Due to sharp increases in data dimensions, working on every data mining or machine
learning (ML) task requires more efficient techniques to get the desired results. Therefore, in …

Disease diagnosis and prediction methods in biotechnology and medicine have significantly
advanced over time. Consequently, analyzing raw gene expression is crucial for identifying …

Feature selection is an important data processing method to reduce dimension of the raw
datasets while preserving the information as much as possible. In this paper, an enhanced …

In classification, regression, and other data mining applications, feature selection (FS) is an
important pre-process step which helps avoid advert effect of noisy, misleading, and …

In recent years, multi-label learning becomes a trending topic in machine learning and data
mining. This type of learning deals with data that each instance is associated with more than …

For high-dimensional data, the traditional feature selection method is slightly inadequate. At
present, most of the existing hybrid search methods have problems of high computational …

The “curse of dimensionality” is one of the largest problems that influences the quality of the
optimization process in most data mining, pattern recognition, and machine learning tasks …

All the educational organizations mainly aim at elevating the academic performance of
students for improving the overall quality of education. In this direction, Educational Data …

Recently, the 6G‐enabled Internet of Medical Things (IoMT) has played a key role in the
development of functional health systems due to the massive data generated daily from the …

This paper presents an alternative event detection model based on the integration between
the DistilBERT and a new meta-heuristic technique named the Hunger Games Search …