Development of Novel Classifying System to Identify the Right Sense of Audio Conversation in Social Networks using Deep Convolution Neural Network
P. Nirupama1, E. Madhusudhana Reddy2

1P. Nirupama, Research Scholar, Department of Computer Science, Bharathiar University, Coimbatore (Tamil Nadu), India.
2E. Madhusudhana Reddy, Professor, Department of Computer Science and Engineering, Guru Nanak Institutions Technical Campus, India.
Manuscript received on 20 August 2019 | Revised Manuscript received on 30 August 2019 | Manuscript Published on 16 September 2019 | PP: 522-525 | Volume-8 Issue-2S6 July 2019 | Retrieval Number: B10990782S619/2019©BEIESP | DOI: 10.35940/ijrte.B1099.0782S619
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Abstract: Social media has paved a new way for communication and interacting with others. The use of social media differs according to the socio-cultural, demographic and psychological aspects of individuals. People chat, share ideas and visual material, and feel that they satisfy their needs of belonging along with the groups they have joined. Social networks is not only a area of freedom where persons express themselves openly or furtively, but also an area where several ways of violence emerge or even a means used for some aspects of violence.. The present research throws light on a few of the regular and trendy methods of abuse and risks faced by the users of social media. Develop a system to identify abusing audio file by an individual on a people/ group based on common language, race, sexual preferences, religion, or nationality. We examine a new model from machine learning, namely deep machine learning by probing design configurations of deep Convolutional Neural Networks (CNN) and the impact of different hyper-parameter settings in identifying the negative aspects in social media. 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. An application of the proposed method demonstrates excellent results with low false alarm rates for Twitter data.
Keywords: Neural Network Deep Classifying Development Machine Learning Communication.
Scope of the Article: Artificial Intelligence