Class imbalance is a key issue affecting the performance of computer vision applications
such as medical image analysis, objection detection and recognition, image segmentation …

Plant disease detection has played a significant role in combating plant diseases that pose a
threat to global agriculture and food security. Detecting these diseases early can help …

The global health implications of fine particulate matter (PM 2.5) underscore the imperative
need for research into its toxicity and chemical composition. In this study, zebrafish embryos …

Evaluating classification accuracy is a key component of the training and validation stages of
thematic map production, and the choice of metric has profound implications for both the …

Imbalanced learning constitutes one of the most formidable challenges within data mining
and machine learning. Despite continuous research advancement over the past decades …

Dataset imbalances pose a significant challenge to predictive modeling in both medical and
financial domains, where conventional strategies, including resampling and algorithmic …

With the continuous development of surface observation methods and technologies, we can
acquire multiple sources of data more effectively in the same geographic area. The quality …

Edge-based and IoT devices have seen phenomenal growth in recent years, driven by the
surge in demand for emerging applications that leverage machine learning models, such as …

Anomaly detection is of paramount importance in many real-world domains characterized by
evolving behavior, such as monitoring cyber-physical systems, human conditions and …

Due to disparities in area among various land cover types, class imbalance has always
existed in crop mapping research, posing uncertainties in extracting minority classes which …