Development of Novel Classifying System to Identify the Right Sense of Image Sharing in Social Networks Using Deep Convolution Neural Network Nirupama P.1, Reddy E. Madhusudhana2,* 1Research Scholar, Computer Science, Bharathiar University, India 2Professor, Computer Science and Engineering, Guru Nanak Institutions Technical Campus, India *Corresponding Author: E. Madhusudhana Reddy, Professor, Computer Science and Engineering, Guru Nanak Institutions Technical Campus, India, Email: e_mreddy@yahoo.com
Online published on 19 August, 2019. Abstract Introduction The use of social networks differs according to the socio-cultural, demographic and psychological aspects of individuals. People share photos and feel that they satisfy their needs of belonging along with the groups they have joined. Social media is not only a domain of freedom where individuals express themselves overtly or secretly, but also an area where several ways of violence emerge or even a means used for some aspects of violence. Aim Being an interactive medium and addressing quite a large number of users, social media issue has become rather sophisticated and problematic. Developed a system to identify abusing images shared/posted by an individual on a people/group based on common language, race, sexual preferences, religion, or nationality. Method We investigate a new paradigm from machine learning, namely deep machine learning by examining design configurations of deep Convolutional Neural Networks (CNN) and the impact of different hyper-parameter settings towards identifying the negative aspects in social networks. Result & Conclusion Deep CNN automatically generate powerful features by hierarchical learning strategies from massive amounts of training data with a minimum of human interaction or expert process knowledge. Top Keywords Deep Convolutional Neural Networks (CNN), Social Networks, Softmax, image classification. Top |