Active learning (AL) attempts to maximize a model's performance gain while annotating the
fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount …

The field of machine learning is witnessing its golden era as deep learning slowly becomes
the leader in this domain. Deep learning uses multiple layers to represent the abstractions of …

Brain diseases, including tumors and mental and neurological disorders, seriously threaten
the health and well-being of millions of people worldwide. Structural and functional …

Abstract Since 2006, deep learning (DL) has become a rapidly growing research direction,
redefining state-of-the-art performances in a wide range of areas such as object recognition …

Model learning from class imbalanced training data is a long-standing and significant
challenge for machine learning. In particular, existing deep learning methods consider …

Industrial automation effectively reduces the human effort in various activities of the industry.
In many autonomous systems, object recognition plays a vital role. Thus, finding a solution …

Data in real-world application often exhibit skewed class distribution which poses an intense
challenge for machine learning. Conventional classification algorithms are not effective in …

Class imbalance is a long-standing problem relevant to a number of real-world applications
of deep learning. Oversampling techniques, which are effective for handling class imbalance …

Motivation Computational methods for phosphorylation site prediction play important roles in
protein function studies and experimental design. Most existing methods are based on …

Most of the traditional pattern classifiers assume their input data to be well-behaved in terms
of similar underlying class distributions, balanced size of classes, the presence of a full set of …