Přírodovědecká fakulta http://hdl.handle.net/20.500.12603/46 2026-07-13T09:54:14Z 2026-07-13T09:54:14Z 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.

2022-01-01T00:00:00Z Zeidabadi, Fatemeh Ahmadi Dehghani, Mohammad Trojovský, Pavel Hubálovský, Štěpán Leiva, Victor Dhiman, Guarav http://hdl.handle.net/20.500.12603/2652 2026-07-08T07:49:54Z 2022-01-01T00:00:00Z

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.

2022-01-01T00:00:00Z Bhuvaneshwari, K. S. Lima, J. Kandasamy, Venkatachalam Masud, Mehedi Abouhawwash, Mohamed Logeswaran, T. http://hdl.handle.net/20.500.12603/2651 2026-07-08T07:49:40Z 2022-01-01T00:00:00Z

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%.

2022-01-01T00:00:00Z Devipriya, A. Prabu, P. Kandasamy, Venkatachalam Ibrahim, Ahmed Zohair http://hdl.handle.net/20.500.12603/2650 2026-07-08T07:49:29Z 2022-01-01T00:00:00Z

Kernel Granulometric Texture Analysis and Light RES-ASPP-UNET Classification for Covid-19 Detection Devipriya, A.; Prabu, P.; Kandasamy, Venkatachalam; Ibrahim, Ahmed Zohair This research article proposes an automatic frame work for detecting COVID-19 at the early stage using chest X-ray image. It is an undeniable fact that coronovirus is a serious disease but the early detection of the virus present in human bodies can save lives. In recent times, there are so many research solutions that have been presented for early detection, but there is still a lack in need of right and even rich technology for its early detection. The proposed deep learning model analysis the pixels of every image and adjudges the presence of virus. The classifier is designed in such a way so that, it automatically detects the virus present in lungs using chest image. This approach uses an image texture analysis technique called granulometric mathematical model. Selected features are heuristically processed for optimization using novel multi scaling deep learning called light weight residual-atrous spatial pyramid pooling (LightRES-ASPP-Unet) Unet model. The proposed deep LightRES-ASPPUnet technique has a higher level of contracting solution by extracting major level of image features. Moreover, the corona virus has been detected using high resolution output. In the framework, atrous spatial pyramid pooling (ASPP) method is employed at its bottom level for incorporating the deep multi scale features in to the discriminative mode. The architectural working starts from the selecting the features from the image using granulometric mathematical model and the selected features are optimized using LightRESASPP-Unet. ASPP in the analysis of images has performed better than the existing Unet model. The proposed algorithm has achieved 99.6% of accuracy in detecting the virus at its early stage.

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