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What is Recurrent Neural Network (RNN)

Chapter 11
Trends, Applications, and Challenges of Chatbot Technology
A subset of artificial neural networks where the connections between the nodes can cycle, allowing the output from one node to influence the input to another node. It can display temporal dynamic behavior because of this. RNNs, which are derived from feedforward neural networks, may process input sequences of different lengths by using their internal state (memory).
Published in Chapter:
Recent Advances in Chatbot Algorithms, Techniques, and Technologies: Designing Chatbots
Guendalina Caldarini (University of Sunderland, UK) and Sardar Jaf (University of Sunderland, UK)
Copyright: © 2023 |Pages: 29
DOI: 10.4018/978-1-6684-6234-8.ch011
Abstract
Intelligent conversational computer systems, known as chatbots, have always been at the forefront of artificial intelligence. They are made to sound like humans in order for machines to communicate with humans. Because of the rising benefits of chatbots, numerous sectors have adopted them to give virtual support to clients. They are also used as companions and virtual assistants. Natural language processing and deep learning are two artificial intelligence disciplines that are used in chatbots. This chapter will examine current advancements in chatbot algorithms, approaches, and technologies that use artificial intelligence and/or natural language processing.
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Chapter 178
Neural Networks and Equilibria, Synchronization, and Time Lags
Neural networks which display feedback interconnections among their units (neurons). Due to these cyclic connections RNNs are nonlinear dynamical systems with very rich spatial and temporal behaviors: stable and unstable fixed points, limit cycles and chaotic behavior. These behaviors make them suitable for modeling certain cognitive functions such as associative memory, unsupervised learning, self-organizing maps and temporal reasoning.
Published in Chapter: Neural Networks and Equilibria, Synchronization, and Time Lags; From: Encyclopedia of Artificial Intelligence
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Chapter 24
Intelligent Techniques for the Analysis of Power Quality Data in Electrical Power Distribution System
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Chapter 5
Predictions For COVID-19 With Deep Learning Models of Long Short-Term Memory (LSTM)
Recurrent neural network is a class of artificial neural networks (ANN) where connections between nodes form a directed graph along a temporal sequence.
Published in Chapter: Predictions For COVID-19 With Deep Learning Models of Long Short-Term Memory (LSTM); From: Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning
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Chapter 9
Predicting the Future Research Gaps Using Hybrid Approach: Machine Learning and Ontology - A Case Study on Biodiversity
A recurrent neural network (RNN) is a type of artificial neural network that uses sequential data or time-series data. These deep learning algorithms are widely used for ordinal or temporal concerns, such as language translation, natural language processing (NLP), speech recognition, and image captioning; they are implemented into popular applications such as Siri, voice search, and Google Translate.
Published in Chapter: Predicting the Future Research Gaps Using Hybrid Approach: Machine Learning and Ontology - A Case Study on Biodiversity; From: Handbook of Research on Knowledge and Organization Systems in Library and Information Science
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Chapter 3
Comparative Analysis of Value at Risk(VaR) of MSCI-EMI With Traditional Time Series Methods and ANN
It is a type of neural network which is used to model sequence or time series data. It has a recursive structure. And, the neural network use previous data to understand future data.
Published in Chapter: Comparative Analysis of Value at Risk(VaR) of MSCI-EMI With Traditional Time Series Methods and ANN; From: Financial Management and Risk Analysis Strategies for Business Sustainability
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Chapter 5
Deep Learning in Instructional Analysis, Design, Development, Implementation, and Evaluation (ADDIE)
The recurrent neural network (e.g., RNN) is an ANN that can recognize a data’s sequential characteristics and use patterns to predict the next likely scenario. It is generally used in the speech recognition and natural language processing (e.g., NLP).
Published in Chapter: Deep Learning in Instructional Analysis, Design, Development, Implementation, and Evaluation (ADDIE); From: Applications of Machine Learning and Artificial Intelligence in Education
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Chapter 2
Deep Learning on Edge: Challenges and Trends
A class of deep neural networks consisting of dense networks with state.
Published in Chapter: Deep Learning on Edge: Challenges and Trends; From: Smart Systems Design, Applications, and Challenges
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Chapter 11
Hybrid Neural Networks for Renewable Energy Forecasting: Solar and Wind Energy Forecasting Using LSTM and RNN
RNN is a type of ANN, usually used for the forecasting of time series data. It utilizes the feedback provided by one or more units of its network as input in selecting the succeeding output. In RNN, the hidden neurons connect the hidden layer from previous time step to current time step, which is why it is called recurrent.
Published in Chapter: Hybrid Neural Networks for Renewable Energy Forecasting: Solar and Wind Energy Forecasting Using LSTM and RNN; From: Research Advancements in Smart Technology, Optimization, and Renewable Energy
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