Hybrid Depression Detection Framework Using BILSTM - Bin Abdul Hamid, Danniel Shazmeer;Goyal, Shyam Bihari
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Nowadays, due to mental stress, a significant section of society is affected by depression. There may be several reasons for depression, especially in adults. As a different person has different symptoms, and its identification is a significant challenge. Most people feel shy to accept that they are suffering from depression, while others are unaware of their depressed mental health. The objective of this work is to design and develop a practical tool or model to diagnose depression. In this work, a hybrid system is designed and simulated for detecting depression using EEG features, and facial…mehr

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Produktbeschreibung
Nowadays, due to mental stress, a significant section of society is affected by depression. There may be several reasons for depression, especially in adults. As a different person has different symptoms, and its identification is a significant challenge. Most people feel shy to accept that they are suffering from depression, while others are unaware of their depressed mental health. The objective of this work is to design and develop a practical tool or model to diagnose depression. In this work, a hybrid system is designed and simulated for detecting depression using EEG features, and facial features as a biological feature give an accurate diagnosis. EEG (Electroencephalogram) is the most adaptive way that can reflect the actual mental state among all biological signals.
Autorenporträt
Me llamo Danniel Shazmeer. He completado mi Master en Tecnología de la Información en la Universidad de la Ciudad de Malasia. Me gusta conocer gente nueva y encontrar formas de ayudarles a tener una experiencia edificante. Atribuyo este éxito a mi habilidad para planificar, programar y manejar muchas tareas diferentes a la vez.