2020 International Conference on Advanced Science and Engineering (ICOASE)最新文献

Data Security System for IoT Applications 物联网应用的数据安全系统

2020 International Conference on Advanced Science and Engineering (ICOASE) Pub Date : 2020-12-23 DOI: 10.1109/ICOASE51841.2020.9436579

Shahed Mohammed, M. H. Al-Jammas

引用次数: 0

Transcript Validation System using biometric characteristics 使用生物特征的转录验证系统

2020 International Conference on Advanced Science and Engineering (ICOASE) Pub Date : 2020-12-23 DOI: 10.1109/ICOASE51841.2020.9436576

Zahraa T. Al Ali, Ahmad M. Al Kababji, Mohammad B. Shukur

{"title":"Transcript Validation System using biometric characteristics","authors":"Zahraa T. Al Ali, Ahmad M. Al Kababji, Mohammad B. Shukur","doi":"10.1109/ICOASE51841.2020.9436576","DOIUrl":"https://doi.org/10.1109/ICOASE51841.2020.9436576","url":null,"abstract":"Recently transcripts and confirmations verification become one of the important issues for universities, institutes and organizations. This problem arises due to the spread out of many programs and modern technologies that allows the forgers to forge transcripts and confirmations which cannot be recognized by registrar persons how work for institutions or other entities. So, it becomes necessary for the entities to insure the validation of transcripts, confirmations or other documents from the original source. In this paper, we design a transcript validation system that can be used by different entities to reduce the time required for transcripts verification based on the information saved in a database. The system has been supported by a Graphic User Interface (GUI) to make the system as easy as possible to be used by users. The system works to verify the existing signatures on the transcript and compare them with the database by using offline signature verification depends on neural network method, also the system verifies the existing photo on the transcript using regression method in order to increase the reliability. The system has proven its reliability through high acceptance ratio of the genuine signatures and high rejection ratio for forged and random signatures.","PeriodicalId":126112,"journal":{"name":"2020 International Conference on Advanced Science and Engineering (ICOASE)","volume":"30 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":"114685066","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

Twitter Sentiment Analysis using an Ensemble Weighted Majority Vote Classifier 使用集成加权多数投票分类器的Twitter情感分析

2020 International Conference on Advanced Science and Engineering (ICOASE) Pub Date : 2020-12-23 DOI: 10.1109/ICOASE51841.2020.9436590

R. H. H. Aziz, Nazife Dimililer

{"title":"Twitter Sentiment Analysis using an Ensemble Weighted Majority Vote Classifier","authors":"R. H. H. Aziz, Nazife Dimililer","doi":"10.1109/ICOASE51841.2020.9436590","DOIUrl":"https://doi.org/10.1109/ICOASE51841.2020.9436590","url":null,"abstract":"Sentiment analysis extracts the emotions expressed in text and has been employed in many fields including politics, elections, movies, retail businesses and in recent years microblogs to understand, track and control the human sentiments or reactions toward products events or ideas. Nevertheless challenges such as different styles of writing, use of negation and sarcasm, existence of spelling mistakes, invention of new words etc. provide obstacle in the correct classification of sentiments. This paper provides an ensemble of classifiers framework for sentiment analysis. The proposed weighted majority voting ensemble method combines six models including Naïve Bayes, Logistic Regression, Stochastic Gradient Descent, Random Forest, Decision Tree and Support Vector Machine to form a single classifier. Weights of the individual classifiers of the ensemble are chosen as accuracy or Fl-score by optimizing their performance. This approach combines models based on the simple majority voting as opposed to the one based on weighted majority voting. Additionally, a comparison is drawn among these six individual classifiers to evaluate their performance. The proposed ensemble model is tested on some existing sentiment datasets, including SemEval 2017 Task 4A, 4B and 4C. The results demonstrate that the Logistic Regression classifier is optimal as compared to other individual classifiers. Furthermore, the proposed ensemble weighted majority voting classifier with the six individual classifiers performs better compared to the simple majority voting and all independent classifiers.","PeriodicalId":126112,"journal":{"name":"2020 International Conference on Advanced Science and Engineering (ICOASE)","volume":"33 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":"128137020","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: 7

Email Common Weaknesses and Enumeration Through Software Customer Perspective 从软件客户的角度分析电子邮件的常见弱点和列举

