Every single word and sentence of eloquent words have been selected and arranged in a purposeful and logical way. The Quranic researchers have sought to discover the way of relation and the philosophy of the arrangement (ORDER and formation) of the Qur'anic verses since the Quran is the most eloquent word. The process (SEMANTIC relations) is also defined in this way in ORDER to provide a simple understanding of how the verses of the Qur'anic chapters are related. The content of each sura is divided into several SEMANTIC domains and the relationship between these relations is examined based on this new process. In this paper, the process (SEMANTIC relations) is adapted to the two suras (Arabic: سورة, chapters) of Ash-Shams (Arabic: الشمس) and al-Layl (Arabic: اللیل) in a descriptive analytical method to find an answer to the question of how the application of the process of SEMANTIC relations leads to the discovery of the textual coherence of Suras Ash-Shams and al-Layl. Finally, it is concluded that there is a central SEMANTIC relation in each of these two surahs that the rest of the relations are arranged in different methods of expressing in ORDER to prove or believability and better understanding of that central meaning.

Intelligent agents are considered as significant means towards realizing the SEMANTIC web vision. On the SEMANTIC Web, integrating ontologies and rules enables software agents to interoperate between them, however, this leads to a problem, that no studies have focused on effective distributed reasoning for integrating ontologies and rules in multiple knowledge-bases. The methods that have been presented for distributed reasoning not only get a lot of times and memory, but also do not lead to a complete and sound reasoning. In this paper, to solve this problem, we present a distributed reasoning system that deals with the representation of the knowledge-base of ORDER sorted logic. This logic is able to describe the hierarchy of predicates and inheritance of expressions that there are in our natural language. To have a distributed reasoning, our proposed method uses the expansion of rigid and valid-non-rigid properties between knowledge-bases. Furthermore, with considering time and the situation of properties for reasoning, the non-rigid properties have not been ignored, in fact, in their valid time and situation, they are used. With this method, we achieve a complete reasoning and, moreover, the extracted knowledge is completely considered in the knowledge-bases and we have a distributed reasoning with high efficiency and sound without missing any information.

Nowadays, with considerable developments in technology, the accessibility and usage of positioning devices has been increased. These systems facilitate generation of position data as streams of spatio-temporal data, so-called trajectory. In recent years, research related to trajectory data management has mostly focused on the techniques of storage/retrieval, modeling, and data mining and knowledge discovery from trajectory data. These studies mainly emphasize on geometric aspects. However, emerge of different applications area from shipment tracking to geo-social networks generate motifs to detect SEMANTIC behavior (pattern) of moving objects. SEMANTIC patterns include not only geometric patterns, but also the knowledge derived from the data relating to the geographical and application domains. Most of the studies in the field of SEMANTIC trajectory are based on offering different ways to add meaning to trajectories and little work has been carried out on preparing data for the SEMANTIC enrichment. Actually, process of knowledge discovery and SEMANTIC enrichment is a computing framework that commonly involves several steps.In this paper, an effective method as a multi-step computing framework is proposed and the results have been evaluated. In the proposed methodology, the outputs of levels are raw cleared data, spatio-temporal trajectory, and structured trajectory, respectively. At the first level, which includes techniques for cleaning raw data collected by moving objects, the algorithms for data cleaning from outliers are assisted and removal of unnecessary data are provided. To identify and eliminate the outliers from data set, a two-step algorithm is provided based on three sigma rule. Another step that needs to be done is the use of compression techniques for detection and removal of additional data. Compression techniques are typically based on linear simplification and use distance functions to approximate data. However, these methods are not able to keep stop and move points of trajectories. In this study, a compression method based on velocity of points is provided. The proposed method is based on combination of two functions. The first function is based on instant velocity of points to calculate distance function. Another function is perpendicular Euclidean distance. However, in this function instead of constant velocity, assumption of constant acceleration has been used in the interpolation. The results of implementation show that the suggested algorithm is able to reduce the number of points at the same time keep important point in trajectories. At the second level of abstraction, spatio-temporal trajectories have been derived from cleaned data. In this stage, trajectory identification is based on type of data and application. In this work, based on the specified application, which is transportation and traffic management, daily trajectories were identified from cleaned data. The final step in preparation of data for SEMANTIC enrichment is producing structured trajectories as stop and move episodes. For this task, a method based on velocity of points is implemented. In this method, the moving object data and environment in which it moves is used to identify episodes. In this paper, the proposed algorithm in the SEMANTIC enrichment framework has been implemented on a real trajectory data and evaluation results show the effectiveness of the method.

