Grey Wolf Optimizer (GWO) simulates the grey wolves' nature in leadership and hunting manners. GWO showed a good performance in the literature as a meta-heuristic algorithm for feature selection problems, however, it shows low precision and slow convergence. This paper proposes a Modified Binary GWO (MbGWO) based on Stochastic Fractal Search (SFS) to identify the main features by achieving the exploration and exploitation balance. First, the modified GWO is developed by applying an exponential form for the number of iterations of the original GWO to increase the search space accordingly exploitation and the crossover/mutation operations to increase the diversity of the population to enhance exploitation capability. Then, the diffusion procedure of SFS is applied for the best solution of the modified GWO by using the Gaussian distribution method for random walk in a growth process. The continuous values of the proposed algorithm are then converted into binary values so that it can be used for the problem of feature selection. To ensure the stability and robustness of the proposed MbGWO-SFS algorithm, nineteen datasets from the UCI machine learning repository are tested. The K-Nearest Neighbor (KNN) is used for classification tasks to measure the quality of the selected subset of features. The results, compared to binary versions of the-state-of-the-art optimization techniques such as the original GWO, SFS, Particle Swarm Optimization (PSO), hybrid of PSO and GWO, Satin Bowerbird Optimizer (SBO), Whale Optimization Algorithm (WOA), Multiverse Optimization (MVO), Firefly Algorithm (FA), and Genetic Algorithm (GA), show the superiority of the proposed algorithm. The statistical analysis by Wilcoxon's rank-sum test is done at the 0.05 significance level to verify that the proposed algorithm can work significantly better than its competitors in a statistical way.
The world is still trying to recover from the devastation caused by the wide spread of COVID-19, and now the monkeypox virus threatens becoming a worldwide pandemic. Although the monkeypox virus is not as lethal or infectious as COVID-19, numerous countries report new cases daily. Thus, it is not surprising that necessary precautions have not been taken, and it will not be surprising if another worldwide pandemic occurs. Machine learning has recently shown tremendous promise in image-based diagnosis, including cancer detection, tumor cell identification, and COVID-19 patient detection. Therefore, a similar application may be implemented to diagnose monkeypox as it invades the human skin. An image can be acquired and utilized to further diagnose the condition. In this paper, two algorithms are proposed for improving the classification accuracy of monkeypox images. The proposed algorithms are based on transfer learning for feature extraction and meta-heuristic optimization for feature selection and optimization of the parameters of a multi-layer neural network. The GoogleNet deep network is adopted for feature extraction, and the utilized meta-heuristic optimization algorithms are the Al-Biruni Earth radius algorithm, the sine cosine algorithm, and the particle swarm optimization algorithm. Based on these algorithms, a new binary hybrid algorithm is proposed for feature selection, along with a new hybrid algorithm for optimizing the parameters of the neural network. To evaluate the proposed algorithms, a publicly available dataset is employed. The assessment of the proposed optimization of feature selection for monkeypox classification was performed in terms of ten evaluation criteria. In addition, a set of statistical tests was conducted to measure the effectiveness, significance, and robustness of the proposed algorithms. The results achieved confirm the superiority and effectiveness of the proposed methods compared to other optimization methods. The average classification accuracy was 98.8%.
Lemniscate chaotic map (LCM) provides a wide range of control parameters, canceling the need for several rounds of substitutions, and excellent performance in the confusion process. Unfortunately, the hardware model of LCM is complex and consumes high power. This paper presents a proposed low power hardware model of LCM called practical lemniscate chaotic map (P-LCM) depending on trigonometric identities to reduce the complexity of the conventional model. The hardware model designed and implement into the field programmable gate array (FPGA) board, Spartan-6 SLX45FGG484-3. The proposed model achieves a 48.3 % reduction in used resources and a 34.6 % reduction in power consumption compared to the conventional LCM. We also introduce a new pseudo-random number generator based on a proposed low power P-LCM model and perform the randomization tests for the proposed encryption system.
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