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Using Text's Terms and Syntactical Properties for Document Similarity
Issue:
Volume 5, Issue 6, December 2016
Pages:
82-87
Received:
4 October 2016
Accepted:
5 November 2016
Published:
5 December 2016
Abstract: This paper reports on experiments performed to investigate the use of syntactical structures of sentences combined with sentences' terms for document similarity calculation. The document's sentences were first converted into ordered Part of Speech (POS) tags that were then fed into the Longest Common Subsequence (LCS) algorithm to determine the size and count of the LCSs found when comparing the document sentence by sentence. As a first stage, these syntactical features of the text were used as a structural representation of the document’s text. However, the produced strings of tags not only work as text representative but also provide for text size reduction. This improves the processing efficiency of comparing the document's representative strings using the LCS. A score is generated by computing an accumulative value based on the number of the LCSs found. In the second stage, documents that score well in the first stage are subjected to further comparison using the actual words of the sentences (content) in a sentence by sentence fashion. An overall final is generated as a measure of similarity using the common words (accumulated for the whole document) and the total number of LCSs from the first step. Experiments were done on two different corpora. Results obtained have showed the utility of the proposed procedure in calculating similarities between written documents. The overall discrimination power was maintained while the size of the documents was reduced using only a representative of the document based on the tagged string.
Abstract: This paper reports on experiments performed to investigate the use of syntactical structures of sentences combined with sentences' terms for document similarity calculation. The document's sentences were first converted into ordered Part of Speech (POS) tags that were then fed into the Longest Common Subsequence (LCS) algorithm to determine the siz...
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Study of Effects of Blood Amino Acid and Hormone Level for Controlling Triglyceride Accumulation in the Liver of Rats Using Self-Organizing Map
Masato Masuda,
Yasushi Nakabayashi,
Ryuji Shioya,
Hiroki Nishi,
Shinichiro Takahashi,
Fumihiko Hakuno
Issue:
Volume 5, Issue 6, December 2016
Pages:
88-93
Received:
17 August 2016
Accepted:
11 November 2016
Published:
12 December 2016
Abstract: Effects of blood amino acid and hormone level for triglyceride accumulation in the liver are revealed in some previous studies by experimental method. Since there are large individual differences in these effects, it needs a lot of rats for such experiments. Simulation can help, sometimes substitute experiment in various fields especially in engineering field. Recently, combining a supercomputer and Artificial Intelligence technique, simulation is expanding its scope. In this study, one of such a simulation technique, Self-Organizing Map (SOM), proposed by Kohonen and it is a kind of the Neural Networks and using for the competitive learning, is applied for classifying effects of blood amino acid and hormone level for controlling triglyceride accumulation in the liver of rats.
Abstract: Effects of blood amino acid and hormone level for triglyceride accumulation in the liver are revealed in some previous studies by experimental method. Since there are large individual differences in these effects, it needs a lot of rats for such experiments. Simulation can help, sometimes substitute experiment in various fields especially in engine...
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A Fuzzy Scheduling Approach for Medicine Supply in Hospital Management Information System with Uncertain Demand
Ping Hu,
Yufang Li,
Ximin Zhou,
Zhengying Cai
Issue:
Volume 5, Issue 6, December 2016
Pages:
94-103
Received:
23 October 2016
Accepted:
7 November 2016
Published:
29 December 2016
Abstract: Generally, it is very difficult to determine the medicine supply in hospital under uncertain environment. Here, the medical supply decision problem in uncertain environment is modeled as a fuzzy multi-objective linear programming model. First, the medicine supply in hospital management system is analyzed and the uncertainties in medicine supply are modeled as fuzzy numbers. Second, a fuzzy medicine scheduling is built to fit the uncertain demand and the solving steps are illustrated too. Third, a numerical example is presented to demonstrate the proposed model, and the compared results verify its effectiveness. Last, some important conclusions and future work are sum up at the end of the paper.
Abstract: Generally, it is very difficult to determine the medicine supply in hospital under uncertain environment. Here, the medical supply decision problem in uncertain environment is modeled as a fuzzy multi-objective linear programming model. First, the medicine supply in hospital management system is analyzed and the uncertainties in medicine supply are...
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Artificial Neural Networks in E-Learning Personalization: A Review
Rana Khudhair Abbas Ahmed
Issue:
Volume 5, Issue 6, December 2016
Pages:
104-108
Received:
16 December 2016
Accepted:
28 December 2016
Published:
19 January 2017
Abstract: Finding the appropriate personalized learning resources is a difficult process for users and learners on the web. Artificial Neural Networks show a great significance in helping users in personalizing their own learning interests from a large number of resources by giving suggestions to users and learners based on their preferences and all of this with less time and effort. This paper discusses the importance of using neural networks in E-Learning personalization and shows some current applications of them with their improvements and limitations.
Abstract: Finding the appropriate personalized learning resources is a difficult process for users and learners on the web. Artificial Neural Networks show a great significance in helping users in personalizing their own learning interests from a large number of resources by giving suggestions to users and learners based on their preferences and all of this ...
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