Abstract: Traditional k-means clustering is widely used to analyze regional and temporal variations in time series data, such as sea levels. However, its accuracy can be affected by limitations, ...

Background Suicide rates have increased over the last couple of decades globally, particularly in the United States and among populations with lower economic status who present at safety-net ...

️ An intelligent system that takes a document and classifies different writing styles within the document using stylometric techniques.

ABSTRACT: From the perspective of student consumption behavior, a data-driven framework for screening student loan eligibility was developed using K-means clustering analysis and decision tree models.

Rocky high steep slopes are among the most dangerous disaster-causing geological bodies in large-scale engineering projects, like water conservancy and hydropower projects, railway tunnels, and metal ...

Learn the Adagrad optimization algorithm, how it works, and how to implement it from scratch in Python for machine learning models. #Adagrad #Optimization #Python Here Are the States That Won't Tax ...

Accurately identifying fracture zones and their types in strata is of great significance for enhancing oil and gas recovery efficiency. Due to its complicated geological structure and long-term ...

Trump to sign executive order allowing crypto, real estate, and private equity in 401(k) retirement plans. Fiduciaries may receive updated guidance clarifying how ERISA applies to crypto in retirement ...

Clustering is an unsupervised machine learning technique used to organize unlabeled data into groups based on similarity. This paper applies the K-means and Fuzzy C-means clustering algorithms to a ...