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

Impact strength of self-compacted concrete is a field of interest, mostly when the concrete is produced from sustainable materials. This research's main objective is to clarify the ability to use two types of Portland limestone cement (Karasta and Tasluja) in self compacted concrete under impact loading, further to the economic and environmental benefits of the limestone cement. The impact loading was applied by a low-speed test, using the drop ball on concrete. Moreover, the study reveals the resistance of the grids reinforced concrete to impact loading by using polymer grid, and steel grid reinforced concrete slabs. Mixes reinforced by steel mesh had the highest results, indicating that the steel mesh was more robust because it had

Rutting is a crucial concern impacting asphalt concrete pavements’ stability and long-term performance, negatively affecting vehicle drivers’ comfort and safety. This research aims to evaluate the permanent deformation of pavement under different traffic and environmental conditions using an Artificial Neural Network (ANN) prediction model. The model was built based on the outcomes of an experimental uniaxial repeated loading test of 306 cylindrical specimens. Twelve independent variables representing the materials’ properties, mix design parameters, loading settings, and environmental conditions were implemented in the model, resulting in a total of 3214 data points. The network accomplished high prediction accuracy with an R

Dielectric barrier discharges (DBD) can be described as the presence of contact with the discharge of one or more insulating layers located between two cylindrical or flat electrodes connected to an AC/pulse dc power supply. In this work, the properties of the plasma generated by dielectric barrier discharge (DBD) system without and with a glass insulator were studied. The plasma was generated at a constant voltage of 4 kV and fixed distance between the electrodes of 5 mm, and with a variable flow rate of argon gas (0.5, 1, 1.5, 2 and 2.5) L/min. The emission spectra of the DBD plasmas at different flow rates of argon gas have been recorded. Boltzmann plot method was used to calculate the plasma electron temperature (Te), and Stark broadeni

One of the most important problems facing the world today is the energy problem. The solution was in finding renewable energy sources such as solar energy. The solar energy applications in Iraq is facing many problems . One of the most important problems is the accumulation of dust on the solar panels surface which causes decreasing its performance sharply. In the present work, a new technique was presented by using two-axis solar tracking system to reduce the accumulated dust on the solar panel surface and compared it with the fixed solar panels which installed at tilt angles 30° and 45°. The results indicated that the maximum losses of the output power due to accumulation of dust on the fixed solar panels is about 31.4% and 23.1% res

Colorectal cancer (CRC) is the most common gastrointestinal malignancy and one of the top ten common cancers worldwide with approximately 2 million cases. There are multiple risk factors that could lead to CRC emergence; of which are genetic polymorphisms. Excision repair cross-complementing group 2 (ERCC2) gene encodes for ERCC2 enzyme which plays a crucial role in maintaining genomic integrity by removing DNA adducts. Several studies suggested that there could be a link between genetic polymorphisms of ERCC2 gene and the risk of CRC development. Hence the present study aims to validate the relationship between the following ERCC2 single nucleotide polymorphisms (rs13181, rs149943175, rs530662943, and rs1799790) and CRC susceptibility. A t

Light isotopes, especially closed shell nuclei, have significance in thermonuclear reactions of the Carbon-Nitrogen-Oxygen (CNO) cycle in stars. In this research, 12C(p, γ) 13N and 14N(p, γ) 15O reactions have been calculated by means of Matlab codes to find the reaction rate across a temperature range of 0.006 to 10 GK using non-resonant parts, as well as the astrophysical S- factor S(E) at low energies. It was concluded that the high binding energy of 12C and 14N nuclei make the reaction less probable thus enabling other competitive processes to develop, which enhances the probability of other competitive proton reactions in the CNO cycle.

Productivity estimating of ready mixed concrete batch plant is an essential tool for the successful completion of the construction process. It is defined as the output of the system per unit of time. Usually, the actual productivity values of construction equipment in the site are not consistent with the nominal ones. Therefore, it is necessary to make a comprehensive evaluation of the nominal productivity of equipment concerning the effected factors and then re-evaluate them according to the actual values.

In this paper, the forecasting system was employed is an Artificial Intelligence technique (AI). It is represented by Artificial Neural Network (ANN) to establish the predicted model to estimate wet ready mixe