In the past decade, wired and wireless computer networks have substantially evolved
because of the rapid development of technologies such as the Internet of Things (IoT) …

Abstract k-nearest neighbors (k-NN) is a well-known classification algorithm that is widely
used in different domains. Despite its simplicity, effectiveness and robustness, k-NN is …

KNN classification is an improvisational learning mode, in which they are carried out only
when a test data is predicted that set a suitable K value and search the K nearest neighbors …

In missing data contexts, k-nearest neighbours imputation has proven beneficial since it
takes advantage of the similarity between patterns to replace missing values. When dealing …

Abstract k-NN is a widely used supervised machine learning method in different domains.
Despite its simplicity, effectiveness, and robustness, k-NN is limited to the use of the …

Semi-supervised learning (SSL) focuses on the way to improve learning efficiency through
the use of labeled and unlabeled samples concurrently. However, recent research indicates …

The problem of k-nearest neighbor (k-NN) search is fundamental in graph databases, which
has numerous real-world applications, such as bioinformatics, computer vision, and software …

Polyamorphism, the existence of multiple amorphous states in a single material, has been
observed in the glass-forming system GeO 2. This study investigates the intermediate range …

In an era where machine learning critically underpins business operations, detecting
vulnerabilities introduced by malicious code has become increasingly essential. Although …

In literature, many studies try to analyze healthcare usage and generated decision support
systems. In this paper, the aim is to generate a decision support system for insurance …