Results 61 to 70 of about 15,127 (194)
AlgorithmsThe density-based spatial clustering of applications with noise (DBSCAN) is able to cluster arbitrarily structured datasets. However, the clustering result of this algorithm is exceptionally sensitive to the neighborhood radius (Eps) and noise points ...
Zicheng Huang +3 more
doaj +1 more source
Mapping Non‐Linear ESG Trajectories in OECD Countries: A Topological Entropy Approach
Business Strategy and the Environment, EarlyView.ABSTRACT This study examines the evolution of Environmental, Social and Governance (ESG) performance across 38 OECD countries between 2010 and 2024 using a longitudinal and multi‐dimensional analytical framework. Moving beyond static ESG benchmarking approaches, it investigates how sustainability trajectories evolve under changing institutional ...
Sana Gaied Chortane +2 more
wiley +1 more source
Enhanced Botnet Detection and Neutralization through Machine Learning: A Synergistic Analysis of Host Activity, Network Patterns with Explainable Insights [PDF]
Computer Science Journal of MoldovaBotnets continue to be one of the biggest cybersecurity risks since they provide a platform for a number of unlawful operations. The growing sophistication and stealth of contemporary botnet networks, which frequently elude conventional detection tools ...
B. Gomathy +4 more
doaj +1 more source
Enhancing DBSCAN Accuracy and Computational Efficiency Using Closest Access Point Pre-Clustering for Fingerprint-Based Localization [PDF]
Within the context of fingerprint database clustering, the density-based spatial clustering of applications with noise (DBSCAN) is notable for its robustness to outliers and ability to handle clusters of different sizes and shapes.
Prazak, Pavel +2 more
core +1 more source
Corporate Social Responsibility and Environmental Management, EarlyView.ABSTRACT Despite the growing emphasis on corporate sustainability, it remains unclear whether firms will align with common ESG standards. The Phillips–Sul convergence methodology, in combination with dynamic time‐warping clustering, was utilized to analyze the ESG trajectories of 430 S&P 500 firms from 2019 to 2024.
Abdullah Kürşat Merter +1 more
wiley +1 more source
Choosing DBSCAN parameters automatically using differential evolution [PDF]
, 2014 Over the last several years, DBSCAN (Density-Based Spatial Clusteringof Applications with Noise) has been widely applied inmany areas of science due to its simplicity, robustness againstnoise (outlier) and ability to discover clusters of arbitrary shapes.
Johansson, Ronnie, Karami, Amin
core +1 more source
NET-DBSCAN: Clustering the nodes of a dynamic linear network
, 2022 S.427-442NET-DBSCAN, a method for clustering the nodes of a linear network, whose edges may be temporarily inaccessible, is introduced. The new method extends the idea of a well-known spatial clustering method, named density-based spatial clustering of ...
Stefanakis, E.
core +1 more source
Deep Underground Science and Engineering, EarlyView.This study investigated the spatial patterns and development mechanisms of urban underground space (UUS) in Shanghai using underground points of interest data. It revealed distinct development mechanisms between the main city and suburbs, highlighting the need for differentiated UUS planning and policies to optimize underground space resources ...
Chenxiao Ma +3 more
wiley +1 more source
An Improved DBSCAN Clustering Algorithm [PDF]
, 2008 摘要: 针对 “基于密度的带有噪声的空间聚类” (DBSCAN)算法存在的不足 ,提出 “分而治之” 和 高效的并行方法对DBSCAN 算法进行改进.通过对数据进行划分,利用 “分而治之” 思想减少全 局变量 Ep s值的影响;利用并行处理方法和降维技术提高聚类效率 ,降低 DBSCAN 算法对内存 的较高要求;采用增量式处理方式解决数据对象的增加和删除对聚类的影响.结果表明:新方法 有效地解决了DBSCAN 算法存在的问题 ,其聚类效率和聚类效果明显优于传统 DBSCAN 聚类 算法 ...
肖文俊, 冯少荣, 肖荣俊
core
AI‐based localization of the epileptogenic zone using intracranial EEG
Epilepsia Open, EarlyView.Abstract Artificial intelligence (AI) is rapidly transforming our lives. Machine learning (ML) enables computers to learn from data and make decisions without explicit instructions. Deep learning (DL), a subset of ML, uses multiple layers of neural networks to recognize complex patterns in large datasets through end‐to‐end learning.
Atsuro Daida +5 more
wiley +1 more source