Abstract With the explosive growth of the World Wide Web, Information Retrieval Systems (IRS) have recently become a focus of research. Query expansion is defined as the process of supplementing additional terms or phrases to the original query to improve the information retrieval performance. Arabic is highly inflectional and derivational language which makes the query expansion process a hard task. In this paper, the well known approach, Pseudo Relevance Feedback (PRF) is adopted to be applied on Arabic. Prior to applying PRF, first; datasets (three collections of Arabic documents) are pre-processed to create documents inverted index vocabularies, then, the normal indexing process is carried out. The PRF is applied to create a modified (expanded) query of the original one and the target collection is indexed once more. To judge the enhancement of retrieval process, the results of normal indexing and those of applying PRF are evaluated against each other using precision and recall measures. The results have shown that the PRF method has significantly enhanced the performance of the Arabic Information Retrieval (AIR) System. As the number of expansion terms increases up to a certain extent (35 terms), the performance has been improved. On the other hand, the performance will not be affected, or grow insignificantly as the number of expansion terms exceeds this limit. 

Abstract: Nowadays, enormous amounts of accessible textual information available on the Internet are phenomenal. Automatic text classification is considered an important application in natural language processing. It is the process of assigning a document to predefined categories based on its content. In this paper, the well-known Centroid-based technique developed for text classification is considered to be applied on Arabic text. Arabic language is highly inflectional and derivational which makes text processing a complex and challenging task. In the proposed work, the Centroid-based Algorithm is adopted and adapted to be applied to classify Arabic documents. The implemented algorithm is evaluated using a corpus containing a set of Arabic documents. The experimental results against a dataset of 1400 Arabic text documents covering seven distinct categories reveal that the adapted Centroid-based algorithm is applicable to classify Arabic documents. The performance criteria of the implemented Arabic classifier achieved roughly figures of 90.7%, 87.1%, 88.9%, 94.8%, and 5.2% of Micro-averaging recall, precision, F measure, accuracy, and error rates respectively.

The building projects are differentiated, each building project is unique in terms of size, the level of complexity, procurement systems required. The characteristics of building projects could have major influence on overall project performance. Building projects that incorporate green designs, which are complicated to understand or manage adequately and are more complex compared with typical building projects. Therefore, To achieve high-green design performance, understand crucial characteristics of building projects that may have affect performance level of green design is required. The propose of this study to identify key project characteristics of building projects influencing green design performance. To achieve mentioned aim a questionnaire survey was conducted to collect dada required. A sample of 274 respondents has been covered under the study, including architects and engineers practicing design and consultancy building sectors. Prior to analysis of data WINSTEPS software were used to determine validity and reliability of date. Descriptive analysis data includes quantitative and qualitative. The results revealed that the Project Size can influence the Green Design Performance. The type of Building and size of the project are the key factors influencing Green Design Performance. The Availability of Design Information was moderate. The Reused and Recycled Materials were hard to find, while Regional Materials were available in the market.

Abstract— Automatic text categorization is considered an important application in natural language processing. It is the process of assigning a document to predefined categories based on its content. In this research, some well-known techniques developed for classifying English text are considered to be applied on Arabic. This work focuses on applying the well-known Rocchio (Centroid-based) technique on Arabic documents. This technique uses centroids to define good class boundaries. The centroid of a class c is computed as center of mass of its members. Arabic language is highly inflectional and derivational which makes text processing a complex task. In the proposed work, first Arabic text is preprocessed using tokenization and stemming techniques. Then, the Rocchio Algorithm is adopted and adapted to be applied to classify Arabic documents. The implemented algorithm is evaluated using a corpus containing a set of actual documents. The results show that the adapted Rocchio algorithm is applicable to categorize Arabic text. Ratios of 92.2%, 92.7%, and 92.1% of Micro-averaging recall, precision, and F-measure respectively are achieved, against a data set of 500 Arabic text documents covering five distinct categories.