| Abstrakt: |
The most frequent psychiatric disorders are depression and post-traumatic stress disorder. Machine learning algorithms are effective tools for predicting psychiatric diseases. The extent to which society is not widely known, most of the time disguised, many more victims does not mention in a public health concern is child abuse and neglect. This Project is mainly focused to rehabilitate the Child, Abuse show various methods is bodily, mind, and several abuse, as well as domestic violence. A child who is subjected to any of the aforementioned sorts of abuse is at danger of being isolated, developing anxiety, depression, and mental trauma, as well as having difficulty learning and building social relationships. Anxiety and depression in children are frequently misdiagnosed. In this study, we used the LSTM algorithm to create a Child Abuse Depression Detection and Recommendation Model that can recognise signals of anxiety and depression in young children's symptoms. On Child Abuse Depression Symptom instances in India, the proposed Deep-LSTM ensemble model is assessed using several forms. [ABSTRACT FROM AUTHOR] |
