Fuzzy C-means Research Articles

Efficient Liver Segmentation using Advanced 3D-DCNN Algorithm on CT Images

According to the latest global cancer statistics for 2022, liver cancer ranks as the ninth most common disease in women. Segmenting the liver and distinguishing it from tumors within it pose a significant challenge due to the complex nature of liver imaging. Common imaging methods such as Magnetic Resonance Imaging (MRI), Computer Tomography (CT), and Ultrasound (US) are employed to distinguish liver tissue from liver tumors after collecting a sample. Attempting to partition the liver and tumor based on grayscale shades or shapes in abdominal CT images is not ideal because of the overlapping intensity levels and the variability in the location and shape of soft tissues. To address this issue, this study introduces an effective method for liver image segmentation using a 3D deep Convolutional Neural Network (3D-DCNN). The process involves several stages. First, liver images undergo preprocessing to enhance image quality, including median filtering, adaptive filtering, and converting them to grayscale. The feature extraction phase focuses on extracting four sets of features, such as the Local Binary Pattern (LBP) and the Gray-Level Co-occurrence Matrix (GLCM). Additionally, an Iterative Region Growing (IRG) technique is developed to improve the Dice Similarity Coefficient (DSC) prediction by enhancing the quality of the input images obtained from segmented images. This method enables the segmentation of the liver in abdominal CT image volumes and can subsequently be used to segment liver tumor images to evaluate the performance of the proposed 3D-DLNN approach. This method was implemented in MATLAB, and its performance was evaluated using various metrics. In experimental analysis, the proposed technique outperformed other methods, including Jaccard with JISTS-FCM, Fuzzy C-Means (FCM), and FCM with Cluster Size Adjustment (FCM-CSA).

Data-Driven Clustering of Plantar Thermal Patterns in Healthy Individuals: An Insole-Based Approach to Foot Health Monitoring

Monitoring plantar foot temperatures is essential for assessing foot health, particularly in individuals with diabetes at increased risk of complications. Traditional thermographic imaging measures foot temperatures in unshod individuals lying down, which may not reflect thermal characteristics of feet in shod, active, real-world conditions. These controlled settings limit understanding of dynamic foot temperatures during daily activities. Recent advancements in wearable technology, such as insole-based sensors, overcome these limitations by enabling continuous temperature monitoring. This study leverages a data-driven clustering approach, independent of pre-selected foot regions or models like the angiosome concept, to explore normative thermal patterns in shod feet with insole-based sensors. Data were collected from 27 healthy participants using insoles embedded with 21 temperature sensors. The data were analysed using clustering algorithms, including k-means, fuzzy c-means, OPTICS, and hierarchical clustering. The clustering algorithms showed a high degree of similarity, with variations primarily influenced by clustering granularity. Six primary thermal patterns were identified, with the “butterfly pattern” (elevated medial arch temperatures) predominant, representing 51.5% of the dataset, aligning with findings in thermographic studies. Other patterns, like the “medial arch + metatarsal area” pattern, were also observed, highlighting diverse yet consistent thermal distributions. This study shows that while normative thermal patterns observed in thermographic imaging are reflected in insole data, the temperature distribution within the shoe may better represent foot behaviour during everyday activities, particularly when enclosed in a shoe. Unlike thermal imaging, the proposed in-shoe system offers the potential to capture dynamic thermal variations during ambulatory activities, enabling richer insights into foot health in real-world conditions.

Determination of biomass energy potential based on regional characteristics using adaptive clustering method

Determining the energy potential of biomass is the first step in selecting the most suitable and efficient energy conversion technology based on regional characteristics. The approach to estimating and determining biomass potential generally uses geospatial technology related to collecting and processing data about mapping an area. Unfortunately, this method is inadequate for simulating the interaction between variables, nor can it provide accurate predictions for the biomass supply chain. As a result, the results obtained from this method tend to be biased and macro, particularly in regions experiencing rapid land-use development. In this paper, the author has developed a clustering methodology with a fuzzy c-means (FCM) algorithm to determine biomass energy potential based on regional characteristics to produce data clusters with high accuracy. Grouping the characteristics of clustering-based areas involves grouping physical or abstract objects into classes or similar objects.

