Mohammed Debakla^1,*, Ali Mezaghrani¹, Khalifa Djemal², Imane Zouaneb¹

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-21, 2026, DOI:10.32604/cmc.2025.071927 - 09 December 2025

Abstract Magnetic Resonance Imaging (MRI) has a pivotal role in medical image analysis, for its ability in supporting disease detection and diagnosis. Fuzzy C-Means (FCM) clustering is widely used for MRI segmentation due to its ability to handle image uncertainty. However, the latter still has countless limitations, including sensitivity to initialization, susceptibility to local optima, and high computational cost. To address these limitations, this study integrates Grey Wolf Optimization (GWO) with FCM to enhance cluster center selection, improving segmentation accuracy and robustness. Moreover, to further refine optimization, Fuzzy Entropy Clustering was utilized for its distinctive features… More >

Jyun-Jie Wang¹, Yin-Chen Lin¹, Chi-Chun Chen^2,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5595-5624, 2025, DOI:10.32604/cmc.2025.066160 - 23 October 2025

Abstract Digital watermarking must balance imperceptibility, robustness, complexity, and security. To address the challenge of computational efficiency in trellis-based informed embedding, we propose a modified watermarking framework that integrates fuzzy c-means (FCM) clustering into the generation off block codewords for labeling trellis arcs. The system incorporates a parallel trellis structure, controllable embedding parameters, and a novel informed embedding algorithm with reduced complexity. Two types of embedding schemes—memoryless and memory-based—are designed to flexibly trade-off between imperceptibility and robustness. Experimental results demonstrate that the proposed method outperforms existing approaches in bit error rate (BER) and computational complexity under More >

Yuhan Xia¹, Xu Li¹, Ye Liu¹, Wenbo Zhou², Yiming Tang^1,3,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 625-651, 2025, DOI:10.32604/cmc.2025.065358 - 09 June 2025

Abstract Applying domain knowledge in fuzzy clustering algorithms continuously promotes the development of clustering technology. The combination of domain knowledge and fuzzy clustering algorithms has some problems, such as initialization sensitivity and information granule weight optimization. Therefore, we propose a weighted kernel fuzzy clustering algorithm based on a relative density view (RDVWKFC). Compared with the traditional density-based methods, RDVWKFC can capture the intrinsic structure of the data more accurately, thus improving the initial quality of the clustering. By introducing a Relative Density based Knowledge Extraction Method (RDKM) and adaptive weight optimization mechanism, we effectively solve the… More >

Sanxia Shi, Yinglei Song^*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2911-2934, 2024, DOI:10.32604/cmc.2024.055363 - 18 November 2024

Abstract Infrared small target detection technology plays a pivotal role in critical military applications, including early warning systems and precision guidance for missiles and other defense mechanisms. Nevertheless, existing traditional methods face several significant challenges, including low background suppression ability, low detection rates, and high false alarm rates when identifying infrared small targets in complex environments. This paper proposes a novel infrared small target detection method based on a transformed Gaussian filter kernel and clustering approach. The method provides improved background suppression and detection accuracy compared to traditional techniques while maintaining simplicity and lower computational costs.… More >

Kusum Yadav¹, Yasser Alharbi¹, Eissa Jaber Alreshidi¹, Abdulrahman Alreshidi¹, Anuj Kumar Jain², Anurag Jain³, Kamal Kumar⁴, Sachin Sharma⁵, Brij B. Gupta^6,7,8,*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2665-2683, 2024, DOI:10.32604/cmc.2024.053565 - 18 November 2024

Abstract In today’s world, image processing techniques play a crucial role in the prognosis and diagnosis of various diseases due to the development of several precise and accurate methods for medical images. Automated analysis of medical images is essential for doctors, as manual investigation often leads to inter-observer variability. This research aims to enhance healthcare by enabling the early detection of diabetic retinopathy through an efficient image processing framework. The proposed hybridized method combines Modified Inertia Weight Particle Swarm Optimization (MIWPSO) and Fuzzy C-Means clustering (FCM) algorithms. Traditional FCM does not incorporate spatial neighborhood features, making More >

