Fuzzy C-means Clustering Research Articles

Segmented MR Images by RG-FCM subjected to Non-Uniform Compression comprising Cascade of different Encoders.

The fundamental problem with the transmission and storage of medical images is their inherent redundancy and large size necessitating higher bandwidth and a significant amount of storage space. The main objective is to enhance the compression efficiency through accurate segmentation followed by non-uniform compression through a cascade of encoders. Due to a sharp growth in digital imaging data, it is highly desirable to reduce the size of medical images by a significant amount, without losing clinically important diagnostic information. The majority of the compression techniques reported in the literature use either manual or traditional segmentation techniques to extract the informative parts of the images. The methods based upon non-uniform compression require accurate extraction of the informative part of the image to achieve higher compression rate. This research proposes unsupervised machine learning modified fuzzy c-means (FCM) clustering-based segmentation for accurate extraction of informative parts of MR images. The spatial constraints of the images are extracted using an automated region-growing algorithm and incorporated into the objective function of FCM clustering (RG-FCM) to enhance the performance of the segmentation process even in the presence of noise. Further, informative and background parts are subjected to two separate series of encoders, with higher bit rates for the informative part of the image. Empirical analysis was done on the Magnetic Resonance Imaging (MRI)dataset, and experimental results indicate that the proposed technique outperforms similar existing techniques in terms of segmentation and compression metrics. This integration of different segmentation techniques exhibits improvement in Jaccard and dice indexes, and cascade of different encoders endorse the superior performance of the proposed compression technique. The proposed technique can help in achieving higher compression of medical images without compromising clinically significant information.

Early warning strategies for corporate operational risk: A study by an improved random forest algorithm using FCM clustering.

To enhance the accuracy and response speed of the risk early warning system, this study develops a novel early warning system that combines the Fuzzy C-Means (FCM) clustering algorithm and the Random Forest (RF) model. Firstly, based on operational risk theory, market risk, research and development risk, financial risk, and human resource risk are selected as the primary indicators for enterprise risk assessment. Secondly, the Criteria Importance Through Intercriteria Correlation (CRITIC) weight method is employed to determine the importance of these risk indicators, thereby enhancing the model's prediction ability and stability. Following this, the FCM clustering algorithm is utilized for pre-processing sample data to improve the efficiency and accuracy of data classification. Finally, an improved RF model is constructed by optimizing the parameters of the RF algorithm. The data selected is mainly from RESSET/DB, covering the issuance, trading, and rating data of fixed-income products such as bonds, government bonds, and corporate bonds, and provides basic information, net value, position, and performance data of funds. The experimental results show that the model achieves an F1 score of 87.26%, an accuracy of 87.95%, an Area under the Curve (AUC) of 91.20%, a precision of 89.29%, and a recall of 87.48%. They are respectively 6.45%, 4.45%, 5.09%, 4.81%, and 3.83% higher than the traditional RF model. In this study, an improved RF model based on FCM clustering is successfully constructed, and the accuracy of risk early warning models and their ability to handle complex data are significantly improved.

Multi-UAV Delivery Path Optimization Based on Fuzzy C-Means Clustering Algorithm Based on Annealing Genetic Algorithm and Improved Hopfield Neural Network

This study develops an MTSP model for multi-UAV delivery optimization from a central hub, proposing a hybrid algorithm that integrates genetic simulated annealing-enhanced clustering with an improved Hopfield neural network to minimize the total flight distance. The proposed methodology initially employs an enhanced fuzzy C-means clustering technique integrated with genetic simulated annealing (GSA) to effectively partition the MTSP formulation into multiple discrete traveling salesman problem (TSP) instances. The subsequent phase implements an enhanced Hopfield neural network (HNN) architecture incorporating three key modifications: data normalization procedures, adaptive step-size control mechanisms, and simulated annealing integration, collectively improving the TSP solution quality and computational efficiency. The proposed algorithm’s effectiveness is validated through comprehensive case studies, demonstrating significant performance improvements in the computational efficiency and solution quality compared to conventional methods. The results show that during clustering, the improved clustering algorithm is more stable in its clustering effect. With regard to path optimization, the improved neural network algorithm has a higher computational efficiency and makes it easier to obtain the global optimal solution. Compared with the genetic algorithm and ant colony algorithm, its iteration times, path length, and delivery time are reduced to varying degrees. To sum up, the hybrid optimization algorithm has obvious advantages for solving a multi-UAV collaborative distribution path optimization problem.

