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Register a new userمقارنة التصنيف المتعدد باستخدام طريقة المركبات الأساسية (PCA)وطريقة تحليل التمايز الخطي لفيشر (Fisher-LDA)
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University of Aleppo أطروحة دكتوراه
Publication date
2017
fields
Mathematical Statistics
and research's language is
العربية
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مقارنة التصنيف المتعدد باستخدام طريقة المركبات الأساسية (PCA)وطريقة تحليل التمايز الخطي لفيشر (Fisher-LDA)
No English abstract
References used
Alpaydin, Ethen (2010). "Introduction to Machine Learning". MIT Press. p. 9.
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The basic step in this algori thm is determining the number of clusters
(K) then calculating the distance between each cluster
center and the elements of images to join each element
to the closest cluster depending on threshold distance.
Stem cells have unique capability to differentiate into many cell types that can
normally replace the loss in some cells of the body due to tissue injury. Umbilical cord blood (UCB) and
umbilical cord (UC) are the two main sources for hematopoietic
stem cells (HSCs) and mesenchymal stem
cells (MSCs), respectively, which constitutes the basis for stem cell banks that have been established
worldwide and very recently in Syria. Research in our region has mainly focused on cell storage and
freezing protocols, and only few studies were conducted to prove the ability of the stored cells to
differentiate into their destined lineages. This study aimed to test the potential of cryopreserved MSCs
isolated from an umbilical cord taken from new delivery at Maternity University Hospital in Damascus, to
differentiate into various types of cells in response to growth and induction factors specific to cell lineages.
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 study aims at comparing ARIMA models and the exponential
smoothing method in forecasting. This study also highlights the special
and basic concepts of ARIMA model and the exponential smoothing
method.
The comparison focuses on the ability
of both methods to forecast
the time series with a narrow range of one point to another and the time
series with a long range of one point to another, and also on the different
lengths of the forecasting periods. Currency exchange rates of Shekel to
American dollar were used to make this comparison in the period
between 25/1/2010 to 22/10/2016. In addition, weekly gold prices were
considered in the period between 10/1/2010 to 23/10/2016. RMSE
standard was used in order to compare between both methods. In this
study, the researcher came up with the conclusion that ARIMA models
give a better forecasting for the time series with a long range of one point
to another and for long term forecasting, but cannot produce a better
forecasting for time series with a narrow range of one point to another as
in currency exchange prices.
On the contrary, exponential smoothing method can give better
forecasting for Exchange Rates that has a narrow range of one point to
another for its time series, while it cannot give better forecasting for long
term forecasting periods
In this paper, we propose an efficient strategy for calculating
quasi-optimal stepsizes for the Givens-parameterized deflationary
ICA algorithm, DelLR.
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