In safety-critical but computationally resource-constrained applications, deep learning faces
two key challenges: lack of robustness against adversarial attacks and large neural network …

Despite the impressive performance of Artificial Intelligence (AI) systems, their robustness
remains elusive and constitutes a key issue that impedes large-scale adoption. Besides …

The introduction of the Industry 4.0 and Society 5.0 concepts has been undoubtedly
challenging, and the readiness towards them could be fundamentally enhanced by strategic …

Palm oil is one of the world's highest vegetable oil producers. Providing accurate oil palm
plantation statistics is essential to supporting effective and efficient decision-making …

Experimental studies were carried out to analyze the impact of optimizers and learning rate
on the performance of deep learning-based algorithms for rock thin-section image …

Palm oil is a commodity that plays an important role in economic activity. The oil palm tree is
capable of producing palm oil and is the most widely consumed vegetable oil in the world …

Increasing the model capacity is a known approach to enhance the adversarial robustness
of deep learning networks. On the other hand, various model compression techniques …

Deep neural networks have achieved impressive performance in many applications but their
large number of parameters lead to significant computational and storage overheads …

This work evaluates the compression techniques on ConvNeXt models in image
classification tasks using the CIFAR-10 dataset. Structured pruning, unstructured pruning …

Convolutional neural networks (CNNs) achieve state-of-the-art accuracy in a variety of tasks
in computer vision and beyond. One of the major obstacles hindering the ubiquitous use of …