Automatic Identification of Rock Discontinuity Sets by a Fuzzy C-Means Clustering Method Based on Artificial Bee Colony Algorithm

The identification and classification of rock discontinuities are crucial for studying rock mechanical properties and rock engineering optimization design and safety assessment. An improved artificial bee colony (ABC) algorithm is proposed and combined with the fuzzy C-means (FCM) clustering method to develop an FCM clustering method for automatically identifying rock discontinuity sets based on the ABC algorithm (FCM-ABC method). All the equations of the method are fully developed, and the methodology is presented in its entirety. Moreover, the rock structural planes are investigated in a gold mine in China using a ShapeMetriX 3D system. Based on the measured structural plane data, the specific calculation process, selection of parameters, effectiveness of grouping, and the dominant orientation of the proposed method for structural plane occurrence classification are analyzed and discussed, and satisfactory clustering results are achieved. This validates the validity and reliability of the method. Furthermore, multiple aspects of the excellent performance of this method for the identification of structural plane sets compared to traditional clustering methods are demonstrated. In addition, the significance of structural plane identification in the prevention and control of rock engineering disasters is discussed. This new method theoretically expands the technology of rock mass structural plane identification and has important application value in practical engineering.

Unconstrained Fuzzy C-Means Based on Entropy Regularization: An Equivalent Model

Unconstrained Fuzzy C-Means Based on Entropy Regularization: An Equivalent Model

Deep learning-based robust brain tumor detection via fuzzy C-means and LSTM networks

Deep learning-based robust brain tumor detection via fuzzy C-means and LSTM networks

Density based spatial clustering of applications with noise and fuzzy C-means algorithms for unsupervised mineral prospectivity mapping

Density based spatial clustering of applications with noise and fuzzy C-means algorithms for unsupervised mineral prospectivity mapping

Utilizing linear and nonlinear ultrasound for improved estimation of concrete strength

ABSTRACT This study investigated non-destructive concrete strength evaluation using in-situ cored samples. Data from various mix proportions and design strengths revealed inconsistent correlations between compressive strength and linear ultrasonic wave velocity (V p). This inconsistency arises because V p captures only linear elastic behaviour, whereas concrete strength reflects both linear and nonlinear behaviours. In contrast, the acoustic nonlinearity parameter (β re) exhibits superior sensitivity to microstructural conditions owing to its direct link to material nonlinearity. We proposed a new integrity index (M/β*) by combining the constrained modulus (M) derived from V p with β re to capture the entire elastic behaviour. This index demonstrated statistically significant correlation with the factor of safety (FS) across different design strengths and mix proportions. Next, we developed a risk stratification system based on M/β* using fuzzy c-means clustering, categorising concrete into three risk zones: critical (M/β* <40), moderate (40 < M/β* <75) and low (M/β* >75), providing a quantitative tool for assessing concrete quality. The results highlight the importance of integrating linear and nonlinear ultrasonic parameters for accurate strength prediction, particularly in field conditions where traditional V p–strength correlations are unreliable. This approach offers a new method for non-destructive concrete strength detection, thereby enhancing structural safety assessments.

Classification of human monkeypox with the Fuzzy C-Means Algorithm using image processing methods and Haralick texture parameters

Aim: Human monkeypox can cause skin lesions in the form of blisters of different shapes on various parts of the body. Due to the fact that the skin lesions caused by human monkeypox have a very similar appearance to lesions caused by chickenpox and measles, the study includes images of chickenpox and measles as well as images of human monkeypox. The aim of this study is to distinguish human monkeypox virus skin lesion images from other viral diseases with similar images. Methods: For this study, the Monkeypox Skin Lesion Dataset, which consists of binary classification data for monkeypox and non-monkeypox (chickenpox, measles) skin lesions, is accessed from the Kaggle.com website. In total, 228 images are processed, with 101 images in the monkeypox group and 127 images in the non-monkeypox group. The images in the Monkeypox Skin Lesion Dataset are processed using image analysis methods and Haralick texture parameters are calculated to create 13 different features for each image. For the classification process in the statistical analysis part of the study, Fuzzy C-Means algorithm is used. Results: The images used in the study belong to individuals with varying skin tones and from different parts of the body, and the algorithm provides encouraging results in determining the type of skin lesions in the images. The overall classification accuracy rate is 61.8%, and the highest accuracy (76.2%) is achieved in the monkeypox class. Conclusion: This study demonstrates that images of viral diseases with similar skin lesions can be classified using various image-processing techniques and different statistical methods.

