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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.

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ResultsThe metabolic risk score was determined; patients with a significant metabolic score of at least 3 risk score constituted 66.4% of the total cohort (n = 81 patients).Patients were subjected to coronary angiography. Totally occluded vessels were found in 33.3% of metabolic syndrome patients and in 26.8% of non metabolic syndrome patients (P < 0.05). The SYNTAX score was used to assess the severity of CAD; it was found to be statistically significantly higher in patients with metabolic syndrome than those without (P = 0.001). ConclusionPatients with metabolic syndrome have more severe CADs. Preventive measures against metabolic syndrome and its components are very important and could help avoid the large economic burden of secondary prevention.

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