A Novel Fuzzy C-Means Clustering Framework for Accurate Road Crack Detection: Incorporating Pixel Augmentation and Intensity Difference Features

A Novel Fuzzy C-Means Clustering Framework for Accurate Road Crack Detection: Incorporating Pixel Augmentation and Intensity Difference Features

Lung Cancer Segmentation Using Improved Golden Eagle Optimization with Clustering Model

Introduction: In recent times, lung cancer has emerged as a widespread and concerning ailment, significantly impacting global health. Early detection is currently recognized as the most effective strategy to enhance the survival rate among cancer patients. The conventional methods for diagnosing lung cancer are time-consuming. Objectives: To develop a Computer Aided Diagnosis (CAD) based lung nodule segmentation and classification. Methods: Initially, the input lung images are pre-processed by the median filtering. Then, the lung nodules are segmented using the Enhanced Local Information Weighted Intuitionistic with Fuzzy C-means clustering) (ELWI-FCM) with Improved Golden Eagle Optimization) (IGEO) algorithm used for lesion segmentation. The IGEO is the combination of GEO and OBL (opposition based learning). Finally, the Deep Learning (DL) model DenseNet201 is utilized for classifying the lung nodules as normal and abnormal classes. Results: The experimentation is carried out on the LIDC-IDRI dataset and achieved better accuracy and dice values of 98.7% and 98.9% respectively. Conclusions: This work presents an automatic earlier detection of lung cancer thus reducing the risks of deaths.

Comparative transcriptome analysis reveals potential regulatory genes involved in the development and strength formation of maize stalks

BackgroundStalk strength is a critical trait in maize that influences plant architecture, lodging resistance and grain yield. The developmental stage of maize, spanning from the vegetative stage to the reproductive stage, is critical for determining stalk strength. However, the dynamics of the genetic control of this trait remains unclear.ResultsHere, we report a temporal resolution study of the maize stalk transcriptome in one tropical line and one non-stiff-stalk line using 53 transcriptomes collected covering V7 (seventh leaf stage) through silking stage. The time-course transcriptomes were categorized into four phases corresponding to stalk early development, stalk early elongation, stalk late elongation, and stalk maturation. Fuzzy c-means clustering and Gene Ontology (GO) analyses elucidated the chronological sequence of events that occur at four phases of stalk development. Gene Ontology analysis suggests that active cell division occurs in the stalk during Phase I. During Phase II, processes such as cell wall extension, lignin deposition, and vascular cell development are active. In Phase III, lignin metabolic process, secondary cell wall biogenesis, xylan biosynthesis process, cell wall biogenesis, and polysaccharide biosynthetic process contribute to cell wall strengthening. Defense responses, abiotic stresses, and transport of necessary nutrients or substances are active engaged during Phase IV. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the two maize lines presented significant gene expression differences in the phenylpropanoid biosynthesis pathway and the flavonoid biosynthesis pathway. Certain differentially expressed genes (DEGs) encoding transcription factors, especially those in the NAC and MYB families, may be involved in stalk development. In addition, six potential regulatory genes associated with stalk strength were identified through weighted gene co-expression network analysis (WGCNA).ConclusionThe data set provides a high temporal-resolution atlas of gene expression during maize stalk development. These phase-specific genes, differentially expressed genes, and potential regulatory genes reported in this study provide important resources for further studies to elucidate the genetic control of stalk development and stalk strength formation in maize.

