Byzantine Neurobiological Phenomenon Analysis and Factors Prediction for Social Network based Adult’s Suicides and Cyber Dismay by Hypercritical Machine Learning Techniques

International Journal of Computer Science and Engineering

© 2017 by SSRG - IJCSE Journal

Volume 4 Issue 4

Year of Publication : 2017

Authors : R. Anjit raja, B. Nagarajan, R. Dhanappriya

10.14445/23488387/IJCSE-V4I4P106

How to Cite?

R. Anjit raja, B. Nagarajan, R. Dhanappriya, "Byzantine Neurobiological Phenomenon Analysis and Factors Prediction for Social Network based Adult’s Suicides and Cyber Dismay by Hypercritical Machine Learning Techniques," SSRG International Journal of Computer Science and Engineering , vol. 4,  no. 4, pp. 24-29 , 2017. Crossref, https://doi.org/10.14445/23488387/IJCSE-V4I4P106

Abstract:

Suicide is a complex neurobiological phenomenon, and there is great changeability in validity of Web Mining based appraisal tools to predict suicidal risk from social medias core. In our research methods provides evidence that we can predict suicide attempts and their depressive mind set accurately. In recent years, the World Health Organization (WHO) Global Mental Health Action Plan, 2013–2020, has been a major step forward in pushing the docket of suicide hindrance globally (WHO, 2013; Saxena, Funk, & Chisholm,2013). This plan was adopted by health ministers in all 194 WHO member states to formally recognize the importance of mental health, which was an extraordinary accomplishment. Generally, Machine learning was primarily used to build faster search engines like Google, for signal detection and for many other engineering achievements. But in our technical scenario, for predicting the suicidal trials using Social Network data that can automatically analyze the sentiments of these social communication. Then we investigate a tool of web data mining to extract beneficial evidence for classification of social communications collected from various social-networks based on Hypercritical machine learning classification algorithms. People are often victims of annoyance or cyberbullying; social networks would thus implement a real-time observation with respect to different risk factors. In this research paper discussed and the terms used by cyber depression and suicidal are well known.

Keywords:

Suicide, Web Mining, Machine Learning, Social Network, Hypercritical, Cyber Depression

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