Abstract
The use of conversational agents is recognized as a significant technological achievement that makes use of recent advances in machine learning and processing of natural languages. These “agents” which are considered to be computer programs enable effortless communication with users in natural language. Conversational bots have a lot of potential thanks to the recent integration of the processing of natural languages and artificial intelligence. In order to create an intelligent conversational bot, this research paper delves deeply into the incorporation of deep learning techniques. The implementation of a sequence-to-sequence simulation strengthened by a structure consisting of encoders and decoders is the main focus. A long-short cell memory recurrent neural network occupies the focal point of this architecture. The encoder facet is in charge of understanding user inquiries, and the decoder facet produces appropriate responses, resulting in an expert conversational system.
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Gangadharan, S.M.P., Gupta, S.C., Thankachan, B., Agarwal, R., Chaturvedi, R.K., Singh, J. (2024). Fusing Management and Deep Learning to Develop Cutting-Edge Conversational Agents. In: Shaw, R.N., Siano, P., Makhilef, S., Ghosh, A., Shimi, S.L. (eds) Innovations in Electrical and Electronic Engineering. ICEEE 2023. Lecture Notes in Electrical Engineering, vol 1115. Springer, Singapore. https://doi.org/10.1007/978-981-99-8661-3_14
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DOI: https://doi.org/10.1007/978-981-99-8661-3_14
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