http://hdl.handle.net/20.500.12603/3 Sun, 12 Jul 2026 01:35:44 GMT 2026-07-12T01:35:44Z http://hdl.handle.net/20.500.12603/2654 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. Sat, 01 Jan 2022 00:00:00 GMT http://hdl.handle.net/20.500.12603/2654 2022-01-01T00:00:00Z http://hdl.handle.net/20.500.12603/2653 Physical Fitness Improvement after 8 Weeks of High-intensity Interval Training with Air Bike Schlegel, Petr; Křehký, Adam; Hiblbauer, Jan Physical fitness is an important part of overall health. High-intensity interval training (HIIT) is a popular form of exercise that has been repeatedly proven as a functional way of developing cardiorespiratory fitness. Air bike is a widespread cardio machine suitable for HIIT. The aim of this research was to verify the effect of HIIT using air bike on the develop- ment of selected physical fitness parameters and compare it to moderate-intensity continuous training (MICT). Twenty active young adults (age 22.1±2.5) were the subject of the research in the research. The participants underwent a com- plex strength and endurance test, a spiroergometric examination, and a body composition analysis. The experimental group (EG) did HIIT twice a week with work intervals (15–45 seconds), while the control group did MICT in a comparable time period. The results have shown significant improvement in back squat (8.25%), pulling strength (7.07%), aerobic endurance (18.74%), and VO2peak (10.62%). Comparison of the groups has shown a significant difference in bench press (ES=1.01), back squat (ES=0.68), anaerobic endurance (ES=0.97), aerobic endurance (ES=1.456), and VO2peak (ES=0.92). According to the results, we can conclude that HIIT using air bike is an effective way of developing multiple aspects of physical fitness and is thus suitable for training programs that aim to develop health and sports performance. Sat, 01 Jan 2022 00:00:00 GMT http://hdl.handle.net/20.500.12603/2653 2022-01-01T00:00:00Z http://hdl.handle.net/20.500.12603/2652 Archery Algorithm: A Novel Stochastic Optimization Algorithm for Solving Optimization Problems Zeidabadi, Fatemeh Ahmadi; Dehghani, Mohammad; Trojovský, Pavel; Hubálovský, Štěpán; Leiva, Victor; Dhiman, Guarav Finding a suitable solution to an optimization problem designed in science is a major challenge. Therefore, these must be addressed utilizing proper approaches. Based on a random search space, optimization algorithms can find acceptable solutions to problems. Archery Algorithm (AA) is a new stochastic approach for addressing optimization problems that is discussed in this study. The fundamental idea of developing the suggested AA is to imitate the archer's shooting behavior toward the target panel. The proposed algorithm updates the location of each member of the population in each dimension of the search space by a member randomly marked by the archer. The AA is mathematically described, and its capacity to solve optimization problems is evaluated on twenty-three distinct types of objective functions. Furthermore, the proposed algorithm's performance is compared vs. eight approaches, including teaching-learning based optimization, marine predators algorithm, genetic algorithm, grey wolf optimization, particle swarm optimization, whale optimization algorithm, gravitational search algorithm, and tunicate swarm algorithm. According to the simulation findings, the AA has a good capacity to tackle optimization issues in both unimodal and multimodal scenarios, and it can give adequate quasi-optimal solutions to these problems. The analysis and comparison of competing algorithms' performance with the proposed algorithm demonstrates the superiority and competitiveness of the AA. Sat, 01 Jan 2022 00:00:00 GMT http://hdl.handle.net/20.500.12603/2652 2022-01-01T00:00:00Z http://hdl.handle.net/20.500.12603/2651 Gaussian Support Vector Machine Algorithm Based Air Pollution Prediction Bhuvaneshwari, K. S.; Lima, J.; Kandasamy, Venkatachalam; Masud, Mehedi; Abouhawwash, Mohamed; Logeswaran, T. Air pollution is one of the major concerns considering detriments to human health. This type of pollution leads to several health problems for humans, such as asthma, heart issues, skin diseases, bronchitis, lung cancer, and throat and eye infections. Air pollution also poses serious issues to the planet. Pollution from the vehicle industry is the cause of greenhouse effect and CO2 emissions. Thus, real-time monitoring of air pollution in these areas will help local authorities to analyze the current situation of the city and take necessary actions. The monitoring process has become efficient and dynamic with the advancement of the Internet of things and wireless sensor networks. Localization is the main issue in WSNs; if the sensor node location is unknown, then coverage and power and routing are not optimal. This study concentrates on localization-based air pollution prediction systems for real-time monitoring of smart cities. These systems comprise two phases considering the prediction as heavy or light traffic area using the Gaussian support vector machine algorithm based on the air pollutants, such as PM2.5 particulate matter, PM10, nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3), and sulfur dioxide (SO2). The sensor nodes are localized on the basis of the predicted area using the meta-heuristic algorithms called fast correlation-based elephant herding optimization. The dataset is divided into training and testing parts based on 10 cross-validations. The evaluation on predicting the air pollutant for localization is performed with the training dataset. Mean error prediction in localizing nodes is 9.83 which is lesser than existing solutions and accuracy is 95%. Sat, 01 Jan 2022 00:00:00 GMT http://hdl.handle.net/20.500.12603/2651 2022-01-01T00:00:00Z