2020 International Conference on Advanced Science and Engineering (ICOASE)最新文献_第4页

A Correlation Analysis Between ISO 25010 based Modularity and CK Metrics in Object-Oriented Software 面向对象软件中基于ISO 25010的模块化与CK度量的相关性分析

2020 International Conference on Advanced Science and Engineering (ICOASE) Pub Date : 2020-12-23 DOI: 10.1109/ICOASE51841.2020.9436617

Dini Yuniasri, S. Rochimah, A. B. Raharjo

引用次数: 1

A Fusion Scheme of Texture Features for COVID-19 Detection of CT Scan Images 基于纹理特征融合的CT扫描图像COVID-19检测方法

2020 International Conference on Advanced Science and Engineering (ICOASE) Pub Date : 2020-12-23 DOI: 10.1109/ICOASE51841.2020.9436538

D. A. Zebari, A. Abdulazeez, D. Zeebaree, Merdin Shamal Salih

{"title":"A Fusion Scheme of Texture Features for COVID-19 Detection of CT Scan Images","authors":"D. A. Zebari, A. Abdulazeez, D. Zeebaree, Merdin Shamal Salih","doi":"10.1109/ICOASE51841.2020.9436538","DOIUrl":"https://doi.org/10.1109/ICOASE51841.2020.9436538","url":null,"abstract":"Coronavirus (COVID-19) is a new contagious disease reasoned by a new virus that is widely spread over the world, this virus never has been identified in humans before. Respiratory disease can be affected by this virus such as flu with several symptoms, for example, fever, headache, cough, and pneumonia. COVID-19 presence in humans can be tested through blood samples or sputum while the result can be obtained in days. Further, biomedical image analysis assists in showing signs of pneumonia in a patient. Therefore, this paper aims to provide a fully automatic COVID-19 identification system by proposing a new fusion scheme of texture features for CT scan images. This paper presents a fusion scheme based on a machine learning system using three significant texture features, namely, Local Binary Pattern (LBP), Fractal Dimension (FD), and Grey Level Co-occurrence Matrices (GLCM). In experimental results, to demonstrate the efficiency of the proposed scheme we have collected 300 CT scan images from a publicly available database. The experimental result shows the performance of LBP, FD, and GLCM obtained an accuracy of 89.87%, 87.84%, and 90.98%, respectively while the proposed scheme yields better results by achieving 96.91% accuracy.","PeriodicalId":126112,"journal":{"name":"2020 International Conference on Advanced Science and Engineering (ICOASE)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132615579","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: 13

Semantic Document Clustering using K-means algorithm and Ward's Method 基于K-means算法和Ward方法的语义文档聚类

2020 International Conference on Advanced Science and Engineering (ICOASE) Pub Date : 2020-12-23 DOI: 10.1109/ICOASE51841.2020.9436588

Niyaz Salih, Karwan Jacksi

{"title":"Semantic Document Clustering using K-means algorithm and Ward's Method","authors":"Niyaz Salih, Karwan Jacksi","doi":"10.1109/ICOASE51841.2020.9436588","DOIUrl":"https://doi.org/10.1109/ICOASE51841.2020.9436588","url":null,"abstract":"Nowadays in the age of technology, textual documents are rapidly growing over the internet. Offline and online documents, websites, e-mails, social network and blog posts, are archived in electronic structured databases. It is very hard to maintain and reach these documents without acceptable ranking and provide demand clustering while there is classification without any details. This paper presents an approach based on semantic similarity for clustering documents using the NLTK dictionary. The procedure is done by defining synopses from IMDB and Wikipedia datasets, tokenizing and stemming them. Next, a vector space is constructed using TFIDF, and the clustering is done using the ward's method and K-mean algorithm. WordNet is also used to semantically cluster documents. The results are visualized and presented as an interactive website describing the relationship between all clusters. For each algorithm three scenarios are considered for the implementations: 1) without preprocessing, 2) preprocessing without stemming, and 3) preprocessing with stemming. The Silhouette metric and seven other metrics are used to measure the similarity with the five different datasets. Using the K-means algorithm, the best similarity ratio acquired from the Silhouette metric with (nltk-Reuters) dataset for all clusters, and the highest ratio is when k=10. Similarly, with Ward's algorithm, the highest similarity ratio of the Silhouette metric obtained using (IMDB and Wiki top 100 movies, and nltk-brown) datasets together for all clusters, and best similarity ratio is obtained when k=5 using the (IMDB and Wiki top 100 movies) dataset. The results are compared with the literature, and the outcome exposed that the Ward's method outperforms the results of K-means for small datasets.","PeriodicalId":126112,"journal":{"name":"2020 International Conference on Advanced Science and Engineering (ICOASE)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132361898","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

Video Delivery Based on Random Linear Network Coding 基于随机线性网络编码的视频传输

2020 International Conference on Advanced Science and Engineering (ICOASE) Pub Date : 2020-12-23 DOI: 10.1109/ICOASE51841.2020.9436533

Amenah M. Younus, M. H. Al-Jammas

引用次数: 0

COVID-19 Diagnosis from Chest X-ray Images Using Deep Learning Approach 基于深度学习方法的胸部x线图像COVID-19诊断

2020 International Conference on Advanced Science and Engineering (ICOASE) Pub Date : 2020-12-23 DOI: 10.1109/ICOASE51841.2020.9436614

