Selected article for: "deep learning analysis and learning analysis"

Author: Sriram, S.; Manikandan, J.; Hemalatha, P.; Leema Roselin, G.
Title: A Chatbot Mobile Quarantine App for Stress Relief
  • Cord-id: tp4l97lq
  • Document date: 2021_1_1
  • ID: tp4l97lq
    Snippet: The COVID-19 epidemic has had a dreadful effect on psychological and physical health on the patient, as a public health disaster of international significance, and this trauma can even lead to death. Studies in COVID patients on psychiatric therapies are still relatively uncommon. During a COVID-19 pandemic, the efficacy of cognitive conduct therapy was investigated in this project. So, introduce chatbot that questions the user with a certain time interval and play their mood with music. If they
    Document: The COVID-19 epidemic has had a dreadful effect on psychological and physical health on the patient, as a public health disaster of international significance, and this trauma can even lead to death. Studies in COVID patients on psychiatric therapies are still relatively uncommon. During a COVID-19 pandemic, the efficacy of cognitive conduct therapy was investigated in this project. So, introduce chatbot that questions the user with a certain time interval and play their mood with music. If they do not react to the situation, the camera is immediately activated and their face gestures recognized by sentimental analysis and encourage them to alleviate the tension and transmit the message to others. In this pandemic situation, this is very helpful for all people to distract their stress. Detecting and analyzing the emotional state of quarantined people is a huge task and it is one of the major issues faced by non-quarantine people who belong to the same family. Various applications of big data analytics in healthcare have lot of positive and lifesaving outcomes. But still does not get proper clarity about detecting the emotional stress range of patients. So, this proposed work combined deep learning technique with sentimental analysis in order to analyze and detect patients stress level. Also enables a time counter in which identifies the depth of the stress. Deep learning techniques have found to be effective and superior for many applications. © 2021 IEEE.

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