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

Facial emotion recognition finds many real applications in the daily life like human robot interaction, eLearning, healthcare, customer services etc. The task of facial emotion recognition is not easy due to the difficulty in determining the effective feature set that can recognize the emotion conveyed within the facial expression accurately. Graph mining techniques are exploited in this paper to solve facial emotion recognition problem. After determining positions of facial landmarks in face region, twelve different graphs are constructed using four facial components to serve as a source for sub-graphs mining stage using gSpan algorithm. In each group, the discriminative set of sub-graphs are selected and fed to Deep Belief Network (DBN) f

Its well known that understanding human facial expressions is a key component in understanding emotions and finds broad applications in the field of human-computer interaction (HCI), has been a long-standing issue. In this paper, we shed light on the utilisation of a deep convolutional neural network (DCNN) for facial emotion recognition from videos using the TensorFlow machine-learning library from Google. This work was applied to ten emotions from the Amsterdam Dynamic Facial Expression Set-Bath Intensity Variations (ADFES-BIV) dataset and tested using two datasets.

• ِAbstract:

Autonomic neuropathy, vasculogenic causes, endocrine deficit, drug-induced, psychosocial problems, or a combination of these causes might lead to Erectile dysfunction (ED).

• Aim:

This study aimed to compare between pudendal neuropathy against limb neuropathy in patients with diabetics and complaining from Erectile dysfunction by considering the Electrophysiological studies.

• Methodology:

84 diabetic subjects were enrolled in this analytic cross-sectional study with an average age of 38.4 years presented with impotence. All of them were assessed by Electrophysiological stud

In the image processing’s field and computer vision it’s important to represent the image by its information. Image information comes from the image’s features that extracted from it using feature detection/extraction techniques and features description. Features in computer vision define informative data. For human eye its perfect to extract information from raw image, but computer cannot recognize image information. This is why various feature extraction techniques have been presented and progressed rapidly. This paper presents a general overview of the feature extraction categories for image.

Cholinesterases are among the most efficient enzymes known. They are divided into two groups: acetylcholinesterase (AChE) involved in the hydrolysis of the neurotransimitter acetylcholine, and butyrylcholinesterase (BChE) of unknown function. Several crystal structures of the former have shown that the active site is located at the bottom of a deep and narrow gorge. Human BChE has attracted attention because it can hydrolyze toxic esters and nerve agents. Here we analyze the complexes of cholinesterase with soman by describing the 3D geometry of the complex, the active site, the changes happened through the inhibition and provide a description for the mechanism of inhibition. Soman undergoes degradation in the active site of the AChE and B

Facial recognition has been an active field of imaging science. With the recent progresses in computer vision development, it is extensively applied in various areas, especially in law enforcement and security. Human face is a viable biometric that could be effectively used in both identification and verification. Thus far, regardless of a facial model and relevant metrics employed, its main shortcoming is that it requires a facial image, against which comparison is made. Therefore, closed circuit televisions and a facial database are always needed in an operational system. For the last few decades, unfortunately, we have experienced an emergence of asymmetric warfare, where acts of terrorism are often committed in secluded area with no

Most recognition system of human facial emotions are assessed solely on accuracy, even if other performance criteria are also thought to be important in the evaluation process such as sensitivity, precision, F-measure, and G-mean. Moreover, the most common problem that must be resolved in face emotion recognition systems is the feature extraction methods, which is comparable to traditional manual feature extraction methods. This traditional method is not able to extract features efficiently. In other words, there are redundant amount of features which are considered not significant, which affect the classification performance. In this work, a new system to recognize human facial emotions from images is proposed. The HOG (Histograms of Or

Takbiratul Ehram "The First Takbeer to Start Prayer" means: the words that the worshiper says to start his prayers, and refrain from anything invalidates it. the findings revealed that the four school jurists agreed that the prayer is not valid without Takbiratul Ehram "The First Takbeer to Start Prayer", and they disagreed on its description, so the majority of jurists said that it is a pillar, and some of them called it an obligatory, but Hanafi made it a condition. Likewise, the four jurists agreed that the one who articulates Takbiratul Ehram "The First Takbeer to Start Prayer" with the word: “Allahu Akbar,”; his Takbeer is correct, and they disagreed about the one who adds a word, or replaced it with another, where the m