Advanced Computer and Digital Technologies

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Systems".

Deadline for manuscript submissions: 30 December 2024 | Viewed by 10617

Special Issue Editors


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Guest Editor
Institute for Research and Applications of Fuzzy Modeling, University of Ostrava, 701 03 Ostrava, Czech Republic
Interests: fuzzy logic; fuzzy modeling; image processing; computer graphics
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Mathematics, COMSATS University Islamabad, Islamabad 45550, Pakistan
Interests: biomathematics; fuzzy mathematics; computational mathematics; group theory and generalizations

Special Issue Information

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • 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

Published Papers (4 papers)

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Research

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24 pages, 2536 KiB  
Article
Enhancing Network Intrusion Detection: A Genetic Programming Symbolic Classifier Approach
by Nikola Anđelić and Sandi Baressi Šegota
Information 2024, 15(3), 154; https://doi.org/10.3390/info15030154 - 9 Mar 2024
Viewed by 1131
Abstract
This investigation underscores the paramount imperative of discerning network intrusions as a pivotal measure to fortify digital systems and shield sensitive data from unauthorized access, manipulation, and potential compromise. The principal aim of this study is to leverage a publicly available dataset, employing [...] Read more.
This investigation underscores the paramount imperative of discerning network intrusions as a pivotal measure to fortify digital systems and shield sensitive data from unauthorized access, manipulation, and potential compromise. The principal aim of this study is to leverage a publicly available dataset, employing a Genetic Programming Symbolic Classifier (GPSC) to derive symbolic expressions (SEs) endowed with the capacity for exceedingly precise network intrusion detection. In order to augment the classification precision of the SEs, a pioneering Random Hyperparameter Value Search (RHVS) methodology was conceptualized and implemented to discern the optimal combination of GPSC hyperparameter values. The GPSC underwent training via a robust five-fold cross-validation regimen, mitigating class imbalances within the initial dataset through the application of diverse oversampling techniques, thereby engendering balanced dataset iterations. Subsequent to the acquisition of SEs, the identification of the optimal set ensued, predicated upon metrics inclusive of accuracy, area under the receiver operating characteristics curve, precision, recall, and F1-score. The selected SEs were subsequently subjected to rigorous testing on the original imbalanced dataset. The empirical findings of this research underscore the efficacy of the proposed methodology, with the derived symbolic expressions attaining an impressive classification accuracy of 0.9945. If the accuracy achieved in this research is compared to the average state-of-the-art accuracy, the accuracy obtained in this research represents the improvement of approximately 3.78%. In summation, this investigation contributes salient insights into the efficacious deployment of GPSC and RHVS for the meticulous detection of network intrusions, thereby accentuating the potential for the establishment of resilient cybersecurity defenses. Full article
(This article belongs to the Special Issue Advanced Computer and Digital Technologies)
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17 pages, 4108 KiB  
Article
HIL Flight Simulator for VTOL-UAV Pilot Training Using X-Plane
by Daniel Aláez, Xabier Olaz, Manuel Prieto, Pablo Porcellinis and Jesús Villadangos
Information 2022, 13(12), 585; https://doi.org/10.3390/info13120585 - 16 Dec 2022
Cited by 4 | Viewed by 4122
Abstract
With the increasing popularity of vertical take-off and landing unmanned aerial vehicles (VTOL UAVs), a new problem arises: pilot training. Most conventional pilot training simulators are designed for full-scale aircrafts, while most UAV simulators are just focused on conceptual testing and design validation. [...] Read more.
With the increasing popularity of vertical take-off and landing unmanned aerial vehicles (VTOL UAVs), a new problem arises: pilot training. Most conventional pilot training simulators are designed for full-scale aircrafts, while most UAV simulators are just focused on conceptual testing and design validation. The X-Plane flight simulator was extended to include new functionalities such as complex wind dynamics, ground effect, and accurate real-time weather. A commercial HIL flight controller was coupled with a VTOL convertiplane UAV model to provide realistic flight control. A real flight case scenario was tested in simulation to show the importance of including an accurate wind model. The result is a complete simulation environment that has been successfully deployed for pilot training of the Marvin aircraft manufactured by FuVeX. Full article
(This article belongs to the Special Issue Advanced Computer and Digital Technologies)
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24 pages, 7937 KiB  
Article
SOCRAT: A Dynamic Web Toolbox for Interactive Data Processing, Analysis and Visualization
by Alexandr A. Kalinin, Selvam Palanimalai, Junqi Zhu, Wenyi Wu, Nikhil Devraj, Chunchun Ye, Nellie Ponarul, Syed S. Husain and Ivo D. Dinov
Information 2022, 13(11), 547; https://doi.org/10.3390/info13110547 - 19 Nov 2022
Cited by 2 | Viewed by 2475
Abstract
Many systems for exploratory and visual data analytics require platform-dependent software installation, coding skills, and analytical expertise. The rapid advances in data-acquisition, web-based information, and communication and computation technologies promoted the explosive growth of online services and tools implementing novel solutions for interactive [...] Read more.
Many systems for exploratory and visual data analytics require platform-dependent software installation, coding skills, and analytical expertise. The rapid advances in data-acquisition, web-based information, and communication and computation technologies promoted the explosive growth of online services and tools implementing novel solutions for interactive data exploration and visualization. However, web-based solutions for visual analytics remain scattered and relatively problem-specific. This leads to per-case re-implementations of common components, system architectures, and user interfaces, rather than focusing on innovation and building sophisticated applications for visual analytics. In this paper, we present the Statistics Online Computational Resource Analytical Toolbox (SOCRAT), a dynamic, flexible, and extensible web-based visual analytics framework. The SOCRAT platform is designed and implemented using multi-level modularity and declarative specifications. This enables easy integration of a number of components for data management, analysis, and visualization. SOCRAT benefits from the diverse landscape of existing in-browser solutions by combining them with flexible template modules into a unique, powerful, and feature-rich visual analytics toolbox. The platform integrates a number of independently developed tools for data import, display, storage, interactive visualization, statistical analysis, and machine learning. Various use cases demonstrate the unique features of SOCRAT for visual and statistical analysis of heterogeneous types of data. Full article
(This article belongs to the Special Issue Advanced Computer and Digital Technologies)
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Review

