Digital images are the most frequently used signals to convey information in the internet era. The security of these images is the primary concern in rapidly changing networked environments. In this research, we present a novel approach to secure images by integrating a scanning technique, the El-Gamal public key cryptosystem and chaotic systems. In brief, zigzag and spiral scanning are used first to construct a permuted image. Then, the El-Gamal encryption algorithm is exploited to encrypt the permuted image. Finally, Lorenz and Rössler chaotic sequences are utilized to scramble the pixel locations in the confusion and diffusion stages. This last step that mixes two stages can fortify the entire security performance and enlarge the key size. Exhaustive analysis has been carried out on the SIPI (signal and image processing institute) dataset to assess the efficiency and security of the proposed method. Numerical and visual results indicate the capability of the proposed image cryptosystem to protect images against several known attacks. In addition, the comparative analysis results indicate that the proposed approach outperforms the compared approaches in terms of the visual quality and security criteria.INDEX TERMS Image security, scanning, chaotic, Lorenz and Rössler sequences, El-Gamal algorithm.

Diabetes mellitus is a chronic, life-threatening, and complicated condition. Around 1.5 million deaths due to diabetes have been documented, according to a World Health Organization (WHO) estimation in 2019. In the world of medicine, predicting diabetes risk is a difficult and time-consuming task. Many past studies have been conducted to investigate and clarify diabetes symptoms and variables. To solve these persisting issues, however, more critical clinical criteria must be considered. A comparative analysis based on three soft computing strategies for diabetes prediction has been carried out and achieved in this work. Among the computational intelligence methods used in this study are fuzzy analytical hierarchy processes (FAHP), support vector machine (SVM), and artificial neural networks (ANNs). The techniques reveal promising performance in predicting diabetes reliably and effectively in terms of several classification evaluation metrics, according to experimental analysis and assessment conducted on 520 participants using a publicly available dataset.

Image enhancement is one of the most critical subjects in computer vision and image processing fields. It can be considered as means to enrich the perception of images for human viewers. All kinds of images typically suffer from different problems such as weak contrast and noise. The primary purpose of image enhancement is to change an image's visual appearance. Many algorithms have recently been proposed for enhancing medical images. Image enhancement is still deemed a challenging task. In this paper, the fuzzy c-means clustering (FCM) technique is utilized to enhance the medical images. The method of enhancement consists of two stages. The proposed algorithm conducts a cluster test on the image pixels. It then increases the difference of gray level between the diverse objects to accomplish the enhancement purpose of the medical images. The experimental results have been tested using various images. The algorithm enhanced the small target of the image to a reasonable limit and revealed favorable performance. The results of image enhancement techniques were evaluated by using terms of different criteria such as peak signal to noise ratio (PSNR), mean square error (MSE) and average information contents (AIC), showing promising performance.