[HTML][HTML] A systematic review of data fusion techniques for optimized structural health monitoring
Advancements in structural health monitoring (SHM) techniques have spiked in the past few
decades due to the rapid evolution of novel sensing and data transfer technologies. This …
decades due to the rapid evolution of novel sensing and data transfer technologies. This …
A hybrid deep learning model for brain tumour classification
A brain tumour is one of the major reasons for death in humans, and it is the tenth most
common type of tumour that affects people of all ages. However, if detected early, it is one of …
common type of tumour that affects people of all ages. However, if detected early, it is one of …
Finger pinching and imagination classification: A fusion of CNN architectures for IoMT-enabled BCI applications
Abstract A Brain–Computer Interface (BCI), integrated with the Internet of Medical Things
(IoMT) and based on electroencephalogram (EEG) technology, allows users to control …
(IoMT) and based on electroencephalogram (EEG) technology, allows users to control …
Pneumonia detection on chest X-ray images using ensemble of deep convolutional neural networks
Pneumonia is a life-threatening lung infection resulting from several different viral infections.
Identifying and treating pneumonia on chest X-ray images can be difficult due to its similarity …
Identifying and treating pneumonia on chest X-ray images can be difficult due to its similarity …
A novel chaos-based privacy-preserving deep learning model for cancer diagnosis
Early cancer identification is regarded as a challenging problem in cancer prevention for the
healthcare community. In addition, ensuring privacy-preserving healthcare data becomes …
healthcare community. In addition, ensuring privacy-preserving healthcare data becomes …
A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods
The comprehensive integration of machine learning healthcare models within clinical
practice remains suboptimal, notwithstanding the proliferation of high-performing solutions …
practice remains suboptimal, notwithstanding the proliferation of high-performing solutions …
Early detection of red palm weevil infestations using deep learning classification of acoustic signals
Abstract The Red Palm Weevil (RPW), also known as the palm weevil, is considered among
the world's most damaging insect pests of palms. Current detection techniques include the …
the world's most damaging insect pests of palms. Current detection techniques include the …
Conflicting evidence fusion using a correlation coefficient-based approach in complex network
Y Tang, G Dai, Y Zhou, Y Huang, D Zhou - Chaos, Solitons & Fractals, 2023 - Elsevier
Dempster–Shafer evidence theory (D–S theory) can effectively deal with uncertain
information and it is one of the effective data fusion methods. However, Dempster's …
information and it is one of the effective data fusion methods. However, Dempster's …
A novel detection and multi-classification approach for IoT-malware using random forest voting of fine-tuning convolutional neural networks
The Internet of Things (IoT) is prone to malware assaults due to its simple installation and
autonomous operating qualities. IoT devices have become the most tempting targets of …
autonomous operating qualities. IoT devices have become the most tempting targets of …
[HTML][HTML] TNN-IDS: Transformer neural network-based intrusion detection system for MQTT-enabled IoT Networks
Abstract The Internet of Things (IoT) is a global network that connects a large number of
smart devices. MQTT is a de facto standard, lightweight, and reliable protocol for machine-to …
smart devices. MQTT is a de facto standard, lightweight, and reliable protocol for machine-to …