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What is Recurrent Neural Networks (RNNs)

Design and Development of Emerging Chatbot Technology
A unique type of neural network called a recurrent neural network is made to process sequential data. RNNs possess an internal memory that enables them to process data sequences, where the output is dependent on the current input and the network's memory of previous inputs. This is in contrast to typical neural networks, which analyze individual data points.
Published in Chapter:
Natural Language Processing (NLP) in Chatbot Design: NLP's Impact on Chatbot Architecture
Rajesh Kanna Rajendran (Christ University, India), Mohana Priya T. (Christ University, India), and Karthick Chitrarasu (Christ University, India)
Copyright: © 2024 |Pages: 12
DOI: 10.4018/979-8-3693-1830-0.ch006
Abstract
The creation and development of chatbots, which are the prevalent manifestations of artificial intelligence (AI) and machine learning (ML) technologies in today's digital world, are built on Natural Language Processing (NLP), which serves as a cornerstone in the process. This chapter investigates the significant part that natural language processing (NLP) plays in determining the development and effectiveness of chatbots, beginning with their beginnings as personal virtual assistants and continuing through their seamless incorporation into messaging platforms and smart home gadgets. The study delves into the technological complexities and emphasizes the problems and improvements in natural language processing (NLP) algorithms and understanding (NLU) systems. These systems are essential in enabling chatbots to grasp context, decode user intent, and provide replies that are contextually appropriate in real time. In spite of the substantial progress that has been made, chatbots continue to struggle with constraints.
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Artificial Intelligence (AI)-Integrated Biosensors and Bioelectronics for Agriculture
These are a type of neural network architecture specifically designed for sequential data processing. They utilize recurrent connections to allow information to persist, making them suitable for tasks such as natural language processing, speech recognition, and time series analysis. RNNs excel in capturing patterns and dependencies in sequential data due to their ability to maintain a memory of previous inputs in the network's hidden state.
Full Text Chapter Download: US $37.50 Add to Cart
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