[HTML][HTML] Deep learning for object detection and scene perception in self-driving cars: Survey, challenges, and open issues
This article presents a comprehensive survey of deep learning applications for object
detection and scene perception in autonomous vehicles. Unlike existing review papers, we …
detection and scene perception in autonomous vehicles. Unlike existing review papers, we …
An insight into crash avoidance and overtaking advice systems for autonomous vehicles: A review, challenges and solutions
Emergence of communication technologies made the automotive industries across the
globe to embrace Advanced Driver Assistance Systems (ADAS) by considerable …
globe to embrace Advanced Driver Assistance Systems (ADAS) by considerable …
Milestones in autonomous driving and intelligent vehicles: Survey of surveys
Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace
due to the convenience, safety, and economic benefits. Although a number of surveys have …
due to the convenience, safety, and economic benefits. Although a number of surveys have …
A survey of deep learning and its applications: a new paradigm to machine learning
Nowadays, deep learning is a current and a stimulating field of machine learning. Deep
learning is the most effective, supervised, time and cost efficient machine learning approach …
learning is the most effective, supervised, time and cost efficient machine learning approach …
Activation functions: Comparison of trends in practice and research for deep learning
Deep neural networks have been successfully used in diverse emerging domains to solve
real world complex problems with may more deep learning (DL) architectures, being …
real world complex problems with may more deep learning (DL) architectures, being …
Arrhythmia detection using deep convolutional neural network with long duration ECG signals
This article presents a new deep learning approach for cardiac arrhythmia (17 classes)
detection based on long-duration electrocardiography (ECG) signal analysis …
detection based on long-duration electrocardiography (ECG) signal analysis …
A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification
Ö Yildirim - Computers in biology and medicine, 2018 - Elsevier
Long-short term memory networks (LSTMs), which have recently emerged in sequential data
analysis, are the most widely used type of recurrent neural networks (RNNs) architecture …
analysis, are the most widely used type of recurrent neural networks (RNNs) architecture …
A deep bidirectional GRU network model for biometric electrocardiogram classification based on recurrent neural networks
In this paper, we propose a deep Recurrent Neural Networks (RNNs) based on Gated
Recurrent Unit (GRU) in a bidirectional manner (BGRU) for human identification from …
Recurrent Unit (GRU) in a bidirectional manner (BGRU) for human identification from …
Face recognition based on convolutional neural network
Face recognition is of great importance to real world applications such as video surveillance,
human machine interaction and security systems. As compared to traditional machine …
human machine interaction and security systems. As compared to traditional machine …
An efficient compression of ECG signals using deep convolutional autoencoders
Background and objective Advances in information technology have facilitated the retrieval
and processing of biomedical data. Especially with wearable technologies and mobile …
and processing of biomedical data. Especially with wearable technologies and mobile …