Support Vector Regression (SVR) called COA-SVR for improving the prediction accuracy of the battery capacity. COA is used for finding the optimal parameters of SVR as the parameter selection has a critical impact on the predictive accuracy of SVR. The performance of COA-SVR has been experimented using NASA's Lithiumion batteries dataset, the experimental results with different evaluation measures showed that the high efficiency of prediction with good stability and low time complexity have been achieved with the COA-SVR. In addition, the prediction accuracy of COA-SVR is compared with those of the basic SVR algorithm with randomized parameter selection and a relevance vector machine (RVM) that has been recently applied in some related work, the comparative results demonstrate that the highest prediction accuracy has been achieved with the proposed model COA-SVR.","PeriodicalId":200604,"journal":{"name":"2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116757854","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

Steganography in DNA Sequence on the Level of Amino acids 氨基酸水平上DNA序列的隐写

2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2019-12-01 DOI: 10.1109/ICICIS46948.2019.9014843

M. Sabry, T. Nazmy, M. E. Khalifa

引用次数: 5

A Unified Access to Heterogeneous Big Data through Ontology-Based Semantic Integration 基于本体语义集成的异构大数据统一访问

2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2019-12-01 DOI: 10.1109/ICICIS46948.2019.9014856

Naglaa Fathy, Walaa K. Gad, N. Badr

引用次数: 7

Glaucoma Detection from Fundus Camera Image 眼底相机图像中的青光眼检测

2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2019-12-01 DOI: 10.1109/ICICIS46948.2019.9014738

Mohamad Aouf, Sultan Almotatiri, A. Bajahzar, Ghada Kareem

引用次数: 0

High performance computing satellite orbit determination using ground station observations 使用地面站观测的高性能计算卫星轨道确定

2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2019-12-01 DOI: 10.1109/ICICIS46948.2019.9014751

M. Mahmoud, H. Hendy, Y. Elhalwagy, A. Elfarouk

引用次数: 0

Hybrid Method for Modeling User Interests based on Social Network 基于社交网络的用户兴趣建模混合方法

2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2019-12-01 DOI: 10.1109/ICICIS46948.2019.9014724

Marina Shafik, R. Elgohary, I. Moawad, Mohamed Roushdy

引用次数: 1

Enhanced Skin Lesions Classification Using Deep Convolutional Networks 使用深度卷积网络增强皮肤病变分类

2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2019-12-01 DOI: 10.1109/ICICIS46948.2019.9014823

E. Mohamed, Wessam H. El-Behaidy

{"title":"Enhanced Skin Lesions Classification Using Deep Convolutional Networks","authors":"E. Mohamed, Wessam H. El-Behaidy","doi":"10.1109/ICICIS46948.2019.9014823","DOIUrl":"https://doi.org/10.1109/ICICIS46948.2019.9014823","url":null,"abstract":"The recent development of machine learning and deep learning techniques for medical image analysis has led to the development of intelligent diagnosis systems that can help doctors make a better diagnosis to the patients' diseases. In particular, skin diagnostics is a field where these new techniques can be applied with a high rate of accuracy. This study aims to enhance the accuracy of skin lesions classification based on two factors. The first is deeply trained all layers of implemented pre-trained models. Whereas, the second is balancing the number of images within the seven classes of dataset used. The state-of-the-art convolutional neural networks MobileNet and DenseNet-121 were trained on HAM10000 dataset. The two models pass through three phases; preprocessing, training and evaluation. Firstly, the dataset is down sampled, splitted and augmented to resolve misbalancing problem. Then, both models are deeply trained and finally they are evaluated against baseline models without balancing the classes. Multiple metrics were used to evaluate our models; precision, recall, F1-score, specificity and ROC AUC. In addition, the micro-average and macro-average of all previous metrics to extend to multi-classification. The accuracy of MobileNet and DenseNet-121 reach 82.6% and 71.9% on unseen testing images, respectively on the original dataset (i.e. before balancing the dataset). Whereas, they reach 92.7% and 91.2% on unseen testing images, respectively after balancing the dataset. This enhancement proves the necessity of existence of balanced dataset for training, to have better performance. Furthermore, MobileNet after balancing dataset has out performed the highest accuracy of ISIC 2018 challenge on the same dataset by 4.2%. For that, this model is the recommended one as it is a light-weight model, suitable for mobile applications used by dermatologists and its accuracy is comparably equal to DenseNet121.","PeriodicalId":200604,"journal":{"name":"2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS)","volume":"583 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115824125","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: 25

