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An optimal deep feature–based AI chat conversation system for smart medical application.

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Τίτλος: An optimal deep feature–based AI chat conversation system for smart medical application.
Συγγραφείς: Lal, Mily, Neduncheliyan, S.
Πηγή: Personal & Ubiquitous Computing; Aug2023, Vol. 27 Issue 4, p1483-1494, 12p
Θεματικοί όροι: Chatbots, Artificial intelligence, Online chat, Python programming language, Conversation
Περίληψη: An artificial intelligence (AI)–based Chatbot system plays a vital role in customer support. In the medical sector, it helps the patients/users get relevant information related to their queries. Although different AI-based Chatbot models have been developed in the past to provide accurate answers to the user, they face some issues. Thus, the novel hybrid Lion-based Deep Belief Chatbot (LbDBC) model is developed in this presented article to support users in retrieving relevant answers related to their queries. Here, the medical QA dataset is considered to validate the designed approach. Incorporating the stemming and tokenization method helps extract root words from the text data. Moreover, the integration of lion fitness provides the finest answer retrieval rate. The presented approach is implemented in Python software version 3.10, and the outcomes are estimated. In addition, a case study is developed to explain the functioning of the designed model. Also, a comparative assessment is produced by comparing the results of the designed model with existing approaches. The comparative assessment verifies that the presented Chatbot model earned better results. [ABSTRACT FROM AUTHOR]
Copyright of Personal & Ubiquitous Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Βάση Δεδομένων: Complementary Index
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  – Url: https://dx.doi.org/doi:10.1007/s00779-023-01713-4
    Name: EDS - Springer Nature Journals (s7799221)
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  Data: An optimal deep feature–based AI chat conversation system for smart medical application.
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  Data: <searchLink fieldCode="AR" term="%22Lal%2C+Mily%22">Lal, Mily</searchLink><br /><searchLink fieldCode="AR" term="%22Neduncheliyan%2C+S%2E%22">Neduncheliyan, S.</searchLink>
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  Data: Personal & Ubiquitous Computing; Aug2023, Vol. 27 Issue 4, p1483-1494, 12p
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  Data: <searchLink fieldCode="DE" term="%22Chatbots%22">Chatbots</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Online+chat%22">Online chat</searchLink><br /><searchLink fieldCode="DE" term="%22Python+programming+language%22">Python programming language</searchLink><br /><searchLink fieldCode="DE" term="%22Conversation%22">Conversation</searchLink>
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  Data: An artificial intelligence (AI)–based Chatbot system plays a vital role in customer support. In the medical sector, it helps the patients/users get relevant information related to their queries. Although different AI-based Chatbot models have been developed in the past to provide accurate answers to the user, they face some issues. Thus, the novel hybrid Lion-based Deep Belief Chatbot (LbDBC) model is developed in this presented article to support users in retrieving relevant answers related to their queries. Here, the medical QA dataset is considered to validate the designed approach. Incorporating the stemming and tokenization method helps extract root words from the text data. Moreover, the integration of lion fitness provides the finest answer retrieval rate. The presented approach is implemented in Python software version 3.10, and the outcomes are estimated. In addition, a case study is developed to explain the functioning of the designed model. Also, a comparative assessment is produced by comparing the results of the designed model with existing approaches. The comparative assessment verifies that the presented Chatbot model earned better results. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Personal & Ubiquitous Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Artificial intelligence
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      – SubjectFull: Online chat
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      – SubjectFull: Conversation
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              Text: Aug2023
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              Y: 2023
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