2020 International Conference on Advanced Science and Engineering (ICOASE) Pub Date : 2020-12-23 DOI: 10.1109/ICOASE51841.2020.9436624

Falak Uossien Hasan

引用次数: 0

Distributed Denial of Service Attack Mitigation using High Availability Proxy and Network Load Balancing 使用高可用性代理和网络负载平衡缓解分布式拒绝服务攻击

2020 International Conference on Advanced Science and Engineering (ICOASE) Pub Date : 2020-12-23 DOI: 10.1109/ICOASE51841.2020.9436545

R. Zebari, Subhi R. M. Zeebaree, A. Sallow, Hanan M. Shukur, Omar M. Ahmad, Karwan Jacksi

{"title":"Distributed Denial of Service Attack Mitigation using High Availability Proxy and Network Load Balancing","authors":"R. Zebari, Subhi R. M. Zeebaree, A. Sallow, Hanan M. Shukur, Omar M. Ahmad, Karwan Jacksi","doi":"10.1109/ICOASE51841.2020.9436545","DOIUrl":"https://doi.org/10.1109/ICOASE51841.2020.9436545","url":null,"abstract":"Nowadays, cybersecurity threat is a big challenge to all organizations that present their services over the Internet. Distributed Denial of Service (DDoS) attack is the most effective and used attack and seriously affects the quality of service of each E-organization. Hence, mitigation this type of attack is considered a persistent need. In this paper, we used Network Load Balancing (NLB) and High Availability Proxy (HAProxy) as mitigation techniques. The NLB is used in the Windows platform and HAProxy in the Linux platform. Moreover, Internet Information Service (IIS) 10.0 is implemented on Windows server 2016 and Apache 2 on Linux Ubuntu 16.04 as web servers. We evaluated each load balancer efficiency in mitigating synchronize (SYN) DDoS attack on each platform separately. The evaluation process is accomplished in a real network and average response time and average CPU are utilized as metrics. The results illustrated that the NLB in the Windows platform achieved better performance in mitigation SYN DDOS compared to HAProxy in the Linux platform. Whereas, the average response time of the Window webservers is reduced with NLB. However, the impact of the SYN DDoS on the average CPU usage of the IIS 10.0 webservers was more than those of the Apache 2 webservers.","PeriodicalId":126112,"journal":{"name":"2020 International Conference on Advanced Science and Engineering (ICOASE)","volume":"54 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":"123909223","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: 14

Selective Crystallization of Highly Dispersed Silicoaluminophosphate SAPO-11 高分散磷酸硅铝SAPO-11的选择性结晶

2020 International Conference on Advanced Science and Engineering (ICOASE) Pub Date : 2020-12-23 DOI: 10.1109/ICOASE51841.2020.9436625

Z. Khayrullina, K. Ahmed, M. Agliullin

引用次数: 0

Privacy Preserving Association Rules based on Compression and Cryptography (PPAR-CC) 基于压缩和加密的隐私保护关联规则(PPAR-CC)

2020 International Conference on Advanced Science and Engineering (ICOASE) Pub Date : 2020-12-23 DOI: 10.1109/ICOASE51841.2020.9436603

W. A. Salman, S. Sadkhan

{"title":"Privacy Preserving Association Rules based on Compression and Cryptography (PPAR-CC)","authors":"W. A. Salman, S. Sadkhan","doi":"10.1109/ICOASE51841.2020.9436603","DOIUrl":"https://doi.org/10.1109/ICOASE51841.2020.9436603","url":null,"abstract":"Privacy-Preserving Data Mining (PPDM) is a modern technique through which data is mined while maintaining the confidentiality and privacy of sensitive information from unauthorized persons. The Privacy-Preserving Association Rules Mining (PPARM) is the most important technique for privacy-preserving data mining. PPARM means the mining of association rules with preserving the non-disclosure of sensitive correlations among items or features for competitors or the public, especially data of sensitive organizations such as financial organizations and others. In this paper, we propose an approach to hiding association rules after performing the mining process and obtaining knowledge through vertical and horizontal compressing then encoded the compressing form by using cryptography methods. The proposed approach is resistant to many known attacks and is undetectable because it includes three stages of compression and encryption in which the basic representation and size of the data change dramatically. The proposed approach significantly reduces storage space, maintains knowledge security, reduces transmission time, and facilitates the transmission of knowledge over any network.","PeriodicalId":126112,"journal":{"name":"2020 International Conference on Advanced Science and Engineering (ICOASE)","volume":"8 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":"134265974","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

