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Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities
In recent years, machine learning (ML) and deep learning (DL) have been the leading
approaches to solving various challenges, such as disease predictions, drug discovery …
approaches to solving various challenges, such as disease predictions, drug discovery …
Deep learning in EEG-based BCIs: A comprehensive review of transformer models, advantages, challenges, and applications
Brain-computer interfaces (BCIs) have undergone significant advancements in recent years.
The integration of deep learning techniques, specifically transformers, has shown promising …
The integration of deep learning techniques, specifically transformers, has shown promising …
Physics-informed attention temporal convolutional network for EEG-based motor imagery classification
The brain-computer interface (BCI) is a cutting-edge technology that has the potential to
change the world. Electroencephalogram (EEG) motor imagery (MI) signal has been used …
change the world. Electroencephalogram (EEG) motor imagery (MI) signal has been used …
[HTML][HTML] Towards a blockchain-SDN-based secure architecture for cloud computing in smart industrial IoT
Some of the significant new technologies researched in recent studies include BlockChain
(BC), Software Defined Networking (SDN), and Smart Industrial Internet of Things (IIoT). All …
(BC), Software Defined Networking (SDN), and Smart Industrial Internet of Things (IIoT). All …
Tuberculosis detection in chest radiograph using convolutional neural network architecture and explainable artificial intelligence
SI Nafisah, G Muhammad - Neural Computing and Applications, 2024 - Springer
In most regions of the world, tuberculosis (TB) is classified as a malignant infectious disease
that can be fatal. Using advanced tools and technology, automatic analysis and …
that can be fatal. Using advanced tools and technology, automatic analysis and …
Medformer: A multi-granularity patching transformer for medical time-series classification
Medical time series (MedTS) data, such as Electroencephalography (EEG) and
Electrocardiography (ECG), play a crucial role in healthcare, such as diagnosing brain and …
Electrocardiography (ECG), play a crucial role in healthcare, such as diagnosing brain and …
A multi-branch convolutional neural network with squeeze-and-excitation attention blocks for EEG-based motor imagery signals classification
Electroencephalography-based motor imagery (EEG-MI) classification is a critical
component of the brain-computer interface (BCI), which enables people with physical …
component of the brain-computer interface (BCI), which enables people with physical …
Toward reliable signals decoding for electroencephalogram: A benchmark study to EEGNeX
This study examines the efficacy of various neural network (NN) models in interpreting
mental constructs via electroencephalogram (EEG) signals. Through the assessment of 16 …
mental constructs via electroencephalogram (EEG) signals. Through the assessment of 16 …
Dynamic convolution with multilevel attention for EEG-based motor imagery decoding
Brain–computer interface (BCI) is an innovative technology that utilizes artificial intelligence
(AI) and wearable electroencephalography (EEG) sensors to decode brain signals and …
(AI) and wearable electroencephalography (EEG) sensors to decode brain signals and …
Attention-inception and long-short-term memory-based electroencephalography classification for motor imagery tasks in rehabilitation
In recent years, the contributions of deep learning have had a phenomenal impact on
electroencephalography-based brain-computer interfaces. While the decoding accuracy of …
electroencephalography-based brain-computer interfaces. While the decoding accuracy of …