2020 Conference on Information Communications Technology and Society (ICTAS)最新文献

The Adoption of Automation in Cyber Forensics 自动化在网络取证中的应用

2020 Conference on Information Communications Technology and Society (ICTAS) Pub Date : 2020-03-01 DOI: 10.1109/ICTAS47918.2020.233977

D. Hayes, M. Kyobe

引用次数: 3

ICTAS 2020 Breaker Page ICTAS 2020断路器页面

2020 Conference on Information Communications Technology and Society (ICTAS) Pub Date : 2020-03-01 DOI: 10.1109/ictas47918.2020.9082443

引用次数: 0

ICTAS 2020 Preface

2020 Conference on Information Communications Technology and Society (ICTAS) Pub Date : 2020-03-01 DOI: 10.1109/ictas47918.2020.9082471

引用次数: 0

Impact of demographics on patients' acceptance of ICT for diabetes self-management: Applying the UTAUT model in low socio-economic areas 人口统计学对糖尿病患者接受ICT自我管理的影响:在低社会经济地区应用UTAUT模型

2020 Conference on Information Communications Technology and Society (ICTAS) Pub Date : 2020-03-01 DOI: 10.1109/ICTAS47918.2020.233975

Fazlyn Petersen, Ziyaad Luckan, S. Pather

引用次数: 3

Online Signature Verification using Deep Descriptors 使用深度描述符的在线签名验证

2020 Conference on Information Communications Technology and Society (ICTAS) Pub Date : 2020-03-01 DOI: 10.1109/ICTAS47918.2020.233999

Abigail Singh, Serestina Viriri

引用次数: 3

ICTAS 2020 Index ICTAS 2020指数

2020 Conference on Information Communications Technology and Society (ICTAS) Pub Date : 2020-03-01 DOI: 10.1109/ictas47918.2020.9082464

引用次数: 0

Big Data and Machine Learning for Forestalling Customer Churn Using Hybrid Software 使用混合软件预防客户流失的大数据和机器学习

2020 Conference on Information Communications Technology and Society (ICTAS) Pub Date : 2020-03-01 DOI: 10.1109/ICTAS47918.2020.233972

L. Butgereit

{"title":"Big Data and Machine Learning for Forestalling Customer Churn Using Hybrid Software","authors":"L. Butgereit","doi":"10.1109/ICTAS47918.2020.233972","DOIUrl":"https://doi.org/10.1109/ICTAS47918.2020.233972","url":null,"abstract":"The term customer churn is used to describe a situation where a customer leaves one merchant or supplier and moves to a competitor of that original merchant or supplier. This is also know as customer attrition. Prior to churning, however, there are often hints or clues in the customer’s buying patterns that he or she is ready to leave the supplier. This paper looks at the use of Machine Learning algorithms to predict when customers are ready to churn or in the process of churning. These predictions are then used to look at free text unformatted log data to find any reasons why this customer might be churning. This free text log data would include textual error messages that the customer might have received or financial problems which might have arisen such as not having sufficient funds in his or her account. This merged information is then forwarded to an outbound call queue so that trained call center agents could make human-to-human voice calls to the customer and entice them to stay with the merchant or supplier by offering some financial incentive. All of the technicalities were orchestrated using Spring Boot microservices. Design Science Research was used for the this project and a number of iterations were executed until results were satisfactory. These iterations included changing from an AutoEncoder to a MultiLayerPerceptron, included changing from one Java library providing neural network objects to another Java library, included better searching of log files for possible reasons that customers were churning and included many experiments with the quantity of sales data required in order for the neural networks to create reasonable predictions.","PeriodicalId":431012,"journal":{"name":"2020 Conference on Information Communications Technology and Society (ICTAS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126315898","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: 2

A conceptual model for the use of artificial intelligence for credit card fraud detection in banks 人工智能在银行信用卡欺诈检测中的应用概念模型

2020 Conference on Information Communications Technology and Society (ICTAS) Pub Date : 2020-03-01 DOI: 10.1109/ICTAS47918.2020.233980

Busisizwe Kelvin Nkomo, T. Breetzke

{"title":"A conceptual model for the use of artificial intelligence for credit card fraud detection in banks","authors":"Busisizwe Kelvin Nkomo, T. Breetzke","doi":"10.1109/ICTAS47918.2020.233980","DOIUrl":"https://doi.org/10.1109/ICTAS47918.2020.233980","url":null,"abstract":"Credit cards play a role in economic growth because they allow for a cashless society which in turn reduces government expenditure on the manufacturing and distribution of monetary notes. A cashless society would allow governments to save billions of money that can be ploughed back into the economy for other purposes. However, mediums of achieving a cashless society such as credit cards are under attack from fraudsters. Recent studies show that more and more money is being fraudulently withdrawn from accounts. This paper aims to evaluate the credit card fraud detection methods used by banks and the difficulties in implementing the said methods. The study suggests the use of artificial intelligence, geolocation and data mining in credit card fraud detection methods to mitigate the weaknesses that current credit card fraud detection methods have. The use of artificial intelligence, data mining and geolocation would enable credit card fraud detection methods to analyse and identify trends in customer spending to identify fraudulent transactions. A model is introduced to help mitigate the weaknesses. An indepth literature review was undertaken and secondary research was used throughout the study as the main source of information.","PeriodicalId":431012,"journal":{"name":"2020 Conference on Information Communications Technology and Society (ICTAS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132190751","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: 2

ICTAS 2020 Ad Page ICTAS 2020广告页

2020 Conference on Information Communications Technology and Society (ICTAS) Pub Date : 2020-03-01 DOI: 10.1109/ictas47918.2020.9082479

引用次数: 0

A Privacy and Security Preservation Framework for D2D Communication Based Smart Grid Services 基于D2D通信的智能电网服务的隐私和安全保护框架

2020 Conference on Information Communications Technology and Society (ICTAS) Pub Date : 2020-03-01 DOI: 10.1109/ICTAS47918.2020.233995

L. Bopape, B. Nleya, Phumzile P. Khumalo

{"title":"A Privacy and Security Preservation Framework for D2D Communication Based Smart Grid Services","authors":"L. Bopape, B. Nleya, Phumzile P. Khumalo","doi":"10.1109/ICTAS47918.2020.233995","DOIUrl":"https://doi.org/10.1109/ICTAS47918.2020.233995","url":null,"abstract":"Long-Term-Evolution (LTE) based Device-to-Device (D2D) communication in future generation networks are envisaged to become the basis for deployment of various applications and services in Smart Grids (SGs). However related privacy and security aspects are also under serious consideration especially when dealing with large-scale deployment of services and applications related D2D groups. Current and legacy related algorithms cannot be applied directly to this new paradigm shift (i.e D2D communication and group formations). Using the IoT as the pillar communication subsystem for SGs, the service providers can deploy several applications and services some of which may include the acquisition and storage of personal information of individual SG users. However, the challenge will always be in the strict preservation of privacy and security of their personal data and thus a necessity in eliminating such concerns. In this paper we propose a general framework that employs a Group Key Management (GKM) mechanism to ensure enhanced privacy and security especially during the discovery and communication phases. We further mitigate on the impact of enhanced privacy and security in SG services and applications.","PeriodicalId":431012,"journal":{"name":"2020 Conference on Information Communications Technology and Society (ICTAS)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122881635","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