International Journal of

Automation and Smart Technology

Shruti Patil1, Venkatesh Mudaliar2, and Pooja Kamat1

1Assistant Professor, Symbiosis Institute of Technology, Pune
2Mtech Research Scholar, Symbiosis Institute of Technology

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ABSTRACT

A chatbot is a software that can reproduce a discussion portraying a specific dimension of articulation among people and machines utilizing Natural Human Language. With the advent of AI, chatbots have developed from being minor guideline based models to progressively modern models. A striking highlight of the current chatbot frameworks is their capacity to maintain and support explicit highlights and settings of the discussions empowering them to have a human contact through the span of involvement. The paper expects to build up a detailed database with respect to the models utilized to deal with the learning of long haul conditions in a chatbot. The paper proposes a crossbreed Long Short Term Memory based Ensemble Network arrangement model to save the continuation of the specific situation. The proposed model uses a characterized number of Long Short Term Memory Networks as a major aspect of the amassed model working as one to create the aggregate forecast class for the info inquiry handled.


Keywords: Chatbot, AI, LSTM, Ensemble Method, GRU


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ARTICLE INFORMATION

Received: 2020-02-09

Accepted: 2020-05-17
Available Online: 2022-01-02


Cite this article:

Shruti. P., Venkatesh. M. and Pooja. K. (2022) LSTM based Ensemble Network to enhance the learning of Long-term Dependencies in Chatbot. Int. j. autom. smart technol. https://doi.org/10.5875/ausmt.v12i1.2286

  Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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