Vol. 27, No. 1

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Now showing 1 - 5 of 7

Proposal of a Relational Database (SQL) for Zoological Research of Epigeic Synusion
(Institute of Automation and Computer Science, Brno University of Technology, 2021-06-21) Langraf, Vladimír; Petrovičová, Kornélia; David, Stanislav; Krumpálová, Zuzana; Purkart, Adrián; Schlarmannová, Janka
In recent years, developments in the field of molecular biology and genetics have led to the increase in biological information stored in databases. The same increase in the volume of information occurred in the field of zoology, but the development of databases was not addressed in this area. We prepared a relational database and its diagram in the Microsoft SQL Server Management Studio (SSMS) database program. Our results represent experience with construction of a new database design for the zoology field with a focus on research of epigeic groups. The structure of the database will help with meta-analyzes with the objective to identify zoological and ecological relationships and responses to anthropic intervention.
Three Steps to Improve Jellyfish Search Optimiser
(Institute of Automation and Computer Science, Brno University of Technology, 2021-06-21) Bujok, Petr
This paper describes three different mechanisms used in Jellyfish Search (JS) optimiser. At first, an archive of good old solutions is used to prevent getting stuck in the local-optima area. Further, a distribution coefficient beta is adapted during the search process to control population diversity. Finally, an Eigen transformation of individuals in the reproduction process is used occasionally to cope with rotated functions. Three proposed variants of the JS optimiser are compared with the original JS algorithm and nine various well-known Nature-inspired optimisation methods when solving real-world problems of CEC 2011. Provided results achieved by statistical comparison show efficiency of the individual newly employed mechanisms.
Intelligent Malware - Trends and Possibilities
(Institute of Automation and Computer Science, Brno University of Technology, 2021-06-21) Plucar, Jan; Frank, Jiří; Walter, Daniel; Zelinka, Ivan
In recent months and years, with more and more computers and computer systems becoming the target of cyberattacks. These attacks are gaining strength and the sophistication of the approach in terms of how to attack. Attackers and Defenders are increasingly using artificial intelligence methods to maximize the success of their actions. For a successful defence, we must be able to anticipate future threats that may come. For these reasons, our research group is engaged in creating experimental software with artificial intelligence to test the possibilities and capabilities of such malware in the event of its deployment. This software has not only malware capabilities but also antimalware and can be used on both sides. This article introduces the reader to the main principles of our design, which can serve as a future platform for cyber defence systems.
Employing Texture Features of Chest X-Ray Images and Machine Learning in COVID-19 Detection and Classification
(Institute of Automation and Computer Science, Brno University of Technology, 2021-06-21) Alquran, Hiam; Alsleti, Mohammad; Alsharif, Roaa; Abu Qasmieh, Isam; Alqudah, Ali Mohammad; Binti Harun, Nor Hazlyna
The novel coronavirus (nCoV-19) was first detected in December 2019. It had spread worldwide and was declared coronavirus disease (COVID-19) pandemic by March 2020. Patients presented with a wide range of symptoms affecting multiple organ systems predominantly the lungs. Severe cases required intensive care unit (ICU) admissions while there were asymptomatic cases as well. Although early detection of the COVID-19 virus by Real-time reverse transcription-polymerase chain reaction (RT-PCR) is effective, it is not efficient; as there can be false negatives, it is time consuming and expensive. To increase the accuracy of in-vivo detection, radiological image-based methods like a simple chest X-ray (CXR) can be utilized. This reduces the false negatives as compared to solely using the RT-PCR technique. This paper employs various image processing techniques besides extracted texture features from the radiological images and feeds them to different artificial intelligence (AI) scenarios to distinguish between normal, pneumonia, and COVID-19 cases. The best scenario is then adopted to build an automated system that can segment the chest region from the acquired image, enhance the segmented region then extract the texture features, and finally, classify it into one of the three classes. The best overall accuracy achieved is 93.1% by exploiting Ensemble classifier. Utilizing radiological data to conform to a machine learning format reduces the detection time and increase the chances of survival.
Mixed-Integer Programming Model for Ranking Universities: Letting Universities Choose the Weights
(Institute of Automation and Computer Science, Brno University of Technology, 2021-06-21) Kudela, Jakub
Regardless of the shortcomings and criticisms of world university rankings, these metrics are still widely used by students and parents to select universities and by universities to attract talented students and researchers, as well as funding. This paper proposes a new mixed-integer programming model for ranking universities. The new approach alleviates one of the criticisms -- the issue of the ``arbitrariness'' of the weights used for aggregation of the individual criteria (or indicators) utilized in the contemporary rankings. Instead, the proposed model uses intervals of different sizes for the weights and lets the universities themselves ``choose'' the weights to optimize their position in the rankings. A numerical evaluation of the proposed ranking, based on the indicator values and weights from the Times Higher Education World University Ranking, is presented.

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