[HTML][HTML] Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which caused the
coronavirus disease 2019 (COVID-19) pandemic, has affected more than 400 million people …
coronavirus disease 2019 (COVID-19) pandemic, has affected more than 400 million people …
Fault tolerance structures in wireless sensor networks (WSNs): Survey, classification, and future directions
The Industrial Revolution 4.0 (IR 4.0) has drastically impacted how the world operates. The
Internet of Things (IoT), encompassed significantly by the Wireless Sensor Networks …
Internet of Things (IoT), encompassed significantly by the Wireless Sensor Networks …
An Intelligent Framework for Fault Diagnosis of Centrifugal Pump Leveraging Wavelet Coherence Analysis and Deep Learning
This paper proposes an intelligent framework for the fault diagnosis of centrifugal pumps
(CPs) based on wavelet coherence analysis (WCA) and deep learning (DL). The fault …
(CPs) based on wavelet coherence analysis (WCA) and deep learning (DL). The fault …
A fault diagnosis framework for centrifugal pumps by scalogram-based imaging and deep learning
Centrifugal pumps are the most vital part of any process industry. A fault in centrifugal pump
can affect imperative industrial processes. To ensure reliable operation of the centrifugal …
can affect imperative industrial processes. To ensure reliable operation of the centrifugal …
Wasserstein GAN-based digital twin-inspired model for early drift fault detection in wireless sensor networks
In this Internet of Things (IoT) era, the number of devices capable of sensing their
surroundings is increasing day by day. Based on the data from these devices, numerous …
surroundings is increasing day by day. Based on the data from these devices, numerous …
Discrete human activity recognition and fall detection by combining FMCW RADAR data of heterogeneous environments for independent assistive living
Human activity monitoring is essential for a variety of applications in many fields, particularly
healthcare. The goal of this research work is to develop a system that can effectively detect …
healthcare. The goal of this research work is to develop a system that can effectively detect …
Multistage centrifugal pump fault diagnosis using informative ratio principal component analysis
This study proposes a fault diagnosis method (FD) for multistage centrifugal pumps (MCP)
using informative ratio principal component analysis (Ir-PCA). To overcome the interference …
using informative ratio principal component analysis (Ir-PCA). To overcome the interference …
Portable UWB RADAR sensing system for transforming subtle chest movement into actionable micro-doppler signatures to extract respiratory rate exploiting ResNet …
Contactless or non-invasive technology for the monitoring of anomalies in an inconspicuous
and distant environment has immense significance in health-related applications, in …
and distant environment has immense significance in health-related applications, in …
Improving energy efficiency prediction under aberrant measurement using deep compensation networks: A case study of petrochemical process
Artificial intelligence-based methods have progressed rapidly to become leading tools for
energy analysis. However, information from the petrochemical processes is commonly …
energy analysis. However, information from the petrochemical processes is commonly …
Sensors faults classification and faulty signals reconstruction using deep learning
Sensor fault classification and reconstruction frameworks are crucial for the stable, safe, and
reliable operations of Structural Health Monitoring (SHM) systems. Existing literature …
reliable operations of Structural Health Monitoring (SHM) systems. Existing literature …