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Similarity profiled association mining from time stamped transaction databases is an important topic of research relatively less addressed in the field of temporal data mining. Mining temporal patterns from these time series databases requires choosing and applying similarity measure for similarity computations and subsequently pruning temporal patterns. This research proposes a novel z-space based interest measure named as Krishna Sudarsana for time-stamped transaction databases by extending interest measure Srihass proposed in previous research. Krishna Sudarsana is designed by using the product based fuzzy Gaussian membership function and performs similarity computations in z-space to determine the similarity degree between any two temporal patterns. The interest measure is designed by considering z-values between z = 0 and z = 3.09. Applying the Krishna Sudarsana requires moving the threshold value given by user to a different transformation space (z-space) which is a defined as a function of standard deviation. In addition to proposing interest measure, new expressions for standard deviation and equivalent z-space threshold are derived for similarity computations. For experimental evaluation, we considered Naïve, Sequential and Spamine algorithms that applies Euclidean distance function and compared performance of these three approaches to Z-Spamine algorithm that uses Krishna Sudarsana by choosing various test cases. Experiment results proved the performance of the proposed approach is better to Sequential approach that uses snapshot database scan strategy and Spamine approach that uses lattice based database scan strategy., Author(s): Radhakrishna Vangipuram [sup.1], P. V. Kumar [sup.2], Vinjamuri Janaki [sup.3], Shadi. A. Aljawarneh [sup.4], Juan A. Lara [sup.5], Khalaf Khatatneh [sup.6] Author Affiliations: (1) grid.411828.6, 0000 0001 0683 7715, [...]

Problem statement: Text compression permits representing a document by using less space. This is useful not only to save disk space, but more importantly, to save disk transfer and network transmission time. With the continues increase in the number of Arabic short text messages sent by mobile phones, the use of a suitable compression scheme would allow users to use more characters than the default value specified by the provider. The development of an efficient compression scheme to compress short Arabic texts is not a straight forward task. Approach: This study combined the benefits of pre-processing, entropy reduction through splitting files and hybrid dynamic coding: A new technique proposed in this study that uses the fact that Arabic texts have single case letters. Experimental tests had been performed on short Arabic texts and a comparison with the well known plain Huffman compression was made to measure the performance of the proposed schema for Arabic short text. Results: The proposed schema can achieve a compression ratio around 4.6 bits [byte.sup.-1] for very short Arabic text sequences of 15 bytes and around 4 bits [byte.sup.-1] for 50 bytes text sequences, using only 8 Kbytes overhead of memory. Conclusion: Furthermore, a reasonable compression ratio can be achieved using less than 0.4 KB of memory overhead. We recommended the use of proposed schema to compress small Arabic text with recourses limited. Key words: Short text compression, Huffman coding, Arabic language, dynamic hybrid coding, INTRODUCTION Text compression permits representing a document by using less space. This is useful not only to save disk space, but more importantly, to save disk transfer and network transmission [...]

Perinatal mortality is a fundamental indicator of the quality of the healthcare provided to women during pregnancy and childbirth, as well as the healthcare provided to neonates in the first week of life. At the national level, determining the direct and indirect causes of these deaths is vital, as it will assist in tracking the quality of antenatal, natal and postnatal care and help to detect the areas for avoidance. This study aimed to identify the main determinants of perinatal deaths in Jordan from the perspectives of health care providers (HCPs). A descriptive qualitative approach using focus group discussion was used. Four focus groups were conducted in each of the four hospitals where the approached HCPs are employed. An average of 5 HCPs were interviewed in each focus group with a total of 80 HCPs participating in the 16 focus groups. Thematic analysis was carried out to analyze the data. The HCPs provided a detailed description of the determinants of perinatal and neonatal death from their points of view. Four main themes with multiple subthemes emerged, namely maternal factors (ignorance, concealment of medical condition, and husbands' negligence), sociocultural factors (socioeconomic status, tribal and consanguineous marriage, and harmful cultural practices), political factors (early marriage driven by displacement and war consequences on maternal health), and health system-related factors (services management including capabilities and logistics, overcrowding of emergency rooms, discharge against medical advice, and unskilled general practitioners in private maternity clinics). As perceived by HCPs, maternal factors, sociocultural factors, political factors, and health system-related factors are the main determinants of perinatal deaths in Jordan. Improvement in the quality of maternal and neonatal health care services, maternal health education, and maternity staff training are strongly recommended. [ABSTRACT FROM AUTHOR]

Abstract: The objective of this study was to present a new technique assists in developing a recognition system for handling the Arabic Hand Written text. The proposed system is called [...]

Phoneme recognition is a very popular area of research, especially in the field of machine intelligence, as it adds a distinguish touch in technology world in many fields that provides many features, This paper discussed a phoneme recognition using neural network technique by using a strong algorithm in pre-processing stage to get good results. Moreover, it explained the stages of phoneme recognition, pre-processing procedures such as getting a signal, sampling, quantization, determine an energy then using neural network to get a good results and enhancements. Gaussian Low Pass filtering was used to get enhanced results of voice signal and lowering the noisy; and then use neural network in a training stage to train a system to recognize the speech signal, this paper has benefited from some of the previous results for other researchers and entered good ideas to enhance Arabic voice recognition. [ABSTRACT FROM AUTHOR]