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DC Field | Value | Language |
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dc.contributor.author | Bagul, Jagruti | - |
dc.date.accessioned | 2022-10-06T08:46:31Z | - |
dc.date.available | 2022-10-06T08:46:31Z | - |
dc.date.issued | 2022-06-01 | - |
dc.identifier.uri | http://10.1.7.192:80/jspui/handle/123456789/11320 | - |
dc.description.abstract | Real-time Conversational AI Application for Healthcare Domain's main goal is to create a real-time communication system to reduce low-pitch sounds and noise levels. We're using Nvidia Nemo SDK, Nvidia Riva, Amazon Services, and open-source platforms like RASA and Facebook Blender 2.0 chatbot. Our chatbot differs from conversational AI-based chatbots because neural networks are used in text mining consumer feedback. We can say Conversational AI applications an Intelligent Virtual Assistants(IAV), which converse like a human being. An IAV is used for computer programs that conduct a natural language via speech to text, understand the user's intents, and respond based on the organization's/business company's and healthcare patients/doctors with rules and data. We have used Bert and GPT ConvAI models for finetune and pretrained them with some datasets. Neurodegenerative disease patients need a therapist for recovery, so our conversational IAV helps them cure, and the patients do not feel lonely. | en_US |
dc.publisher | Institute of Technology | en_US |
dc.relation.ispartofseries | 20MCEC02; | - |
dc.subject | Computer 2020 | en_US |
dc.subject | Project Report 2020 | en_US |
dc.subject | Computer Project Report | en_US |
dc.subject | Project Report | en_US |
dc.subject | 20MCE | en_US |
dc.subject | 20MCEC | en_US |
dc.subject | 20MCEC02 | en_US |
dc.title | Real-time Conversational AI Application for Healthcare Domain | en_US |
dc.type | Dissertation | en_US |
Appears in Collections: | Dissertation, CE |
Files in This Item:
File | Description | Size | Format | |
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20MCEC02.pdf | 20MCEC02 | 5.82 MB | Adobe PDF | ![]() View/Open |
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