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A novel method based on the local nonlinear mapping is presented in this research. The method is called Locally Linear Discriminate Embedding (LLDE). LLDE preserves a local linear structure of a high-dimensional space and obtains a compact data representation as accurately as possible in embedding space (low dimensional) before recognition. For computational simplicity and fast processing, Radial Basis Function (RBF) classifier is integrated with the LLDE. RBF classifier is carried out onto low-dimensional embedding with reference to the variance of the data. To validate the proposed method, CMU-PIE database has been used and experiments conducted in this research revealed the efficiency of the proposed methods in face recognition, as compared to the linear and non-linear approaches.

Feature extraction techniques are widely used to reduce the complexity high dimensional data. Nonlinear feature extraction via Locally Linear Embedding (LLE) has attracted much attention due to their high performance. In this paper, we proposed a novel approach for face recognition to address the challenging task of recognition using integration of nonlinear dimensional reduction Locally Linear Embedding integrated with Local Fisher Discriminant Analysis (LFDA) to improve the discriminating power of the extracted features by maximize between-class while within-class local structure is preserved. Extensive experimentation performed on the CMU-PIE database indicates that the proposed methodology outperforms Benchmark methods such as Principal Component Analysis (PCA), Fisher Discrimination Analysis (FDA). The results showed that 95% of recognition rate could be obtained using our proposed method.

The Sultanate of Oman is one of the first countries in the Middle East to utilize technology in the management of the education process. Over time, education data have accumulated, and at the present, large volumes of data with numerous types of statistics have been collected as operational data. This research article takes the advantage of these data and applied predictive data mining approaches to study the performance of general education diploma students (year 12 of school). The decision tree as a classification method has been applied and a prediction model is constructed with ability to show relatively high accuracy of students' performance prior to year 12 of school, using 30% test data of nearly 6000 student's records. The significant variables that influence students' performance are identified which would help stakeholders and decision makers plan ahead and have more time to prepare for decisions.

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