Pasi Fränti^1,*, Claude Cariou², Qinpei Zhao³

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4687-4704, 2025, DOI:10.32604/cmc.2025.066534 - 23 October 2025

Abstract K-means uses the sum-of-squared error as the objective function to minimize within-cluster distances. We show that, as a consequence, it also maximizes between-cluster variances. This means that the two measures do not provide complementary information and that using only one is enough. Based on this property, we propose a new objective function called cluster overlap, which is measured intuitively as the proportion of points shared between the clusters. We adopt the new function within k-means and present an algorithm called overlap k-means. It is an alternative way to design a k-means algorithm. A localized variant is also More >

Xuejun Guo^1,2, Yilin Fu³, Natalia Baran^4,5, Wenxue Ma^6,*

Oncology Research, Vol.33, No.11, pp. 3375-3385, 2025, DOI:10.32604/or.2025.071708 - 22 October 2025

Abstract Cluster of differentiation 47 (CD47), an immune checkpoint commonly referred to as the “don’t eat me” signal, plays a pivotal role in tumor immune evasion by inhibiting phagocytosis through interaction with signal regulatory protein alpha (SIRPα) on macrophages and dendritic cells (DCs). Although early enthusiasm drove broad clinical development, recent discontinuations of major CD47-targeted programs have prompted re-evaluation of its therapeutic potential. The purpose of this commentary is to contextualize the setbacks observed with first-generation CD47 inhibitors and to highlight strategies aimed at overcoming their limitations. Clinical challenges, including anemia, thrombocytopenia, suboptimal pharmacokinetics, and limited… More >

Ana Clara Ciglioni Salustiano^1,2, Gabriela Barbosa^1,3,4, Rodolfo Borges dos Reis^2,4, Amílcar Castro de Mattos^5,6, Athanase Billis⁶, Leonardo O. Reis^1,3,4,*

Oncology Research, Vol.33, No.11, pp. 3417-3428, 2025, DOI:10.32604/or.2025.068023 - 22 October 2025

Abstract Objective: The tumor microenvironment plays a pivotal role in prostate cancer progression and may differ across metastatic sites. This study aimed to evaluate and compare the primary and metastatic prostate adenocarcinoma tumor microenvironment. Methods: A total of 27 formalin-fixed paraffin-embedded tissue samples derived from 17 patients diagnosed with prostate adenocarcinoma, including the primary tumors, and the corresponding metastatic lymphatic and hematogenous lesions from various anatomical sites. Immunohistochemical labeling was performed using antibodies against Cluster of Differentiation 3 epsilon chain (CD3e), CD8 alpha chain (CD8a), Cluster of Differentiation 68 (CD68), Cluster of Differentiation 163 (CD163), Forkhead… More > Graphic Abstract

Zhe Liu^1,2,*, Jiahao Shi³, Dania Santina⁴, Yulong Huang¹, Nabil Mlaiki⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3531-3555, 2025, DOI:10.32604/cmes.2025.071145 - 30 September 2025

Abstract The increasing prevalence of multi-view data has made multi-view clustering a crucial technique for discovering latent structures from heterogeneous representations. However, traditional fuzzy clustering algorithms show limitations with the inherent uncertainty and imprecision of such data, as they rely on a single-dimensional membership value. To overcome these limitations, we propose an auto-weighted multi-view neutrosophic fuzzy clustering (AW-MVNFC) algorithm. Our method leverages the neutrosophic framework, an extension of fuzzy sets, to explicitly model imprecision and ambiguity through three membership degrees. The core novelty of AW-MVNFC lies in a hierarchical weighting strategy that adaptively learns the contributions More >

Naif Almusallam^1,*, Junaid Qayyum^2,3

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3653-3687, 2025, DOI:10.32604/cmc.2025.067220 - 23 September 2025

Abstract This paper proposes a novel hybrid fraud detection framework that integrates multi-stage feature selection, unsupervised clustering, and ensemble learning to improve classification performance in financial transaction monitoring systems. The framework is structured into three core layers: (1) feature selection using Recursive Feature Elimination (RFE), Principal Component Analysis (PCA), and Mutual Information (MI) to reduce dimensionality and enhance input relevance; (2) anomaly detection through unsupervised clustering using K-Means, Density-Based Spatial Clustering (DBSCAN), and Hierarchical Clustering to flag suspicious patterns in unlabeled data; and (3) final classification using a voting-based hybrid ensemble of Support Vector Machine (SVM),… More >

