This paper proposes two hybrid feature subset selection approaches based on the combination (union or intersection) of both supervised and unsupervised filter approaches before using a wrapper, aiming to obtain low-dimensional features with high accuracy and interpretability and low time consumption. Experiments with the proposed hybrid approaches have been conducted on seven high-dimensional feature datasets. The classifiers adopted are support vector machine (SVM), linear discriminant analysis (LDA), and K-nearest neighbour (KNN). Experimental results have demonstrated the advantages and usefulness of the proposed methods in feature subset selection in high-dimensional space in terms of the number of selected features and time spe

God sent the Prophet Muhammad as a teacher and promising harbinger {that is sent to the illiterate messenger reciting to them His signs and purifies them and teaches them the Book and Wisdom} [Friday: 2]; the wisdom of Baos Prophet him peace be and am back to teach people, - Education and therefore his life was very rich educational tactics And has passed the Prophet in different circumstances and conditions that can be experienced by a teacher or educator in any time, anywhere; there is no case of going through the educator or teacher only finds the same or similar or likened to, or close to it in the life of the Prophet peace be upon him Prophet has lived God's peace be upon his strengths and weaknesses, victory and defeat, he lived or

To maintain a sustained competitive position in the contemporary environment of  knowledge  economy,  organizations  as an open social systems must have an ability to learn and know  how to adapt to rapid changes  in a proper fashion so that organizational objectives will be achieved efficiently and effectively.  A multilevel approach is adopted proposing that organizational learning suffers from the lack of interest about the strategic competitive performance of the organization. This remains implicit almost in all models of organizational learning and there is little focus on how learning organizations achieve sustainable competitive advantage . A dynamic model that captures t

In this paper, we will present proposed enhance process of image compression by using RLE algorithm. This proposed yield to decrease the size of compressing image, but the original method used primarily for compressing a binary images [1].Which will yield increasing the size of an original image mostly when used for color images. The test of an enhanced algorithm is performed on sample consists of ten BMP 24-bit true color images, building an application by using visual basic 6.0 to show the size after and before compression process and computing the compression ratio for RLE and for the enhanced RLE algorithm.

Verrucae vulgares are commonly encountered. The present work is designed in an attempt to build a systematic procedure for treating warts by carbon dioxide laser regarding dose parameters, application parameters and laser safety.
Patients and Methods: The study done in the department of dermatology in Al-Najaf Teaching Hospital in Najaf, Iraq. Forty-two patients completed the study and follow up period for 3 months. Recalcitrant and extensive warts were selected to enter the study. Carbon dioxide laser in a continuous mode, in non-contact application, with 1 mm spot size was used. The patients were divided into two groups. The first group of patients consisted of 60 lesions divided to 6 equal groups, in whom we use different outputs a

BP algorithm is the most widely used supervised training algorithms for multi-layered feedforward neural net works. However, BP takes long time to converge and quite sensitive to the initial weights of a network. In this paper, a modified cuckoo search algorithm is used to get the optimal set of initial weights that will be used by BP algorithm. And changing the value of BP learning rate to improve the error convergence. The performance of the proposed hybrid algorithm is compared with the stan dard BP using simple data sets. The simulation result show that the proposed algorithm has improved the BP training in terms of quick convergence of the solution depending on the slope of the error graph.

Churning of employees from organizations is a serious problem. Turnover or churn of employees within an organization needs to be solved since it has negative impact on the organization. Manual detection of employee churn is quite difficult, so machine learning (ML) algorithms have been frequently used for employee churn detection as well as employee categorization according to turnover. Using Machine learning, only one study looks into the categorization of employees up to date.  A novel multi-criterion decision-making approach (MCDM) coupled with DE-PARETO principle has been proposed to categorize employees. This is referred to as SNEC scheme. An AHP-TOPSIS DE-PARETO PRINCIPLE model (AHPTOPDE) has been designed that uses 2-stage MCDM s