IJCI. International Journal of Computers and Information最新文献_第6页

Frequent Pattern Mining over Streaming Data: From models to research challenges 流式数据的频繁模式挖掘:从模型到研究挑战

IJCI. International Journal of Computers and Information Pub Date : 2021-12-01 DOI: 10.21608/ijci.2021.207862

A. Saad, Rashed K. Salem, H. Abdel-Kader

引用次数: 0

Collaborative Filtering Using Explicit and Implicit Ratings for Arabic Dataset 使用显式和隐式评级的阿拉伯语数据集协同过滤

IJCI. International Journal of Computers and Information Pub Date : 2021-12-01 DOI: 10.21608/ijci.2021.207735

Rouhia M. Sallam, M. Hussien, Hamdy M. Mousa

{"title":"Collaborative Filtering Using Explicit and Implicit Ratings for Arabic Dataset","authors":"Rouhia M. Sallam, M. Hussien, Hamdy M. Mousa","doi":"10.21608/ijci.2021.207735","DOIUrl":"https://doi.org/10.21608/ijci.2021.207735","url":null,"abstract":"As the amount of digital information recorded on the internet increases, the need for flexible recommender systems is growing. Collaborative Filtering (CF) has been widely used in the E-commerce industry. A variety of input data was used, either implicitly or explicitly, to provide personalized recommendations for specific users and helped the system to improve its performance. Traditional CF algorithms relied solely on users' numeric ratings to identify user preferences. The majority of current research in recommender systems is focusing on a single implicit or explicit rating. In this paper, we combine explicit rating and implicit rating for user reviews to build the best recommender system using a large Arabic dataset. In addition, we employ two powerful techniques in the creation of our recommender system. First, we use Item-based CF and use cosine vector similarity to calculate the similarity between items. Second, we use Singular Value Decomposition (SVD) to reduce dimensionality, boost efficiency, and solve scalability and sparsity problems in CF. The proposed approach improves the experiment results by reducing mean absolute and root mean squared errors. The experimental results show to perform better when using both explicit and implicit ratings compared with using only one type of ratings. Keywords— Collaborative filtering (CF) Explicit and Implicit Ratings, A Large-Scale Arabic Book Reviews (LABR), LABR Lexicon.","PeriodicalId":137729,"journal":{"name":"IJCI. International Journal of Computers and Information","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116867889","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

Enhanced Security System of Internet of Things data streaming 增强物联网数据流安全体系

IJCI. International Journal of Computers and Information Pub Date : 2021-12-01 DOI: 10.21608/ijci.2021.207860

A. Saada, Hatem Abdel-Kader, A. Ali

引用次数: 0

COVID-19 Detection Based on Chest X-Ray Image Classification using Tailored CNN Model 基于定制CNN模型的胸片图像分类的COVID-19检测

IJCI. International Journal of Computers and Information Pub Date : 2021-12-01 DOI: 10.21608/ijci.2021.207825

M. Zaki, Khalid Amin, A. Hamad

引用次数: 1

Diffusion Models for Social Analysis, Influence and Learning 社会分析、影响和学习的扩散模型

IJCI. International Journal of Computers and Information Pub Date : 2021-12-01 DOI: 10.21608/ijci.2021.207863

N. Badr, Hatem Abdel-Kader, Asmaa Ali

{"title":"Diffusion Models for Social Analysis, Influence and Learning","authors":"N. Badr, Hatem Abdel-Kader, Asmaa Ali","doi":"10.21608/ijci.2021.207863","DOIUrl":"https://doi.org/10.21608/ijci.2021.207863","url":null,"abstract":"Social networks are complicated by millions of users interacting and creating massive amounts of content. The problem is that any unobservable changes in network structure can result in dramatic swings in the spread of new ideas and behaviors between users. This diffusion process leads to numerous latent information that can be extracted, analyzed, and used in different applications, including market forecasting, rumor control, disease modeling, and opinion monitoring. Furthermore, mining social media big data helps to ease tracking propagated data and trends across the world. In this article, we address the study of diffusion models in social networks. We discuss three significant categories of diffusion models: contagion, social influence, and social learning models with different enhancements applied to improve performance. The aim is to study diffusion models in social networks to effectively understand how innovation and information spread over individuals and predict future trends. Also, identifying the most influential users in social networks is addressed to help spread knowledge faster and prevent harmful content like viruses or bad online behavior from spreading. Keywords—Social Network, Information Diffusion, social influence, Predictive Models, Contusion.","PeriodicalId":137729,"journal":{"name":"IJCI. International Journal of Computers and Information","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131473019","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

