Data hiding and watermarking are considered one of the most important topics in cyber security. This article proposes an optimized method for embedding a watermark image in a cover medium (color image). First, the color of the image is separated into three components (RGB). Consequently, the discrete wavelet transform is applied to each component to obtain four bands (high-high, high-low, low-high, and low-low), resulting in 12 bands in total. By omitting the low-low band from each component, a new square matrix is formed from the rest bands to be used for the hiding process after adding keys to it. These keys are generated using a hybrid approach, combining two chaotic functions, namely Gaussian and exponential maps. The embedding matrix is divided into square blocks with a specific length, each of which is converted using Hessenberg transform into two matrices, P and H. For each block, a certain location within the H-matrix is used for embedding a secret value; the updated blocks are assembled, and the reverse process is performed. An optimization method is applied, through the application of the firework algorithm, on the set of the initial values that generate keys. Using an optimization procedure to obtain keys requires performing lowest possible change rate in an image and maintain the quality of the image. To analyze and test the efficiency of the proposed method, mean-square error (MSE) and peak signal-to-noise ratio (PSNR) measurements are calculated. Furthermore, the robustness of the watermark is computed by applying several attacks. The experimental results show that the value of the MSE is reduced by about 0.01 while the value of the PSNR is increased by about 1.25 on average. Moreover, the proposed method achieved a high-retrieval rate in comparison with the non-optimization approach.

Diagnosis in healthcare systems relies heavily on the use of medical images. Images such as X-rays, ultrasounds, computed tomography (CT) scans, magnetic resonance imaging (MRIs), and other scans of the brain and other internal organs of patients include private and personal information. However, these images are vulnerable to unauthorized users who unlawfully use them for non-diagnostic reasons due to the lack of security in communication routes and the gaps in the storage systems of hospitals or medical centers. Image encryption is a prominent technique used to protect medical images from unauthorized access in addition to enhancing the security of communication networks. In this paper, researchers offer a lightweight cryptosystem for the secure encryption of medical images that makes use of the present block cipher and a five-dimensional chaotic map. More than 25 images from the open science framework (OSF) public database of patients with coronavirus disease 2019 (COVID-19) were used to evaluate the proposed system. DICOM stands for “digital imaging and communications in medicine”. The efficiency of the proposed system is proved in terms of adjacent pixels’ correlation analysis, National Institute of Standards and Technology (NIST) analysis, mean square error, information entropy, unified average changing intensity, peak-to-signal noise ratio, entropy, and structure similarity index image.

Scheduling is critical part in most creation frameworks and information processing as sequencing of tasks or jobs framework executed on a grouping of processors. One of the NP-hard problem is “Job Shop Scheduling Problem”. In this work, a method of optimization proposed called “Fireworks Algorithm”. The solutions divided into fireworks and each one applied sparks to find the best solution. For some selected spark applied Gaussian mutation to find enhanced solution and find optimum solution. FWA tested on dataset to improve performance and it do well with respect to some other algorithm like Meerkat Clan Algorithm (MCA), Camel Herds Algorithm) CHA(, and Cukoo Search Algorithm (CSA).

Speech coding plays a crucial role in maintaining speech quality while optimizing network resources and expediting transmission, as well as facilitating the storage of speech data. In this paper, an adaptive method for speech coding using singular value decomposition (SVD), grey wolf optimization (GWO), and run-length encoding (RLE) was proposed. The proposed method streamlines the speech matrix through preprocessing, converting it into short intervals. Subsequently, each interval undergoes decomposition using SVD, followed by optimization of compression quality using GWO. Finally, RLE is employed as the last step to increase space-saving. The developed method was conducted on two datasets: Quran and LibriSpeech using PSNR, PSEQ, and MOS tests. The results demonstrate promising outcomes, achieving space-saving up to 89.80, 84.04, 74.76, 67.24, and 59.52, respectively, for different values of quality (10, 20, 30, 40, and 50). GWO was used to optimize the quality factor which varies in each block, further increasing the space-saving up to 85.77. The average value of PSNR was equal to 21.3, MOS = 4.71, and PSEQ was equal to 3.95. Lastly, the RLE method effectively reduced the size of speech matrices, yielding a highly satisfactory space saving of up to 90.77, while maintaining excellent speech quality.

Lightweight block encryption algorithms, such as PRESENT, KEILN, and LED, are pivotal in securing resource-constrained devices and small-scale systems relevance for environments with limited computational resources. This work presents a performance comparison in execution time, memory usage, and implementations in single-core and multicore systems. Our approach involves executing encryption for fixed text file sizes in single-mode and multicore-mode processing. The implemented Multicore technique is notable for its efficient adjustment of text files with a fixed size, showcasing its adaptability. The evaluation criteria encompass the execution time of encryption and memory utilization, continually showcasing the performance analysis of algorithms prioritizing lightweight encryption. These algorithms exhibit significant improvements while handling Multicore execution fixed text data files, which minimally impact encryption and decryption time. These values can be influenced by factors such as the encryption technique, file content, and system resources. We performed simulations using an Arduino Nano, which operates on an ATmega328P microcontroller as the principal device, and several microcontroller assessments within the IoT sector. This research additionally established additional measurements, covering energy utilization, security robustness, and attack resilience of three lightweight encryption methods. Our findings indicate that, in terms of various metrics, the LED approach appears to be the most effective algorithm in a single model. It has the quickest encryption time and uses the least amount of RAM.KEILN seems to be the best choice for applications where memory usage is critical because of its efficient use of resources. LEDs provide competitive speeds; however, a slight increase in memory usage is allowed. Because of its large memory requirements, PRESENT might not be as enticing even though it is reasonably quick. This paper will aid IoT developers in choosing a suitable platform and encryption method for creating a secure network, considering several resource constraint factors. It facilitates educated decision-making concerning algorithm choice, microcontroller selection, and optimization methodologies to attain the required equilibrium of performance, security, and resource utilization.

The paper's subject is one of the points of fundamental significance in improving the development of complex computer systems is the effect of errors since their probability of occurrence in a widely distributed system is exceptionally high. Thus, the computer system must be dependable and provide comprehensive support for fault tolerance, fault detection, signal recovery mechanisms, failure prediction, etc. The goal is to develop a way to evaluate the reliability of the Red Hat Linux operating system utilizing data sets of system failures obtained from system injection with error reports using the Fault Injection Benchmarking and Reliability) FIBR( (for a data set of system failures). It allows users to inject various faults into a system to simulate failures and analyse their impact on system reliability. First, identify the type and frequency of failures observed in the data set. This can be done by analysing the error reports generated by the FIBR tool. The categorization of the failures is based on their severity and impact on system performance by assigning a score or rank to each failure based on its severity and impact on system performance. This can be done using statistical methods like failure distribution analysis or reliability block diagrams. Finally, use the reliability model to calculate key reliability metrics, such as mean time between failures (MTBF) and failure rate. These metrics can be used to evaluate the overall reliability of the Red Hat Linux operating system under different scenarios and conditions. Moreover, compare the results obtained from the reliability analysis with industry standards and benchmarks to determine if the Red Hat Linux operating system's reliability meets the required performance level for the intended use case. The method is a technique founded on two reliability evaluation models: defect count and failure prediction. An Operatingreliability evaluation technique based on two models: defect count and failure prediction. In the defect count model, operating system defects or errors are counted and used to estimate the operating system's reliability. This is done by analysing the number of defects found during the functional testing. This information is used to predict the number of defects that may occur in the future. The defect count model assumes that the number of defects in the operating system is proportional to its size and complexity and that reducing defects leads to increased reliability. The defect count model provides a quantitative estimate of the number of defects in the operating