Strategic leadership is the main source to enable organizations of excellence in light of turbulent environment, it is also the capabilities of organizational renewal organization’s ability to anticipate changes that take place or possible occurrence.

As a result of the many changes that characterize the environment where they operate researched hospitals, many of them began looking for ways to help it achieve a lot of their own competitive advantages.

The subject of organizational renewal capabilities of subjects that are not obvious interest in the Arab environment in general and Iraq in particular, and longer. The problem of the research and field presence of deficiencies in

The research aims to identify the level of educational supervisors' practices of their supervisory duties according to the requirements of comprehensive quality from the viewpoint of teachers in Oman. Moreover, identify the differences between the responses of the sample about these practices, which are attributed to gender, and years of experience. Thus, a questionnaire was administered to teachers, consisting of (48) items distributed on (5) main fields related to the educational supervisor's practices. The sample consisted of (220) teachers who were randomly chosen from the schools in Dhofar region. Data were analyzed using means, standard divisions, T-test, and One Way ANOVA. The research concluded that the level of educational super

Recurrent strokes can be devastating, often resulting in severe disability or death. However, nearly 90% of the causes of recurrent stroke are modifiable, which means recurrent strokes can be averted by controlling risk factors, which are mainly behavioral and metabolic in nature. Thus, it shows that from the previous works that recurrent stroke prediction model could help in minimizing the possibility of getting recurrent stroke. Previous works have shown promising results in predicting first-time stroke cases with machine learning approaches. However, there are limited works on recurrent stroke prediction using machine learning methods. Hence, this work is proposed to perform an empirical analysis and to investigate machine learning al

APDBN Rashid, Review of International Geographical Education Online (RIGEO), 2021

This work implements an Electroencephalogram (EEG) signal classifier. The implemented method uses Orthogonal Polynomials (OP) to convert the EEG signal samples to moments. A Sparse Filter (SF) reduces the number of converted moments to increase the classification accuracy. A Support Vector Machine (SVM) is used to classify the reduced moments between two classes. The proposed method’s performance is tested and compared with two methods by using two datasets. The datasets are divided into 80% for training and 20% for testing, with 5 -fold used for cross-validation. The results show that this method overcomes the accuracy of other methods. The proposed method’s best accuracy is 95.6% and 99.5%, respectively. Finally, from the results, it

Traffic classification is referred to as the task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Most systems of network traffic identification are based on features. These features may be static signatures, port numbers, statistical characteristics, and so on. Current methods of data flow classification are effective, they still lack new inventive approaches to meet the needs of vital points such as real-time traffic classification, low power consumption, ), Central Processing Unit (CPU) utilization, etc. Our novel Fast Deep Packet Header Inspection (FDPHI) traffic classification proposal employs 1 Dimension Convolution Neural Network (1D-CNN) to automatically learn more representational c