Chatbots Employing Deep Learning for Big Data
Prince Verma1, Kiran Jyoti2

1Prince Verma, Department of Computer Science and Engineering, IKG Punjab Technical University, Jalandhar, India.
2Kiran Jyoti, Department of Information Technology, GNDEC, Ludhiana, India.
Manuscript received on 22 August 2019. | Revised Manuscript received on 03 September 2019. | Manuscript published on 30 September 2019. | PP: 1005-1010 | Volume-8 Issue-11, September 2019. | Retrieval Number: I8017078919/2019©BEIESP | DOI: 10.35940/ijitee.I8017.0981119
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Abstract: With the evolution of artificial intelligence to deep learning, the age of perspicacious machines has pioneered that can even mimic as a human. A Conversational software agent is one of the best-suited examples of such intuitive machines which are also commonly known as chatbot actuated with natural language processing. The paper enlisted some existing popular chatbots along with their details, technical specifications, and functionalities. Research shows that most of the customers have experienced penurious service. Also, the inception of meaningful cum instructive feedback endure a demanding and exigent assignment as enactment for chatbots builtout reckon mostly upon templates and hand-written rules. Current chatbot models lack in generating required responses and thus contradict the quality conversation. So involving deep learning amongst these models can overcome this lack and can fill up the paucity with deep neural networks. Some of the deep Neural networks utilized for this till now are Stacked Auto-Encoder, sparse auto-encoders, predictive sparse and denoising auto-encoders. But these DNN are unable to handle big data involving large amounts of heterogeneous data. While Tensor Auto Encoder which overcomes this drawback is time-consuming. This paper has proposed the Chatbot to handle the big data in a manageable time.
Keywords: Chatbot, Big Data Analytics, Artificial Intelligence, Deep Learning, Auto Encoder, Tensor flow, Neural Networks, Deep Neural Networks, Feed-Forward Networks.
Scope of the Article: Deep Learning