92 citations · 130 across the 9 of their papers we have counts for
10 papers
VGS-ATD: Robust Distributed Learning for Multi-Label Medical Image Classification Under Heterogeneous and Imbalanced Conditions
Zehui Zhao, Laith Alzubaidi, Haider A. Alwzwazy +2
In recent years, advanced deep learning architectures have shown strong performance in medical imaging tasks. However, the traditional centralized learning paradigm poses serious p…
A Review on Deep Learning Autoencoder in the Design of Next-Generation Communication Systems
Omar Alnaseri, Laith Alzubaidi, Yassine Himeur +3
Traditional mathematical models used in designing next-generation communication systems often fall short due to inherent simplifications, narrow scope, and computational limitation…
A Scalable and Generalized Deep Learning Framework for Anomaly Detection in Surveillance Videos
Sabah Abdulazeez Jebur, Khalid A. Hussein, Haider Kadhim Hoomod +3
Anomaly detection in videos is challenging due to the complexity, noise, and diverse nature of activities such as violence, shoplifting, and vandalism. While deep learning (DL) has…
Robust and Explainable Framework to Address Data Scarcity in Diagnostic Imaging
Zehui Zhao, Laith Alzubaidi, Jinglan Zhang +3
Deep learning has significantly advanced automatic medical diagnostics and released the occupation of human resources to reduce clinical pressure, yet the persistent challenge of d…
Investigating Imperceptibility of Adversarial Attacks on Tabular Data: An Empirical Analysis
Zhipeng He, Chun Ouyang, Laith Alzubaidi +2
Adversarial attacks are a potential threat to machine learning models by causing incorrect predictions through imperceptible perturbations to the input data. While these attacks ha…
Transfer or Self-Supervised? Bridging the Performance Gap in Medical Imaging
Zehui Zhao, Laith Alzubaidi, Jinglan Zhang +3
Recently, transfer learning and self-supervised learning have gained significant attention within the medical field due to their ability to mitigate the challenges posed by limited…
Physics-informed radial basis network (PIRBN): A local approximating neural network for solving nonlinear PDEs
Jinshuai Bai, Gui-Rong Liu, Ashish Gupta +3
Our recent intensive study has found that physics-informed neural networks (PINN) tend to be local approximators after training. This observation leads to this novel physics-inform…
Utilising physics-guided deep learning to overcome data scarcity
Jinshuai Bai, Laith Alzubaidi, Qingxia Wang +3
Deep learning (DL) relies heavily on data, and the quality of data influences its performance significantly. However, obtaining high-quality, well-annotated datasets can be challen…
An introduction to programming Physics-Informed Neural Network-based computational solid mechanics
Jinshuai Bai, Hyogu Jeong, C. P. Batuwatta-Gamage +6
Physics-informed neural network (PINN) has recently gained increasing interest in computational mechanics. In this work, we present a detailed introduction to programming PINN-base…
MedNet: Pre-trained Convolutional Neural Network Model for the Medical Imaging Tasks
Laith Alzubaidi, J. Santamaría, Mohamed Manoufali +6
Deep Learning (DL) requires a large amount of training data to provide quality outcomes. However, the field of medical imaging suffers from the lack of sufficient data for properly…