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Many algorithms have been implemented for the problem of text classification. Most of the work in this area was carried out for English text. Very little research has been carried out on Arabic text. The nature of Arabic text is different than that of English text, and preprocessing of Arabic text is more challenging. This paper presents an implementation of three automatic text-classification techniques for Arabic text. A corpus of 1445Arabic text documents belonging to nine categories has been automatically classified using the kNN, Rocchio, and naïve Bayes algorithms. The research results reveal that Naïve Bayes was the best performer, followed by kNN and Rocchio.

Heart diagnosis is not always possible at every medical center, especially in the rural areas where less support and care, due to lack of advanced heart diagnosis equipment. Also, physician intuition and experience are not always sufficient to achieve high quality medical procedures results. Therefore, medical errors and undesirable results are reasons for a need for unconventional computer-based diagnosis systems, which in turns reduce medical fatal errors, increasing the patient safety and save lives. The proposed solution, which is based on an Artificial Neural Networks (ANNs), provides a decision support system to identify three main heart diseases: mitral stenosis, aortic stenosis and ventricular septal defect. Furthermore, the system deals with an encouraging opportunity to develop an operational screening and testing device for heart disease diagnosis and can deliver great assistance for clinicians to make advanced heart diagnosis. Using real medical data, series of experiments have been conducted to examine the performance and accuracy of the proposed solution. Compared results revealed that the system performance and accuracy are acceptable, with a heart diseases classification accuracy of 92%.

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PurposeThe purpose of this paper is to develop an innovative information hiding algorithm.Design/methodology/approachThe proposed algorithm is based on image histogram statistics. Cumulative‐peak histogram regions are utilized to hide multiple bits of the secret message by performing histogram bin substitution. The embedding capacity, otherwise known as payload, and peak signal to noise ratio (PSNR), as well as security, are the main metrics used to evaluate the performance of the proposed algorithm.FindingsAccording to the obtained results, the proposed algorithm shows high embedding capacity and security at comparable PSNR compared with existing hiding information techniques.Originality/valueThe simplicity, security, random distribution of embedding pixels, and on‐demand high capacity are the key advantages of the proposed approach.

The development of an efficient compression scheme to process the Arabic language represents a difficult task. This paper employs the dynamic Huffman coding on data compression with variable length bit coding, on the Arabic language. Experimental tests have been performed on both Arabic and English text. A comparison was made to measure the efficiency of compressing data results on both Arabic and English text. Also a comparison was made between the compression rate and the size of the file to be compressed. It has been found that as the file size increases, the compression ratio decreases for both Arabic and English text. The experimental results show that the average message length and the efficiency of compression on Arabic text was better than the compression on English text. Also, results show that the main factor which significantly affects compression ratio and average message length was the frequency of the symbols on the text

Much attention has been paid to the relative effectiveness of Interactive Query Expansion (IQE) versus Automatic Query Expansion (AQE). This research has been shown that automatic query expansion (collection dependent) strategy gives better performance than no query expansion. The percentage of queries that are improved by AQE strategy is 57% with average precision equal to 43.2. Compared against AQE (collection dependent) strategy, IQE gives better average precision than AQE strategy. The percentage of queries that are improved by best IQE decision is 86% with average precision equal to 44.1. Evaluation process reveals that the value of n in AQE strategy that gave the optimal value of average precision for the whole query set is equal to one.

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