Background A regional trial indicated that implementing at-risk asthma registers in primary care could reduce hospital ...

Using the visual programming language Pure Data, I created a tough, low-level K-Clustering algorithm. This refers to an unsupervised AI algorithm that identifies bundles of data points from one source ...

ABSTRACT: Data mining has been a popular research area for more than a decade. There are several problems associated with data mining. Among them clustering is one of the most interesting problems.

Automated apple harvesting is hindered by clustered fruits, varying illumination, and inconsistent depth perception in complex orchard environments. While deep learning models such as Faster R-CNN and ...

Abstract: Based on K-means clustering algorithm and Yolov3 model, this paper proposes a traffic improvement scheme combining congestion warning and emergency lane optimization. By collecting the key ...

ABSTRACT: As a highly contagious respiratory disease, influenza exhibits significant spatiotemporal fluctuations in incidence, posing a persistent threat to public health and placing considerable ...

Clustering is a fundamental task in data science that aims to group data based on their similarities. However, defining similarity is often ambiguous, making it challenging to determine the most ...

Abstract: The K-means is sensitive to the initial choice of cluster centers, leading to the results to be different every time. To address this, a new K-means variant based on decision values is ...