Background and Aim: Laboratories, on average, allocated for about 4 percent of Hospital's budget and are often considered the main focus of health care spending. There is a wide range of laboratory automation options available today that Designed to improve the quality and efficiency of laboratory tests. The aim of this study was to investigate The effect of Implementation of Automatic Stop ORDER program on the management of the request and the cost of frequent and costly tests in Shahid Rajaei Educational and medical center. Research Methods: This research is analytical and comparative. In terms of time, the work is cross-sectional-longitudinal. After of data collection of before and after of Implementation of Automatic Stop ORDER program, analysis was carried out with the SPSS 19&Excel softwares. Results: In ORDER to establish the Auto Stop ORDER program to control the amount of requests and the cost of frequent tests, first the number of requests and the total cost of tests in Shahid Rajaei Medical Training Center were examined and all the laboratory services requested in the hospital, 27 laboratory services were selected to apply in the Auto Stop ORDER program. The total number of requests after the implementation of the Auto Stop ORDER program decreased by 11% and 8100 laboratory services. Also, after the implementation of the Auto Stop ORDER program on 27 costly tests, the cost of the tests was reduced by 16% and the amount was 445, 004, 725 Rials. Conclusion: Using the Auto Stop ORDER program can lead to rational prescribing of tests according to clinical guidelines, reducing the cost of patients' and resource control, and saving hospital laboratory costs. Therefore, managers and policy makers of the health system should create a suitable platform to use this program as a way to reduce costs in the laboratories of public hospitals in the country.

This article is an attempt to give pragmatic criteria for the recognition of the polysemy and homonymy. The difference between polysemy words and the homonymy ones, is one of the common issues in books and some articles of SEMANTICs, but the criteria that linguists have put forward in this regard is not unpopular when tested. The manner in which this paper proposes to re-examine the causes of polysemy in words. By providing examples of poetry Dehkhoda’ s dictionary, we emphasized the conjugal relations of words and tissues of the language, in which the term is contained. therefore, it is possible to use the same criteria in the separation of the polysemy and homonymy. However, some of the meanings that the authors of the Dehkhoda’ s dictionary have brought can be a source of doubt, but a lot of evidence is useful. We did not refer to the texts because of the choice of the dictionary.

Exploiting SEMANTIC content of texts due to its wide range of applications such as finding related documents to a query, document classification and computing SEMANTIC similarity of documents has always been an important and challenging issue in Natural Language Processing. In this paper, using Wikipedia corpus and organizing it by three-dimensional tensor structure, a novel corpus-based approach for computing SEMANTIC similarity of texts is proposed. For this purpose, first the SEMANTIC vector of available words in documents are obtained from the vector space derived from available words in Wikipedia articles, then the SEMANTIC vector of documents is formed according to their words vector. Consequently, SEMANTIC similarity of a pair of documents is computed by comparing their corresponding SEMANTIC vectors. Moreover, due to existence of high dimensional vectors, the vector space of Wikipedia corpus will cause curse of dimensionality. On the other hand, vectors in high-dimension space are Usually very similar to each other. In this way, it would be meaningless and vain to identify the most appropriate SEMANTIC vector for the words. Therefore, the proposed approach tries to improve the effect of the curse of dimensionality by reducing the vector space dimensions through random indexing. Moreover, the random indexing makes significant improvement in memory consumption of the proposed approach by reducing the vector space dimensions. Additionally, the capability of addressing synonymous and polysemous words will be feasible in the proposed approach by means of the structured co-occurrence through random indexing.

Improvement in information retrieval performance relates to the method of knowledge extraction from large amounts of text information on web. Text classification is a way of knowledge extraction with supervised machine learning methods. This paper proposed Kullback-Leibler divergence KNN for classifying extracted features based on term weighting with Latent Dirichlet Allocation algorithm. LDA is Non-Negative matrix factorization method proposed for topic modeling and dimension reduction of high dimensional feature space. In traditional LDA, each component value is assigned using the information retrieval Term Frequency measure. While this weighting method seems very appropriate for information retrieval, it is not clear that it is the best choice for text classification problems. Actually, this weighting method does not leverage the information implicitly contained in the categorization task to represent documents. In this paper, we introduce a new weighting method based on Point wise Mutual Information for accessing the importance of a word for a specific latent concept, then each document classified based on probability distribution over the latent topics. Experimental result investigated when we used Pointwise Mutual Information measure for term weighing and K Nearest Neighbor with Kullback-Leibler distance for classification, accuracy has been 82. 5%, with the same accuracy versus probabilistic deep learning methods.