Ajeet Ram Pathak

Work place: School of Computer Engineering, Kalinga Institute of Industrial Technology University (KIIT), Bhubaneswar, India

E-mail: ajeet.pathak44@gmail.com


Research Interests: Autonomic Computing, Computing Platform, Data Structures and Algorithms, Information-Theoretic Security


Ajeet Ram Pathak is currently pursuing Ph.D. from School of Computer Engineering, KIIT University, Bhubaneswar, India. He received his Master of Engineering degree in Computer Engineering from University of Pune, India in 2014. His research interests include big data analytics, cloud computing, information security, and deep learning. He has published more than 15 international journal and conferences as the first author. He also received best paper awards for research work.

Author Articles
Adaptive Model for Dynamic and Temporal Topic Modeling from Big Data using Deep Learning Architecture

By Ajeet Ram Pathak Manjusha Pandey Siddharth Rautaray

DOI: https://doi.org/10.5815/ijisa.2019.06.02, Pub. Date: 8 Jun. 2019

Due to freedom to express views, opinions, news, etc and easier method to disseminate the information to large population worldwide, social media platforms are inundated with big streaming data characterized by both short text and long normal text. Getting the glimpse of ongoing events happening over social media is quintessential from the viewpoint of understanding the trends, and for this, topic modeling is the most important step. With reference to increase in proliferation of big data streaming from social media platforms, it is crucial to perform large scale topic modeling to extract the topics dynamically in an online manner. This paper proposes an adaptive framework for dynamic topic modeling from big data using deep learning approach. Approach based on approximation of online latent semantic indexing constrained by regularization has been put forth. The model is designed using deep network of feed forward layers. The framework works in an adaptive manner in the sense that model is extracts incrementally according to streaming data and retrieves dynamic topics. In order to get the trends and evolution of topics, the framework supports temporal topic modeling, and enables to detect implicit and explicit aspects from sentences also.

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