BACKGROUND: Mental stress-induced myocardial ischemia is often clinically silent and associated with increased cardiovascular risk, particularly in women. Conventional ECG-based detection is limited, ...
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 ...
ABSTRACT: This paper proposes a structured data prediction method based on Large Language Models with In-Context Learning (LLM-ICL). The method designs sample selection strategies to choose samples ...
Learn the Adagrad optimization algorithm, how it works, and how to implement it from scratch in Python for machine learning models. #Adagrad #Optimization #Python Trump administration looking to sell ...
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 ...
Embedding-based search outperforms traditional keyword-based methods across various domains by capturing semantic similarity using dense vector representations and approximate nearest neighbor (ANN) ...
It's quick and easy to access Live Science Plus, simply enter your email below. We'll send you a confirmation and sign you up for our daily newsletter, keeping you up to date with the latest science ...
Abstract: This study is intended to provide a novel random forest-based strategy for software bug detection and contrast its efficiency to the conventional K-Nearest Neighbor (KNN) technique. The ...
This project demonstrates how to implement the K-Nearest Neighbors (KNN) algorithm for classification on a customer dataset. The program iterates through different values of k (number of neighbors) ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results