Since many different emotional expressions can be found in artistic and design compositions, it can be challenging to effectively extract and analyze emotional information from such a wide range of artworks. This study uses deep learning approaches to ...

Extracting surfaces from 3D point clouds is significant in reconstructing and transforming these discrete points into their corresponding models. Scanned point clouds are often accompanied by noise, and the existing methods mainly rely on local ...

• A normal estimation method based on neighborhood information reconstruction is proposed to provide reliable feature information for subsequent segmentation.
• A subdivision strategy is designed to extend the DBSCAN clustering algorithm ...

Explainable clustering provides human-understandable reasons for decisions in black-box learning models. In a previous work, a decision tree built on the set of dimensions was used to define ranges of values for k-means clusters. For explainable ...

• A general method for explainable clustering of high-dimensional data.
• A fixed-parameter algorithms for explainable graph clustering.
• A Massively Parallel Computation (MPC) algorithm for explainable clustering.
• An approximation ...

Density-Based Spatial Clustering of Applications with Noise (DBSCAN) identifies high-density connected areas as clusters, so that it has advantages in discovering arbitrary-shaped clusters. However, it has difficulty in adjusting parameters and ...

An approach to speed up the DBSCAN algorithm is suggested. The planar clusters to be revealed are assumed to be tightly packed and correlated constituting, thus, a serpentine dataset developing rightwards or leftwards as time goes on. The ...

WiFi-based indoor localization has gained widespread attention in the recent past with the ubiquitous deployment of WLAN. However, for sustainable performance, it is crucial to identify and maintain the important WiFi Access Points (APs). ...

• A region-wise indoor localization system is designed for user localization.
• Works on two granularities- sub-region in building and exact location in sub-region.
• Adopts unsupervised learning to determine optimal number and size of ...

Federated learning (FL) is susceptible to backdoor attacks, where malicious model updates are covertly inserted into the model’s aggregation process, which can result in inaccurate predictions for certain inputs, compromising the integrity of FL. ...

Outlier reduction is crucial in computer science for improving data quality, analysis accuracy, and modeling robustness. Selection and modification of DBSCAN parameters are essential for optimal clustering accuracy and outlier detection. We developed an ...

DBSCAN is a widely used clustering algorithm based on density metrics that can efficiently identify clusters with uniform density. However, if the densities of different clusters are varying, the corresponding clustering results may be not good. ...

• We present an improved breast ultrasound image denoising algorithm combining multi-filter DFrFT (Discrete fractional Fourier Transform) and the adaptive fast BM3D (Block matching and 3D collaborative filtering) method. It includes three ...

Improving the quality of breast ultrasound images is of great significance for clinical diagnosis which can greatly boost the diagnostic accuracy of ultrasonography. However, due to the influence of ultrasound imaging principles and acquisition ...

Cardiac arrhythmias such as atrial fibrillation (AF) are recognised to be associated with re-entry or rotors. A rotor is a wave of excitation in the cardiac tissue that wraps around its refractory tail, causing faster-than-normal periodic ...

• DGM obtains F2-scores ≥ 0.931 for 64 rotor simulations of various complexities.
• DGM detects more rotors that are closer to the ground truth than PM.
• PM suffers from false positives especially in high meandering, fibrosis and noise ...

Clustering is an important data analytics technique and has numerous use cases. It leads to the determination of insights and knowledge which would not be readily discernible on routine examination of the data. Enhancement of clustering ...

This paper presents a clustering algorithm for non-uniformly distributed point clouds in road scenes, which is used to alleviate the performance effect of classical Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm in non-...

With the emergence of remote storage, computation facilities, and the availability of high-speed data connectivity — cloud computation has become the call for the day. In this scenario, security and computability of data have emerged as two ...

Many density-based clustering algorithms already proposed in the literature are capable of finding clusters with different shapes, sizes, and densities. Also, the noise points are detected well. However, many of these methods require input ...

From the perspective of distance optimisation, a density-based spatial clustering applied noise algorithm is proposed for clustering analysis and academic prediction of mathematical student behaviour. This algorithm improves the clustering effect and ...

Detecting and identifying manipulated portions within images poses a formidable challenge in research. Manipulated images, often created using image editing software such as Picasa or Photoshop, serve to obscure information and intentionally ...

With the rapid development and widespread adoption of wearable technology, a new type of lifelog data is being collected and used in numerous studies. We refer to these data as informative lifelog which usually contain GPS, images, videos, text, etc. GPS ...

• Developed prediction model for congenital heart disease.
• A cluster based oversampling approach has been proposed.
• Captures intricate details from the mothers’ lifestyle dataset.
• The proposed clustering-based approach gave the ...

One of the most prevalent birth disorders is congenital heart diseases (CHD). Although CHD risk factors have been the subject of numerous studies, their propensity to cause CHD has not been tested. Particularly few research has ...

In the existing statistics-based PRI estimation method, it is difficult to improve the PRI estimation accuracy due to the contradiction between the width of the statistical interval and the PRI extraction accuracy. In order to improve the accuracy of PRI ...