This abstract focuses on the significance of wireless body area networks (WBANs) as a cutting-edge and self-governing technology, which has garnered substantial attention from researchers. The central challenge faced by WBANs revolves around upholding quality of service (QoS) within rapidly evolving sectors like healthcare. The intricate task of managing diverse traffic types with limited resources further compounds this challenge. Particularly in medical WBANs, the prioritization of vital data is crucial to ensure prompt delivery of critical information. Given the stringent requirements of these systems, any data loss or delays are untenable, necessitating the implementation of intelligent algorithms. These algorithms play a pivota

Steganography is a mean of hiding information within a more obvious form of
communication. It exploits the use of host data to hide a piece of information in such a way
that it is imperceptible to human observer. The major goals of effective Steganography are
High Embedding Capacity, Imperceptibility and Robustness. This paper introduces a scheme
for hiding secret images that could be as much as 25% of the host image data. The proposed
algorithm uses orthogonal discrete cosine transform for host image. A scaling factor (a) in
frequency domain controls the quality of the stego images. Experimented results of secret
image recovery after applying JPEG coding to the stego-images are included.

Digital image is widely used in computer applications. This paper introduces a proposed method of image zooming based upon inverse slantlet transform and image scaling. Slantlet transform (SLT) is based on the principle of designing different filters for different scales.

      First we apply SLT on color image, the idea of transform color image into slant, where large coefficients are mainly the   signal and smaller one represent the noise. By suitably modifying these coefficients , using scaling up image by  box and Bartlett filters so that the image scales up to 2X2 and then inverse slantlet transform from modifying coefficients using to the reconstructed image .

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This study offers a new Mixed Meta Heuristics algorithm (HGSABAT) for estimating the parameter values of each of the six categories of Non-Linear regression models examined (Misrald, Meyer4, Meyer7, Militky4, Militky2, and MGH09) by combining the Gravitational Search Algorithm and Bat Algorithm. Some models have different numbers of parameters. For example, the Misrald and Militky2 models of the Non-Linear Regression model have two parameters (Bl, B2). In contrast, the MGH09 and Militky4 models have four parameters (MGHl, MGH2, MGH3, and MGH4), in which location as the Meyer4 and Meyer7 models have three attributes (Meyerl, MGH2, and MGH3). To examine the effectiveness of the suggested Hybrid Meta Heuristics algorithm (HGSABAT), a simulatio

This research aims to know the effectiveness of teaching with a proposed strategy according to the common Knowledge construction modelin mathematical proficiency among students of the second middle class. The researchers adopted the method of the experimental approach, as the experimental design was used for two independent and equal groups with a post-test. The experiment was applied to a sample consisting of (83) students divided into two groups: an experimental comprising (42) students and a control group, the second comprising (41) students., from Badr Shaker Al-Sayyab Intermediate School for Boys, for the first semester of the academic year (2021-2022), the two groups were rewarded in four variables: (chronological age calculated in mo

Metasurface polarizers are essential optical components in modern integrated optics and play a vital role in many optical applications including Quantum Key Distribution systems in quantum cryptography. However, inverse design of metasurface polarizers with high efficiency depends on the proper prediction of structural dimensions based on required optical response. Deep learning neural networks can efficiently help in the inverse design process, minimizing both time and simulation resources requirements, while better results can be achieved compared to traditional optimization methods. Hereby, utilizing the COMSOL Multiphysics Surrogate model and deep neural networks to design a metasurface grating structure with high extinction rat

The application of the test case prioritization method is a key part of system testing intended to think it through and sort out the issues early in the development stage. Traditional prioritization techniques frequently fail to take into account the complexities of big-scale test suites, growing systems and time constraints, therefore cannot fully fix this problem. The proposed study here will deal with a meta-heuristic hybrid method that focuses on addressing the challenges of the modern time. The strategy utilizes genetic algorithms alongside a black hole as a means to create a smooth tradeoff between exploring numerous possibilities and exploiting the best one. The proposed hybrid algorithm of genetic black hole (HGBH) uses the