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Educational data mining is a specific data mining field applied to data originating from educational environments, it relies on different approaches to discover hidden knowledge from the available data. Among these approaches are machine learning techniques which are used to build a system that acquires hidden knowledge from previous data. Machine learning can be applied to solve different regression, classification, clustering and optimization problems. In our research, we propose a "Student Advisory Framework" that utilizes classification and clustering. This system can be used to guide the first year university students to the more suitable educational track. The classification phase will predict the department which is most likely to be chosen by a student and the clustering phase will recommend a department to student by showing his expected rate of success for each department, this recommendation aims to decrease the high rate of academic failure for first year students. Our approach is tested using a real case study from

The biometric person identification technique based on the pattern of the human iris is a very costly top secure application. This paper describes a personal identification system based on the human iris imaging through providing a set of algorithms that describes image acquisition, image segmentation, feature extraction and pattern forming. For image acquisition, we present an image enhancement algorithm in order to get more accurate image feature results. In addition, we propose a boundary localization algorithm, which used to find the pupil boundary. A new iris recognition method that analyzes local variations of the iris is used to construct a Feature Selection Vector (FSV) that can be used to extract features of any iris image size. Extensive experimental results using Pearson's correlation coefficient to verify one's identity on CASIA iris images database shows that the proposed system is effective and encouraging.

In this paper, we present a new approach for tracking a moving target in forwardlooking infrared (FLIR) imagery taken from an anti-tank moving platform. First, the target image is captured in a frame with resolution 128×128 pixels by using IR camera, and then the image is enhanced by using a non uniformity correction algorithm to reduce the main dominant noises associated with infrared images. Second, the existing moving target is detected with time ( ) within the image by using 2-D Gabor filter kernels. Next, Gabor filter with 4 orientations applying on the window created with resolution 32×32 pixels around the predicted target center using Kalman filter estimator. Subtraction process is performed between the predicted center using Kalman filter estimator and the center of the detected target using Gabor filter, which is used to predict the next center by using Kalman filter, and to guide the detection of the target location in the tracking window of the next frame respectively. The proposed technique fasts and reduces the detection time into 1/16 of with high performance of tracking. The experiments performed on several real image data of an existing infrared imaging system. Results show the robustness of the proposed method, which combines high speed of detection and good performance of tracking even with strong ego-motion.

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