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.

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).

Pattern Analysis and detection in images using neural networks

A new face detection system is presented. The system combines several techniques for face detection to achieve better detection rates, a skin colormodel based on RGB color space is built and used to detect skin regions. The detected skin regions are the face candidate regions. Neural network is used and trained with training set of faces and non-faces that projected into subspace by principal component analysis technique. we have added two modifications for the classical use of neural networks in face detection. First, the neural network tests only the face candidate regions for faces, so the search space is reduced. Second, the window size used by the neural network in scanning the input image is adaptive and depends on the size of the face candidate region. This enables the face detection system to detect faces with any size.

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.

Improved the Automatic Number Plate Recognition System (ANPR)

The Automatic recognition System to vehicles through its number is an important topic, because of its important uses, such as security applications by monitoring the entrances of a important institutions, monitor the vehicles on the road, detection of stolen cars, and even that could be useful in statistical studies, where we can study the traffic congestion in an area. In this work we offer an overview of the Automatic Number Plate Recognition System (ANPR) through to identify the license plate number, and also recognize the color of car. The focus of this research on the stage of converting the numbers into a picture of a car plate to actual figures, to improve the performance of all system, where many of errors that occur at this stage. In this search was used the algorithm of Principle component analysis (PCA) to identify the numbers plate inside the picture. and its integration with optical character Recognition algorithm(OCR) which usually used for recognition , to minimize errors in recognition numbers and thus improve the performance of the automatic number plate system.and also we add color car recognize(which another important parameter of car) , this helps after return to data base detect stolen vehicles and improve the reliability of system

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.

Applying Factor Analysis for Studying the Most Important Factors Economic Affecting the number of employees within period 2000 till 2009 in Syria

The objective of this research is applying Factor Analysis for Studying the most important economic factors affecting the number of employees within period 2000 till 2009 in Syria, to propose a methodological framework for constructing the integrated factor analysis model system (FAMS) that can be used as a decision support tool in employment year examination and supervision process for detection of years, which are experiencing serious problems. Sample and variable set of the study contains 17 economic variables. Study years (10 years during the period 2000–2009) and their economic variables. Well known multivariate statistical technique (principal component analysis), was used to explore the basic economic characteristics of the theses years, and discriminant models were estimated based on these characteristics to construct FAMS. The importance of factor analysis model system in employment year examination was evaluated with respect to defining the non-employment years for deciding the most important employment policy for reducing unemployment rates in future. Results of the study show that, if FAMS was effectively employed within studied years, It is possible in this case to identify weaknesses, according to the years that have the number of employees is less than the overall average calculated over the period.