Data security pressing issue, particularly in terms of ensuring secure and reliable data transfer over a network. Encryption and seganography play a fundamental role in the task of securing data exchanging. In this article, both steganography and cryptography were combined to produce a powerful hybrid securing stego-system. Firstly, a text message is encrypted with a new method using a bits cycling operation to give a cipher text. In the second stage, an enhanced LSB method is used to hide the text bits randomly in an audio file of a wav format. This hybrid method can provide effectually secure data. Peak signal-to-noise ratio (PSNR), mean squared error (MSE) and structural similarity (SSIM) were employed to evaluate the performance of the proposed system. A PSNR was in range (60-65) dB with the enhanced least significant bit (LSB) and the SSIM had been invested to calculate the signal quality, which scored 0.999. The experimental results demonstrated that our algorithm is highly effective in securing data and the capacity size of the secured text. Furthermore, the time consumption was considerably low, at less than 0.3 seconds.

<span lang="EN-US">The advancement of systems with the capacity to compress audio signals and simultaneously secure is a highly attractive research subject. This is because of the need to enhance storage usage and speed up the transmission of data, as well as securing the transmission of sensitive signals over limited and insecure communication channels. Thus, many researchers have studied and produced different systems, either to compress or encrypt audio data using different algorithms and methods, all of which suffer from certain issues including high time consumption or complex calculations. This paper proposes a compressing sensing-based system that compresses audio signals and simultaneously provides an encryption system. The audio signal is segmented into small matrices of samples and then multiplied by a non-square sensing matrix generated by a Gaussian random generator. The reconstruction process is carried out by solving a linear system using the pseudoinverse of Moore-Penrose. The statistical analysis results obtaining from implementing different types and sizes of audio signals prove that the proposed system succeeds in compressing the audio signals with a ratio reaching 28% of real size and reconstructing the signal with a correlation metric between 0.98 and 0.99. It also scores very good results in the normalized mean square error (MSE), peak signal-to-noise ratio metrics (PSNR), and the structural similarity index (SSIM), as well as giving the signal a high level of security.</span>

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Deep learning models on the same database have varied accuracy ratings; as such, additional parameters, such as pre-processing, data augmentation and transfer learning, can influence the models’ capacity to obtain higher accuracy. In this paper, a fully automated model is designed using deep learning algorithm to capture images from patients and pre-process, segment and classify the intensity of cancer spread. In the first pre-processing step, pectoral muscles are removed from the input images, which are then downsized. The removal of pectoral muscles after identification may become crucial in classification systems. Finally, the pectoral musclesaredeleted from the picture by using an area expanding segmentation. All mammograms are downsized to reduce processing time. Each stage of the fully automated model uses an optimisation approach to obtain highaccuracy results at respective stages. Simulation is conducted to test the efficacy of the model against state-of-art models, and the proposed fully automated model is thoroughly investigated. For a more accurate comparison, we include the model in our analysis. In a nutshell, this work offers a wealth of information as well as review and discussion of the experimental conditions used by studies on classifying breast cancer images.

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