Recent Trends in Mathematics

A fractional Riemannian fuzzy c-means framework is proposed for uncertain economic forecasting on non-Euclidean data domains.

Matrix factorization and tensor decomposition techniques have emerged as fundamental tools in machine learning and data science for handling high dimensional data efficiently.

A new discrete-mathematical framework is developed for resilient network design on intuitionistic fuzzy hypergraphs, where uncertainty is explicitly represented through membership, non-membership, and hesitation degrees associated with both vertices and hyperedges.

Cloud computing underpins modern social-media platforms by providing elastic compute, storage, and data-processing pipelines capable of absorbing highly bursty workloads.

Machine learning models such as Decision Trees, Logistic Regression, and K-Nearest Neighbors are widely used for classification tasks due to their simplicity and interpretability.

The proliferation of machine learning applications in various scientific and industrial domains has given rise to an urgent need for developing principled, automated techniques for optimal architecture selection and hyperparameter tuning for machine learning models without human expert intervention.