scholarly journals Application of Redundancy and Information Retrieval Systems in Congestion Control Based on Combined Grey Neural Network and CC and RBF-DDA Algorithms

Author(s):  
Feihua Qu
Author(s):  
Dr. V. Suma

The recent technology development fascinates the people towards information and its services. Managing the personal and pubic data is a perennial research topic among researchers. In particular retrieval of information gains more attention as it is important similar to data storing. Clustering based, similarity based, graph based information retrieval systems are evolved to reduce the issues in conventional information retrieval systems. Learning based information retrieval is the present trend and in particular deep neural network is widely adopted due to its retrieval performance. However, the similarity between the information has uncertainties due to its measuring procedures. Considering these issues also to improve the retrieval performance, a hybrid deep fuzzy hashing algorithm is introduced in this research work. Hashing efficiently retrieves the information based on mapping the similar information as correlated binary codes and this underlying information is trained using deep neural network and fuzzy logic to retrieve the necessary information from distributed cloud. Experimental results prove that the proposed model attains better retrieval accuracy and accuracy compared to conventional models such as support vector machine and deep neural network.


Author(s):  
Iuliia Bruttan ◽  
Igor Antonov ◽  
Dmitry Andreev ◽  
Victor Nikolaev ◽  
Tatyana Klets

The paper is devoted to the problems of orientation and navigation in the world of verbal presentation of scientific knowledge. The solution of these problems is currently hampered by the lack of intelligent information retrieval systems that allow comparing descriptions of various scientific works at the level of coincidence of semantic situations, rather than keywords. The article discusses methods for the formation and recognition of semantic images of scientific publications belonging to specific subject areas. The method for constructing a semantic image of a scientific text developed by Iuliia Bruttan allows to form an image of the text of a scientific publication, which can be used as input data for a neural network. Training of this neural network will automate the processes of pattern recognition and classification of scientific publications according to specified criteria. The approaches to the recognition of semantic images of scientific publications based on neural networks considered in the paper can be used to organize the semantic search for scientific publications, as well as in the design of intelligent information retrieval systems.


1967 ◽  
Vol 06 (02) ◽  
pp. 45-51 ◽  
Author(s):  
A. Kent ◽  
J. Belzer ◽  
M. Kuhfeerst ◽  
E. D. Dym ◽  
D. L. Shirey ◽  
...  

An experiment is described which attempts to derive quantitative indicators regarding the potential relevance predictability of the intermediate stimuli used to represent documents in information retrieval systems. In effect, since the decision to peruse an entire document is often predicated upon the examination of one »level of processing« of the document (e.g., the citation and/or abstract), it became interesting to analyze the properties of what constitutes »relevance«. However, prior to such an analysis, an even more elementary step had to be made, namely, to determine what portions of a document should be examined.An evaluation of the ability of intermediate response products (IRPs), functioning as cues to the information content of full documents, to predict the relevance determination that would be subsequently made on these documents by motivated users of information retrieval systems, was made under controlled experimental conditions. The hypothesis that there might be other intermediate response products (selected extracts from the document, i.e., first paragraph, last paragraph, and the combination of first and last paragraph), that would be as representative of the full document as the traditional IRPs (citation and abstract) was tested systematically. The results showed that:1. there is no significant difference among the several IRP treatment groups on the number of cue evaluations of relevancy which match the subsequent user relevancy decision on the document;2. first and last paragraph combinations have consistently predicted relevancy to a higher degree than the other IRPs;3. abstracts were undistinguished as predictors; and4. the apparent high predictability rating for citations was not substantive.Some of these results are quite different than would be expected from previous work with unmotivated subjects.


2005 ◽  
Vol 14 (5) ◽  
pp. 335-346
Author(s):  
Por Carlos Benito Amat ◽  
Por Carlos Benito Amat

Libri ◽  
2020 ◽  
Vol 70 (3) ◽  
pp. 227-237
Author(s):  
Mahdi Zeynali-Tazehkandi ◽  
Mohsen Nowkarizi

AbstractEvaluation of information retrieval systems is a fundamental topic in Library and Information Science. The aim of this paper is to connect the system-oriented and the user-oriented approaches to relevant philosophical schools. By reviewing the related literature, it was found that the evaluation of information retrieval systems is successful if it benefits from both system-oriented and user-oriented approaches (composite). The system-oriented approach is rooted in Parmenides’ philosophy of stability (immovable) which Plato accepts and attributes to the world of forms; the user-oriented approach is rooted in Heraclitus’ flux philosophy (motion) which Plato defers and attributes to the tangible world. Thus, using Plato’s theory is a comprehensive approach for recognizing the concept of relevance. The theoretical and philosophical foundations determine the type of research methods and techniques. Therefore, Plato’s dialectical method is an appropriate composite method for evaluating information retrieval systems.


Sign in / Sign up

Export Citation Format

Share Document