Export Citations
Save this search
Please login to be able to save your searches and receive alerts for new content matching your search criteria.
- research-articleJuly 2024
3D surface segmentation from point clouds via quadric fits based on DBSCAN clustering
AbstractExtracting 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 ...
Highlights- 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 ...
- research-articleJuly 2024
Explainable graph clustering via expanders in the massively parallel computation model
Information Sciences: an International Journal (ISCI), Volume 677, Issue CAug 2024https://doi.org/10.1016/j.ins.2024.120897AbstractExplainable 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 ...
Highlights
- 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 ...
- research-articleJuly 2024
GB-DBSCAN: A fast granular-ball based DBSCAN clustering algorithm
Information Sciences: an International Journal (ISCI), Volume 674, Issue CJul 2024https://doi.org/10.1016/j.ins.2024.120731AbstractDensity-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 ...
- research-articleJuly 2024
A region-wise indoor localization system based on unsupervised learning and ant colony optimization technique
Applied Soft Computing (APSC), Volume 157, Issue CMay 2024https://doi.org/10.1016/j.asoc.2024.111509AbstractWiFi-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). ...
Highlights- 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 ...
- research-articleJune 2024
FLMAAcBD: Defending against backdoors in Federated Learning via Model Anomalous Activation Behavior Detection
Knowledge-Based Systems (KNBS), Volume 289, Issue CApr 2024https://doi.org/10.1016/j.knosys.2024.111511AbstractFederated 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. ...
-
- posterMay 2024
M-DBSCAN: Modified DBSCAN Clustering Algorithm for Detecting and Controlling Outliers
- Momotaz Begum,
- Mehedi Hasan Shuvo,
- Md. Golam Mostofa,
- Abm Kamrul Islam Riad,
- Md Arabin Islam Talukder,
- Mst Shapna Akter,
- Hossain Shahriar
SAC '24: Proceedings of the 39th ACM/SIGAPP Symposium on Applied ComputingApril 2024, Pages 1034–1035https://doi.org/10.1145/3605098.3636188Outlier 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 ...
- research-articleJune 2024
MDBSCAN: A multi-density DBSCAN based on relative density
AbstractDBSCAN 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. ...
- research-articleJune 2024
Breast ultrasound image despeckling using multi-filtering DFrFT and adaptive fast BM3D
Computer Methods and Programs in Biomedicine (CBIO), Volume 246, Issue CApr 2024https://doi.org/10.1016/j.cmpb.2024.108042Highlights- 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 ...
- research-articleJuly 2024
Directed Graph Mapping exceeds Phase Mapping for the detection of simulated 2D meandering rotors in fibrotic tissue with added noise
- Sebastiaan Lootens,
- Iris Janssens,
- Robin Van Den Abeele,
- Eike M. Wülfers,
- Arthur Santos Bezerra,
- Bjorn Verstraeten,
- Sander Hendrickx,
- Arstanbek Okenov,
- Timur Nezlobinsky,
- Alexander V. Panfilov,
- Nele Vandersickel
Computers in Biology and Medicine (CBIM), Volume 171, Issue CMar 2024https://doi.org/10.1016/j.compbiomed.2024.108138AbstractCardiac 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 ...
Highlights- 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 ...
- research-articleJuly 2024
Multivariate hierarchical DBSCAN model for enhanced maritime data analytics
Data & Knowledge Engineering (DAKE), Volume 150, Issue CMar 2024https://doi.org/10.1016/j.datak.2024.102282AbstractClustering 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 ...
- research-articleJune 2024
Research on clustering of non-uniformly distributed point clouds in road scenes
ICMLC '24: Proceedings of the 2024 16th International Conference on Machine Learning and ComputingFebruary 2024, Pages 376–381https://doi.org/10.1145/3651671.3651717This 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-...
- research-articleApril 2024
Approximate DBSCAN on obfuscated data
Journal of Information Security and Applications (JISA), Volume 80, Issue CFeb 2024https://doi.org/10.1016/j.jisa.2023.103664AbstractWith 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 ...
- research-articleJanuary 2024
A dynamic density-based clustering method based on K-nearest neighbor
Knowledge and Information Systems (KAIS), Volume 66, Issue 5May 2024, Pages 3005–3031https://doi.org/10.1007/s10115-023-02038-7AbstractMany 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 ...
- research-articleJuly 2024
Modified Keypoint-Based Copy Move Area Detection
Procedia Computer Science (PROCS), Volume 235, Issue C2024, Pages 3389–3396https://doi.org/10.1016/j.procs.2024.04.319AbstractDetecting 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 ...
- research-articleMarch 2024
Research and application of the global positioning system (GPS) clustering algorithm based on multilevel functions
Journal of Computational Methods in Sciences and Engineering (JOCMSE), Volume 24, Issue 12024, Pages 357–368https://doi.org/10.3233/JCM-237061With 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 ...
- research-articleMarch 2024
A cluster-based ensemble approach for congenital heart disease prediction
Computer Methods and Programs in Biomedicine (CBIO), Volume 243, Issue CJan 2024https://doi.org/10.1016/j.cmpb.2023.107922Highlights- 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 ...
- research-articleJanuary 2024
A PRI estimation and signal deinterleaving method based on density-based clustering
International Journal of Information and Communication Technology (IJICT), Volume 24, Issue 12024, Pages 72–85https://doi.org/10.1504/ijict.2024.135307In 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 ...
- ArticleFebruary 2024
Clustered Federated Learning with Inference Hash Codes Based Local Sensitive Hashing
AbstractFederated Learning (FL) is a distributed paradigm enabling clients to train a global model collaboratively while protecting client privacy. During the FL training process, the statistical heterogeneity between different clients can compromise the ...
- research-articleDecember 2023
Detecting New Points of Interest Using Taxi GPS Data
IAIT '23: Proceedings of the 13th International Conference on Advances in Information TechnologyDecember 2023, Article No.: 40, Pages 1–7https://doi.org/10.1145/3628454.3631858Nowadays, online mapping services, such as navigation, rely significantly on points of interest (POIs) data to enhance their utility and efficiency. However, maintaining an up-to-date POI dataset is a challenging task. Recognizing this, we observed that ...
- research-articleFebruary 2024
STIOCS: Active learning-based semi-supervised training framework for IOC extraction
Computers and Electrical Engineering (CENG), Volume 112, Issue CDec 2023https://doi.org/10.1016/j.compeleceng.2023.108981AbstractCyber Threat Intelligence (CTI) contains numerous Indicators of Compromise (IOCs) and contextual information, crucial for understanding threat actors’ behavior and intentions. However, current information extraction predominantly relies on ...
Graphical abstractDisplay Omitted
Highlights- This paper proposes a semi-supervised active learning framework, STIOCS, aimed at improving the efficiency of IOC extraction in CTI and addressing the challenge of model degradation due to inadequate IOC annotation data.
- Firstly, we ...