Harnessing Spectral Libraries From AVIRIS-NG Data for Precise PFT Classification: A Deep Learning Approach.

The generation of spectral libraries using hyperspectral data allows for the capture of detailed spectral signatures, uncovering subtle variations in plant physiology, biochemistry, and growth stages, marking a significant advancement over traditional land cover classification methods. These spectral libraries enable improved forest classification accuracy and more precise differentiation of plant species and plant functional types (PFTs), thereby establishing hyperspectral sensing as a critical tool for PFT classification. This study aims to advance the classification and monitoring of PFTs in Shoolpaneshwar wildlife sanctuary, Gujarat, India using Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and machine learning techniques. A comprehensive spectral library was developed, encompassing data from 130 plant species, with a focus on their spectral features to support precise PFT classification. The spectral data were collected using AVIRIS-NG hyperspectral imaging and ASD Handheld Spectroradiometer, capturing a wide range of wavelengths (400-1600 nm) to encompass the key physiological and biochemical traits of the plants. Plant species were grouped into five distinct PFTs using Fuzzy C-means clustering. Key spectral features, including band reflectance, vegetation indices, and derivative/continuum properties, were identified through a combination of ISODATA clustering and Jeffries-Matusita (JM) distance analysis, enabling effective feature selection for classification. To assess the utility of the spectral library, three advanced machine learning classifiers-Parzen Window (PW), Gradient Boosted Machine (GBM), and Stochastic Gradient Descent (SGD)-were rigorously evaluated. The GBM classifier achieved the highest accuracy, with an overall accuracy (OAA) of 0.94 and a Kappa coefficient of 0.93 across five PFTs.

Evaluation of the Possibility of Using Fuzzy C-Means Clustering, AMMI Analysis and GGE Biplot Methods to Predict the Yield of Chickpea Genotypes Cultivated in Different Environments

The purpose of this study was to evaluate the potential of using fuzzy C-means clustering, AMMI and GGE biplot analysis methods to predict the yield of chickpea (Cicer arietinum L.) genotypes grown in various environments. The trials were conducted in the Central, Silvan and Hazro districts of Diyarbakir province and Kiziltepe district of Mardin province in the Southeastern Anatolia Region of Türkiye. During the 2016 growing season, 19 chickpea genotypes were tested across four distinct environments. Multiple location experiments were used to assess the genotypes’ performance and stability. The study employed a two-factor experimental design in randomized blocks with four replications in each environment. As a result, the genotype FLIP98-206C showed the highest performance for yield (1727.3 kg ha−1) at the Diyarbakır location among all locations. On the other hand, the Diyar-95 variety showed the lowest yield (723.5 kg ha−1) at the Hazro location among all locations. The Diyarbakir location was determined as an ideal test environment for genotype selection in fuzzy C-means clustering, AMMI and GGE biplot analysis. The Silvan region was determined as the weakest environment for this purpose. It is considered that the determination of genotypes with high yield and stability in this research, in which different analysis methods were used in combination, will contribute to agricultural production.

Long-term creep life prediction of P91 steel using domain knowledge and back propagation artificial neural network

ABSTRACT In this work, the long-term creep life of P91 steels with the limited experiment data was predicted using the domain knowledge and back propagation artificial neural networks (BP-ANN). Based on the traditional creep life models of Larson-Miller (L-M) parameter and θ projection, the domain knowledge was incorporated to expand the creep dataset. Then, the fuzzy C-means (FCM) clustering algorithm was introduced for training data collection. Consequently, the creep life prediction was conducted with the corresponding dataset and BP-ANN. The obtained results demonstrated that in contrast to the conventional prediction models of creep life, the prediction accuracy for long-term creep life of P91 steels can be improved significantly by the present model. For instance, the predicted creep lives 200,000 h exhibit the average errors of 6.0%. In addition, the present study offers a convenient tool to solve the issue of limited experiment data, particularly the long-term creep of P91 steels.

