Alam, Md. Golam RabiulRasel, Mr. Annajiat AlimKhan, RafiurSohel, Abdullah AlShreyashee, Farhana AzadHossain, ShamimaFiaz, Mahin2021-09-052021-09-0520212021-01ID 16101087ID 19341008ID 16101096ID 17101429ID 16101269http://hdl.handle.net/10361/14973Cataloged from PDF version of thesis.Includes bibliographical references (pages 60-62).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.Mental disorders are an unfortunate reality among the general population nowadays. Conditions like anxiety; depression may seem trivial on the surface but have serious consequences on an individual’s life. These disorders have shown to be detrimental to health and hamper a person’s general well being. In severe cases, if mental disorders go unnoticed and untreated they can cause permanent damage to one’s personality, drive him/her to social isolation and in worst cases compel the person to commit suicide as a means to end their suffering. Therefore, a need for proper detection and awareness of such diseases in a person emerges. Mental disorders may not show physical symptoms in a person but it is possible to find patterns in people with a potential mental disorder and detect them with the help of modern Machine learning techniques. In addition to this, such methods are completely automated and non-invasive; as a result these systems can also help continuously monitor a person’s mental state. We propose a system that can take various physiological signal readings from the human body as a way to predict distress. Upon detecting a user’s distress, the system tries to converse with the user trained by a knowledge base of conversations of people suffering from mental disorders and can interact with the user in a conversation-like interface as a companion. For this we used a system consisting of BioBERT models(separately for questions and answers) and a couple of FCNN layers.62 pagesenBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.BioBERTTransformerMental healthMachine learning techniquesSignalsDeep LearningThesis