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An Optimal Approach to Enhance Context Aware Description Administration Service for Cloud Robots in a Deep Learning Environment

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Abstract

As the advancements in the field of artificial intelligence technologies continue to grow, robots are being built by the researchers as an attempt to render services to the people. In this regard, the robots can communicate effectively with the people and be able to complete all the tasks adequately given to them. These service robots while being developed requires the dialogue services to be developed to interact effectively with human beings providing far better user experience. Thus, the robot been built can provide domain-specific knowledge as well as able to provide consultations in various domains. We in this paper adopted a service-oriented approach for developing context-aware communication services for the cloud robot. The proposed work aims at training the context-aware model developed. The context-aware model is responsible for answering the questions put forward by the users and possess the ability to exploit the answers corresponding to the questions presented. An integrated framework is used to combine the contextual information and communication services. The performance of the proposed model can be evaluated based on Inverse Rank Mean (IRM). Evolutionary testing methods are used for testing the data in the proposed model. The results thus obtained shows the effectiveness of the proposed approach.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India

    R. Subha & Anandakumar Haldorai

  2. Department of Computer Science and Engineering, Presidency University, Bangalore, India

    Arulmurugan Ramu

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  1. R. Subha
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  2. Anandakumar Haldorai
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  3. Arulmurugan Ramu
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Correspondence to R. Subha.

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Subha, R., Haldorai, A. & Ramu, A. An Optimal Approach to Enhance Context Aware Description Administration Service for Cloud Robots in a Deep Learning Environment. Wireless Pers Commun 117, 3343–3358 (2021). https://doi.org/10.1007/s11277-021-08073-3

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