Extracting concepts for software components
Authors : Ahmad Kayed, Nael Hirzalla, Hadeel Ahmad, Enas Al Faisal
Abstract : Ontologies enhance searching information on the Web since they provide relationships and semantics among them. Ontology is required to describe the semantics of concepts and properties used in a web document. It is needed to describe products, services, processes and practices for any software components. Software components are essential part of software development process. Using component is essential in nowadays software development. This paper demonstrates several experiments to extract concepts to build ontologies that improve the description process for software components embedded in a web document. In this paper we built ontology (mainly concepts) for some software components then used them to solve some semantic problems. We collected many documents that describe components in .Net and Java from several and different resources. Concepts were extracted and used to decide which domain of any given description (semantic) is close or belong to.
Keywords : Ontology,Semantic Web,Component Description,Software Engineering
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Multilayer perceptron artificial neural networks-based model for credit card fraud detection
Authors : Bassam Kasasbeh, Balqees Aldabaybah, Hadeel Ahmad
Abstract : Nowadays, credit card fraud has emerged as a major problem. People are becoming increasingly using credit cards to pay for their transactions, it has become more popular and essential in our lives. Fraudsters are developing new strategies and techniques over time, and it is not easy for humans to manually check out all transactions. The cost of fraudulent transactions is significant and without prevention mechanisms it is rising. Finding the best methodology to detect fraudulent transactions is a crucial asset to the industry to reduce the fraud financial loss. Artificial neural networks (ANN) technique is considered as one of the effective techniques that has proved its efficiency in detecting credit card fraud transactions with high precision and minimum cost. In this paper, we propose a multilayer perceptron (MLP) ANN-based model solution to improve the accuracy of the detection process. The performance of the methodology is measured based on the precision, sensitivity, specificity, accuracy, F-measure, area under curve (AUC) and root mean square error (RMSE). Moreover, we illustrate the performance results of these measures with a descriptive analysis. Experimental results have shown that the proposed ANN-based model is efficient and does improve the accuracy of the detection of fraudulent transactions.
Keywords : Artificial neural networks,Credit card fraud,Machine learning,Multilayer perceptron online,transaction
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