Face recognition is a crucial biometric technology used in various security and identification applications. Ensuring accuracy and reliability in facial recognition systems requires robust feature extraction and secure processing methods. This study presents an accurate facial recognition model using a feature extraction approach within a cloud environment. First, the facial images undergo preprocessing, including grayscale conversion, histogram equalization, Viola-Jones face detection, and resizing. Then, features are extracted using a hybrid approach that combines Linear Discriminant Analysis (LDA) and Gray-Level Co-occurrence Matrix (GLCM). The extracted features are encrypted using the Data Encryption Standard (DES) for security

The study was conducted at research station A, department of field crops, college of agricultural engineering sciences, university of Baghdad during summer 2021 to evaluate the effect of boron and some growth regulators on some growth criteria and yield of soybean crop (cv. shimaa). The experiment  was carried out according to split plots by using randomized complete block design with three replications. The main plots included three concentrations of boron (75, 150 and 225) mg.L^-1, the sub-plots included three levels of growth regulators, spraying kinetin (100 mg. L^-1), spraying ethrel (200 mg.L^-1) and spraying kinetin (100 mg.L^-1) + spraying ethrel (200 mg.L^-1) as

Abstract

Most universities in the world are largely committed to creating credible and transparent admission standards that provide justice in admission and have the ability to predict students' performance in their chosen programs. Hence, this study aimed to reveal the predictive ability of the acceptance criteria for the level of performance of master's students in the College of Education at Sultan Qaboos University. Quantitative data were collected from (115) students' admission documents for those accepted in the postgraduate programs for the academic year 2019-2020, and GPA data was collected from students’ transcripts for the fall semester of 2019. Qualitative data were also collected from the interviews

Sorting and grading agricultural crops using manual sorting is a cumbersome and arduous process, in addition to the high costs and increased labor, as well as the low quality of sorting and grading compared to automatic sorting. the importance of deep learning, which includes the artificial neural network in prediction, also shows the importance of automated sorting in terms of efficiency, quality, and accuracy of sorting and grading. artificial neural network in predicting values and choosing what is good and suitable for agricultural crops, especially local lemons.

Diagnosing heart disease has become a very important topic for researchers specializing in artificial intelligence, because intelligence is involved in most diseases, especially after the Corona pandemic, which forced the world to turn to intelligence. Therefore, the basic idea in this research was to shed light on the diagnosis of heart diseases by relying on deep learning of a pre-trained model (Efficient b3) under the premise of using the electrical signals of the electrocardiogram and resample the signal in order to introduce it to the neural network with only trimming processing operations because it is an electrical signal whose parameters cannot be changed. The data set (China Physiological Signal Challenge -cspsc2018) was ad

To date, comprehensive reviews and discussions of the strengths and limitations of Remote Sensing (RS) standalone and combination approaches, and Deep Learning (DL)-based RS datasets in archaeology have been limited. The objective of this paper is, therefore, to review and critically discuss existing studies that have applied these advanced approaches in archaeology, with a specific focus on digital preservation and object detection. RS standalone approaches including range-based and image-based modelling (e.g., laser scanning and SfM photogrammetry) have several disadvantages in terms of spatial resolution, penetrations, textures, colours, and accuracy. These limitations have led some archaeological studies to fuse/integrate multip

Diabetic retinopathy is an eye disease in diabetic patients due to damage to the small blood vessels in the retina due to high and low blood sugar levels. Accurate detection and classification of Diabetic Retinopathy is an important task in computer-aided diagnosis, especially when planning for diabetic retinopathy surgery. Therefore, this study aims to design an automated model based on deep learning, which helps ophthalmologists detect and classify diabetic retinopathy severity through fundus images. In this work, a deep convolutional neural network (CNN) with transfer learning and fine tunes has been proposed by using pre-trained networks known as Residual Network-50 (ResNet-50). The overall framework of the proposed

The purpose of this article was to identify and assess the importance of risk factors in the tendering phase of construction projects. The construction project cannot succeed without the identification and categorization of these risk elements. In this article, a questionnaire for likelihood and impact was designed and distributed to a panel of specialists to analyze risk factors. The risk matrix was also used to research, explore, and identify the risks that influence the tendering phase of construction projects. The probability and impact values assigned to risk are used to calculate the risk's score. A risk matrix is created by combining probability and impact criteria. To determine the main risk elements for the tender phase of

The purpose of this article was to identify and assess the importance of risk factors in the tendering phase of construction projects. The construction project cannot succeed without the identification and categorization of these risk elements. In this article, a questionnaire for likelihood and impact was designed and distributed to a panel of specialists to analyze risk factors. The risk matrix was also used to research, explore, and identify the risks that influence the tendering phase of construction projects. The probability and impact values assigned to risk are used to calculate the risk's score. A risk matrix is created by combining probability and impact criteria. To determine the main risk elements for the tend