In IoT environment applications generate continuous non-stationary data streams with in-
built problems of concept drift and class imbalance which cause classifier performance …

Multi-label data classification has received much attention due to its wide range of
application domains. Unfortunately, a class imbalance problem often occurs in multi-label …

Wheat stripe rust (WSR) is a foliar disease that causes destructive damage in the wheat
production context. Accurately estimating the severity of WSR in the autumn growing stage …

The Narrative question answering (QA) problem involves generating accurate, relevant, and
human-like answers to questions based on the comprehension of a story consisting of …

The predictive performance of machine learning (ML) models is challenged when trained on
class imbalance real-world construction datasets, reducing the accuracy of relevant …

Drug addiction is a major problem in many countries, with rehabilitation and treatment clinics
playing a critical role in aiding drug addicts' recovery. Thus, the issue requires an effective …

Credit card fraud detection plays a crucial role in safeguarding the financial security of
individuals and organizations. However, imbalanced datasets pose significant challenges to …

One of the most prevalent issues in the classification field is an imbalanced dataset.
Because an instance can be assigned to more than one class label, multi-label …

The performance of semantic segmentation in remote sensing, based on deep learning
models, depends on the training data. A commonly encountered issue is the imbalanced …

In multilabel classification, each sample can be allocated to multiple class labels at the same
time. However, one of the prominent problems of multilabel classification is missing labels …