الملخص: The need for a proper design and implementation of adequate surveillance system for detecting and categorizing explosion phenomena is nowadays rising as a part of the development planning for risk reduction processes including mitigation and preparedness. In this context, we introduce state-of-the-art explosions classification using pattern recognition techniques. Consequently, we define seven patterns for some of explosion and non-explosion phenomena including: pyroclastic density currents, lava fountains, lava and tephra fallout, nuclear explosions, wildfires, fireworks, and sky clouds. Towards the classification goal, we collected a new dataset of 5327 2D RGB images that are used for training the classifier. Furthermore, in order to achieve high reliability in the proposed explosion classification system and to provide multiple analysis for the monitored phenomena, we propose employing multiple approaches for feature extraction on images including texture features, features in the spatial domain, and features in the transform domain. Texture features are measured on intensity levels using the Principal Component Analysis (PCA) algorithm to obtain the highest 100 eigenvectors and eigenvalues. Moreover, features in the spatial domain are calculated using amplitude features such as the YCbCr color model; then, PCA is used to reduce vectors’ dimensionality to 100 features. Lastly, features in the transform domain are calculated using Radix-2 Fast Fourier Transform (Radix-2 FFT), and PCA is then employed to extract the highest 100 eigenvectors. In addition, these textures, amplitude and frequency features are combined in an input vector of length 300 which provides a valuable insight into the images under consideration. Accordingly, these features are fed into a combiner to map the input frames to the desired outputs and divide the space into regions or categories. Thus, we propose to employ one-against-one multi-class degree-3 polynomial kernel Support Vector Machine (SVM). The efficiency of the proposed research methodology was evaluated on a totality of 980 frames that were retrieved from multiple YouTube videos. These videos were taken in real outdoor environments for the seven scenarios of the respective defined classes. As a result, we obtained an accuracy of 94.08%, and the total time for categorizing one frame was approximately 0.12 s

الملخص: Face detection is a significant research topic to recognize the identity for many automated systems. In this paper, we propose a face detection algorithm to detect a single face in an image sequence in the real-time environment by finding unique structural features. The proposed method allows the user to detect the face in case the lighting conditions, pose, and viewpoint vary. Two methods are combined in the proposed approach. First, we use the components Y, Cb, and Cr in YCbCr color space as threshold conditions to segment the image into luminance and chrominance components. Second, we use Roberts cross operator to approximate the magnitude of the gradient of the test image and outline the edges of the face. Experimental results show that the proposed algorithm achieves high detection rate and low false positive rate.

الملخص: This paper investigates the role of the scientific signs in the holy Quran in improving the creative thinking skills for the deaf children using multimedia. The paper investigates if the performance made by the experimental group’s individuals is statistically significant compared with the performance made by the control group’s individuals on Torrance Test for creative thinking (fluency, flexibility, originality and the total degree) in two cases: 1) Without considering the gender of the population, and, 2) Considering the gender of the population

الملخص: Explosion phenomena today are considered a significant concern that needs to be detected and analyzed with a prompt response. We develop a multiclass categorization system for explosion phenomena using color images. Consequently, we describe four patterns of explosion phenomena, including pyroclastic density currents, lava fountains, lava and tephra fallout, and nuclear explosions, against three patterns of non-explosion phenomena, including wildfires, fireworks, and sky clouds. The classification task was handled through extracting different types of features, including texture features, amplitude features, frequency features, and histogram features. Then, these features were fed into several multiclass classification methods. In addition, we present a new data set for volcanic and nuclear explosions that includes 10654 samples. Evaluation results show the one-against-one multiclass support vector machine with degree 3 polynomial kernel outperforms other classification methods. It produces the highest classification rate of 90.85% to categorize 5327 images of the data set. A reasonable execution time of approximately 117 ms was accomplished to classify one input test image