2021 2nd Information Technology To Enhance e-learning and Other Application (IT-ELA)最新文献_第4页

Pedestrian and Objects Detection by Using Learning Complexity-Aware Cascades 基于学习复杂性感知级联的行人和物体检测

2021 2nd Information Technology To Enhance e-learning and Other Application (IT-ELA) Pub Date : 2021-12-28 DOI: 10.1109/IT-ELA52201.2021.9773589

M. F. Alrifaie, Omar Ayad Ismael, Asaad Shakir Hameed, Mustafa B. Mahmood

{"title":"Pedestrian and Objects Detection by Using Learning Complexity-Aware Cascades","authors":"M. F. Alrifaie, Omar Ayad Ismael, Asaad Shakir Hameed, Mustafa B. Mahmood","doi":"10.1109/IT-ELA52201.2021.9773589","DOIUrl":"https://doi.org/10.1109/IT-ELA52201.2021.9773589","url":null,"abstract":"Due to the considerable technological development in all joints of life, the trend has become significant towards automating various processes in daily life, such as smart cities, the Internet of things, and cloud services. One of the most crucial applications is self-driving cars, which will be a quantum leap in this field. The main problem with these vehicles will be how to provide the necessary accuracy to deal with various situations, such as sudden stops and pedestrian crossing. In this paper, we propose an effective method for automating autonomous vehicles by improving their ability to make appropriate decisions at the right time. For this, we rely on sequential training that is aware of the complexity. The system is trained and provided to the vehicles, where the presence of pedestrians is detected using machine learning algorithms, such as a deep convolutional neural network (CNN). The findings obtained in this research suggest a clear improvement in the vehicle's ability to make decisions and a great speed in responding to the event and parking the vehicle when passing by.","PeriodicalId":330552,"journal":{"name":"2021 2nd Information Technology To Enhance e-learning and Other Application (IT-ELA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122570887","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

Android Mobile Applications Vulnerabilities and Prevention Methods: A Review Android移动应用程序漏洞及防范方法综述

2021 2nd Information Technology To Enhance e-learning and Other Application (IT-ELA) Pub Date : 2021-12-28 DOI: 10.1109/IT-ELA52201.2021.9773615

Hilmi Abdullah, Subhi R. M. Zeebaree

{"title":"Android Mobile Applications Vulnerabilities and Prevention Methods: A Review","authors":"Hilmi Abdullah, Subhi R. M. Zeebaree","doi":"10.1109/IT-ELA52201.2021.9773615","DOIUrl":"https://doi.org/10.1109/IT-ELA52201.2021.9773615","url":null,"abstract":"The popularity of mobile applications is rapidly increasing in the age of smartphones and tablets. Communication, social media, news, sending emails, buying, paying, viewing movies and streams, and playing games are just a few of the many uses for them. Android is currently the most popular mobile operating system on the planet. The android platform controls the mobile operating system market, and the number of Android Mobile applications grows day by day. At the same time, the number of attacks is also increasing. The attackers take advantage of vulnerable mobile applications to execute malicious code, which could be harmful and access sensitive private data. Security and privacy of data are critical and must be prioritized in mobile application development. To cope with the security threats, mobile application developers must understand the various types of vulnerabilities and prevention methods. Open Web Application Security Project (OWASP) lists the top 10 mobile applications security risks and vulnerabilities. Therefore, this paper investigates mobile applications vulnerabilities and their solutions.","PeriodicalId":330552,"journal":{"name":"2021 2nd Information Technology To Enhance e-learning and Other Application (IT-ELA)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120966475","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

A Hybrid Load Balancing Scheme for Software Defined Networking 一种用于软件定义网络的混合负载均衡方案

2021 2nd Information Technology To Enhance e-learning and Other Application (IT-ELA) Pub Date : 2021-12-28 DOI: 10.1109/IT-ELA52201.2021.9773387

Thaeer Ghyadh Thajeel, A. Abdulhassan

{"title":"A Hybrid Load Balancing Scheme for Software Defined Networking","authors":"Thaeer Ghyadh Thajeel, A. Abdulhassan","doi":"10.1109/IT-ELA52201.2021.9773387","DOIUrl":"https://doi.org/10.1109/IT-ELA52201.2021.9773387","url":null,"abstract":"Software-Defined Networking (SDN) has attracted great interest as a new paradigm in networking. In SDN, the control and data planes are decoupled, network intelligence and state are logically centralized, and the underlying network infrastructure is abstracted from the applications. An SDN controller is an application in a software-defined networking (SDN) architecture that manages flow control for improved network management and application performance. The overloading problem and SDN controller lacks a flexible mechanism to balance load among the distributed controllers. In this paper, it introduced a server load balancer technique using a hybrid algorithm as Least connection load-balancing algorithm and Weighted Round Robin load-balancing algorithm. The performance evaluation reveals that the proposed case study based on the hybrid algorithms with weighted WRR, and LC provided the better results of minimum time of Average ping for 100 packets of size 10240 Bytes is 80.766 ms, Max Bandwidth is 16.76 (Mb/sec), Max Transfer 30.05 (MB), and Max Interval is 9 Seconds, and Speed is 0.00555 Gb/sec.","PeriodicalId":330552,"journal":{"name":"2021 2nd Information Technology To Enhance e-learning and Other Application (IT-ELA)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126888772","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

