[HTML][HTML] A blockchain-enabled deep residual architecture for accountable, in-situ quality control in industry 4.0 with minimal latency
Real-time and vision-based quality control for industrial processes has drawn great interest
from both scientists and practitioners, particularly following the transition to Zero Defect …
from both scientists and practitioners, particularly following the transition to Zero Defect …
[HTML][HTML] A novel deep learning model to detect COVID-19 based on wavelet features extracted from Mel-scale spectrogram of patients' cough and breathing sounds
M Aly, NS Alotaibi - Informatics in Medicine Unlocked, 2022 - Elsevier
The goal of this paper is to classify the various cough and breath sounds of COVID-19
artefacts in the signals from dynamic real-life environments. The main reason for choosing …
artefacts in the signals from dynamic real-life environments. The main reason for choosing …
Resource allocation in ordinal classification problems: a prescriptive framework utilizing machine learning and mathematical programming
Ordinal classification tasks that require the allocation of limited resources are prevalent in
various real-world scenarios. Examples include assessing disease severity in the context of …
various real-world scenarios. Examples include assessing disease severity in the context of …
NSLNet: An improved deep learning model for steel surface defect classification utilizing small training datasets
Manufacturing industries contemplate integrating computer vision and artificial intelligence
into shop floor operations, such as steel surface defect identification, to realize smart …
into shop floor operations, such as steel surface defect identification, to realize smart …
[HTML][HTML] An automated CAD-to-XR framework based on generative AI and Shrinkwrap modelling for a User-Centred design approach
CAD-to-XR is the workflow to generate interactive Photorealistic Virtual Prototypes (iPVPs)
for Extended Reality (XR) apps from Computer-Aided Design (CAD) models. This process …
for Extended Reality (XR) apps from Computer-Aided Design (CAD) models. This process …
Effective Fabric Defect Detection Model for High-Resolution Images
L Li, Q Li, Z Liu, L Xue - Applied Sciences, 2023 - mdpi.com
Featured Application The research results can quickly and accurately detect defects in the
fabric production process. Abstract The generation of defects during fabric production …
fabric production process. Abstract The generation of defects during fabric production …
Convolutional-and Deep Learning-Based Techniques for Time Series Ordinal Classification
R Ayllón-Gavilán, D Guijo-Rubio… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
Time-series classification (TSC) covers the supervised learning problem where input data is
provided in the form of series of values observed through repeated measurements over time …
provided in the form of series of values observed through repeated measurements over time …
Age prediction in healthy subjects using RR intervals and heart rate variability: A pilot study based on deep learning
Autonomic cardiac regulation is affected by advancing age and can be observed by
variations in R-peak to R-peak intervals (RRIs). Heart rate variability (HRV) has been …
variations in R-peak to R-peak intervals (RRIs). Heart rate variability (HRV) has been …
Integrating machine learning in business decision making: Application and future directions
A Singh, A Dwivedi, S Dubey… - 2023 International …, 2023 - ieeexplore.ieee.org
The copious amount of data generated as a result of Industry 4.0 revolution across every
domain including Internet of things (IoT) data, cybersecurity data, business data, social data …
domain including Internet of things (IoT) data, cybersecurity data, business data, social data …
[HTML][HTML] Fusion of standard and ordinal dropout techniques to regularise deep models
Dropout is a popular regularisation tool for deep neural classifiers, but it is applied
regardless of the nature of the classification task: nominal or ordinal. Consequently, the …
regardless of the nature of the classification task: nominal or ordinal. Consequently, the …