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

An analytical approach based on field data was used to determine the strength capacity of large diameter bored type piles. Also the deformations and settlements were evaluated for both vertical and lateral loadings. The analytical predictions are compared to field data obtained from a proto-type test pile used at Tharthar –Tigris canal Bridge. They were found to be with acceptable agreement of 12% deviation.

               Following ASTM standards D1143M-07e1,2010, a test schedule of five loading cycles were proposed for vertical loads and series of cyclic loads to simulate horizontal loading .The load test results and analytical data of 1.95

Recently, the internet has made the users able to transmit the digital media in the easiest manner. In spite of this facility of the internet, this may lead to several threats that are concerned with confidentiality of transferred media contents such as media authentication and integrity verification. For these reasons, data hiding methods and cryptography are used to protect the contents of digital media. In this paper, an enhanced method of image steganography combined with visual cryptography has been proposed. A secret logo (binary image) of size (128x128) is encrypted by applying (2 out 2 share) visual cryptography on it to generate two secret share. During the embedding process, a cover red, green, and blue (RGB) image of size (512

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 aim of this research is to assess the validity of Detailed Micro-Modeling (DMM) as a numerical model for masonry analysis. To achieve this aim, a set of load-displacement curves obtained based on both numerical simulation and experimental results of clay masonry prisms loaded by a vertical load. The finite element method was implemented in DMM for analysis of the experimental clay masonry prism. The finite element software ABAQUS with implicit solver was used to model and analyze the clay masonry prism subjected to a vertical load. The load-displacement relationship of numerical model was found in good agreement with those drawn from experimental results. Evidence shows that load-displacement curvefound from the finite element m

Abstract

The current research is attempt to test the reflection of the lean management on the human resources management practices of two of the most important communication companies operating in Iraq (`Zain & Asia cell), The research aims to Determine the extent of adoption of the lean management approach in the two researched companies, as it improving human resource management practices. The research problem represented in the existence of lack of in some aspects of the application the lean management approach in service sector and neglecting the impact of its tools on the human resource management practices. For this purpose three principle research hypotheses has been formulated, first there is a correlation rel

The effect of short range correlations on the inelastic longitudinal Coulomb form
factors for the lowest four excited 2+ states in 18O is analyzed. This effect (which
depends on the correlation parameter β) is inserted into the ground state charge
density distribution through the Jastrow type correlation function. The single particle
harmonic oscillator wave function is used with an oscillator size parameter b. The
parameters β and b are, considered as free parameters, adjusted for each excited state
separately so as to reproduce the experimental root mean square charge radius of
18O. The model space of 18O does not contribute to the transition charge density. As
a result, the inelastic Coulomb form factor of 18

The research endeavors to harness the benefits stemming from the integration of constraint theory into construction project management, with the primary goal of mitigating project completion delays. Additionally, it employs fuzzy analysis to determine the relative significance of fundamental constraints within projects by assigning them appropriate weights. The research problem primarily revolves around two key issues. Firstly, the persistent utilization of outdated methodologies and a heavy reliance on workforce experience without embracing modern computerized technologies. Secondly, the recurring problem of project delivery delays. Construction projects typically encompass five fundamental constraint types: cost restrictions, tim

The proposal of nonlinear models is one of the most important methods in time series analysis, which has a wide potential for predicting various phenomena, including physical, engineering and economic, by studying the characteristics of random disturbances in order to arrive at accurate predictions.

In this, the autoregressive model with exogenous variable was built using a threshold as the first method, using two proposed approaches that were used to determine the best cutting point of [the predictability forward (forecasting) and the predictability in the time series (prediction), through the threshold point indicator]. B-J seasonal models are used as a second method based on the principle of the two proposed approaches in dete