IJNRD Research Journal

WhatsApp
Click Here

WhatsApp editor@ijnrd.org
IJNRD
INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)

Call For Paper

For Authors

Forms / Download

Published Issue Details

Editorial Board

Other IMP Links

Facts & Figure

Impact Factor : 8.76

Issue per Year : 12

Volume Published : 9

Issue Published : 95

Article Submitted :

Article Published :

Total Authors :

Total Reviewer :

Total Countries :

Indexing Partner

Join RMS/Earn 300

Licence

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Published Paper Details
Paper Title: Document Cognition - Information Search and Retrieval in Chatbots
Authors Name: P. V. Koundinya
Download E-Certificate: Download
Author Reg. ID:
IJNRD_198310
Published Paper Id: IJNRD2306099
Published In: Volume 8 Issue 6, June-2023
DOI:
Abstract: In today's digital age, the abundance of information poses a significant challenge in efficiently accessing and retrieving relevant documents, which has led to a growing market demand for advanced document information search and retrieval systems. This project aims to address this market need by developing a robust and accurate system, using various machine learning techniques and natural language processing algorithms, that enables users to quickly find and retrieve pertinent information from large document repositories. The methodology employed in this project involves evaluating and benchmarking various document information search and retrieval models. The project starts with extensive data preprocessing to ensure data quality and consistency. Next, a range of models, including Deeplake and ChromaDB, are trained using state-of-the-art techniques. The trained models are then rigorously evaluated based on their performance metrics, including accuracy and retrieval time, to determine their effectiveness in real-world scenarios.  The quantitative results obtained from the evaluation phase demonstrate the capabilities of the models in terms of accuracy and efficiency. Deeplake achieved an impressive accuracy of 90.25% with an average retrieval time of 0.6 seconds, while ChromaDB demonstrated a remarkable accuracy of 91.4% with an average retrieval time of 0.8 seconds. These results highlight the potential of these models to deliver accurate and timely search results, providing significant value to users in terms of time savings and enhanced information retrieval capabilities.
Keywords:
Cite Article: "Document Cognition - Information Search and Retrieval in Chatbots", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 6, page no.a862-a866, June-2023, Available :http://www.ijnrd.org/papers/IJNRD2306099.pdf
Downloads: 000118755
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publication Details: Published Paper ID:IJNRD2306099
Registration ID: 198310
Published In: Volume 8 Issue 6, June-2023
DOI (Digital Object Identifier):
Page No: a862-a866
Country: Bangalore, Karnataka, India
Research Area: Computer Science & Technology 
Publisher : IJ Publication
Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2306099
Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2306099
Share Article:
Share

Click Here to Download This Article

Article Preview
Click Here to Download This Article

Major Indexing from www.ijnrd.org
Semantic Scholar Microsaoft Academic ORCID Zenodo
Google Scholar ResearcherID Thomson Reuters Mendeley : reference manager Academia.edu
arXiv.org : cornell university library Research Gate CiteSeerX PUBLON
DRJI SSRN Scribd DocStoc

ISSN Details

ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
Journal Starting Year (ESTD) : 2016

DOI (A digital object identifier)


Providing A digital object identifier by DOI
How to Get DOI? DOI

Conference

Open Access License Policy

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Creative Commons License This material is Open Knowledge This material is Open Data This material is Open Content

Important Details

Social Media

Licence

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Join RMS/Earn 300

IJNRD