In light of the development in computer science and modern technologies, the impersonation crime rate has increased. Consequently, face recognition technology and biometric systems have been employed for security purposes in a variety of applications including human-computer interaction, surveillance systems, etc. Building an advanced sophisticated model to tackle impersonation-related crimes is essential. This study proposes classification Machine Learning (ML) and Deep Learning (DL) models, utilizing Viola-Jones, Linear Discriminant Analysis (LDA), Mutual Information (MI), and Analysis of Variance (ANOVA) techniques. The two proposed facial classification systems are J48 with LDA feature extraction method as input, and a one-dimensional Convolutional Neural Network Hybrid Model (1D-CNNHM). The MUCT database was considered for training and evaluation. The performance, in terms of classification, of the J48 model reached 96.01% accuracy whereas the DL model that merged LDA with MI and ANOVA reached 100% accuracy. Comparing the proposed models with other works reflects that they are performing very well, with high accuracy and low processing time.
The support vector machine, also known as SVM, is a type of supervised learning model that can be used for classification or regression depending on the datasets. SVM is used to classify data points by determining the best hyperplane between two or more groups. Working with enormous datasets, on the other hand, might result in a variety of issues, including inefficient accuracy and time-consuming. SVM was updated in this research by applying some non-linear kernel transformations, which are: linear, polynomial, radial basis, and multi-layer kernels. The non-linear SVM classification model was illustrated and summarized in an algorithm using kernel tricks. The proposed method was examined using three simulation datasets with different sample
... Show MorePhosphorus (P) is an element that is potatoes require in large amounts. Soil pH is a crucial factor impacting phosphorus availability in potato production. This study was conducted to evaluate the influence of P application rates on the P efficiency for tuber yield, specific gravity, and P uptake. Additionally, the relationship between soil pH and total potato tuber yield was determined. Six rates of P fertilization (0–280 kg P ha−1) were applied at twelve different sites across Northern Maine. Yield parameters were not responsive to P application rates. However, regression analysis showed that soil pH was significantly correlated with total potato tuber yield(R2 = 0.38). Sites with soil pH values < 6 had total tuber yields,
... Show MoreGas lift is one of the oldest methods used to produce hydrocarbon fluids from wells experiencing declining production. It works by aerating the mixture inside production tubing and forcing it to the surface. One of the reasons for declining production in oil wells is an increase in water cut or a drop in reservoir pressure, which requires one of the artificial lift methods to restore production. (A) oil field suffers from an increase in water cut, which has affected well productivity. The wells under study have a water cut of up to 66%, resulting in very low production rates. In this research, Pipesim software was used to build a physical and fluid model, then design an optimal gas lift system that achieves the highest possible prod
... Show MoreThis study looks into the many methods that are used in the risk assessment procedure that is used in the construction industry nowadays. As a result of the slow adoption of novel assessment methods, professionals frequently resort to strategies that have previously been validated as being successful. When it comes to risk assessment, having a precise analytical tool that uses the cost of risk as a measurement and draws on the knowledge of professionals could potentially assist bridge the gap between theory and practice. This step will examine relevant literature, sort articles according to their published year, and identify domains and qualities. Consequently, the most significant findings have been presented in a manne
... Show MoreThe gravity method is a measurement of relatively noticeable variations in the Earth’s gravitational field caused by lateral variations in rock's density. In the current research, a new technique is applied on the previous Bouguer map of gravity surveys (conducted from 1940–1950) of the last century, by selecting certain areas in the South-Western desert of Iraqi-territory within the provinces' administrative boundary of Najaf and Anbar. Depending on the theory of gravity inversion where gravity values could be reflected to density-contrast variations with the depths; so, gravity data inversion can be utilized to calculate the models of density and velocity from four selected depth-slices 9.63 Km, 1.1 Km, 0.682 Km and 0.407 Km.
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Travel Time estimation and reliability measurement is an important issues for improving operation efficiency and safety of traffic roads networks. The aim of this research is the estimation of total travel time and distribution analysis for three selected links in Palestine Arterial Street in Baghdad city. Buffer time index results in worse reliability conditions. Link (2) from Bab Al Mutham intersection to Al-Sakara intersection produced a buffer index of about 36% and 26 % for Link (1) Al-Mawall intersection to Bab Al- Mutham intersection and finally for link (3) which presented a 24% buffer index. These illustrated that the reliability get worst for link
... Show MoreArtificial Neural Networks (ANN) is one of the important statistical methods that are widely used in a range of applications in various fields, which simulates the work of the human brain in terms of receiving a signal, processing data in a human cell and sending to the next cell. It is a system consisting of a number of modules (layers) linked together (input, hidden, output). A comparison was made between three types of neural networks (Feed Forward Neural Network (FFNN), Back propagation network (BPL), Recurrent Neural Network (RNN). he study found that the lowest false prediction rate was for the recurrentt network architecture and using the Data on graduate students at the College of Administration and Economics, Univer
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