2 research outputs found
A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRU
Deep learning (DL) has emerged as a powerful subset of machine learning (ML)
and artificial intelligence (AI), outperforming traditional ML methods,
especially in handling unstructured and large datasets. Its impact spans across
various domains, including speech recognition, healthcare, autonomous vehicles,
cybersecurity, predictive analytics, and more. However, the complexity and
dynamic nature of real-world problems present challenges in designing effective
deep learning models. Consequently, several deep learning models have been
developed to address different problems and applications. In this article, we
conduct a comprehensive survey of various deep learning models, including
Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs),
Generative Models, Deep Reinforcement Learning (DRL), and Deep Transfer
Learning. We examine the structure, applications, benefits, and limitations of
each model. Furthermore, we perform an analysis using three publicly available
datasets: IMDB, ARAS, and Fruit-360. We compare the performance of six renowned
deep learning models: CNN, Simple RNN, Long Short-Term Memory (LSTM),
Bidirectional LSTM, Gated Recurrent Unit (GRU), and Bidirectional GRU.Comment: 16 pages, 29 figure
A Survey on Multi-Resident Activity Recognition in Smart Environments
Human activity recognition (HAR) is a rapidly growing field that utilizes
smart devices, sensors, and algorithms to automatically classify and identify
the actions of individuals within a given environment. These systems have a
wide range of applications, including assisting with caring tasks, increasing
security, and improving energy efficiency. However, there are several
challenges that must be addressed in order to effectively utilize HAR systems
in multi-resident environments. One of the key challenges is accurately
associating sensor observations with the identities of the individuals
involved, which can be particularly difficult when residents are engaging in
complex and collaborative activities. This paper provides a brief overview of
the design and implementation of HAR systems, including a summary of the
various data collection devices and approaches used for human activity
identification. It also reviews previous research on the use of these systems
in multi-resident environments and offers conclusions on the current state of
the art in the field.Comment: 16 pages, to appear in Evolution of Information, Communication and
Computing Systems (EICCS) Book Serie