Advanced text documents information retrieval system for search services

Abstract

Information technology has explored the growth of text documents data in many organizations and the structural arrangement of voluminous data is a complex task. Handling the text document data is a challenging process involving not only the training of models but also numerous additional procedures, e.g., data pre-processing, transformation, and dimensionality reduction. In this paper, we describe the system's architecture, the technical challenges, and the novel solution we have built. We propose a Recurrent Convolutional Neural network (RCNN), based text information retrieval system which efficiently retrieves the text documents and information for the user query. Pre-processing using tokenization and stemming, retrieval using TF-IDF (Term Frequency-Inverse Document Frequency), and RCNN classifier which captures the contextual information is implemented. A real-time advanced search system is developed on a huge set of MAHE University dataset. The performance of the proposed text document retrieval system is compared with other existing algorithms and the efficacy of the method is discussed. The proposed RCNN-based text document information retrieval model performs better in terms of precision, recall, and F-measure. A high-quality and high-performance text document retrieval search system is presented. Chiranjeevi H S ABOUT THE AUTHOR Chiranjeevi H S is an Experienced Director with a demonstrated history of working in the information technology and services industry for over 10 years. Skilled in Business development, Databases, Information engineering, and Management. Strong business and operational professional, He is a founder director for Mythinit Technology and DataSciii Solutions Private Limited, where he is currently on sabbatical. His industry-oriented work has led onto extended research as a Ph.D. candidate in MIT, Manipal Academy of Higher Education, Manipal.

Optimal Intelligent Information Retrieval and Reliable Storage Scheme for Cloud Environment And E-Learning Big Data Analytics

“…In this section, we validate the performance of our OIIRS scheme using different benchmark datasets are MAHE University [30], Kaggle web content [31] and CISI test set [32]. Our OIIRS scheme is implemented in Google Colab simulation environment with the python programming language.…”

“…Y are two members of the HM population chosen at random probability, QBR is defined as: (30) where min QBR and are the probabilities at their lowest and highest, respectively. In optimization problems, this function is one of the standard test functions that is used the most.…”

“…In order to validate the performance of proposed OIIRS scheme through the benchmark datasets are MAHE University [30], Kaggle web content [31] and CISI test set [32]. 3.…”

“…To check how well the proposed OIIRS information retrieval method works the existing state-ofarts recurrent convolutional neural network (RCNN) [30], K-nearest neighbor (KNN) [30], long short-term memory (LSTM) [30],convolutional neural network CNN [30], text-block FCN [30],…”

Information Retrieval in Text-Based Document using Boyer Moore Algorithm

“…In the studies on the IR system, Bunyamin and Negara [3], also Amin and Purwaningtyas [4] used the vector space model method that uses the concept of vector space which converts documents into vectors that are used as references in determining the relevance of input to documents. While Saadah et al [5] and Chiranjeevi and Shenoy [6] did so using the Term-Frequency-Inverse Document Frequency (TF-IDF) method which considered the occurrence of the same word order between the query and the text in the document. Danuri [7] conducted research on content-based text search using the Brute Force algorithm and Aruleba et al [8] using a full text retrieval system in the digital library.…”