Mini Jarvis Patrick -Based Graph Clustering for Scientific Institutions 基于微型Jarvis Patrick的科研机构图聚类

2020 International Conference on Advanced Science and Engineering (ICOASE) Pub Date : 2020-12-23 DOI: 10.1109/ICOASE51841.2020.9436589

Hussein Z. Almngoshi, Eman S. Alshamery

{"title":"Mini Jarvis Patrick -Based Graph Clustering for Scientific Institutions","authors":"Hussein Z. Almngoshi, Eman S. Alshamery","doi":"10.1109/ICOASE51841.2020.9436589","DOIUrl":"https://doi.org/10.1109/ICOASE51841.2020.9436589","url":null,"abstract":"The competition between scientific institutions is increased every day. Every institution tends to improve its reputation by producing and publishing high-quality scientific research. Clustering and evaluating the educational institutions are important for professors, policymakers, as well as students. This research aims to develop a Jarvis-Patrick algorithm for scientific institutions clustering, which is one of the graph-based techniques. It suffers from the problem of a large number of clusters. In addition to the Shared Nearest Neighbor (SNN) similarity included in the standard Mini Jarvis-Patrick (MJP) algorithm, the merging clusters of low separation are proposed to improve algorithm performance. The SNN similarity measures the number of shared neighbors between every two points in the data. Besides that, the merging is implemented by combining the clusters that have low separation. The proposed algorithm takes advantage of cluster validity measures (separation) to produce rational and reasonable clusters. The SciVal dataset for USA scientific institutions 2016–2018 dataset is used. The proposed MJP detected 8 clusters (Cluster0 %6, Cluster16%, Cluster2 6%, Cluster3 2%, Cluster4 7%, Cluster5 7.3%, Cluster6 26.6%, Cluster7 32%). In addition to the standard MJP, the proposed technique is compared with known methods; the cobweb, DBSCAN, and HierarchicalClusterer. The results have proved that the MJP is superior to other methods.","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":"123092257","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

Decision Making Approaches in Cognitive Radio-Status, Challenges and Future Trends 认知无线电中的决策方法——现状、挑战和未来趋势

2020 International Conference on Advanced Science and Engineering (ICOASE) Pub Date : 2020-12-23 DOI: 10.1109/ICOASE51841.2020.9436597

Akeel Bdrany, S. Sadkhan

引用次数: 3

Brain Tumor Detection and Classifiaction Using CNN Algorithm and Deep Learning Techniques 基于CNN算法和深度学习技术的脑肿瘤检测与分类

2020 International Conference on Advanced Science and Engineering (ICOASE) Pub Date : 2020-12-23 DOI: 10.1109/ICOASE51841.2020.9436599

Sultan B. Fayyadh, A. Ibrahim

{"title":"Brain Tumor Detection and Classifiaction Using CNN Algorithm and Deep Learning Techniques","authors":"Sultan B. Fayyadh, A. Ibrahim","doi":"10.1109/ICOASE51841.2020.9436599","DOIUrl":"https://doi.org/10.1109/ICOASE51841.2020.9436599","url":null,"abstract":"Detection of brain tumors through image processing is done by using an integrated approach. This work was planned to present a system to classify and detect brain tumors using the CNN algorithm and deep learning techniques from MRI images to the most popular tumors in the world. This work was performed using an MRI image dataset as input, Preprocessing and segmentation were performed to enhance the images. Our neural network design is simpler to train and it's possible to run it on another computer because the designed algorithm requires fewer resources. The dataset was used contains 3064 images related to different tumors meningioma (708 slices), glioma (1426 slices), and pituitary tumor (930 slices), the convolution neural network (CNN) was used through which the brain tumor is classified according to a special structure of this algorithm consisting of several layers, The implementation of the neural network consist blocks each block include many types of layer, first, the input layer then followed by convolution layer, then the activation function that used was Rectified Linear Units (ReLU), normalization layer, and pooling layer. Also, it contains the classification layer fully connected and softmax layer the overall accuracy rate obtained from the proposed approach was (98,029%) in the testing stage and (98.29%) in the training stage for the data set were used.","PeriodicalId":126112,"journal":{"name":"2020 International Conference on Advanced Science and Engineering (ICOASE)","volume":"7 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":"127739640","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