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

The introduction of concrete damage plasticity material models has significantly improved the accuracy with which the concrete structural elements can be predicted in terms of their structural response. Research into this method's accuracy in analyzing complex concrete forms has been limited. A damage model combined with a plasticity model, based on continuum damage mechanics, is recommended for effectively predicting and simulating concrete behaviour. The damage parameters, such as compressive and tensile damages, can be defined to simulate concrete behavior in a damaged-plasticity model accurately. This research aims to propose an analytical model for assessing concrete compressive damage based on stiffness deterioration. The prop

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

 Fluoroscopic images are a field of medical images that depends on the quality of image for correct diagnosis; the main trouble is the de-nosing and how to keep the poise between degradation of noisy image, from one side, and edge and fine details preservation, from the other side, especially when fluoroscopic images contain black and white type noise with high density. The previous filters could usually handle low/medium black and white type noise densities, that expense edge, =fine details preservation and fail with high density of noise that corrupts the images. Therefore, this paper proposed a new Multi-Line algorithm that deals with high-corrupted image with high density of black and white type noise. The experiments achieved i

As one type of heating furnaces, the electric heating furnace (EHF) typically suffers from time delay, non-linearity, time-varying parameters, system uncertainties, and harsh en-vironment of the furnace, which significantly deteriorate the temperature control process of the EHF system. In order to achieve accurate and robust temperature tracking performance, an integration of robust state feedback control (RSFC) and a novel sliding mode-based disturbance observer (SMDO) is proposed in this paper, where modeling errors and external disturbances are lumped as a lumped disturbance. To describe the characteristics of the EHF, by using convection laws, an integrated dynamic model is established and identified as an uncertain nonlinear second ord

     The open hole well log data (Resistivity, Sonic, and Gamma Ray) of well X in Euphrates subzone within the Mesopotamian basin are applied to detect the total organic carbon (TOC) of Zubair Formation in the south part of Iraq. The mathematical interpretation of the logs parameters helped in detecting the TOC and source rock productivity. As well, the quantitative interpretation of the logs data leads to assigning to the organic content and source rock intervals identification. The reactions of logs in relation to the increasing of TOC can be detected through logs parameters. By this way, the TOC can be predicted with an increase in gamma-ray, sonic, neutron, and resistivity, as well as a decrease in the density log

Research on sustainable design in Iraqi craft industries and ways to develop them is essential to preserving the cultural heritage and authenticity of these industries while promoting environmentally sustainable practices. Lack of access to modern technologies, knowledge and resources may hinder the growth of these industries and their ability to compete in the global market. The research problem revolves around finding ways to develop sustainable design in the Iraqi craft industries. The expected outcomes of this research include a clear definition of sustainable design, understanding the history of sustainable design in the craft industry, identifying different types of craft industries in Iraq, exploring the basic concepts of sustaina

This research discusses the subject of emotional stimulation factors through the stages of product design interaction with the consumer and what they reflect in the formation of a self-consideration of the design in his emotional memory regarding the specific brand through the following question (What is the emotional stimulus in the design of certain products and its reflection on the consumer during the processes of receiving and using it?), The importance of the research comes in the connection of design elements with many sensory factors used and motivating to attract the consumer to the acquisition of a specific design and the demand for its experience, the study aims Detection and access to emotional stimulation in the design of th

The research on coordination polymers chemistry based on organic-metal framework with bridging ligands has accelerated during the past two decades. It is an interested hot topic in the synthetic inorganic chemistry, which allowed the fabrication of a variety of interested materials. These materials have shown a range of applications including light harvesting and magnetic properties. The thesis is divided in three chapters. The first chapter gives a general introduction on the development of self-assembled polymeric species based on transition metal. It is also covered a summary of the literature review on the current state of the art on self-assembled coordination ploymers. Furthermore, it includes a description on various relevant topics