Xianghui Hu¹, Yiming Tang^2,3, Witold Pedrycz^3,4, Jiuchuan Jiang^5,*, Yichuan Jiang^1,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4917-4945, 2024, DOI:10.32604/cmc.2024.054775 - 12 September 2024

Abstract Traditional Fuzzy C-Means (FCM) and Possibilistic C-Means (PCM) clustering algorithms are data-driven, and their objective function minimization process is based on the available numeric data. Recently, knowledge hints have been introduced to form knowledge-driven clustering algorithms, which reveal a data structure that considers not only the relationships between data but also the compatibility with knowledge hints. However, these algorithms cannot produce the optimal number of clusters by the clustering algorithm itself; they require the assistance of evaluation indices. Moreover, knowledge hints are usually used as part of the data structure (directly replacing some clustering centers),… More >

Liang Zhu¹, Junyang Liu¹, Chen Hu¹, Yanli Zhi², Yupeng Liu^3,*

Energy Engineering, Vol.121, No.9, pp. 2639-2653, 2024, DOI:10.32604/ee.2024.041441 - 19 August 2024

Abstract Studying user electricity consumption behavior is crucial for understanding their power usage patterns. However, the traditional clustering methods fail to identify emerging types of electricity consumption behavior. To address this issue, this paper introduces a statistical analysis of clusters and evaluates the set of indicators for power usage patterns. The fuzzy C-means clustering algorithm is then used to analyze 6 months of electricity consumption data in 2017 from energy storage equipment, agricultural drainage irrigation, port shore power, and electric vehicles. Finally, the proposed method is validated through experiments, where the Davies-Bouldin index and profile coefficient More >

Jili Chen^1,2, Hailan Wang², Xiaolan Xie^1,2,*

Computer Systems Science and Engineering, Vol.48, No.3, pp. 645-663, 2024, DOI:10.32604/csse.2023.037957 - 20 May 2024

Abstract Fuzzy C-Means (FCM) is an effective and widely used clustering algorithm, but there are still some problems. considering the number of clusters must be determined manually, the local optimal solutions is easily influenced by the random selection of initial cluster centers, and the performance of Euclid distance in complex high-dimensional data is poor. To solve the above problems, the improved FCM clustering algorithm based on density Canopy and Manifold learning (DM-FCM) is proposed. First, a density Canopy algorithm based on improved local density is proposed to automatically deter-mine the number of clusters and initial cluster… More >

Muhammad Waqas Ahmed¹, Nouf Abdullah Almujally², Abdulwahab Alazeb³, Asaad Algarni⁴, Jeongmin Park^5,*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3315-3331, 2024, DOI:10.32604/cmc.2024.046501 - 15 May 2024

Abstract Advances in machine vision systems have revolutionized applications such as autonomous driving, robotic navigation, and augmented reality. Despite substantial progress, challenges persist, including dynamic backgrounds, occlusion, and limited labeled data. To address these challenges, we introduce a comprehensive methodology to enhance image classification and object detection accuracy. The proposed approach involves the integration of multiple methods in a complementary way. The process commences with the application of Gaussian filters to mitigate the impact of noise interference. These images are then processed for segmentation using Fuzzy C-Means segmentation in parallel with saliency mapping techniques to find… More >

Thanh-Lam Nguyen¹, Hao Kao¹, Thanh-Tuan Nguyen², Mong-Fong Horng^1,*, Chin-Shiuh Shieh^1,*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2181-2205, 2024, DOI:10.32604/cmc.2024.047387 - 27 February 2024

Abstract Since its inception, the Internet has been rapidly evolving. With the advancement of science and technology and the explosive growth of the population, the demand for the Internet has been on the rise. Many applications in education, healthcare, entertainment, science, and more are being increasingly deployed based on the internet. Concurrently, malicious threats on the internet are on the rise as well. Distributed Denial of Service (DDoS) attacks are among the most common and dangerous threats on the internet today. The scale and complexity of DDoS attacks are constantly growing. Intrusion Detection Systems (IDS) have… More >