scholarly journals A Novel Information retrieval system for distributed cloud using Hybrid Deep Fuzzy Hashing Algorithm

2020 ◽  
Vol 02 (03) ◽  
pp. 151-160 ◽  
Author(s):  
Dr. V. Suma
Keyword(s):  
Neural Network ◽  
Information Retrieval ◽  
Deep Neural Network ◽  
Research Work ◽  
Support Vector ◽  
Retrieval Performance ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
Hashing Algorithm ◽  
Distributed Cloud

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.

scholarly journals An Art of Review on Conceptual based Information Retrieval

Webology ◽  
2021 ◽  
Vol 18 (SI02) ◽  
pp. 21-31
Author(s):  
P. Mahalakshmi ◽  
N. Sabiyath Fathima
Keyword(s):  
Information Retrieval ◽  
Keyword Search ◽  
Semantic Representation ◽  
Research Work ◽  
Semantic Search ◽  
Future Research ◽  
Conceptual Information ◽  
Retrieval Systems ◽  
User Query ◽  
Information Retrieval Systems

Basically keywords are used to index and retrieve the documents for the user query in a conventional information retrieval systems. When more than one keywords are used for defining the single concept in the documents and in the queries, inaccurate and incomplete results were produced by keyword based retrieval systems. Additionally, manual interventions are required for determining the relationship between the related keywords in terms of semantics to produce the accurate results which have paved the way for semantic search. Various research work has been carried out on concept based information retrieval to tackle the difficulties that are caused by the conventional keyword search and the semantic search systems. This paper aims at elucidating various representation of text that is responsible for retrieving relevant search results, approaches along with the evaluation that are carried out in conceptual information retrieval, the challenges faced by the existing research to expatiate requirements of future research. In addition, the conceptual information that are extracted from the different sources for utilizing the semantic representation by the existing systems have been discussed.

The Three Faces of Synthesis: Bringing Together Quantitative Findings in the Field of Transport and Environmental Policy

1998 ◽  
Vol 16 (5) ◽  
pp. 517-528 ◽  
Author(s):  
K J Button
Keyword(s):  
Information Retrieval ◽  
Environmental Policy ◽  
Research Work ◽  
Easy Access ◽  
Body Of Knowledge ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
Modern Information ◽  
Fruitful Research ◽  
Work Done

Good policymaking is based on sound information. Seldom, however, does any research work generate findings that are genuinely new. Much research draws upon and extends bodies of existing knowledge to gain better insights or to refine the accuracy of key parameters. Modern information-retrieval systems allow increasingly easy access to the findings of previous work. One of the bigger challenges today is to keep abreast of what is actually going on, and to extract insights from the existing body of knowledge where gaps still exist and the most fruitful research may be conducted. There are three broad approaches towards our existing body of knowledge, and the aim of this paper is to examine the usefulness of these alternatives and to pinpoint the types of circumstance where one may prove more helpful than the others. It places particular attention on work done which relates to the effects that transport has on the environment.

An Overview of Video Information Retrieval Techniques

2011 ◽  
pp. 283-292
Author(s):  
Sagarmay Deb ◽  
Yanchun Zhang
Keyword(s):  
Feature Extraction ◽  
Information Retrieval ◽  
Research Work ◽  
Multimedia Databases ◽  
Video Information ◽  
The Past ◽  
Retrieval Systems ◽  
Indexing And Retrieval ◽  
Information Retrieval Systems ◽  
Efficient Video

Video information retrieval is currently a very important topic of research in the area of multimedia databases. Plenty of research work has been undertaken in the past decade to design efficient video information retrieval techniques from the video or multimedia databases. Although a large number of indexing and retrieval techniques have been developed, there are still no universally accepted feature extraction, indexing, and retrieval techniques available. In this chapter, we present an up-to-date overview of various video information retrieval systems. Since the volume of literature available in the field is enormous, only selected works are mentioned.

Building Proximity Models for Cross Language Information Retrieval

2015 ◽  
Vol 1 ◽  
pp. 8
Author(s):  
Lam Tung Giang ◽  
Vo Trung Hung ◽  
Huynh Cong Phap
Keyword(s):  
Information Retrieval ◽  
Information Systems ◽  
Bag Of Words ◽  
Retrieval Performance ◽  
Average Precision ◽  
Ranking Models ◽  
Retrieval Systems ◽  
Cross Language Information Retrieval ◽  
Information Retrieval Systems ◽  
Cross Language

In information retrieval systems, the proximity of query terms has been employed to enable ranking models to go beyond the ”bag of words” assumption and it can promote scores of documents where the matched query terms are close to each other. In this article, we study the integration of proximity models into cross-language information retrieval systems. The new proximity models are proposed and incorporated into existing cross-language information systems by combining the proximity score and the original score to re-rank retrieved documents. The experiment results show that the proposed models can help to improve the retrieval performance by 4%-7%, in terms of Mean Average Precision.

scholarly journals Comprehensive Review on Effectual Information Retrieval of Semantic Drift using Deep Neural Network

2019 ◽  
Vol 8 (1) ◽  
pp. 32-35
Author(s):  
A. Uma Maheswari ◽  
N. Revathy
Keyword(s):  
Neural Network ◽  
Information Retrieval ◽  
Information Extraction ◽  
Deep Neural Network ◽  
Research Work ◽  
Single Class ◽  
High Recall ◽  
Comprehensive Review ◽  
Substantial Loss ◽  
Semantic Lexicons

Semantic drift is a common problem in iterative information extraction. Unsupervised bagging and incorporated distributional similarity is used to reduce the difficulty of semantic drift in iterative bootstrapping algorithms, particularly when extracting large semantic lexicons. In this research work, a method to minimize semantic drift by identifying the (Drifting Points) DPs and removing the effect introduced by the DPs is proposed. Previous methods for identifying drifting errors can be roughly divided into two categories: (1) multi-class based, and (2) single-class based, according to the settings of Information Extraction systems that adopt them. Compared to previous approaches which usually incur substantial loss in recall, DP-based cleaning method can effectively clean a large proportion of semantic drift errors while keeping a high recall.

scholarly journals RESEARCH OF APPROACHES TO THE RECOGNITION OF SEMANTIC IMAGES OF SCIENTIFIC PUBLICATIONS BASED ON NEURAL NETWORKS

2021 ◽  
Vol 2 ◽  
pp. 38-43
Author(s):  
Iuliia Bruttan ◽  
Igor Antonov ◽  
Dmitry Andreev ◽  
Victor Nikolaev ◽  
Tatyana Klets
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Information Retrieval ◽  
Scientific Publication ◽  
Scientific Publications ◽  
Network Training ◽  
Retrieval Systems ◽  
Intelligent Information Retrieval ◽  
Information Retrieval Systems ◽  
Intelligent Information

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.

Relevance Predictability in Information Retrieval Systems

1967 ◽  
Vol 06 (02) ◽  
pp. 45-51 ◽  
Author(s):  
A. Kent ◽  
J. Belzer ◽  
M. Kuhfeerst ◽  
E. D. Dym ◽  
D. L. Shirey ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Experimental Conditions ◽  
Treatment Groups ◽  
Retrieval Systems ◽  
Significant Difference ◽  
Information Retrieval Systems ◽  
High Predictability ◽  
Intermediate Response ◽  
Quantitative Indicators ◽  
Level Of Processing

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.

Performance of eight information retrieval systems in the Spanish web

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