The aim of this paper is to design artificial neural network as an alternative accurate tool to estimate concentration of Cadmium in contaminated soils for any depth and time. First, fifty soil samples were harvested from a phytoremediated contaminated site located in Qanat Aljaeesh in Baghdad city in Iraq. Second, a series of measurements were performed on the soil samples. The inputs are the soil depth, the time, and the soil parameters but the output is the concentration of Cu in the soil for depth x and time t. Third, design an ANN and its performance was evaluated using a test data set and then applied to estimate the concentration of Cadmium. The performance of the ANN technique was compared with the traditional laboratory inspecting

Prediction of penetration rate (ROP) is important process in optimization of drilling due to its crucial role in lowering drilling operation costs. This process has complex nature due to too many interrelated factors that affected the rate of penetration, which make difficult predicting process. This paper shows a new technique of rate of penetration prediction by using artificial neural network technique. A three layers model composed of two hidden layers and output layer has built by using drilling parameters data extracted from mud logging and wire line log for Alhalfaya oil field. These drilling parameters includes mechanical (WOB, RPM), hydraulic (HIS), and travel transit time (DT). Five data set represented five formations gathered

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-dimen

Any design subject to a set of forces contributing to the establishment of relations working to strengthen the internal elements of the design; any imbalance in these elements can make a fragmented and weak design, thus preventing it from achieving the goal or performance. Poor performance can be attributed to various factors: the extent and function of the elements and principles in the design, realization of the idea, especially in fashion design.
Moreover, there are many aspects of a design that go into achieving the realization of the designer’s idea. The design utilizes a lot of stimulants by drawing attention to its design, which is consistent with the need for psychological and material individuals. In this research, we will

The present study aims to show the importance of ESL reading ability in acquiring English as a second language. The study involves 92 college students (males and females) from the Department of English at Nizwa College of Applied Sciences, Sultanate of Oman. They represent two groups, the foundation year students and the first year English majors. A number of tests were used to measure students’ overall proficiency in English as well as their reading ability (i.e., the ability to contribute to the main idea of the text, scanning, skimming, to derive word meanings from context, to use a dictionary to find meanings, definitions, to identify prefixes, antonyms and synonyms). Students’ ability to read was correlated to their proficiency

Neural cryptography deals with the problem of “key exchange” between two neural networks by using the mutual learning concept. The two networks exchange their outputs (in bits) and the key between two communicating parties ar eventually represented in the final learned weights, when the two networks are said to be synchronized. Security of neural synchronization is put at risk if an attacker is capable of synchronizing with any of the two parties during the training process.

Modified optical fiber sensors received increasing attention because of their superior properties over electrical sensors. These properties include their immunity towards electromagnetic interference and the ability to be deployed in corrosive and volatile environment. Several optical fiber platforms have been developed for chemical sensing applications based on modifying optical fiber cladding layer such as etched, tapered, D-shaped and etched-tapered. The modifications purpose is to extend the evanescent wave propagating out of the core physical dimensions. Thus, evanescent wave interaction with analyte is enhanced. Modified optical transducing platforms are integrated in gas sensing applications, such as ammonia. Modified optical