Consensus methods with Nash and Kalai-Smorodinsky bargaining game for large-scale group decision-making

Consensus methods with Nash and Kalai-Smorodinsky bargaining game for large-scale group decision-making

Two‐Dimensional Joint Inversion of DC Resistivity and Magnetic Data for Archaeological Investigation

ABSTRACTMagnetic data inversion excels in identifying surface magnetic anomalies, yet it lacks crucial depth information. On the other hand, direct current resistivity (DC resistivity) measurements can provide more detailed depth information with high vertical resolution. This study focuses on the joint inversion DC resistivity and magnetic data, which combines complementary information to establish a more robust geological model. This method addresses the inconsistencies inherent in separate inversions, thereby enhancing the resolution, stability and interpretability of geological features. We develop a novel joint inversion cost function for DC resistivity and magnetic data. This cost function leverages a model parameter transformation function to bridge the significant discrepancies between resistivity and magnetic susceptibility, thereby offering practical advantages in enhancing the inversion model's accuracy. By incorporating cross‐gradient and fuzzy C‐means (FCM) clustering, we enhance the coupling between inversion parameters and further improve the efficacy of joint inversion. Theoretical and field data inversion results demonstrate that the hybrid constraints, combining cross‐gradient and FCM clustering, notably enhance the inversion performance of the magnetic susceptibility model. This method is capable of effectively recovering boundary anomalies and physical property values, which also resolves the inconsistencies that are often encountered in separate inversions.

Recognizing the Traffic State of Urban Road Networks: A Resilience-Based Data-Driven Approach

Accurate and timely traffic network state recognition is crucial in supporting intelligent transportation system (ITS) urban traffic control and guidance. Despite their significance, existing methods for traffic state recognition often fall short of practical demands owing to the dynamic nature and unpredictability of traffic flows and the high costs associated with sample processing. This paper introduces a novel resilience-based approach for classifying and identifying link-level traffic states in urban road networks by focusing on these challenges. This approach has two phases: 1) the classification phase introduces a new operational resilience index and uses a hybrid K-means++-fuzzy c-means (FCM) clustering method for traffic state labeling; and 2) the identification phase employs real-time automatic vehicle identification (AVI) data and a transformer-based model to determine current traffic conditions. A case study conducted by Shaoxing validated the effectiveness of this approach. The results show that the objective function value of the hybrid clustering method is 0.168, with a classification performance metric Xie–Beni (XB) index of 0.137 and a Davies–Bouldin index (DBI) of 12.39, indicating high-quality clustering. A comparative analysis with support vector machines, convolutional neural networks, and long short-term memory (LSTM) models revealed the superior identification performance of the transformer-based model, which achieved 93.35% accuracy (increases of 21.44%, 13.01%, and 5.89%, respectively). The proposed method offers a practical reference for real-time traffic condition monitoring from a resilience perspective in traffic management systems.

Highway Typical Scenario Operation and Maintenance Energy Demand Forecasting

Highways play a critical role in global energy transitions and climate change mitigation, making the accurate forecasting of operational energy demand essential for improving energy efficiency and promoting green energy applications. This study develops a multi-scenario energy demand forecasting model focused on five key operational contexts: service areas, tunnels, toll stations, management centers, and roadside facilities. The model integrates user characteristics, behavioral patterns, and meteorological data, employing agent-based modeling (ABM) and the fuzzy C-means (FCM) clustering algorithm to simulate and analyze energy demand. Results indicate that during major holidays, total daily electricity consumption and peak demand increase by 143.2% and 43.8%, respectively, compared to baseline conditions. Conversely, during snowfall events, total electricity consumption and peak demand decrease by 8.8% and 11.7%, respectively. These findings provide valuable data support and a scientific basis for sustainable energy management in highway operations, contributing to the broader application of green energy solutions.

