Kulkarni Jaideep

As a fresher, I would like to choose a field that will challenge me, put me to use creative skills, technologies that I am familiar with, innovative thoughts that will benefit the organization in the long run and also will help me in building up my professional career.

• Completed 14 days ML/AI course at BITS Pillani, Hyderabad conducted by CodeBeat taught by Surya Vamshi (CEO of SuBrains.org) Extra-Curricular Activities AVNIET Organizer for all Technical and Cultural events.
• Major Project: DETECTING OF ONLINE HARRASSMENT THROUGH SMD(Surface Mounted Device) AUTO ENCODER
• Languages used: Python
• Summary: As a side effect of increasingly popular social media, cyber-bullying has emerged as a serious problem afflicting children, adolescents and young adults.
• Machine learning techniques make automatic detection of bullying messages in social media possible, and this could help to construct a healthy and safe social media environment.
• In this meaningful research area, one critical issue is robust and discriminative numerical representation learning of text messages.
• In this paper, we propose a new representation learning method to tackle this problem.
• Our method named Semantic-Enhanced Marginalized De-noising Auto-Encoder (smSDA) is developed via semantic extension of the popular deep learning model stacked de-noising auto encoder.
• The semantic extension consists of semantic dropout noise and sparsity constraints, where the semantic dropout noise is designed based on domain knowledge and the word embedding technique.
• Our proposed method is able to exploit the hidden feature structure of bullying information and learn a robust and discriminative representation of text.
• Comprehensive experiments on two public cyber-bullying corpora (Twitter and MySpace) are conducted.