Segmentation of Brain Tumors MRI Images using a Nonparametric Method 基于非参数方法的脑肿瘤MRI图像分割

2021 2nd Information Technology To Enhance e-learning and Other Application (IT-ELA) Pub Date : 2021-12-28 DOI: 10.1109/IT-ELA52201.2021.9773468

Israa Kazem Rasheed, Haifa Taha Abd

引用次数: 0

Developing a CAD System to Detect Pulmonary Nodules from CT-Scan Images via Employing 3D-CNN 利用3D-CNN从ct扫描图像中检测肺结节的CAD系统

2021 2nd Information Technology To Enhance e-learning and Other Application (IT-ELA) Pub Date : 2021-12-28 DOI: 10.1109/IT-ELA52201.2021.9773749

O. R. Kadhim, H. J. Motlak, K. K. Abdalla

{"title":"Developing a CAD System to Detect Pulmonary Nodules from CT-Scan Images via Employing 3D-CNN","authors":"O. R. Kadhim, H. J. Motlak, K. K. Abdalla","doi":"10.1109/IT-ELA52201.2021.9773749","DOIUrl":"https://doi.org/10.1109/IT-ELA52201.2021.9773749","url":null,"abstract":"Due to high development in machine learning with satisfactory results in medical image detection and segmentation approaches, Several Computer-Aided Diagnosis (CAD) systems are adopted to help detect and diagnose pulmonary lung nodules. This paper proposes two CAD systems (Model-1 and Model-2) to classify benign or malignant tissue by adopting a three-dimension Convolution Neural network (3D-CNN) with a 3D-CT scan image. Initially, a seven convolutional layer was adopted in the first model (Model-1), with one fully connected layer. In terms of accuracy, the first proposed model outperformed the current state-of-the-art by a significant margin (98.9 percent). A block of convolution and max-pooling layers known as the inception layer is employed in the second model (Model -2). Model -2 is developed with two convolution layers and four inceptions layers to train a dense convolution neural network followed by one fully connected layer to detect malignant or benign tissue accurately. The second proposed model achieved state-of-the-art performance and significantly outperformed in accuracy levels of around (99.5%). Finally, the proposed Model (Model -2) performance is compared with some related work that has applied the same dataset or utilized a different dataset and gives a higher performance with classification accuracy reach to 99.5 %. It's also worth noting that sensitivity and specificity came out on top compared to other studies, with a 99.8 and a 99.1 percentage, respectively.","PeriodicalId":330552,"journal":{"name":"2021 2nd Information Technology To Enhance e-learning and Other Application (IT-ELA)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130661377","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

Detection of COVID-19 from X-Ray Images Using Transfer Learning Neural Networks 利用迁移学习神经网络从x射线图像中检测COVID-19

2021 2nd Information Technology To Enhance e-learning and Other Application (IT-ELA) Pub Date : 2021-12-28 DOI: 10.1109/IT-ELA52201.2021.9773657

Sayf A. Majeed, A. Darghaoth, Nama'a M. Z. Hamed, Yahya Ahmed Yahya, Sara Raed, Younis S. Dawood

{"title":"Detection of COVID-19 from X-Ray Images Using Transfer Learning Neural Networks","authors":"Sayf A. Majeed, A. Darghaoth, Nama'a M. Z. Hamed, Yahya Ahmed Yahya, Sara Raed, Younis S. Dawood","doi":"10.1109/IT-ELA52201.2021.9773657","DOIUrl":"https://doi.org/10.1109/IT-ELA52201.2021.9773657","url":null,"abstract":"With the continued rise in the number of infected people and deaths from the coronavirus (COVID-19) daily, along with the collapse of the health care systems in many countries of the world, especially in diagnosing the virus, it becomes necessary to devise an achievable and rapid way for diagnosing the virus. Since radiographs like X-ray images and Computed Tomography (CT) scans are broadly available at public health amenities, hospital Emergency Rooms (ERs), as well as at non-urban clinics. Therefore, they might be utilized for the rapid detection of COVID-19 induced lung infections. In this paper, for automating the detection of COVID-19 from X-ray images, deep learning techniques have been used to distinguish between (COVID-19) and normal cases. A dataset used by this work is publicly published, which comprised 5000 Chest X-ray images with their labels. A subset of 2000 X-ray images was used to train two trendy convolutional neural networks, which are AlexNet and ResNet50. While the remaining 3000 images were used for testing. The parameters of these network models have been adjusted precisely to achieve optimum detection decision. Results show these models can achieve an accuracy of nearly 99.6% with F1-Scores of 0.939 for COVID-19 and 0.998 for non-COVID-19 via the AlexNet model, while the ResNet50 model realized an accuracy of 99.3% with F1-Scores of 0.91 and 0.996 for COVID-19 and non-COVID-19, respectively. From these results, the AlexNet model can be an enthralling tool to assist radiologists in the early diagnosis and detection of COVID-19 cases.","PeriodicalId":330552,"journal":{"name":"2021 2nd Information Technology To Enhance e-learning and Other Application (IT-ELA)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134443545","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

Arabic Sign Language Detection Using Deep Learning Based Pose Estimation 基于姿态估计的深度学习阿拉伯手语检测

2021 2nd Information Technology To Enhance e-learning and Other Application (IT-ELA) Pub Date : 2021-12-28 DOI: 10.1109/IT-ELA52201.2021.9773404

M. Ismail, Shefa A. Dawwd, F. Ali

引用次数: 2