Behavioral Correlation-Based Residential Space Modularization Using Design Structure Matrix and Fuzzy C-Means Clustering Algorithm

This study introduces an automated method for constructing residential functional modules from the perspective of user behavior. By integrating the design structure matrix (DSM) and Fuzzy C-Means Clustering Algorithm (FCM), this approach systematically explores architectural functional modules. The DSM is employed to statistically analyze the correlations between residential behaviors. These correlations are then processed using FCM to generate various module segmentation schemes. The optimal scheme is selected based on modularity calculations. The results demonstrate improved modularity compared to traditional room-based designs, offering a greater variety of combinations and hierarchical organization. This methodology provides architects with a novel approach to address space integration challenges.

Research on Urban Road Design Method in South China Based on Climate Zoning

The urban climate in South China is marked by high complexity and substantial precipitation, posing significant challenges to road performance. This study focuses on the importance of precise climate zoning for urban roads in South China and the application of performance grade (PG) asphalt grading technology to enhance pavement durability. Meteorological data from multiple stations across the region were analyzed to identify key climatic indicators. Using spatial interpolation methods and fuzzy c-means clustering, urban roads were classified into five distinct climate zones. Zone I has the highest temperature; Zone II experiences the lowest temperature, necessitating attention to low-temperature pavement cracking; Zone III exhibits greater temperature variability, requiring consideration of both low-temperature cracking and water stability; Zone IV demonstrates relatively stable climatic conditions; and Zone V receives the highest precipitation, demanding a focus on water stability in pavement design. Trend analysis indicates increasing precipitation across all zones except Zone II and a general rise in minimum temperatures, suggesting a diminishing influence of low-temperature conditions. By integrating the Strategic Highway Research Program temperature conversion method and PG classification technology, this study provides asphalt grade recommendations tailored to each climate zone, addressing diverse environmental challenges and optimizing pavement performance.

Combination of machine learning and data envelopment analysis to measure the efficiency of the Tax Service Office

The Tax Service Office, a division of the Directorate General of Taxes, is responsible for providing taxation services to the public and collecting taxes. Achieving tax targets efficiently while utilizing available resources is crucial. To assess the performance efficiency of decision-making units (DMUs), data envelopment analysis (DEA) is commonly employed. However, ensuring homogeneity among the DMUs is often necessary and requires the application of machine learning clustering techniques. In this study, we propose a three-stage approach: Clustering, DEA, and Regression, to measure the efficiency of all tax service office units. Real datasets from Indonesian tax service offices were used while maintaining strict confidentiality. Unlike previous studies that considered both input and output variables, we focus solely on clustering input variables, as it leads to more objective efficiency values when combining the results from each cluster. The results revealed three clusters with a silhouette score of 0.304 and Davies Bouldin Index of 1.119, demonstrating the effectiveness of fuzzy c-means clustering. Out of 352 DMUs, 225 or approximately 64% were identified as efficient using DEA calculations. We propose a regression algorithm to measure the efficiency of DMUs in new office planning, by determining the values of input and output variables. The optimization of multilayer perceptrons using genetic algorithms reduced the mean squared error by about 75.75%, from 0.0144 to 0.0035. Based on our findings, the overall performance of tax service offices in Indonesia has reached an efficiency level of 64%. These results show a significant improvement over the previous study, in which only about 18% of offices were considered efficient. The main contribution of this research is the development of a comprehensive framework for evaluating and predicting tax office efficiency, providing valuable insights for improving performance.

Fully Incomplete Information for Multiview Clustering in Postoperative Liver Tumor Diagnoses.

