One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p

<span>We present the linearization of an ultra-wideband low noise amplifier (UWB-LNA) operating from 2GHz to 11GHz through combining two linearization methods. The used linearization techniques are the combination of post-distortion cancellation and derivative-superposition linearization methods. The linearized UWB-LNA shows an improved linearity (IIP3) of +12dBm, a minimum noise figure (NF_min.) of 3.6dB, input and output insertion losses (S₁₁ and S₂₂)  below -9dB over the entire working bandwidth, midband gain of 6dB at 5.8GHz, and overall circuit power consumption of 24mW supplied from a 1.5V voltage source. Both UWB-LNA and linearized UWB-LNA designs are

General Background: Deep image matting is a fundamental task in computer vision, enabling precise foreground extraction from complex backgrounds, with applications in augmented reality, computer graphics, and video processing. Specific Background: Despite advancements in deep learning-based methods, preserving fine details such as hair and transparency remains a challenge. Knowledge Gap: Existing approaches struggle with accuracy and efficiency, necessitating novel techniques to enhance matting precision. Aims: This study integrates deep learning with fusion techniques to improve alpha matte estimation, proposing a lightweight U-Net model incorporating color-space fusion and preprocessing. Results: Experiments using the AdobeComposition-1k

A new two-way nesting technique is presented for a multiple nested-grid ocean modelling system. The new technique uses explicit center finite difference and leapfrog schemes to exchange information between the different subcomponents of the nested-grid system. The performance of the different nesting techniques is compared, using two independent nested-grid modelling systems. In this paper, a new nesting algorithm is described and some preliminary results are demonstrated. The validity of the nesting method is shown in some problems for the depth averaged of 2D linear shallow water equation.

Hiding technique for dynamic encryption text using encoding table and symmetric encryption method (AES algorithm) is presented in this paper. The encoding table is generated dynamically from MSB of the cover image points that used as the first phase of encryption. The Harris corner point algorithm is applied on cover image to generate the corner points which are used to generate dynamic AES key to second phase of text encryption. The embedded process in the LSB for the image pixels except the Harris corner points for more robust. Experimental results have demonstrated that the proposed scheme have embedding quality, error-free text recovery, and high value in PSNR.

Deep Learning Techniques For Skull Stripping of Brain MR Images

This study deals with the role that social work profession plays in all its fields to reduce social extremismat home, or school or within society. The study aims to: examine the historical roots of social work in the Iraqi society, investigate the objectives of the developmental role of social work, review the theories of social extremism, its characteristics, and causes, and to analyze the developmental role of social work to limit social extremism. To meet the objectives of the study, a descriptive analytical approach has been adopted. It involves using the social sampling survey method, i.e., a questionnaire tool in the University of Baghdad community-College of Media. The sample was randomly selected to include (100) students from th

Thermal decomposition of used tires was studied under atmospheric pressure and thermal heating program up to 350 °C for 110-120 min in a glass reactor. The effects of using MgO or SiO2 as catalyst in the thermal pyrolysis of tires waste on the yield ratio, reaction time, initial degree of decomposition also the contents of BTEX compounds in resulting pyrolytic oil was estimated via GC-FID. The results showed that the MgO catalyst gave a gas output in proportion of 17.063%, a liquid in a proportion of 38.245% and a solid product in proportion of 44.733%, while the SiO2 catalyst gave proportions of 14.308%, 40.161% and 45.448% for gas, liquid and solid products respectively. The results revealed that the thermal pyrolysis produced minor cont