Asmat Ullah¹, Yar Muhammad^1,*, Bakht Zada¹, Korhan Cengiz², Nikola Ivković^3,*, Mario Konecki³, Abid Yahya⁴

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3767-3786, 2025, DOI:10.32604/cmc.2025.067145 - 23 September 2025

Abstract Low visibility conditions, particularly those caused by fog, significantly affect road safety and reduce drivers’ ability to see ahead clearly. The conventional approaches used to address this problem primarily rely on instrument-based and fixed-threshold-based theoretical frameworks, which face challenges in adaptability and demonstrate lower performance under varying environmental conditions. To overcome these challenges, we propose a real-time visibility estimation model that leverages roadside CCTV cameras to monitor and identify visibility levels under different weather conditions. The proposed method begins by identifying specific regions of interest (ROI) in the CCTV images and focuses on extracting specific… More >

Ning Tang^1,2, Wang Luo^1,2,*, Yiwei Wang^1,2, Bao Feng^1,2, Shuang Yang^1,2, Jiangtao Xu³, Daohua Zhu³, Zhechen Huang³, Wei Liang³

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 4091-4113, 2025, DOI:10.32604/cmc.2025.066241 - 23 September 2025

Abstract With the deep integration of edge computing, 5G and Artificial Intelligence of Things (AIoT) technologies, the large-scale deployment of intelligent terminal devices has given rise to data silos and privacy security challenges in sensing-computing fusion scenarios. Traditional federated learning (FL) algorithms face significant limitations in practical applications due to client drift, model bias, and resource constraints under non-independent and identically distributed (Non-IID) data, as well as the computational overhead and utility loss caused by privacy-preserving techniques. To address these issues, this paper proposes an Efficient and Privacy-enhancing Clustering Federated Learning method (FedEPC). This method introduces… More >

Sami Alrabie^#,*, Ahmed Barnawi^#

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2481-2519, 2025, DOI:10.32604/cmes.2025.067977 - 31 August 2025

Abstract In the effort to enhance cardiovascular diagnostics, deep learning-based heart sound classification presents a promising solution. This research introduces a novel preprocessing method: iterative k-means clustering combined with silhouette score analysis, aimed at downsampling. This approach ensures optimal cluster formation and improves data quality for deep learning models. The process involves applying k-means clustering to the dataset, calculating the average silhouette score for each cluster, and selecting the cluster with the highest score. We evaluated this method using 10-fold cross-validation across various transfer learning models from different families and architectures. The evaluation was conducted on… More >

Kamepalli S. L. Prasanna¹, Vijaya J², Parvathaneni Naga Srinivasu¹, Babar Shah³, Farman Ali^4,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1603-1630, 2025, DOI:10.32604/cmc.2025.068707 - 29 August 2025

Abstract Cardiovascular disease prediction is a significant area of research in healthcare management systems (HMS). We will only be able to reduce the number of deaths if we anticipate cardiac problems in advance. The existing heart disease detection systems using machine learning have not yet produced sufficient results due to the reliance on available data. We present Clustered Butterfly Optimization Techniques (RoughK-means+BOA) as a new hybrid method for predicting heart disease. This method comprises two phases: clustering data using Roughk-means (RKM) and data analysis using the butterfly optimization algorithm (BOA). The benchmark dataset from the UCI More >

Ray-I Chang¹, Chia-Hui Wang^2,*, Yen-Ting Chang¹, Lien-Chen Wei²

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 503-518, 2025, DOI:10.32604/cmc.2025.068516 - 29 August 2025

Abstract As data analysis often incurs significant communication and computational costs, these tasks are increasingly outsourced to cloud computing platforms. However, this introduces privacy concerns, as sensitive data must be transmitted to and processed by untrusted parties. To address this, fully homomorphic encryption (FHE) has emerged as a promising solution for privacy-preserving Machine-Learning-as-a-Service (MLaaS), enabling computation on encrypted data without revealing the plaintext. Nevertheless, FHE remains computationally expensive. As a result, approximate homomorphic encryption (AHE) schemes, such as CKKS, have attracted attention due to their efficiency. In our previous work, we proposed RP-OKC, a CKKS-based clustering… More >