N. Qaqos, O. Kareem

{"title":"COVID-19 Diagnosis from Chest X-ray Images Using Deep Learning Approach","authors":"N. Qaqos, O. Kareem","doi":"10.1109/ICOASE51841.2020.9436614","DOIUrl":"https://doi.org/10.1109/ICOASE51841.2020.9436614","url":null,"abstract":"Coronavirus (COVID-19) disease is an infectious disease caused by the newly and deadly pneumonia type identified Coronavirus2 (SARS-CoV-2). A real-time Reverse Transcription Polymerase Chain Reaction (RT-PCR) is the main method and has been regarded as the gold standard for diagnosing the COVID-19. Strict requirements and the limited supply of RT-PCR kits for the laboratory environment leads to delay in the accurate diagnosis of patients in addition to the test takes 4-6 hours to obtain the results. To tackle this problem, radiological images such as chest X-rays and CT scan could be the answer to test the COVID-19 infection rapidly and more efficiently. In this paper, an efficient proposed Convolution Neural Network (CNN) architecture model for COVID-19 detection based on chest X-ray images is presented. The proposed model is developed to provide accurate detection for binary classification (Normal vs. COVID-19), three class classification (Normal vs. COVID-19 vs. Pneumonia), and four class classification (Normal vs. COVID-19 vs. Pneumonia vs. Tuberculosis (TB)). Our proposed model produced an overall testing accuracy of 99.7%, 95.02%, and 94.53% for binary, three, and four class classifications, respectively. A comparison is made between this work and others shows the superior of this work over the others.","PeriodicalId":126112,"journal":{"name":"2020 International Conference on Advanced Science and Engineering (ICOASE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115339079","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: 9

Trust Evaluation Model Based on Statistical Tests in Social Network 基于统计检验的社会网络信任评价模型

2020 International Conference on Advanced Science and Engineering (ICOASE) Pub Date : 2020-12-23 DOI: 10.1109/ICOASE51841.2020.9436543

Aseel Hussein Zahi, S. T. Hasson

引用次数: 0

Single Heartbeat ECG Biometric Recognition using Convolutional Neural Network 基于卷积神经网络的单次心电生物特征识别

2020 International Conference on Advanced Science and Engineering (ICOASE) Pub Date : 2020-12-23 DOI: 10.1109/ICOASE51841.2020.9436592

Dalal Alduwaile, Md. Saiful Islam

{"title":"Single Heartbeat ECG Biometric Recognition using Convolutional Neural Network","authors":"Dalal Alduwaile, Md. Saiful Islam","doi":"10.1109/ICOASE51841.2020.9436592","DOIUrl":"https://doi.org/10.1109/ICOASE51841.2020.9436592","url":null,"abstract":"Biometrics plays a crucial role in information security to identify and constantly validate individuals using physiological characteristics. During the last decade, Electrocardiogram (ECG) signal has emerged as a biometric modality due to its desirable characteristics for a reliable recognition system. However, the duration of the signal required for the recognition is long, and it is still one of the limitations of existing biometric recognition methods for their acceptability as a biometric modality. In this paper, a method is proposed to use the single heartbeat ECG signal for biometric recognition of a person with the help of deep machine learning technique. We investigate the use of a light and a pre-trained convolutional neural network for the classification of single heartbeat ECG signal segmented based on the R-peak and transformed used continuous wavelet transformation. Different scenarios of segmentations experimented; Fixed length, variable length, blind, and feature depending segmentations. The performance of the proposed method was tested with a landmark dataset available online. We obtained 99.94% and 99.83% recognition accuracy for a window of ECG signal for a single heartbeat outperforming existing methods.","PeriodicalId":126112,"journal":{"name":"2020 International Conference on Advanced Science and Engineering (ICOASE)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116548778","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: 6

Aplicaions of Digital Signal Processing in all sciences 数字信号处理在所有科学中的应用

2020 International Conference on Advanced Science and Engineering (ICOASE) Pub Date : 2020-12-23 DOI: 10.1109/icoase51841.2020.9436607

引用次数: 0

ECN-Marking with CoDel and its Compatibility with Different TCP Congestion Control Algorithms 基于CoDel的ecn标记及其与不同TCP拥塞控制算法的兼容性

2020 International Conference on Advanced Science and Engineering (ICOASE) Pub Date : 2020-12-23 DOI: 10.1109/ICOASE51841.2020.9436575

Dhulfiqar A. Alwahab, S. Laki

{"title":"ECN-Marking with CoDel and its Compatibility with Different TCP Congestion Control Algorithms","authors":"Dhulfiqar A. Alwahab, S. Laki","doi":"10.1109/ICOASE51841.2020.9436575","DOIUrl":"https://doi.org/10.1109/ICOASE51841.2020.9436575","url":null,"abstract":"In order to solve the bufferbloat problem, many Active Queue Management (AQM) algorithms have been proposed, such as Random Early Detection (RED), Proportional Integral Controller Enhanced (PIE), Controlled Delay (CoDel), etc. Among them, CoDel has widely been adapted by devices at the last mile. It maintains the sojourn times of transmitted packets and compares them to a pre-defined target time. When the packet's sojourn time exceeds this target, it is interpreted as an evidence of bufferbloat and CoDel applies its drop strategy. This paper examines how CoDel performs when instead of packet dropping the Explicit Congestion Notification (ECN) marking is applied, considering Reno, Cubic and BRR congestion control algorithms in the TCP flows. The evaluation relies on the Programming Protocol-independent Packet Processors (P4) implementation of CoDel, extending it with mechanisms needed for ECN support. Results show that ECN-CoDel can significantly reduce the number of re-transmissions and properly work with classic congestion controls like TCP Cubic and TCP Reno. For TCP BBR, almost zero packet drop ratio can be experienced, but the queue delay is higher than with the original CoDel. Based on the results of this work, the characteristics of the universe AQM had been drawn in the conclusion.","PeriodicalId":126112,"journal":{"name":"2020 International Conference on Advanced Science and Engineering (ICOASE)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124958847","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