Multiview clustering (MVC) is a proven, effective approach to boosting the various downstream tasks given by unlabeled data. In contemporary society, domain-specific multiview data, such as multiphase postoperative liver tumor contrast-enhanced computed tomography (CECT) images, may be vulnerable to exploitation by illicit organizations or may not be comprehensively collected due to patient privacy concerns. Thus, these can be modeled as incomplete multiview clustering (IMVC) problems. Most existing IMVC methods have three issues: (1) most methods rely on paired views, which are often unavailable in clinical practice; (2) directly predicting the features of missing views may omit key features; and (3) recovered views still have subtle differences from the originals. To overcome these challenges, we proposed a novel framework named fuzzy clustering combined with information theory arithmetic based on feature reconstruction (FCITAFR). Specifically, we propose a method for reconstructing the characteristics of prevailing perspectives for each sample. Based on this, we utilized the reconstructed features to predict the missing views. Then, based on the predicted features, we used variational fuzzy c-means clustering (FCM) combined with information theory to learn the mutual information among views. The experimental results indicate the advantages of FCITAFR in comparison to state-of-the-art methods, on both in-house and external datasets, in terms of accuracy (ACC) (77.5%), normalized mutual information (NMI) (37.9%), and adjusted rand index (ARI) (29.5%).

Multicast Scaling in Heterogeneous Wireless Sensor Networks for Security and Time Efficiency

Abstract Heterogeneous wireless sensor networks (HWSNs) satisfy researchers' requirements for developing real-world solutions that handle unattended challenges. However, the primary constraint of researchers is the privacy of the sensor nodes. It safeguards the sensor nodes and extensions in the HWSNs. Therefore, it is necessary to develop secure operational systems. Multicast scaling with security and time efficiency is described in heterogeneous wireless sensor networks to maximize network performance while also successfully protecting network privacy. This study evaluates the initial security and time efficiency measures, such as execution time, transmission delay, processing delay, congestion level, and trust measure. Subsequently, the optimal location of the heterogeneous nodes is determined using sigmoid-based fuzzy c-means clustering. Finally, successful cluster routing was achieved via support-value-based particle swarm optimization. The experimental results indicate that the proposed strategy surpasses existing strategies in terms of network delivery ratio, end-to-end delay, throughput, packet delivery, and node remaining energy level.

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.

Static and Dynamic Cross-Network Functional Connectivity Shows Elevated Entropy in Schizophrenia Patients.

Schizophrenia (SZ) patientsexhibit abnormal static and dynamic functional connectivity across various brain domains. We present a novel approach based on static and dynamic inter-network connectivity entropy (ICE), which represents the entropy of a given network's connectivity to all the other brain networks. This novel approach enables the investigation of how connectivity strength is heterogeneously distributed across available targets in both SZ patients and healthy controls. We analyzed fMRI data from 151 SZ patients and 160 demographically matched healthy controls (HC). Our assessment encompassed both static and dynamic ICE, revealing significant differences in the heterogeneity of connectivity levels across available functional brain networks between SZ patients and HC. These networks are associated with subcortical (SC), auditory (AUD), sensorimotor (SM), visual (VIS), cognitive control (CC), default mode network (DMN), and cerebellar (CB) functional brain domains. Elevated ICE observed in individuals with SZ suggests that patients exhibit significantly higher randomness in the distribution of time-varying connectivity strength across functional regions from each source network, compared to HC. C-means fuzzy clustering analysis of functional ICE correlation matrices revealed that SZ patients exhibit significantly higher occupancy weights in clusters with weak, low-scale functional entropy correlation, while the control group shows greater occupancy weights in clusters with strong, large-scale functional entropy correlation. K-means clustering analysis on time-indexed ICE vectors revealed that cluster with highest ICE have higher occupancy rates in SZ patients whereas clusters characterized by lowest ICE have larger occupancy rates for control group. Furthermore, our dynamic ICE approach revealed that in HC, the brain primarily communicates through complex, less structured connectivity patterns, with occasional transitions into more focused patterns. Individuals with SZ are significantly less likely to attain these more focused and structured transient connectivity patterns. The proposed ICE measure presents a novel framework for gaining deeper insight into mechanisms of healthy and diseased brain states and represents a useful step forward in developing advanced methods to help diagnose mental health conditions.

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.

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.