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What is Long Short-Term Memory (LSTM)

Chapter 6
Design and Development of Emerging Chatbot Technology
Long short-term memory (LSTM) networks are a specific type of RNN designed to address a common limitation of RNNs - the vanishing gradient problem. This problem can make it difficult for RNNs to learn long-term dependencies in sequences. LSTMs address this problem by introducing a gating mechanism that controls the flow of information through the network. This allows LSTMs to selectively remember or forget information over long periods of time.
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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Chapter 2
Survey of Applications of Neural Networks and Machine Learning to COVID-19 Predictions
A type of Recurrent Neural Network (RNN) capable of learning order dependence in sequence prediction problems as a behavior required in complex problem domains like machine translation, speech recognition, and more ( Brownlee, 2017 ).
Published in Chapter: Survey of Applications of Neural Networks and Machine Learning to COVID-19 Predictions; From: Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning
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Chapter 7
Intelligent Log Analysis Using Machine and Deep Learning
Type of RNN that incorporates multiplicative gates that allows the network to have long- and short-term memory.
Published in Chapter: Intelligent Log Analysis Using Machine and Deep Learning; From: Machine Learning and Cognitive Science Applications in Cyber Security
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Chapter 11
Hybrid Neural Networks for Renewable Energy Forecasting: Solar and Wind Energy Forecasting Using LSTM and RNN
LSTM is explicitly a subfield of RNN architecture, which is more stable and efficient in dealing with both long-term, as well as short-term dependency problems. It is very useful when the gap between the past and the required future values are substantial.
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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Chapter 3
Medical Image Lossy Compression With LSTM Networks
The variant of recurrent neural networks that is capable of learning long term dependencies in sequence.
Published in Chapter: Medical Image Lossy Compression With LSTM Networks; From: Histopathological Image Analysis in Medical Decision Making
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Chapter 11
Time Series Forecasting in Retail Sales Using LSTM and Prophet
A type of recurrent neural network that uses gating mechanisms to register long-term temporal dependencies in training data.
Published in Chapter: Time Series Forecasting in Retail Sales Using LSTM and Prophet; From: Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry
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Chapter 13
A Multifaceted Machine Learning Approach to Understand Road Accident Dynamics Using Twitter Data
LSTM is a Recurrent Neural Network (RNN) architecture specifically designed to handle sequential data with long-term dependencies. RNNs are neural networks that process sequential data, such as time series or natural language, by passing information from one sequence step to the next through hidden states.
Published in Chapter: A Multifaceted Machine Learning Approach to Understand Road Accident Dynamics Using Twitter Data; From: Handbook of Research on Advancements of Contactless Technology and Service Innovation in Library and Information Science
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Chapter 18
NLP for Clinical Data Analysis: Handling the Unstructured Clinical Information
Long short-term memory (LSTM) is artificial recurrent neural network (ARNN) which finds utility in the field of deep learning.
Published in Chapter: NLP for Clinical Data Analysis: Handling the Unstructured Clinical Information; From: Handbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering
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Chapter 9
Predicting the Future Research Gaps Using Hybrid Approach: Machine Learning and Ontology - A Case Study on Biodiversity
LSTM networks are a form of a recurrent neural network capable of learning order dependency on sequence prediction problems. This behavior is required in complex problem domains such as machine translation, speech recognition, and more.
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 5
Predictions For COVID-19 With Deep Learning Models of Long Short-Term Memory (LSTM)
Long short-term memory is an artificial recurrent neural network (RNN) architecture used in deep learning. LSTM has feedback connections so that it’s useful for different handle types of time series data. LSTM unit is composed of a cell, an input gate, an output gate, and a forget gate.
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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