Abstract: Fuzzy clusters, where ambiguous samples belong to multiple clusters, are common in real-world applications. Analyzing such ambiguous samples in large-scale datasets is crucial for practical ...
Abstract: Dempster–Shafer (DS) evidence theory provides a powerful framework for modeling uncertainty, reasoning, and combining information from multiple sources. However, it may yield ...
A hybrid optimizer that combines information-theoretic entropy minimization, Bayesian variational updates, and hierarchical clustering to achieve ultra-fast, deterministic convergence on black-box ...
A hierarchical clustering approach to dissect behavioral symptoms in early-stage breast cancer (BC).
Autoimmune conditions and ‘breast implant illness’ in breast cancer patients with implant-based breast reconstructions. Proportions of patients with clinically meaningful symptoms by CL at Y1 (may not ...
BACKGROUND Leprosy, a neglected tropical disease caused by Mycobacterium leprae, presents significant public health challenges in Brazil due to its slow progression, dermato-neurological ...
Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Multiclass data sets and large-scale studies are increasingly common in omics sciences ...
ABSTRACT: Domaining is a crucial process in geostatistics, particularly when significant spatial variations are observed within a site, as these variations can significantly affect the outcomes of ...
ABSTRACT: The use of machine learning algorithms to identify characteristics in Distributed Denial of Service (DDoS) attacks has emerged as a powerful approach in cybersecurity. DDoS attacks, which ...
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