The primary objective of this paper is to improve a biometric authentication and classification model using the ear as a distinct part of the face since it is unchanged with time and unaffected by facial expressions. The proposed model is a new scenario for enhancing ear recognition accuracy via modifying the AdaBoost algorithm to optimize adaptive learning. To overcome the limitation of image illumination, occlusion, and problems of image registration, the Scale-invariant feature transform technique was used to extract features. Various consecutive phases were used to improve classification accuracy. These phases are image acquisition, preprocessing, filtering, smoothing, and feature extraction. To assess the proposed

 In this paper, we introduce and discuss an algorithm for the numerical solution of some kinds of fractional integral and fractional integrodifferential equations. The algorithm for the numerical solution of these equations is based on iterative approach. The stability and convergence of the fractional order numerical method are described. Finally, some numerical examples are provided to show that the numerical method for solving the fractional integral and fractional integrodifferential equations is an effective solution method.

In an attempt to disposal from nuclear waste which threats our health and environments. Therefore we have to find appropriate method to immobilize nuclear waste. So, in this research the nuclear waste (Strontium hydroxide) was immobilized by Carbon nanotubes (CNTs).  The Nd-YAG laser with wave length 1064 nm, energy 750 mJ and 100 pulses used to prepare CNTs. After that adding Sr(HO)₂ powder to the CNTs colloidal in calculated rate to get homogenous mixing of CNTs-Sr(OH)_2. The Sr(HO)₂ absorbs carbon dioxide from the air to form strontium carbonate so, the  new solution is CNTs-SrCO₃. To dry solution putting three drops from the new solution on the glass slides. To investigate the radi

In this study, we have created a new Arabic dataset annotated according to Ekman’s basic emotions (Anger, Disgust, Fear, Happiness, Sadness and Surprise). This dataset is composed from Facebook posts written in the Iraqi dialect. We evaluated the quality of this dataset using four external judges which resulted in an average inter-annotation agreement of 0.751. Then we explored six different supervised machine learning methods to test the new dataset. We used Weka standard classifiers ZeroR, J48, Naïve Bayes, Multinomial Naïve Bayes for Text, and SMO. We also used a further compression-based classifier called PPM not included in Weka. Our study reveals that the PPM classifier significantly outperforms other classifiers such as SVM and N

Background: Corn Syrup is food syrup higher of carbohydrate, depending on grade. The study aimed to assess efficiency of Corn syrup as cytological fixative.

Subjects and methods: This was laboratory based study, it has been conducted at Elrazi University included   apparently 30 healthy students have been involved in this study.

Results: Out of 30 smears fixed with 95% alcohol, 76.7% (n=23) shows excellent nuclear stain, 23.3% (n= 7)   shows good nuclear stain. 70% (n=21) show excellent cytoplasmic stain, 26.7% (n=8) shows good cytoplasmic stain, 3.3% (n=1) shows poor cytoplasmuc stain.

   Out of 30 smears fixed with corn solution, 60

The goal of this study is to provide a new explicit iterative process method  approach for solving maximal monotone(M.M )operators in Hilbert spaces utilizing a finite family of different types of  mappings as( nonexpansive mappings,resolvent mappings and projection mappings. The findings given in this research strengthen and extend key previous findings in the literature. Then, utilizing various structural conditions in Hilbert space and variational inequality problems, we examine the strong convergence to nearest point projection for these explicit iterative process methods Under the presence of two important conditions for convergence, namely closure and convexity. The findings reported in this research strengthen and extend

     The work in this paper focuses on solving numerically and analytically a  nonlinear social epidemic model that represents an initial value problem  of ordinary differential equations. A recent moking habit model from Spain is applied and studied here. The accuracy and convergence of the numerical and approximation results are investigated for various methods; for example, Adomian decomposition, variation iteration, Finite difference and Runge-Kutta. The discussion of the present results has been tabulated and graphed. Finally, the comparison between the analytic and numerical solutions from the period 2006-2009 has been obtained by absolute and difference measure error.