Skin lesion classification plays a crucial role in diagnosing various gene and related local
medical cases in the field of dermoscopy. In this paper, a new model for the classification of …

Imbalanced data with a skewed class distribution are common in many real-world
applications. Deep Belief Network (DBN) is a machine learning technique that is effective in …

Class imbalance problems, where the number of samples in each class is unequal, is
prevalent in numerous real world machine learning applications. Traditional methods which …

Federated Learning (FL) with mobile computing and the Internet of Things (IoT) is an
effective cooperative learning approach. However, several technical challenges still need to …

Since the last few decades, a class imbalance has been one of the most challenging
problems in various fields, such as data mining and machine learning. The particular state of …

Imbalanced data problem is widely present in network intrusion detection, spam filtering,
biomedical engineering, finance, science, being a challenge in many real-life data-intensive …

This chapter discusses challenges and opportunities in remote sensing big data. Three
challenges are discussed. They are data complexity, data quality, and infrastructure change …

The imbalanced data classification remains a vital problem. The key is to find such methods
that classify both the minority and majority class correctly. The paper presents the classifier …

Traditional approaches tend to cause classier bias in the imbalanced data set, resulting in
poor classification performance for minority classes. In particular, there are many …

Background: Just-In-Time (JIT) defect prediction models predict if a commit will introduce
defects in the future. DeepJIT and CC2Vec are two state-of-the-art JIT Deep Learning (DL) …