<p>Analyzing X-rays and computed tomography-scan (CT scan) images using a convolutional neural network (CNN) method is a very interesting subject, especially after coronavirus disease 2019 (COVID-19) pandemic. In this paper, a study is made on 423 patients’ CT scan images from Al-Kadhimiya (Madenat Al Emammain Al Kadhmain) hospital in Baghdad, Iraq, to diagnose if they have COVID or not using CNN. The total data being tested has 15000 CT-scan images chosen in a specific way to give a correct diagnosis. The activation function used in this research is the wavelet function, which differs from CNN activation functions. The convolutional wavelet neural network (CWNN) model proposed in this paper is compared with regular convol

The main goal of this work is study the land cover changes for "Baghdad city" over a period of (30) years using multi-temporal Landsat satellite images (TM, ETM+ and OLI) acquired in 1984, 2000, and 2015 respectively. In this work, The principal components analysis transform has been utilized as multi operators, (i.e. enhancement, compressor, and temporal change detector). Since most of the image band's information are presented in the first PCs image. Then, the PC1 image for all three years is partitioned into variable sized blocks using quad tree technique. Several different methods of classification have been used to classify Landsat satellite images; these are, proposed method singular value decomposition (SVD) using Visual Basic sof

Image registration plays a significant role in the medical image processing field. This paper proposes a development on the accuracy and performance of the Speeded-Up Robust Surf (SURF) algorithm to create Extended Field of View (EFoV) Ultrasound (US) images through applying different matching measures. These measures include Euclidean distance, cityblock distance, variation, and correlation in the matching stage that was built in the SURF algorithm. The US image registration (fusion) was implemented depending on the control points obtained from the used matching measures. The matched points with higher frequency algorithm were proposed in this work to perform and enhance the EFoV for the US images, since the maximum accurate matching po

Kidney tumors are of different types having different characteristics and also remain challenging in the field of biomedicine. It becomes very important to detect the tumor and classify it at the early stage so that appropriate treatment can be planned. Accurate estimation of kidney tumor volume is essential for clinical diagnoses and therapeutic decisions related to renal diseases. The main objective of this research is to use the Computer-Aided Diagnosis (CAD) algorithms to help the early detection of kidney tumors that addresses the challenges of accurate kidney tumor volume estimation caused by extensive variations in kidney shape, size and orientation across subjects.
In this paper, have tried to implement an automated segmentati

Database is characterized as an arrangement of data that is sorted out and disseminated in a way that allows the client to get to the data being put away in a simple and more helpful way. However, in the era of big-data the traditional methods of data analytics may not be able to manage and process the large amount of data. In order to develop an efficient way of handling big-data, this work studies the use of Map-Reduce technique to handle big-data distributed on the cloud. This approach was evaluated using Hadoop server and applied on EEG Big-data as a case study. The proposed approach showed clear enhancement for managing and processing the EEG Big-data with average of 50% reduction on response time. The obtained results provide EEG r

Performance of gas-solid spouted bed benefit from solids uniformity structure (UI).Therefore, the focus of this work is to maximize UI across the bed based on process variables. Hence, UI is to be considered as the objective of the optimization process .Three selected process variables are affecting the objective function. These decision variables are: gas velocity, particle density and particle diameter. Steady-state solids concentration measurements were carried out in a narrow 3-inch cylindrical spouted bed made of Plexiglas that used 60° conical shape base. Radial concentration of particles (glass and steel beads) at various bed heights and different flow patterns were measured using sophisticated optical probes. Stochastic Genetic

Background: Polyetheretherketone (PEEK) is a promising implant material due to its superior biomechanical strength. However, due to its hydrophobic nature and lack of cellular adhesion properties, it has poor integration with bone tissue. Methods: A fractional CO2 laser was used with various parameters for surface texturing of PEEK substrate to enhance its surface properties. An optical microscope and field-emission scanning electron microscope (FESEM) were used to examine the surface morphology of untextured and laser-textured samples. Energy dispersive X-ray spectroscopy (EDX) was performed to determine the effect of the laser on the microstructure of PEEK. Surface microroughness, atomic force microscopy (AFM), and wettability were invest

Functionalized-multi wall carbon nanotubes (F-MWCNTs) and functionalized-single wall carbon nanotubes (F-SWCNTs) were well enhanced using CoO Nanoparticles. The sensor device consisted of a film of sensitive material (F-MWCNTs/CoONPs) and (F-SWCNTs/CoO NPs) deposited by drop- casting on an n-type porous silicon substrate. The two sensors perform high sensitivity to NO₂ gas at room temperatures. The analysis indicated that the (F-MWCNTs/CoONPs) have a better performance than (F-SWCNTs/CoONPs). The F-SWCNTs/CoONPs gas sensor shows high sensitivity (19.1 %) at RT with response time 17 sec, while F-MWCNTs/CoONPs gas sensor show better sensitivity (39 %) at RT with response time 13 sec. The device shows a very reproducible sensor p