The research deals with the reality of using electronic learning to teach the lessons prescribed for the training courses held at the Continuing Education Center of Middle Technical University, through a survey of the opinions of trainees in training courses held at the center. These trainees are considered the research sample as for the research, community is represented by all participants in the training courses using electronic classes through Google Classroom as a practical application for supporting electronic learning. The questionnaire tool was designed to collect information as a descriptive approach and a set of statistical methods was used to analyze the relationship between variables and test the research hypotheses. This included the simple correlation coefficient Pearson (r) and the significance tests (F, t) to test the significance of the hypotheses. Further more, in order to verify the hypothesis verification; simple linear regression (Regression Linear) was used, as well as interpretation factor (R2) to measure what the independent variable explains from the changes affected by the dependent variable in the research. The research results indicated the importance of using electronic learning in the training process, based on diligence and personal initiative. The research conclusions showed that members of the sample agreed on the importance of supportive (blended) electronic training to enhance the training process and also give them a skill in using electronic learning software.
The method of converting color images from the RGB color system to grayscale images is a simple operation by using the fixed weights method of conversion, but using the same weights to restore the color of the same images is not an effective operation of all types of images because the grayscale image contains little information and it isn't worthy of conversion operation. The basic idea in this paper is to employ the mathematics equations which extracted from the grayscale image in conversion operation, this paper presents the method of coloring the grayscale image by using the weights derived from the characteristics of the grayscale image. Skewness, Mean and Standard deviation moments have been extracted from the features of grayscale images and its adoption the determine weights of the RGB color system. This method proved its success in coloring images compared to the traditional method adoption of fixed weights for coloring images because it relies on fixed weights for converting all grayscale images.
The method of solving volterra integral equation by using numerical solution is a simple operation but to require many memory space to compute and save the operation. The importance of this equation appeares new direction to solve the equation by using new methods to avoid obstacles. One of these methods employ neural network for obtaining the solution. This paper presents a proposed method by using cascade-forward neural network to simulate volterra integral equations solutions. This method depends on training cascade-forward neural network by inputs which represent the mean of volterra integral equations solutions, the target of cascade-forward neural network is to get the desired output of this network. Cascade-forward neural network is trained multi times to obtain the desired output, the training of cascade-forward neural network model terminal when there is no enhancement in result. The model combines all training cascade-forward neural network to obtain the best result. This method proved its successful in training and testing cascade-forward neural network for obtaining the desired output of numerical solution of volterra integral equation for multi intervals. Cascade-forward neural network model measured by calculating MSE to compute the degree of error at each training time.
Recently, there have been several automatic approaches to color grayscale images, which depend on the internal features of the grayscale images. There are several scales which are considered as a prominent key to extract the corresponding chromatic value of the gray level. In this aspect, colorizing methods that rely on automatic algorithms are still under investigation, especially after the development of neural networks used to recognize the features of images. This paper develops a new model to obtain a color image from an original grayscale image through the use of the Support Vector Machine to recognize the features of grayscale images which are extracted from two stages: the first stage is Haar Discrete Wavelets Transform used to configure the vector that combines with six of Statistical Measurements: (Mean, Variance, Skewness, Kurtosis, Energy and Standard Deviation) extracts from the grayscales image in the second stage. After the Support Vector Machine recognition has been done, the colorization process uses the result of Support Vector Machine to recover the color to greyscale images by using YCbCr color system then it converts the color to default color system (RGB) to be more clear. The proposed model will be able to move away from relying on the user to identify the source image which matches in color levels and it exceeds the network determinants of image types with similar color levels. In addition, Support Vector Machine is considered to be more reliable than neural networks in classification algorithms. The model performance is evaluated by using the Root Mean Squared Error (RMSE) in measuring the success of the assumed modal of matching the coloring (resulting) images and the original color images. So, a reality-related result has been obtained at a good rate for all the tested images. This model has proved to be successful in the process of recognizing the chromatic values of greyscale images then retrieving it. It takes less time complexity in trained data, and it isn’t complex in working.
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