Improvement the application of unsupervised classification algorithms on remote sensing images using PCA technique

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.

مقارنة التصنيف المتعدد باستخدام طريقة المركبات الأساسية (PCA ) وطريقة تحليل التمايز الخطي لفيشر (Fisher - LDA )

مقارنة التصنيف المتعدد باستخدام طريقة المركبات الأساسية (PCA ) وطريقة تحليل التمايز الخطي لفيشر (Fisher - LDA )

مقارنة التصنيف المتعدد باستخدام طريقة المركبات الأساسية (PCA)وطريقة تحليل التمايز الخطي لفيشر (Fisher-LDA)

مقارنة التصنيف المتعدد باستخدام طريقة المركبات الأساسية (PCA)وطريقة تحليل التمايز الخطي لفيشر (Fisher-LDA)

Studing the economic factors affecting consumer spending in Syria using factor Analysis

This study aimed at identifying the best indicators representing economic factors using Factor Analysis, as well as developing a mathematical model linking principal components which represent both the economic factors and consumer spending in Syria using Multi-linear regression analysis. A descriptive analytical approach is used in this study. The study results from Factor Analysis show that there are three principal components which best represent the economic factors. The first component includes: the number of workforce working for free, the number of paid workforce, consumer price index, the average annual GDP per capita. The second component includes: interest rate, self-employed workforce. The third component includes the number of employers. A mathematical model is developed to link the above three components of the economic factors and the total monthly household spending average in Syria during ( 2000-2010).

Mathematical modeling of the relationship between number of higher education students and social and economic factors in Syria

This study aims to find the best social and economic factors that affect the number of students in higher education using the descriptive analysis approach, and find the mathematical model that connects the principal components representing the social and economic factors and the number of students in higher education in Syria. The most important results that have reached were the principal components representing the social and economic factors, after doing the orthogonal rotation and was representing the first component (number of members the labor force that are gainfully) employed, the number of population per health doctor, number of members, the labor force that are self-employed, number of members the labor force that are unmarried, number of population per dentist, higher education budget, and number of nurses. And the four thcomponent (number of members the labor force that are married), both components affected positively on the number of students in higher education, the second component (economic activity rate of the human power, average number of people per pharmacist, number of members the labor force that are gainfully unemployed, the third component (number of members the labor force that are divorced and widowed) affected negatively on the number of students in higher education.

Compressive Sensing

Compressive Sensing (CS) shows high promise for fully distributed compression in wireless sensor networks (WSNs). In theory, CS allows the approximation of the readings from a sensor field with excellent accuracy, while collecting only a small fraction of them at a data gathering point. However, the conditions under which CS performs well are not necessarily met in practice. CS requires a suitable transformation that makes the signal sparse in its domain. Also, the transformation of the data given by the routing protocol and network topology and the sparse representation of the signal have to be incoherent, which is not straightforward to achieve in real networks. In this paper we investigated the effectiveness of data recovery through joint Compressive Sensing (CS) and Principal Component Analysis (PCA) in actual WSN deployments. We proposed a novel system, called CS-PCA that embeds a feedback control mechanism to automatically change the compression ratio through changing the number of transmitting sensors, while bounding the reconstruction error. The considered recovery techniques in the proposed system are: biharmonic Spline (Spline), Deterministic Ordinary Least Square (DOLS), Probabilistic Ordinary Least Square (POLS) and Joint CS and PCA (CS-PCA). We found that the later outperform all other interpolation technique in the case of slow varying signals, while POLS was the most effective in case of fast varying signals that( low correlation less than 0.45)

Mathematical Modeling of the Relationship between the Components of Higher Education and the Rate of Economic Activity in Syria

This study aims to find the best indicators representing higher education components using the method of multivariate statistical analysis represented in a manner factor analysis, and create a mathematical model that connects the principal components representing higher education and the rate of economic activity in Syria using multi- linear regression analysis. A descriptive analytical approach is used in this study. The most important results obtained state that the principal components that belong to higher studies and intermediate institutes have a positive impact on the rate of economic activity of manpower, whereas principal components that belong to students of state universities and higher institutes have a negative impact on the rate of economic activity.