Publikační činnost akademických pracovníků PřF http://hdl.handle.net/20.500.12603/50 2026-07-16T20:40:34Z 2026-07-16T20:40:34Z Helvich, Jakub Novák, Lukáš Mikoška, Petr Hubálovský, Štěpán http://hdl.handle.net/20.500.12603/2659 2026-07-16T09:22:25Z 2023-01-01T00:00:00Z

A Systematic Review of Gamification and Its Assessment in EFL Teaching Helvich, Jakub; Novák, Lukáš; Mikoška, Petr; Hubálovský, Štěpán The aim of this study is to examine the satisfaction of EFL teachers with gamification platforms as well as to investigate how EFL teachers perceive gamification and its effects on pupils' motivation and learning outcomes. Five major databases (ERIC, Scopus, WoS, EBSCO, ProQuest) and Google Scholar were used to search for relevant studies. The study followed the PRISMA guidelines and the PICO framework. Inter-rater reliability analyses were performed for both study selection and study quality assessment. Eleven relevant quantitative or mixed studies were identified. The findings indicate that EFL teachers perceived a positive effect of gamification on pupils' motivation and are satisfied with the applicability of gamification platforms. The findings revealed that internet and technology issues and a lack of teachers' skills are the most prominent negative factors when implementing gamification. Further experimental research is needed to provide evidence of the EFL teacher-perceived effectiveness of gamification on learning outcomes.

2023-01-01T00:00:00Z Dehghani, Mohammad Trojovský, Pavel Malik, Om Parkash http://hdl.handle.net/20.500.12603/2658 2026-07-16T09:22:16Z 2023-01-01T00:00:00Z

Green Anaconda Optimization: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems Dehghani, Mohammad; Trojovský, Pavel; Malik, Om Parkash A new metaheuristic algorithm called green anaconda optimization (GAO) which imitates the natural behavior of green anacondas has been designed. The fundamental inspiration for GAO is the mechanism of recognizing the position of the female species by the male species during the mating season and the hunting strategy of green anacondas. GAO’s mathematical modeling is presented based on the simulation of these two strategies of green anacondas in two phases of exploration and exploitation. The effectiveness of the proposed GAO approach in solving optimization problems is evaluated on twenty-nine objective functions from the CEC 2017 test suite and the CEC 2019 test suite. The efficiency of GAO in providing solutions for optimization problems is compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that the proposed GAO approach has a high capability in exploration, exploitation, and creating a balance between them and performs better compared to competitor algorithms. In addition, the implementation of GAO on twenty-one optimization problems from the CEC 2011 test suite indicates the effective capability of the proposed approach in handling real-world applications.

2023-01-01T00:00:00Z Bhuvaneshwari, K. S. Venkatachalam, K. Hubálovský, Štěpán Trojovský, Pavel Prabu, P. http://hdl.handle.net/20.500.12603/2655 2026-07-16T09:00:56Z 2022-01-01T00:00:00Z

Improved Dragonfly Optimizer for Intrusion Detection Using Deep Clustering CNN-PSO Classifier Bhuvaneshwari, K. S.; Venkatachalam, K.; Hubálovský, Štěpán; Trojovský, Pavel; Prabu, P. With the rapid growth of internet based services and the data generated on these services are attracted by the attackers to intrude the networking services and information. Based on the characteristics of these intruders, many researchers attempted to aim to detect the intrusion with the help of automating process. Since, the large volume of data is generated and transferred through network, the security and performance are remained an issue. IDS (Intrusion Detection System) was developed to detect and prevent the intruders and secure the network systems. The performance and loss are still an issue because of the features space grows while detecting the intruders. In this paper, deep clustering based CNN have been used to detect the intruders with the help of Meta heuristic algorithms for feature selection and preprocessing. The proposed system includes three phases such as preprocessing, feature selection and classification. In the first phase, KDD dataset is preprocessed by using Binning normalization and Eigen-PCA based discretization method. In second phase, feature selection is performed by using Information Gain based Dragonfly Optimizer (IGDFO). Finally, Deep clustering based Convolutional Neural Network (CCNN) classifier optimized with Particle Swarm Optimization (PSO) identifies intrusion attacks efficiently. The clustering loss and network loss can be reduced with the optimization algorithm. We evaluate the proposed IDS model with the NSL-KDD dataset in terms of evaluation metrics. The experimental results show that proposed system achieves better performance compared with the existing system in terms of accuracy, precision, recall, f-measure and false detection rate.

2022-01-01T00:00:00Z Bacanin, Nebojsa Budimirovic, Nebojsa Kandasamy, Venkatachalam Strumberger, Ivana Alrasheedi, Adel Fahad Abouhawwash, Mohamed http://hdl.handle.net/20.500.12603/2654 2026-07-08T07:50:22Z 2022-01-01T00:00:00Z

Novel chaotic oppositional fruit fly optimization algorithm for feature selection applied on COVID 19 patients' health prediction Bacanin, Nebojsa; Budimirovic, Nebojsa; Kandasamy, Venkatachalam; Strumberger, Ivana; Alrasheedi, Adel Fahad; Abouhawwash, Mohamed The fast-growing quantity of information hinders the process of machine learning, making it computationally costly and with substandard results. Feature selection is a pre-processing method for obtaining the optimal subset of features in a data set. Optimization algorithms struggle to decrease the dimensionality while retaining accuracy in high-dimensional data set. This article proposes a novel chaotic opposition fruit fly optimization algorithm, an improved variation of the original fruit fly algorithm, advanced and adapted for binary optimization problems. The proposed algorithm is tested on ten unconstrained benchmark functions and evaluated on twenty-one standard datasets taken from the Univesity of California, Irvine repository and Arizona State University. Further, the presented algorithm is assessed on a coronavirus disease dataset, as well. The proposed method is then compared with several well-known feature selection algorithms on the same datasets. The results prove that the presented algorithm predominantly outperform other algorithms in selecting the most relevant features by decreasing the number of utilized features and improving classification accuracy.

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