Lately, a growing interest has been emerging in age estimation from face images because of the wide range of potential implementations in law enforcement, security control, and human computer interactions. Nevertheless, in spite of the advances in age estimation, it is still a challenging issue. This is due to the fact that face aging process is not only set by distinct elements, such as genetic factors, but by extrinsic factors, such as lifestyle, expressions, and environment as well. This paper applied machine learning technique to intelligent age estimation from facial images using J48 classifier on FG_NET dataset. The proposed work consists of three phases; the first phase is image preprocessing which include five stages: gray scale image, noise removable, face detection, image size normalization and clipping process. The second phase is a data mining process which includes three stages: feature extraction, feature selection and classification using j48 classifier. The third phase includes two stages, estimation and evaluation. FG-NET dataset is used which is divided into three classes; first class represents (3-7), (26-30) ages and this class represents the ages from 3 to 7 years and from 26 to 30 years because this class have four attributes from any one of this images, second class represents (8-25) ages and this class represents the ages from 8 to 25 years because this class have five attributes from any one of this images, last class represents (31-50) ages and have nine attributes from any one of this images. The Experimental results illustrate that the proposed system can give results with high precision and low time complexity. The practical evaluation of the proposed system gives accuracy up to 89.13 % with time taken of 0.023.
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Objective: The aim of this study is to evaluate anemia among lactating women and their children less than 2 years of
age.
Methodology: The study was done on (148) lactating mothers and their children under 2 years of age in the primary
health care center at AL-Salam Quarter/Baghdad from l/10/2009 to 15/10/2010.
Result: This study recommends that there is a significant relationship between anemic mothers and their children. The
study also revealed that there is (77.8%) from nursing mothers in the age groups (25-29) who suffered from anemia,
while (23.1%) for the age group (20-24) did not have anemia
Recommendation: We encourage the use of breast milk or iron-fortified infant formula only for any milk-based part
of
Age and growth of a population of Varicorhinus trutta (Heckel) from Tigris river at Salahuldin province have been investigated during the period from September 1999 to August 2000. A total of 156 fishes were collected from two stations at Tigris river using small meshed gill nets. The age data revealed that the species under investigation reached to a maximum age of seven years and approximately 46 cm long. The population of this species in Tigris river at the sites of study was dominated by 3 and 4 years classes. There were no marked differences in growth or longevity between the sexes. Determination of length-weight relationship revealed that the growth of both sexes in the species under investigation was allometric and the values of b
... Show MoreBackground: Placenta is a chief cause of maternal and perinatal mortality and significant factor in fetal growth retardation. It undergoes different variations in weight, volume, structure, shape and function continuously throughout the gestation tosupport the prenatal life. Cautious examination of placenta can give information which can be useful in the management of complications in mother and the newborn. Objective: The present work has been attempted towards determination of the morphological ( macroscopic and microscopic) parameters of human full-term placentae and their relation with different parity and age group of mothers. Patients and Methods: A whole of 40 placentae were recently collected.They were divided into four groups
... Show MoreTo evaluate impact the difference in stages ofage and related incidence of hemodialysis patients.Two hundred and fifty patients undergoing hemodialysis were collected from general hospital in Baghdad city /Iraq. The samples with renal failure before hemodialysis were divided into (138) male,( 112)female. The sera were separated from samples to physiological investigation. We found that renal failure was more predominant among the patients ages group ranging from (51-70) years old. The results shows A significant increase in the levels of urea, creatinine, in younger patients (≤ 30 years) when compared with older patients (>70 years). Furthermore a significant decrease in serum levels of total protein in patients in older patients (>7
... Show MoreFace detection is one of the important applications of biometric technology and image processing. Convolutional neural networks (CNN) have been successfully used with great results in the areas of image processing as well as pattern recognition. In the recent years, deep learning techniques specifically CNN techniques have achieved marvellous accuracy rates on face detection field. Therefore, this study provides a comprehensive analysis of face detection research and applications that use various CNN methods and algorithms. This paper presents ten of the most recent studies and illustrate the achieved performance of each method.
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DBN Rashid, IMPAT: International Journal of Research in Humanities, Arts, and Literature, 2016 - Cited by 5
In this paper, we propose a method using continuous wavelets to study the multivariate fractional Brownian motion through the deviations of the transformed random process to find an efficient estimate of Hurst exponent using eigenvalue regression of the covariance matrix. The results of simulations experiments shown that the performance of the proposed estimator was efficient in bias but the variance get increase as signal change from short to long memory the MASE increase relatively. The estimation process was made by calculating the eigenvalues for the variance-covariance matrix of Meyer’s continuous wavelet details coefficients.
The proliferation of many editing programs based on artificial intelligence techniques has contributed to the emergence of deepfake technology. Deepfakes are committed to fabricating and falsifying facts by making a person do actions or say words that he never did or said. So that developing an algorithm for deepfakes detection is very important to discriminate real from fake media. Convolutional neural networks (CNNs) are among the most complex classifiers, but choosing the nature of the data fed to these networks is extremely important. For this reason, we capture fine texture details of input data frames using 16 Gabor filters indifferent directions and then feed them to a binary CNN classifier instead of using the red-green-blue
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