Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

Deep learning (DL), a branch of machine learning (ML) and artificial intelligence (AI) is
nowadays considered as a core technology of today's Fourth Industrial Revolution (4IR or …

In the last few years, the deep learning (DL) computing paradigm has been deemed the
Gold Standard in the machine learning (ML) community. Moreover, it has gradually become …

Deep Learning (DL) models proved themselves to perform extremely well on a wide variety
of learning tasks, as they can learn useful patterns from large data sets. However, purely …

With the recent advancement in digital technologies, the size of data sets has become too
large in which traditional data processing and machine learning techniques are not able to …

Deep learning (DL) is revolutionizing evidence-based decision-making techniques that can
be applied across various sectors. Specifically, it possesses the ability to utilize two or more …

Deep Learning (DL) has had an immense success in the recent past, leading to state-of-the-
art results in various domains, such as image recognition and natural language processing …

Corrupted labels and class imbalance are commonly encountered in practically collected
training data, which easily leads to over-fitting of deep neural networks (DNNs). Existing …

Deep learning (DL) is affecting each and every sphere of public and private lives and
becoming a tool for daily use. The power of DL lies in the fact that it tries to imitate the …

Deep learning has exploded in the public consciousness, primarily as predictive and
analytical products suffuse our world, in the form of numerous human-centered smart-world …