End-to-end unsupervised learning of latent-space clustering for image segmentation via fully dense-UNet and fuzzy C-means loss

End-to-end unsupervised learning of latent-space clustering for image segmentation via fully dense-UNet and fuzzy C-means loss

Application of Fuzzy C-Means and Borda in Clustering Crime–Prone Areas and Predicting Crime Rates Using Long Short Term Memory in Northern Aceh Regency

North Aceh is a district with diverse geographical conditions, ranging from vast lowland areas in the north stretching from west to east, to mountainous areas in the south. The average altitude in North Aceh is 125 meters. The district covers an area of 2,694.66 km² with a population of 614,640 people in 2022. The issue of crime in North Aceh District has caused significant discomfort among the community. According to data from the Central Bureau of Statistics (BPS) of Aceh Province, the number of criminal cases increased from 6,651 cases in 2022 to 10,137 cases in 2023. Using the Fuzzy C-Means clustering method, the data was grouped into three clusters: cluster 1 represents safe areas, cluster 2 represents moderately vulnerable areas, and cluster 3 represents vulnerable areas. For ranking using the Borda method, the Dewantara Police Sector ranked first for the physical aspect, while the Muara Batu Police Sector ranked first for the item aspect. As for predictions using the LSTM model, almost all subdistricts achieved MAPE values below 20%, indicating that the LSTM model is quite effective in predicting crime-prone areas. For example, Baktiya District recorded a MAPE value of 15.85% for the physical aspect, while the best result was achieved by Simpang Keramat District for the item aspect with a MAPE value of 0.00%. However, in Syamtalira Bayu District, the item aspect reached a MAPE value of 20.07%. Although the MAPE value for the item aspect in Syamtalira Bayu is relatively high, it is still considered acceptable as it remains below 50%.

Evaluation of Water Inrush Risk in the Fault Zone of the Coal Seam Floor in Madaotou Coal Mine, Shanxi Province, China

As coal seams are mined at greater depths, the threat of high water pressure from the confined aquifer in the floor that mining operations face has become increasingly prominent. Taking the Madaotou mine field in the Datong Coalfield as the research object, in the context of mining under pressure, for the main coal seams in the mining area, first of all, an improved evaluation method for the vulnerability of floor water inrush is adopted for hazard prediction. Secondly, numerical simulation is used to conduct a simulation analysis on the fault zones in high-risk areas. By using the fuzzy C-means clustering method (FCCM) to improve the classification method for the normalized indicators in the original variable-weight vulnerability evaluation, the risk zoning for water inrush from the coal seam floor is determined. Then, through the numerical simulation method, a simulation analysis is carried out on high-risk areas to simulate the disturbance changes of different mining methods on the fault zones so as to put forward reasonable mining methods. The results show that the classification of the variable-weight intervals of water inrush from the coal seam floor is more suitable to be classified by using fuzzy clustering, thus improving the prediction accuracy. Based on the time effect of the delayed water inrush of faults, different mining methods determine the duration of the disturbance on the fault zones. Therefore, by reducing the disturbance time on the fault zones, the risk of karst water inrush from the floor of the fault zones can be reduced. Through prediction evaluation and simulation analysis, the evaluation of the risk of water inrush in coal mines has been greatly improved, which is of great significance for ensuring the safe and efficient mining of mines.

Clustering Career Development of HR Graduates: Insights Through Locke's Value Theory

This research uses the framework of Locke's Value Theory to investigate the career development trajectories of HR Management graduates from the University of Mindanao over the past five years. Utilizing Fuzzy C-Means Clustering, five distinct career development clusters were identified, showing variations in job satisfaction, employment status, skill acquisition, and professional engagement. The sample of 16 graduates was surveyed with a standardized tracer study questionnaire administered through Google Forms, and the data were bootstrapped for analysis. The results suggest that the value alignment between people and their careers is essential to ensuring satisfaction and professional development. Recommendations aim to upgrade the training programs and support professional organization memberships with aligned educational offerings that cater to industry requirements, thus helping fulfill UNSDG goals for Quality Education and Decent Work and Economic Growth.

