Manuscript Number : CSEIT1952285
To Alleviate Depression by Interactive Artificial Conversation Entity
Authors(5) :-Uditesh Jha, Keyur Khant, Milan Kotadiya, Kirti Gamdha, Prof. Zalak Kansagra This work is developed to alleviate depression. We provide transparency and privacy between user and chatbot by using the best approach which proves to be more reassuring, empathetic and non-judgmental. This new technology is interactive artificial conversation entity - chatbot. In this project, we are trying to relieve sufferer from depression. It is found that depression is the leading cause of disability globally. Projections indicates that after heart disease, depression is expected to become the second leading cause of disease burden by the year 2020. We will provide psychiatric treatment to users. After understanding the symptoms, causes and treatments of depression, user will be judged and treated according to defined and well-suited treatment. It will be helpful to those having depression, fear of sharing and fear of being judged. Also, it will help at those places where therapists are not easily available and to people who cannot afford therapist. A mobile application will be created for user interface.
Uditesh Jha Depression, Chatbot, CBT, AIML, NLP Publication Details Published in : Volume 5 | Issue 2 | March-April 2019 Article Preview
Department of Computer Science and Engineering, Parul University, Limda, Gujarat, India
Keyur Khant
Department of Computer Science and Engineering, Parul University, Limda, Gujarat, India
Milan Kotadiya
Department of Computer Science and Engineering, Parul University, Limda, Gujarat, India
Kirti Gamdha
Department of Computer Science and Engineering, Parul University, Limda, Gujarat, India
Prof. Zalak Kansagra
Department of Computer Science and Engineering, Parul University, Limda, Gujarat, India
Date of Publication : 2019-04-30
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 1039-1050
Manuscript Number : CSEIT1952285
Publisher : Technoscience Academy
Journal URL : https://res.ijsrcseit.com/CSEIT1952285
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