<p class="0abstract">Image denoising is a technique for removing unwanted signals called the noise, which coupling with the original signal when transmitting them; to remove the noise from the original signal, many denoising methods are used. In this paper, the Multiwavelet Transform (MWT) is used to denoise the corrupted image by Choosing the HH coefficient for processing based on two different filters Tri-State Median filter and Switching Median filter. With each filter, various rules are used, such as Normal Shrink, Sure Shrink, Visu Shrink, and Bivariate Shrink. The proposed algorithm is applied Salt&amp; pepper noise with different levels for grayscale test images. The quality of the denoised image is evaluated by usi

In this paper, a new equivalent lumped parameter model is proposed for describing the vibration of beams under the moving load effect. Also, an analytical formula for calculating such vibration for low-speed loads is presented. Furthermore, a MATLAB/Simulink model is introduced to give a simple and accurate solution that can be used to design beams subjected to any moving loads, i.e., loads of any magnitude and speed. In general, the proposed Simulink model can be used much easier than the alternative FEM software, which is usually used in designing such beams. The obtained results from the analytical formula and the proposed Simulink model were compared with those obtained from Ansys R19.0, and very good agreement has been shown. I

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Objectives: Osteoporosis (OP) is a systemic skeletal disorder characterized with bone mass loss and microstructure, resulting in fragility fractures. Continued secretion of Osteopontin (OPN), osteonectin (ON), osteocalcin (OCN), Parathyroid hormone (PTH) and Ca+2 lead to bone remodeling disorders, followed by bone loss and osteoporosis (OP). The current study aims to investigate the biochemical proteins OPN, OCN, and ON in postmenopausal women with osteoporosis and determine whether we could use them as good indicators for OP diagnostics. Materials and Methods: Case- control study carried out between December 2022 and July 2023. OP disease was confirmed among 108 Iraqi postmenopausal women randomly selected from different Iraqi hospitals, B

Piezoelectric structures are nowadays used in many different applications. A better understanding of the influence of material properties and geometrical design on the performance of these structures helps to develop piezoelectric structures specifically designed for their application. Different equivalent circuits have been introduced in the literature to investigate the behaviour of piezoelectric transducers. The model parameters are usually determined from measurements covering the characteristic frequencies of the piezoelectric transducer. This article introduces an analytical technique for calculating the mechanical and electrical equivalent system parameters and characteristic frequencies based on material properties and geom