Assessment and application of tropical cyclone clustering in the South China Sea

Accurate classification of tropical cyclone (TC) tracks is essential for evaluating and mitigating the potential disaster risks associated with TCs. In this study, three commonly used methods (K-means, Fuzzy C-Means, and Self-Organizing Maps) are assessed for clustering historical TC tracks that originated in the South China Sea from 1949 to 2023. The results show that the K-means method performs the best, while the Fuzzy C-Means and Self-Organizing Maps methods are also viable alternatives. By applying the K-means method, the distinct characteristics of the four cluster types are investigated. Each type has different characteristics in terms of lifespan, wind speed, frequency of occurrence, Power Dissipation Index, and the spatial distribution of accumulated rainfall. The influence of El Niño-Southern Oscillation (ENSO) is evident in the patterns of TC activity. Specifically, there is a higher frequency of TC activity during La Niña years, whereas during El Niño years, the activity is reduced. This observation highlights the important role that ENSO plays in shaping the behavior of TCs and provides valuable information for predicting and preparing for these events. Understanding the unique characteristics of each cluster can help authorities and communities in the region better prepare for and respond to the potential impacts of TCs.

Active contour model based on fuzzy C-means and local pre-fitting energy for image segmentation

Active contour model based on fuzzy C-means and local pre-fitting energy for image segmentation

An Enhanced Segmentation Method Using a Fuzzy Clustering Technique for Colored Satellite Images

Aim: Satellite images are significantly more accessible to collect and include a huge amount of informative data in selected geographical areas. However, because of their vast dimensions and acquisition procedures, information extraction or segmentation is an extremely complicated procedure. So, this paper proposes a satellite image segmentation technique to extract required information that can be applied to real-time applications. Background: Satellite images are vast sources of information that help to perceive the earth’s surface and relevant changes in it. In satellite imaging, image segmentation is crucial as it leads to better classification and understanding of the data present in the considered images. Objective: This paper presents an enhanced segmentation technique based on color-based fuzzy cmean clustering (FCM) presents an improved segmentation technique based on color-based fuzzy c-mean clustering. Method: One of the popular types of soft clustering techniques that is utilized for image segmentation is fuzzy c-mean clustering. It is chosen for its robust features in data categorization. This study suggests an FCM method for segmenting colored satellite images based on clusters created using the colors red, green, and blue. Result: The performance of the proposed system is done with seven test images by comparing the segmented output of each image obtained by the popular threshold technique and the proposed methodology. Four performance metrics are employed in quantitative analysis to assess the effectiveness of the proposed method: entropy, standard deviation (SD), PIQE (Perception Image Quality Evaluator), and NIQE (Naturalness Image Quality Evaluator). A higher value of Entropy denotes better quality of images and a lower value of NIQE shows more intricate image details. In both the parameters, the images obtained by the proposed techniques showed better quality as per an increase in their entropy value and a decrease in NIQE value. Conclusion: The traditional threshold-based methods are applied to assess the performance of the proposed methodology utilizing four image-measuring parameters: entropy, NIQE, PIQE, and standard deviation. Overall, better results are obtained in all test cases using the proposed FCMbased clustering technique.Applying qualitative as well as quantitative analysis, the proposed method has compared the performance of the threshold technique with the proposed approach using seven satellite images. Experimental images taken from the public domain.

Enhanced Clustering Techniques for Marine Ad-Hoc Networks Using Dynamic PFCM

Ship Ad Hoc Networks (SANETs) are an integral part of modern maritime communication and shipping, characterized by dynamic topology and heavy traffic. Accurate node localization in SANETs is of great importance to ensure effective communication, security, and operational decisions. Traditional clustering algorithms, such as Fuzzy C-Means (FCM) and Possibilistic Fuzzy C-Means (PFCM), struggle with the dynamic and collaborative nature of SANETs, being sensitive to noise, outliers, and node distribution of rapidly changing. In this paper, a new clustering algorithm, the Dynamic Weighted Gradient-Based Possibilistic using Fuzzy C-Means (DWGB-PFCM), is specially designed to address the limitations of traditional methods in dynamic SANETs. The DWGB-PFCM contains dynamic weighted distances, flexible membership and uniqueness functions, and enhanced objective functions to improve robustness, adaptability, and efficiency of the cluster. Detailed data processing from the National Buoy Data Center (NDBC) combines spatial environmental parameters such as wind speed, atmospheric pressure, and wave characteristics to simulate real-world ocean challenges. Experimental results show that DWGB-PFCM outperforms traditional methods and separation measurements, with PFCM improving by 15.8%, decreasing by 22.2% in separation entropy, and decreasing by 32.1% in RMSE. In addition, DWGB-PFCM achieves a 15.0% improvement in computational efficiency over FCM. This research lays the foundation for further innovations in clustering algorithms designed for dynamic environments.