Datasets often incorporate various functional patterns related to different aspects or regimes, which are typically not equally present throughout the dataset. We propose a novel partitioning ...

Differential privacy (DP) stands as the gold standard for protecting user information in large-scale machine learning and data analytics. A critical task within DP is partition selection—the process ...

Given an album with more than 1 song, partition the songs into an even number of sides with some sort of max capacity (maybe 22 minutes). The current algorithm is greedy and just places songs until ...

ABSTRACT: Background: Unjustified emergency department (ED) visits are a global issue, contributing to overcrowding, reduced care quality, decreased patient satisfaction, and increased healthcare ...

Section 3(k) excludes pure algorithms and business methods from patentability. Blackberry’s cases test when software inventions qualify for patents in India. Delhi High Court rulings clarify criteria ...

ABSTRACT: 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 ...

The SEO industry is undergoing a profound transformation in 2025. As large language models (LLMs) increasingly power search experiences, success now depends on withstanding traditional algorithm ...

Abstract: Graph partitioning used in many fields is an important problem in graph theory so that an efficient algorithm for graph partitioning is meaningful. But graph partitioning is a NP-complete ...