ABSTRACT: The objective of this work is to determine the true owner of a land—public or private—in the region of Kumasi (Ghana). For this purpose, we applied different machine learning methods to the ...

Abstract: The K-nearest neighbors (kNNs) algorithm, a cornerstone of supervised learning, relies on similarity measures constrained by real-number-based distance metrics. A critical limitation of ...

Machine learning algorithms are often categorized as lazy learners or eager learners based on how they learn and make predictions. Among these, the K-Nearest Neighbor (KNN) algorithm stands out as a ...

The rise of artificial intelligence (AI) deep learning algorithms is helping to accelerate brain-computer interfaces (BCIs). Published in this month’s Nature Neuroscience is new research that shows ...

In the realm of e-commerce, personalized recommendations are a crucial component in enhancing user experience and optimizing sales efficiency. To address the inherent sparsity challenge prevalent in ...

K-Nearest Neighbors (KNN) is a simple yet effective supervised machine learning algorithm used for both regression and classification tasks. The algorithm works by finding the K nearest data points in ...

The goal of a machine learning regression problem is to predict a single numeric value. For example, you might want to predict the price of a particular make and model of a used car based on its ...

Disease risk prediction is a rising challenge in the medical domain. Researchers have widely used machine learning algorithms to solve this challenge. The k-nearest neighbour (KNN) algorithm is the ...

A new artificial intelligence algorithm called MosAIc draws unexpected connections between seemingly disparate works of art: for instance, a piece of Persian glassware that resembles a naturalist’s ...

The nearest neighbor problem asks where a new point fits into an existing data set. A few researchers set out to prove that there was no universal way to solve it. Instead, they found such a way. If ...