Design of high throughput and area efficient advanced encryption system core

Naidu, A. P. Anusha; Deshmukh, Achala; Bhure, Vipin

doi:10.1109/iccsp.2014.6950189

Cited by 6 publications

(1 citation statement)

References 9 publications

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“…H. Vizcaíno et.al [10] explained that Big Data can deal with an aggregate amount of data produced every second of time available from multiple sources and in various formats. Various approaches and skills have been established to make good use of these data, but their acceptance by organizations is complex in many respects.…”

Section: Related Workmentioning

confidence: 99%

Chatbots Employing Deep Learning for Big Data

Verma*¹,

Jyoti²

2019

IJITEE

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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.

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Section: Related Workmentioning

confidence: 99%