An Efficient Person Re-Identification Method Based on Deep Transfer Learning Techniques 基于深度迁移学习技术的高效人员再识别方法

IJCI. International Journal of Computers and Information Pub Date : 2021-12-01 DOI: 10.21608/ijci.2021.207824

Shimaa Saber, Khalid Amin, M. Adel Hammad

{"title":"An Efficient Person Re-Identification Method Based on Deep Transfer Learning Techniques","authors":"Shimaa Saber, Khalid Amin, M. Adel Hammad","doi":"10.21608/ijci.2021.207824","DOIUrl":"https://doi.org/10.21608/ijci.2021.207824","url":null,"abstract":"Person re-identification (re-id) is a significant process in applications of video analysis. Several applications in different areas such as airports and stations are used multiple cameras in different places for monitoring and investigation, which are expensive and can be easily abused. Therefore, automatic person re-identification techniques are highly required. The main issue of this field is to find distinguishing features that represent the person. In this paper, we proposed an efficient method to extract the main features based on the deep transfer learning technique for a person re-id system. In addition, we employed a support vector classifier (SVC) as a separated classifier for the final decision to increase the accuracy of the system. We employed several publicly available datasets, which are the main datasets used for person re-id purposes in the literature. The proposed method achieved the best accuracy of 89.59% for rank-1, which outperforms the state-of the-art methods. Finally, the simulation results reveal that the proposed system is efficient prior to person re-id. Keywords— person re-identification; transfer learning; SVC; deep learning; video analysis.","PeriodicalId":137729,"journal":{"name":"IJCI. International Journal of Computers and Information","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128565042","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

A proposed learning model based on fog computing technology 提出一种基于雾计算技术的学习模型

IJCI. International Journal of Computers and Information Pub Date : 2021-12-01 DOI: 10.21608/ijci.2021.207858

M. Saieed, Hatem Ahmed, N. Abbas

{"title":"A proposed learning model based on fog computing technology","authors":"M. Saieed, Hatem Ahmed, N. Abbas","doi":"10.21608/ijci.2021.207858","DOIUrl":"https://doi.org/10.21608/ijci.2021.207858","url":null,"abstract":"These days, e-learning has become indispensable as it facilitates the learning process and enables the students to obtain educational resources faster. With the increase in the number of learners and the number of requests on the e-learning frameworks, the e-learning framework has become suffering from some shortcomings, which prompted to search for a model that could facilitate students' access to educational resources. Therefore, in this research, a model based on fog computing was proposed, in which the e-learning resources are closer to the end-users. A test of the proposed model was conducted on a sample of students to measure the response time. Result data are collected and analyzed. The response time resulting from the proposed model compared with that resulting from the current model based on cloud computing. It founded that the proposed model has advantages as the number of students is divided on the fog computing nodes, unlike what happens in the cloud-based model in which the students did not split in the required way, as dividing students reduces the response time of the learning framework. Finally, using fog computing in a learning environment makes the learning resources closer to the end-user at the edge layer as described in the results of this paper.","PeriodicalId":137729,"journal":{"name":"IJCI. International Journal of Computers and Information","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116793089","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

A Comparative Analysis for Predicting Airline Arrival Delays 航空公司到达延误预测的比较分析

IJCI. International Journal of Computers and Information Pub Date : 2021-12-01 DOI: 10.21608/ijci.2021.207759

A. Ghalwash, Hamdy M. Mousa, Heba Elbeh

引用次数: 0

New CAPTCHA Approach for Securing Online Social Network and Webpages Using Extended Finite Automata (XFA CAPTCHA) 使用扩展有限自动机(XFA CAPTCHA)保护在线社交网络和网页的新CAPTCHA方法

IJCI. International Journal of Computers and Information Pub Date : 2021-12-01 DOI: 10.21608/ijci.2021.207733

Menna Mostafa, Hamdy M. Mousa, Medhat A. Tawfik

引用次数: 0

A novel optimization algorithm for the missing data in HCC based on multiple imputation and genetic algorithm 一种基于多重插值和遗传算法的肝癌缺失数据优化算法

IJCI. International Journal of Computers and Information Pub Date : 2021-12-01 DOI: 10.21608/ijci.2021.207861

Y. Salah, M. Adel Hammad, Hatem Abdel-Kader

引用次数: 0