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13 pages, 630 KiB  
Review
Subtask Segmentation Methods of the Timed Up and Go Test and L Test Using Inertial Measurement Units—A Scoping Review
by Alexis L. McCreath Frangakis, Edward D. Lemaire and Natalie Baddour
Information 2023, 14(2), 127; https://doi.org/10.3390/info14020127 - 16 Feb 2023
Cited by 2 | Viewed by 1569
Abstract
The Timed Up and Go test (TUG) and L Test are functional mobility tests that allow healthcare providers to assess a person’s balance and fall risk. Segmenting these mobility tests into their respective subtasks, using sensors, can provide further and more precise information [...] Read more.
The Timed Up and Go test (TUG) and L Test are functional mobility tests that allow healthcare providers to assess a person’s balance and fall risk. Segmenting these mobility tests into their respective subtasks, using sensors, can provide further and more precise information on mobility status. To identify and compare current methods for subtask segmentation using inertial sensor data, a scoping review of the literature was conducted using PubMed, Scopus, and Google Scholar. Articles were identified that described subtask segmentation methods for the TUG and L Test using only inertial sensor data. The filtering method, ground truth estimation device, demographic, and algorithm type were compared. One article segmenting the L Test and 24 articles segmenting the TUG met the criteria. The articles were published between 2008 and 2022. Five studies used a mobile smart device’s inertial measurement system, while 20 studies used a varying number of external inertial measurement units. Healthy adults, people with Parkinson’s Disease, and the elderly were the most common demographics. A universally accepted method for segmenting the TUG test and the L Test has yet to be published. Angular velocity in the vertical and mediolateral directions were common signals for subtask differentiation. Increasing sample sizes and furthering the comparison of segmentation methods with the same test sets will allow us to expand the knowledge generated from these clinically accessible tests. Full article
(This article belongs to the Special Issue Advanced Computer and Digital Technologies)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

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.

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