The process of accurate localization of the basic components of human faces (i.e., eyebrows, eyes, nose, mouth, etc.) from images is an important step in face processing techniques like face tracking, facial expression recognition or face recognition. However, it is a challenging task due to the variations in scale, orientation, pose, facial expressions, partial occlusions and lighting conditions. In the current paper, a scheme includes the method of three-hierarchal stages for facial components extraction is presented; it works regardless of illumination variance. Adaptive linear contrast enhancement methods like gamma correction and contrast stretching are used to simulate the variance in light condition among images. As testing material

The condition of Islam and Muslims and what their political, social, cultural and educational status, and even religious and faith status, has led to whoever feels his belonging to this great religion and that middle nation, to try as much as he can to fix what has been corrupted as much as he can, and perhaps in diagnosing illness and illness as It was said half the way to treatment, and from this standpoint I liked to occupy thought, work consideration and harness part of the youth’s life in order to reach what contributes to reforming the situation and the safety of generations, and this is only in seeking knowledge and learning it by following the guidance of the Messenger (r) and his companions ( y) Therefore, the subject of my re

This paper studies a novel technique based on the use of two effective methods like modified Laplace- variational method (MLVIM) and a new Variational method (MVIM)to solve PDEs with variable coefficients. The current modification for the (MLVIM) is based on coupling of the Variational method (VIM) and Laplace- method (LT). In our proposal there is no need to calculate Lagrange multiplier. We applied Laplace method to the problem .Furthermore, the nonlinear terms for this problem is solved using homotopy method (HPM). Some examples are taken to compare results between two methods and to verify the reliability of our present methods.

The experiment aimed to compare different methods of measuring the Feed pellet durability through the effect of pellet die speeds and the particle size (mill sieve holes diameter). Feed pellet durability was studied in four different ways: pellet direct measurement (%), pellet lengths (%), pellet water absorption (%), pellet durability by drop box device (%), pellet durability by air pressure device (%). Three pellet die speeds 280, 300, and 320 rpm, three mill sieve holes diameter 2, 4, and 6 mm, have been used. The results showed that increasing the pellet die speeds from 280 to 300 then to 320 rpm led to a significant decrease in the feed pellet durability by direct measurement, drop box device, and air pressure device, while pel

Porosity is important because it reflects the presence of oil reserves. Hence, the number of underground reserves and a direct influence on the essential petrophysical parameters, such as permeability and saturation, are related to connected pores. Also, the selection of perforation interval and recommended drilling additional infill wells. For the estimation two distinct methods are used to obtain the results: the first method is based on conventional equations that utilize porosity logs. In contrast, the second approach relies on statistical methods based on making matrices dependent on rock and fluid composition and solving the equations (matrices) instantaneously. In which records have entered as equations, and the matrix is sol

In our article, three iterative methods are performed to solve the nonlinear differential equations that represent the straight and radial fins affected by thermal conductivity. The iterative methods are the Daftardar-Jafari method namely (DJM), Temimi-Ansari method namely (TAM) and Banach contraction method namely (BCM) to get the approximate solutions. For comparison purposes, the numerical solutions were further achieved by using the fourth Runge-Kutta (RK4) method, Euler method and previous analytical methods that available in the literature. Moreover, the convergence of the proposed methods was discussed and proved. In addition, the maximum error remainder values are also evaluated which indicates that the propo

Feature selection (FS) constitutes a series of processes used to decide which relevant features/attributes to include and which irrelevant features to exclude for predictive modeling. It is a crucial task that aids machine learning classifiers in reducing error rates, computation time, overfitting, and improving classification accuracy. It has demonstrated its efficacy in myriads of domains, ranging from its use for text classification (TC), text mining, and image recognition. While there are many traditional FS methods, recent research efforts have been devoted to applying metaheuristic algorithms as FS techniques for the TC task. However, there are few literature reviews concerning TC. Therefore, a comprehensive overview was systematicall