This paper presents a new method to recognize human face in different emotional situations. This method is based on proposed algorithm SD.R&C to discover skin and expression classification.
We discussed in this work some predictive methods for time series and it is decomposing time series to its component (trend, Seasonality, cycle, random), Exponential smoothing, ARIMA, then we discussed some combining methods, then we formed a new c
ombine for predict time series which depends on combining exponential smoothing and ARIMA using weighted average with MAPE weights, and applied all methods above on three seasonal time series , first hourly temperature in Aleppo in august 2011 ,second monthly milk production peer cow in Australia from Jan 1962 to Dec 1975,third quartly electricity production in Australia from Mar 1956 to Sep 1994, and compared the results which approved that the suggested method is the best.
In this paper we introduced the notions of: ATL law, RAL law, Semiflower,
Flower, Garden and Farm.
Some new algebraic concepts have been defined. An algorithm for ATL test
has been explained.
Some Lemmas, Propositions and Theorems have been prove
d.
There are many known methods for finding each of:
Determinate for square matrix, Inverse for irregular
square matrix, and Rank for any matrix. but these
methods become difficult to high- order matrices . and
even software gives results are rounde
d due to
recycling numbers several times. The main idea in this
work is finding Determinate, Rank, and Inverse matrix
by reduction the order of matrix.
حظيت نمذجة وتوقع السلاسل الزمنية بأهمية كبيرة في العديد من المجالات التطبيقية كالتنبؤ بالطقس وأسعار العملات ومعدلات استهلاك الوقود والكهرباء، إن توقع السلاسل الزمنية من شأنه أن يزود المنظمات والشركات بالمعلومات الضرورية لاتخاذ القرارات الهامة، وبسبب
أهمية هذا المجال من الناحية التطبيقية فإن الكثير من الأعمال البحثية التي جرت ضمنه خلال السنوات الماضية، إضافةً إلى العدد الكبير من النماذج والخوارزميات التي تم اقتراحها في أدب البحث العلمي والتي كان هدفها تحسين كل من الدقة والكفاءة في نمذجة وتوقع السلاسل الزمنية.
These papers aim to study the estimation of the simple linear regression equation
coefficients using the least square method at different sample sizes and different sampling
methods. And so on, the main goal of this research is to try to determine
the optimum size
and the best sampling method for these coefficients. We used experimental data for a
population consist of 2000 students from different schools all over the country. We had
changed the sample size each time and calculate the coefficients and then compare these
coefficients for different sample sizes with their coefficients of the real population; and the
results have been shown that the estimation of the linear regression equation coefficients
are close from the real values of the coefficients of the regression line equation for the
population when the sample size closes the value (325). As it turns out that the Stratified
random sampling with proportional distribution with class sizes gives the best and most
accurate results to estimate linear regression equation with least square method.
We present in this paper the neutrosophic exponential distribution,
which is an extension of the classical exponential distribution
according to the neutrosophic logic (a new non-classical logic which
was founded by the American philosopher and ma
thematical
Florentin Smarandache, which he introduced as a generalization of
fuzzy logic especially the intuitionistic fuzzy logic), so that it can
handle all the data that it is not precisely defined.
Linear regression methods impose strong constraints on regression models, especially on
the error terms where it assumes that it is independent and follows normal distribution, and
this may not be satisfied in many studies, leading to bias that can
not be ignored from the
actual model, which affects the credibility of the study.
We present in this paper the problem of estimating the regression function using the
Nadarya Watson kernel and k- nearest neighbor estimators as alternatives to the parametric
linear regression estimators through a simulation study on an imposed model, where we
conducted a comparative study between these methods using the statistical programming
language R in order to know the best of these estimations. Where the mean squares errors
(MSE) was used to determine the best estimate.
The results of the simulation study also indicate the effectiveness and efficiency of the
nonparametric in the representation of the regression function as compared to linear
regression estimators, and indicate the convergence of the performance of these two
estimates.
We aimed in this research to suggest an Insurance Regulatory
Information System (IRIS); it is integrated with the nature of the Syrian
insurance market. This system consists of scientific, practical and
separated indicators for evaluating the p
erformance of the Syrian
insurance companies.
Moreover, this system consists of previous indicators which are
divided into four groups. Each group studies a different side of risks that
are faced by the Syrian insurance companies. We devised a mechanism to
distinguish among the studied sample according to the proposed
indicators. Depending on earlier mechanism, the studied companies are
classified into the companies that have positive performance and others
that have negative one.
We have applied the suggested IRIS to the studied sample which
includes one Syrian public insurance company and six private insurance
companies. We depend on appropriate statistical methods to test
hypotheses during the studied period to achieve objectives of the research.
Major findings serve the decision makers in the Syrian insurance
companies. Suggested IRIS is a new scientific method which should be
used in the local insurance field.
Future studies should be interested in developing and increasing
the number of previous indicators.
سلسلة محاضرات في لغة البرمجة الإحصائية R
مقدمة عن لغة R - الأوامر الخاصة في لغة البرمجة
Rstudio & R و تطبيقات عملية
