2022 International Conference on Frontiers of Information Technology (FIT)最新文献

筛选

英文中文

Rumor Source Detection on Interconnected Social Networks 基于互联社交网络的谣言源检测

2022 International Conference on Frontiers of Information Technology (FIT) Pub Date : 2022-12-01 DOI: 10.1109/FIT57066.2022.00030

Amanullah Khan, M. Shaikh, F. Sherwani, S. Hassan, Aymen Kalifa Soluman Ahteewash

{"title":"Rumor Source Detection on Interconnected Social Networks","authors":"Amanullah Khan, M. Shaikh, F. Sherwani, S. Hassan, Aymen Kalifa Soluman Ahteewash","doi":"10.1109/FIT57066.2022.00030","DOIUrl":"https://doi.org/10.1109/FIT57066.2022.00030","url":null,"abstract":"Social networks are the most widely used platform for spreading information but it is also used to spread false rumor, using source identification we can held the source of spreading rumor responsible, also it can defeat the rumor as well, it has been the area of research for many researchers but due to limitation of different proposed study, it could not be used in the real environment, in this study we propose a methodology that is capable of identifying the rumor source on interconnected network. Interconnected network is considered as the network in which a rumor is propagated from one network to another and identifying the rumor from an independent network does not meet the requirement of identifying the real source. In this study we have proposed a methodology by which we can achieve high performance and accuracy based on an ensemble fusion and maximum voting on different centrality measures. We evaluate our model on two real datasets Facebook and U.S. Power Grid using error distance, this is the first attempt identifying the rumor source on interconnected network but for the acceptability of this model, we evaluate our results with the other approaches of rumor source identification on single network and found satisfactory results and our model outperform LPSI, we also compare our ensemble model with classical centrality based model ecc+clo which is most accurate model till date and found that our model outperforms the classical centrality’s model.","PeriodicalId":102958,"journal":{"name":"2022 International Conference on Frontiers of Information Technology (FIT)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128622231","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

Covid severity prediction: Who cares about the data quality? Covid严重性预测:谁关心数据质量?

2022 International Conference on Frontiers of Information Technology (FIT) Pub Date : 2022-12-01 DOI: 10.1109/FIT57066.2022.00049

Teodora Nae, Johannes Krabbe, F. Bukhsh, J. J. Arachchige, Faizan Ahmed

{"title":"Covid severity prediction: Who cares about the data quality?","authors":"Teodora Nae, Johannes Krabbe, F. Bukhsh, J. J. Arachchige, Faizan Ahmed","doi":"10.1109/FIT57066.2022.00049","DOIUrl":"https://doi.org/10.1109/FIT57066.2022.00049","url":null,"abstract":"COVID-19 is an ongoing pandemic disrupting daily life and overwhelming the healthcare infrastructure. Since the outburst of the pandemic, researchers have used various techniques to predict many aspects of the disease, including mortality rate and severity. The reproducibility of this research is challenging due to varying methodologies used to collect data, data quality, vague description of methodological approach to training prediction models, over-relying on data imputation, and over-fitting. This paper focuses on these challenges and provides a short yet comprehensive review of research on COVID mortality and severity prediction. The emphasis is on the reproducibility of the results and data quality issues. To further elaborate on the issue, we report the development of severity prediction models using two data sets. CRISP-DM is used as a methodological approach. We analyze and criticize the quality of the used data sets and how they affect the performance and limitations of the trained models. We conclude this paper with comments on data quality issues, the importance of reproducibility, and suggestions to improve reproducibility.","PeriodicalId":102958,"journal":{"name":"2022 International Conference on Frontiers of Information Technology (FIT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123455961","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 Systematic Literature Review on Block-chain Attacks 区块链攻击的系统文献综述

2022 International Conference on Frontiers of Information Technology (FIT) Pub Date : 2022-12-01 DOI: 10.1109/FIT57066.2022.00050

Muhammad Azeem, Jabar Mahmood, Tayyaba Aslam, K. Shahzadi

引用次数: 0

首页上一页