Traffic Signs Detection and Recognition System using Deep Learning 基于深度学习的交通标志检测与识别系统

2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2019-12-01 DOI: 10.1109/ICICIS46948.2019.9014763

Pavly Salah Zaki, Marco Magdy William, Bolis Karam Soliman, Kerolos Gamal Alexsan, Keroles K. Khalil, M. El-Moursy

{"title":"Traffic Signs Detection and Recognition System using Deep Learning","authors":"Pavly Salah Zaki, Marco Magdy William, Bolis Karam Soliman, Kerolos Gamal Alexsan, Keroles K. Khalil, M. El-Moursy","doi":"10.1109/ICICIS46948.2019.9014763","DOIUrl":"https://doi.org/10.1109/ICICIS46948.2019.9014763","url":null,"abstract":"With the rapid development of technology, automobiles have become an essential asset in our day-to-day lives. One of the more important researches is Traffic Signs Recognition (TSR) systems. This paper describes an approach for efficiently detecting and recognizing traffic signs in real-time, taking into account the various weather, illumination and visibility challenges through the means of transfer learning. We tackle the traffic sign detection problem using the state-of-the-art of multi-object detection systems such as Faster Recurrent Convolutional Neural Networks (F-RCNN) and Single Shot Multi-Box Detector (SSD) combined with various feature extractors such as MobileNet v1 and Inception v2, and also Tiny-YOLOv2. However, the focus of this paper is going to be F-RCNN Inception v2 and Tiny YOLO v2 as they achieved the best results. The aforementioned models were fine-tuned on the German Traffic Signs Detection Benchmark (GTSDB) dataset. These models were tested on the host PC as well as Raspberry Pi 3 Model B+ and the TASS PreScan simulation. We will discuss the results of all the models in the conclusion section.","PeriodicalId":200604,"journal":{"name":"2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117221027","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: 27

Synergy of GIS and IoT for Weather Disasters Monitoring and Management GIS与物联网在天气灾害监测与管理中的协同作用

2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2019-12-01 DOI: 10.1109/ICICIS46948.2019.9014709

Akram M. Nabil, S. Mesbah, Ashraf Sharawi

{"title":"Synergy of GIS and IoT for Weather Disasters Monitoring and Management","authors":"Akram M. Nabil, S. Mesbah, Ashraf Sharawi","doi":"10.1109/ICICIS46948.2019.9014709","DOIUrl":"https://doi.org/10.1109/ICICIS46948.2019.9014709","url":null,"abstract":"Information technology plays a significant role in attaining safety from natural disasters through providing predictions and early warnings to minimize the degree of danger and the damage due to the severe weather conditions. This paper presents an integration of GIS and the Internet of Things (IoT) technologies in the detection and alerting processes in disaster management of severe weather conditions. Data are measured collected by sensors distributed in different locations. The system generates alerts disseminated simultaneously in near real-time to the responsible governmental entities and the public through multiple delivery mechanisms. These available channels include Email notifications, text-based SMS, live alerts via online social media sites. The system has a substantial advantage is that it can operate with any available weather sensors or with automatic weather stations. A multi-layered geodatabase is developed with the integration of the IoT data. Spatial analysis is carried out to identify the vulnerable areas to disasters due to the severe weather conditions.","PeriodicalId":200604,"journal":{"name":"2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128433720","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: 4

Spacecraft Orbital maneuver Flight Dynamics Simulation and Verification 航天器轨道机动飞行动力学仿真与验证

2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2019-12-01 DOI: 10.1109/ICICIS46948.2019.9014702

Hossam M. I. Alshamy, H. Hendy, A. Makled, Y. Elhalwagy

引用次数: 2