Department of Computer Science

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    Can an Enterprise System Persuade? The Role of Perceived Effectiveness and Social Influence
    (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018-04) Dabi, J.; Wiafe, I.; Stibe, A.; Abdulai, J.D.
    This study provides an interpretation to empirically explain and predict use continuance intention of students towards an enterprise resource planning (ERP) system. A research model based on the information system continuance, the social identity theory, and the unified theory of acceptance and use of technology was adopted and analyzed using partial least squares structural equation modeling. The analysis uncovered important roles that perceived effectiveness and social influence play in explaining the intention of students to continue using the ERP. Further, the model demonstrated how primary task support contributes to perceived effort, which helps in explaining perceived effectiveness of the system. Computer-human dialogue support significantly contributes to perceived credibility, primary task support and perceived social influence. Social identification of the students significantly predicts perceived social influence. Research related to continuous usage of an ERP system is viable, as it enables designers and developers building more persuasive enterprise and socially influencing systems. © Springer International Publishing AG, part of Springer Nature 2018.
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    Survey of Mobile Malware Analysis, Detection Techniques and Tool
    (2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2018, 2018-11) Gyamfi, N.K.; Owusu, E.
    The rapid increase in the use of smartphones, has contributed to the increase in mobile attackers. In most situations deceitful applications are infected with malicious contents to cause harm to both the hardware and the software. These malicious programs or malware are usually designed to disrupt or gather information from the device. By attempts to curtail these problems various techniques are proposed. This paper attempts to analyze the most popular and recent techniques and suggests which is better.
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    Bank Fraud Detection Using Support Vector Machine
    (2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2018, 2018-11) Gyamfi, N.K.; Abdulai, J.D.
    With the significant development of communications and computing, bank fraud is growing in its forms and amounts. In this paper, we analyze the various forms of fraud to which are exposed banks d data mining tools allowing its early detection data already accumulated in a bank. We use supervised learning methods Support Vector Machines with Spark (SVM-S) to build models representing normal and abnormal customer behavior and then use it to evaluate validity of new transactions. The results obtained from databases of credit card transactions show that these techniques are effective in the fight against banking fraud in big data. Experiment result from the study show that SVM-S have better prediction performance than Back Propagation Netw orks (BPN). Besides the average prediction, accuracy reaches a maximum when training the data ratio arrives at 0.8.