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@article{162458,
        author = {Kotagiri Geethika and Patnam Rakesh and Jonnalagadda Kruthik Reddy and Mohammed Faisal},
        title = {Mental Health Analyzer Using Facial Images, Deep-CNN & IQ Tests},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {10},
        pages = {161-165},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=162458},
        abstract = {Approximately 280 million individuals worldwide suffer from depression, making it a widespread disorder. Deep convolutional neural networks can recognize depression automatically thanks to its distinct facial traits. A person’s face is a significant component of their body and can reveal a lot about their emotional state. A person’s face is where they convey all of their fundamental feelings. The current system assesses the user’s mental state manually, which has several drawbacks. For example, we are unable to forecast any accurate answers based on the assessment score since we may not be aware of the user’s constant emotional state. This model provides a hybrid design that invokes a facial-based emotion sequence and an IQ test in order to overcome this issue and suggest an effective approach for dynamically forecasting the mental state. The human mental status is routed through regular observation of their emotions and administration of IQ tests. For mental health and self-control, combining these methods mentioned above yields encouraging outcomes. 
In addition to helping individuals with mental health disorders such as anxiety, depression, and addictive behaviors by identifying them, Mental health Analyzer is a platform that supports and encourages healing while also assisting in maintaining a balanced state of mental state of an individual.
},
        keywords = {Convolutional Neural Network, IQ tests, Depression, anxiety, addictive behaviors.},
        month = {},
        }