The purpose of this article is to shed light on the mechanism
and the procedures of a program that classifies an input face into
any of the six basic facial expressions, which are Anger, Disgust,
Fear, Happiness, Sadness and Surprise, in addition
to normal face.
This program works by apply PCA- principal component
analysis algorithm, which is applied of one side of the face, and
depends, on contrast to the traditional studies which rely on the
whole face, on three components: Eyebrows, Eyes and Mouth.
Those out-value are used to determine the facial feature array as
an input to the neural network, and the neural network is trained by
using the back-propagation algorithm. Note that the faces used in
this study belong to people from different ages and races.
The purpose of this article is to shed light on the mechanism
and the procedures of a neuro-fuzzy controller that classifies an
input face into any of the four facial expressions, which are
Happiness, Sadness, Anger and Fear. This program works
a
ccording to the facial characteristic points-FCP which is taken
from one side of the face, and depends, in contrast with some
traditional studies which rely on the whole face, on three
components: Eyebrows, Eyes and Mouth.
In this paper we introduce a comparison for some of data mining algorithm for traffic accidents analysis.
We start by describing available data for entry by analyzing the structure of statistical reports in Lattakia traffic directorate, and proceed
to data mining stage which enables us to smart study of factors that play roles in traffic accident and find its inter-relations and importance for causing traffic accident.
That comes after building data warehouse upon the database we built to store the data we gathered.
In this research we list a some of models was tested which is a sample of a many cases we checked to have the research results.
Rhodotorula yeast was isolated from various local sources. Fifty isolates
were collected during the years 2012-2013 where 13 were isolated from soil, 23
from tree leaves and 14 from food. The isolates AUX system. Were classified
into three Rhodoto
rula species: R. mucilaginosa, R. glutinis and R. minuta with
76%, 20% and 4% respectively using API 20c. Six methods were applied in
breaking down the yeast cells and extracting the carotenoids. It was shown the
quantity of carotenoides extracted with the modified method by adding DMSO
to the yeast biomass and incubating for 24 hrs at 4˚C yielded in higher
quantities when compared with the other five methods. All isolates were able to
produce carotenoides but they varied in their efficiency where the isolate A24
(R.mucilaginosa) isolated from food was distinguished by its high level of
production which reached 658.23 μg/g dry weight compared with the others.
This research concludes an determination of some Culicinae mosquitoes
distributed in south of Syria.
The study show that there are 14 species, 3 of them were recorded for the
first time in the study area, these 3 species were described, illustrate
d , and
distinguished from related species ,the seasonal reproduction and the habitat
types of these were studied .
(Culex perexiguus , Cx.sinaiticus , Cx.territans).
This research was conducted at the Department of Food Science,
Faculty of Agriculture and National commission for posterity
energy. Twenty kg of apple juice concentrate (70%) were tooked
from company of Natural Aljabal Juice from AL-suidaa
Govern
orate and prarerd two concentarate (15% and 35%) by using
distilled –sterlization water according to pirson .
The Brain Computer Interface (BCI) is considered the latest development of the Human Computer Interface (HCI). Unlike traditional input devices (keyboard, mouse, etc.) BCI reads brain signals from different areas of the human head and translates thes
e signals into commands that can control the computer.
The importance of BCI comes from its many applications such as medical applications, especially to assist people with disabilities to help them deal with computers, and help people with Locked-In Syndrome to communicate with the outside world. and advertising applications to see how much the customer appreciates the product, Security applications, or finding a new way to play games using your brain.
The aim of this research is to demonstrate the most recent solutions to the problems faced by computer-brain interfaces and the algorithms used to classify brain signals. The difficulty of this research is the in extracting and processing the signal.
In this study, 200 Tittigoniids specimens were collected from 32 locations in Syrian
coast during 2012 and 2013 seasons. Morphological and taxonomical aspects of
collected specimens have been studied. Identification keys of families, genera,
and s
pecies were recorded according to the most important taxonomic features.
A total of eighth genera recorded and classified, and these were: Phaneroptera
nana, Phaneroptera sparsa, Acrometopa syriaca, Tylopsis lilifolia, Conocephalus
conocephalus, Conocephalus maculatus, Conocephalus concolor. The species
Isopya savigny was considered the first record in Syria.
Data mining techniques have numerous applications in malware detection. Classification method is one of the most popular data mining techniques. In this paper we present a data mining classification approach to detect malware behavior.We suggested di
fferent classification methods in order to detect malware based on the feature and behavior of each malware. A dynamic analysis method has been presented for identifying the malware features.A suggested programhas been presented for converting a malware behavior executive history XML file to a suitable WEKA tool input. To illustrate the performance efficiency as well as training data and test, we apply the proposed approaches to a real case study data set using WEKA tool. The evaluation results demonstrated the availability of the proposed data mining approach. Also our proposed data mining approach is more efficient for detecting malware and behavioral classification of malware can be useful to detect malware in a behavioral antivirus.
