FQRS: Farmer Query Redressal System Using Open-Source Framework Article Swipe
Praveen Gujjar J
,
Guru Prasad M S
,
Prasanna Kumar H R
,
Amith Keerthi Jain
,
Prabhdeep Singh
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/materproc2022010009
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/materproc2022010009
The Farmer query redressal system (FQRS) is an application designed as a chatbot. The FQRS is developed with the help of machine learning and deep learning techniques. The FQRS interacts with the farmers and this system can understand and respond totext or speech. The FQRS is developed using the nlu-based open-source framework called Rasa. The Rasa framework uses building blocks such as the nlu and nlu core. The Rasa nlu and Rasa core are the building blocks for the development of a conversational chatbot. The FQRSwill use natural language processing and mimic conversations with the customers or users in a manner identical to a human being. The FQRS is implemented in Rasa 3.0. In this paper, to address farmer-related query, a chatbot has been created and the results show that the Rasa framework is more versatile than other chatbot developments platforms for creating chatbots. The results show that farmers’queries can be effectively addressed with the proper training of the model.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/materproc2022010009
- https://www.mdpi.com/2673-4605/10/1/9/pdf?version=1659315726
- OA Status
- gold
- Cited By
- 1
- References
- 11
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4289529888
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Raw OpenAlex JSON
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https://openalex.org/W4289529888Canonical identifier for this work in OpenAlex
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https://doi.org/10.3390/materproc2022010009Digital Object Identifier
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FQRS: Farmer Query Redressal System Using Open-Source FrameworkWork title
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articleOpenAlex work type
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enPrimary language
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2022Year of publication
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2022-07-29Full publication date if available
- Authors
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Praveen Gujjar J, Guru Prasad M S, Prasanna Kumar H R, Amith Keerthi Jain, Prabhdeep SinghList of authors in order
- Landing page
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https://doi.org/10.3390/materproc2022010009Publisher landing page
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https://www.mdpi.com/2673-4605/10/1/9/pdf?version=1659315726Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2673-4605/10/1/9/pdf?version=1659315726Direct OA link when available
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Chatbot, Computer science, Natural language understanding, Open source, Core (optical fiber), Artificial intelligence, Natural language, Natural language processing, Human–computer interaction, Telecommunications, Programming language, SoftwareTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2023: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.natural | 87 |
| abstract_inverted_index.respond | 39 |
| abstract_inverted_index.results | 127, 145 |
| abstract_inverted_index.speech. | 42 |
| abstract_inverted_index.FQRSwill | 85 |
| abstract_inverted_index.building | 58, 75 |
| abstract_inverted_index.chatbot. | 12, 83 |
| abstract_inverted_index.creating | 142 |
| abstract_inverted_index.designed | 9 |
| abstract_inverted_index.language | 88 |
| abstract_inverted_index.learning | 22, 25 |
| abstract_inverted_index.training | 156 |
| abstract_inverted_index.addressed | 152 |
| abstract_inverted_index.chatbots. | 143 |
| abstract_inverted_index.customers | 95 |
| abstract_inverted_index.developed | 16, 46 |
| abstract_inverted_index.framework | 51, 56, 132 |
| abstract_inverted_index.identical | 101 |
| abstract_inverted_index.interacts | 29 |
| abstract_inverted_index.nlu-based | 49 |
| abstract_inverted_index.platforms | 140 |
| abstract_inverted_index.redressal | 3 |
| abstract_inverted_index.versatile | 135 |
| abstract_inverted_index.processing | 89 |
| abstract_inverted_index.understand | 37 |
| abstract_inverted_index.application | 8 |
| abstract_inverted_index.development | 79 |
| abstract_inverted_index.effectively | 151 |
| abstract_inverted_index.implemented | 109 |
| abstract_inverted_index.open-source | 50 |
| abstract_inverted_index.techniques. | 26 |
| abstract_inverted_index.developments | 139 |
| abstract_inverted_index.conversations | 92 |
| abstract_inverted_index.conversational | 82 |
| abstract_inverted_index.farmer-related | 118 |
| abstract_inverted_index.farmers’queries | 148 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5091397373 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I106826634 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.6700000166893005 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.53013856 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |