Contrastive learning (CL) has been successfully applied in Natural Language Processing (NLP) as a powerful representation method and shown promising results various downstream tasks. Recent research highlighted the importance of constructing effective contrastive samples through data augmentation. However, current augmentation methods primarily rely on random word deletion, substitution, croppi...

Delivery of justice with the help artificial intelligence is a current research interest. Machine learning natural language processing (NLP) can classify types sexual harassment experiences into quid pro quo (QPQ) and hostile work environments (HWE). However, imbalanced data are often present in classes classification on specific datasets. Data imbalance cause decrease classifier's performance ...

In this article, a Multi-Objective Memetic Algorithm (MA) for rule learning is proposed. Prediction accuracy and interpretation are two measures that conflict with each other. In this approach, we consider accuracy and interpretation of rules sets. Additionally, individual classifiers face other problems such as huge sizes, high dimensionality and imbalance classes’ distribution data sets. This...

there is no doubt that breast-feeding is the best and safest way of feeding infants. physiological weight loss occurs in the first two or three days of life, and the achievement of birth weight is expected towards the end of the first week. hypernatremic dehydration may occur in exclusively breast-fed infants if milk supply is low during these first few days. it is not because of the high sodiu...

Plasma-based semiconductor processing is highly sensitive, thus even minor changes in the procedure can have serious consequences. The monitoring and classification of these equipment anomalies be performed using fault detection (FDC). However, class imbalance process data poses a significant obstacle to introduction FDC into equipment. Overfitting occur machine learning due diversity datasets ...

Datasets are growing in size and complexity at a pace never seen before, forming ever larger datasets known as Big Data. A common problem for classification, especially Data, is that the numerous examples of different classes might not be balanced. Some decades ago, imbalanced classification was therefore introduced, to correct tendency classifiers show bias favor majority class ignore minority...