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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) |
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| Paper Title: | Formulating SQL Queries from Natural Language Processing for Students Using Mobile Learning |
| Authors Name: | Parth Mody , Maanaav Motiramani , Param Sejpal , Abhitay Shinde |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_200294
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| Published Paper Id: | IJNRD2307102 |
| Published In: | Volume 8 Issue 7, July-2023 |
| DOI: | https://doi.org/10.5281/zenodo.8150058 |
| Abstract: | Mobile learning opens new worlds of information and personal development. To enable more personalized learning via mobile devices, several clever approaches should be implemented into mobile-assisted learning systems. This paper examines and explores several systems created with natural language processing (NLP) to extract relevant information from a database by utilizing structured natural language questions as input and SQL queries as output. Natural Language Processing (NLP) tools can be used to evaluate students' flaws throughout the mobile learning assessment process. Furthermore, the approach broadens its use beyond student learning by incorporating CSV data retrieval capabilities. Users, such as placement cell workers dealing with student databases, can perform natural language searches to retrieve useful information from CSV files. The design of the proposed model includes a user interface for submitting English inquiries, followed by NLP modules for analyzing the queries and mapping them to SQL queries. The SQL queries may then be run to obtain the necessary data from the database. It illustrates the power of natural language processing techniques in supporting mobile learning and enhancing data retrieval operations. |
| Keywords: | Natural Language Processing (NLP), Structured Query Language (SQL), CSV, Tokenization, POS tagging, Chunking, Parsing, Featured context free language, Speech to Text, Mobile Learning, User Experience, Data retrieval. |
| Cite Article: | "Formulating SQL Queries from Natural Language Processing for Students Using Mobile Learning", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 7, page no.b1-b7, July-2023, Available :http://www.ijnrd.org/papers/IJNRD2307102.pdf |
| Downloads: | 000118772 |
| 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 |
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Published Paper ID:IJNRD2307102 Registration ID: 200294 Published In: Volume 8 Issue 7, July-2023 DOI (Digital Object Identifier): https://doi.org/10.5281/zenodo.8150058 Page No: b1-b7 Country: Mumbai, Maharashtra, India Research Area: Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2307102 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2307102 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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