Dipper throated optimization (DTO) algorithm is a novel with a very efficient metaheuristic inspired by the dipper throated bird. DTO has its unique hunting technique by performing rapid bowing movements. To show the efficiency of the proposed algorithm, DTO is tested and compared to the algorithms of Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), and Genetic Algorithm (GA) based on the seven unimodal benchmark functions. Then, ANOVA and Wilcoxon rank-sum tests are performed to confirm the effectiveness of the DTO compared to other optimization techniques. Additionally, to demonstrate the proposed algorithm's suitability for solving complex realworld issues, DTO is used to solve the feature selection problem. The strategy of using DTOs as feature selection is evaluated using commonly used data sets from the University of California at Irvine (UCI) repository. The findings indicate that the DTO outperforms all other algorithms in addressing feature selection issues, demonstrating the proposed algorithm's capabilities to solve complex real-world situations.

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In this paper, a novel framework is presented for chaotic image encryption. The proposed method is based on integrating multiple chaotic maps (e.g., logistic, tent, quadratic, cubic, and Bernoulli) to generate more robust chaotic maps in order to increase the security and privacy needed by applying variable keys. The latter are generated by computing the sine square logistic map and are then applied to generate the chaotic maps employed in our framework. For this, we have performed many experiments to achieve the best period for each chaotic map in which it performed the best encryption. Here, we combine multiple chaotic maps to get a new map that works well when X ∈ [0, 1]. For using a chaotic map in the encryption process, it was necessary to find a way to choose the best of those chaotic maps for encryption. This selection was done with the lowest value for the correlation factor because the smaller value of correlation has an impression of good encryption. We have also noted a clear difference in the influence of one of these maps on some pictures from the others. We chose one of those maps according to the correlation value for each encoding process and compared them. Then, we used a chaotic map of the best of these values for encryption and decryption. Numerical results on various gray images showed the robustness of the proposed method to encrypt and decrypt the images based on the evaluation using different performance analyses. We compared our methods against other well-known approaches, e.g., circular mapping, S-boxes, and S-box with Arnold transform. Our pipeline outperforms those methods. Moreover, our results documented that the proposed scheme has an excellent security level with very low correlation coefficients and good information entropy.

A hybrid framework that is based on the integration of geometric deformable models and nonnegative matrix factorization (NMF) is introduced for 3D kidney segmentation from abdominal computed tomography (CT) images. The NMF is employed due to its ability to cluster complex data by extracting discriminative features from higher dimensional space. In this paper, regional features from CT appearance, a kidney shape model, and spatial interactions are fused using the NMF to produce a more robust model to guide the deformable model's evolution. The shape model is constructed using a set of training images and is updated during segmentation using an appearance-based method taking into account both voxels' locations and appearances. The spatial interactions are modeled using a pairwise Potts Markov-Gibbs random field model. Our approach has been tested on 36 in-vivo 3D CT data sets and evaluated using, the Dice coefficient, the 95-percentile Hausdorff distance, and percentage kidney volume difference. Evaluation results show that feature fusion using NMF increases the deformable model's ability to accurately segment complex CT kidney data.

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