The World of Computer Science and Information Technology Journal

4

Authors: Mohammad Said El-Bashir

Abstract— In recent research, the assessment of the trustworthiness of a certain recommender in pervasive environments is examined upon their previous interactions. However, when it is the initial interaction for a certain user, then recommendations cannot be assessed against trustworthiness. One of the approaches is to refer to a previous interaction of one of the users. However, this approach may give good results but it may also lead into wrong recommendations. In this paper, a method for detecting untrustworthiness in pervasive environments is proposed. After digitizing the data attributes, logistic regression is applied. The data attributes used are the recommendations not users information. The proposed method achieved promising results, which are comparable with other research.

7

Authors: Mohammad Said El-Bashir, Atallah. M AL-Shatnawi, Faisal Al-Saqqar, Mohammad I. Nusir

Face recognition has become more important recently than before. The objective of this study was to develop a face recognition model that can achieve high recognition accuracy. The better the extraction of features, the better results achieved. The already existing face recognition systems suffer from the problem of integration of different types of features. In this paper, a fusion feature-level face recognition method (FFLFRM) is proposed. The input face image is detected by applying the Haar-cascade method. After that, the features are extracted by using two statistical methods: local binary pattern (LBP) and principal component analysis (PCA). Then, the Covariance Intersection Fusion (CIF) technique is applied to integrate the LBP-, and PCA-extracted features. Afterwards, the integrated feature vector is input to a Multi-layer Perceptron Artificial Neural Network (MLPANN). Performance of the proposed method was tested using the Olivetti Research Laboratory (ORL) database of face images. To assess robustness of the proposed FFLFRM, it was applied on images with and without illumination and change in pose; and images with different expressions, occlusions, and levels of image resolution. For validation purposes, the proposed method was compared with a method applying LBP only, PCA only, and a combination of LBP and PCA with Frequency Partition (FP. Performance evaluation uncovered that the proposed FFLFRM has an average recognition accuracy of 96.75%. The recognition accuracy of this proposed method is quite good, particularly when compared with the corresponding accuracies reported by other studies that used the same face images.

4

Authors: Mohammad Said El-Bashir, Wael Waheed AlQassas

Handwritten Digit Recognition has been proposed using different techniques that were implemented over the available datasets. Although existing systems reached high recognition accuracy, more efforts regarding speed and memory allocation is required. In this research, we experiment the impact of image resolution reduction on recognition accuracy for handwritten digits. A set of features were extracted, include histogram of pixels for horizontal, vertical, diagonal and inversed diagonal orientations. Feature vector constructed by joining these features. Then, support vector machine is applied for classification. Rescaled handwritten digit images were experimented against recognition accuracy, speed and memory. MNIST database of handwritten digits is utilized for implementation. Results showed that the reduction of the size for the features vector due to image rescaling to quarter of the original size had only about 1% accuracy degradation impact.