Dear Colleagues,

The 1st International Conference Advanced Computer and Digital Technologies—2021 (ACDT-2021) will be held in Belgorod, Russia on September 14–16, 2021. The conference is a scientific event that intends to bring together young and senior scientists in the fields of artificial intelligence and applications. This Special Issue intends to contain a selection of carefully revised and extended best papers, to be presented at ACDT-2021. Paper acceptance for ACDT-2021 will be based on quality, relevance to the conference theme, and originality.

The authors of a number of selected full papers of high quality will be invited after the conference to submit revised and extended versions of their originally accepted conference papers to this Special Issue of Information, published by MDPI in open-access format. The selection of these best papers will be based on their ratings in the conference review process, the quality of their presentation during the conference, and their expected impact on the research community. Each submission to this Special Issue should contain at least 50% new material (e.g., in the form of technical extensions, more in-depth evaluations, or additional use cases) and a change of title, abstract, and keywords. These extended submissions will undergo a peer-review process according to the journal’s rules of action. At least two technical committees will act as reviewers for each extended article submitted to this Special Issue; if needed, additional external reviewers will be invited to guarantee a high-quality review process.

Prof. Dr. Irina Perfilieva
Prof. Dr. Madad Khan
Guest Editors

Manuscript Submission Information

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• Intelligent systems and technologies
• Intelligent data mining and machine learning
• Artificial intelligence in information security
• Computer support of organizational intelligence
• System modelling
• Cyber-physical systems in technologies
• Software for simulations of nonlinear, non-stationary, and non-homogeneous processes

Title: Credit Card Fraud Detection Using Machine Learning Algorithms
Authors: Ivan Lorencin; Nikola Anđelić; Sandi Baressi Šegota; Zlatan Car
Affiliation: RiTeh
Abstract: Online trading has taken a large share in worldwide trade in recent years. More and more different products and services can be bought and sold online. Such a trend reached its peak in the past years due to the COVID-19 pandemic and reduced physical contact. For the above reasons, safe and verified trade is imperative for the exchange of goods and services to be carried out smoothly and without major delays. One of the main challenges for the security of an online business is certainly card fraud, and the timely detection of fraud represents a significant saving of resources and time. For this reason, algorithms based on artificial intelligence and machine learning are being introduced to enable the most accurate and fast detection of card fraud. This paper presents an approach to the detection of card fraud based on machine learning algorithms, more specifically, a multilayer perceptron (MLP) and a decision tree. The aforementioned algorithms were trained and tested using a publicly available data set on card fraud. The data set used consists of 7 characteristics of the card transaction and information on whether there was card fraud or not. In total, the data set contains information on 1,000,000 transactions. From the conducted research, it can be seen how it is possible to apply the mentioned algorithms for the detection of card fraud, which is indicated by the high performance ( $AUC=0.99$ achieved with a decision tree and $AUC=0.95$ achieved with an MLP) achieved with proposed classification algorithms. If the performance of the mentioned algorithms is examined using fewer characteristics of the transaction, it can be seen that by reducing the number of characteristics a significant decrease in classification performances can be noticed if an MLP is used. However, if a decision tree is used, a significantly lower decrease in performance can be observed. In this case, satisfactory performances were achieved (AUC=0.93) even if only two transaction characteristics were used. Such an approach enables a significantly faster initial evaluation of the transaction, even in situations where significantly less data is available.