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Shield tunnelling presents numerous potential risks particularly in complex geological environments. In this study, we propose a novel fuzzy model for assessing the risk of tunnelling in soil-rock mixed strata. The proposed model incorporates the fuzzy setpair analysis (FSPA) method into fuzzy c-means (FCM) clustering to overcome the limitations of conventional data normalisation. Data pertaining to tunnelling machine, deformation, and vibration are employed to construct an index system using mutual information algorithms for feature selection. The intercriteria importance though intercriteria correlation is employed to weight the indicators, and the FSPA method is adopted to calculate the connection number. Subsequently, the results are classified by the FCM with a modified objective function that considers the importance of risk indicators to derive the risk level of each ring in real time. The proposed model is applied to a case study of a shield tunnelling project in Guangzhou, China. The analysis results indicate a higher risk level from Ring 1572 onwards, which necessitates a judicious regulation of the thrust force and earth pressure. This novel method provides a practical and reliable tool for guiding risk decisions during the tunnel construction. [ABSTRACT FROM AUTHOR]

This study conducted a questionnaire survey on the physical condition of high school students based on FIT. The correlation between sedentary behavior, exercise level and physical fitness was studied. In this paper, the fuzzy C-average clustering method of the BP neural network is used to make statistics and classification of students’ physical condition. This paper collates the biological quality data of college students. Then this paper makes a comprehensive classification, classification and quantitative assessment of the data. The study found that college students’ sedentary habits varied from individual and family backgrounds. Family factors influence students’ physical exercise. Physical exercise was positively correlated with sedentary behavior and body mass index in adolescents. The accuracy of clustering combined with the BP neural network can reach 94%.

Introduction: Chilling injury is one of the most common meteorological disasters affecting cucumber production. For implementing remedial measures as soon as possible to minimize production loss, a timely and precise assessment of chilling injury is crucial. Methods: To evaluate the possibility of detecting cucumber chilling injury using chlorophyll fluorescence (ChlF) technology, we investigated the continuous changes in ChlF parameters under various low-temperature conditions and created the criteria for evaluating chilling injury. The ChlF induction curves were first collected before low-temperature as unstressed samples and daily 1 to 5 days after low-temperature as chilling injury samples. Principal component analysis was employed to investigate the public information on ChlF parameters and evaluate the differences between samples with different degrees of chilling injury. The parameters (Fv/Fm, Y(NO), qP, and Fo) accounted for a large proportion in the principal components and could characterize chilling injury. Uniform manifold approximation and projection method was employed to extract new features (Feature 1, Feature 2, Feature 3, and Feature 4) from ChlF parameters for subsequent classification model. Taking four features as input, a classification model based on the Fuzzy C-means clustering algorithm was constructed in order to identify the chilling injury classes of cucumber seedlings. The cucumber seedlings with different chilling injury classes were analyzed for ChlF images, rapid light curves, and malondialdehyde content. Results and discussion: The results demonstrated that the variations in these indicators among the different chilling injury classes supported the validity of the classification model. Our findings provide a better understanding of the relationship between ChlF parameters and the impact of low-temperature treatment on cucumber seedlings. This finding offers an additional perspective that can be used to evaluate the responses and damage that plants experience under stress. [ABSTRACT FROM AUTHOR]

A wide variety of natural catastrophes are induced by coal mining, with fire hazard being one of the most significant threats to underground engineering structures. In recent years, there has been an alarming rise in mine fire accidents due to the abundance of coal deposits around the world. Underground fires and explosions have continuously been the primary reason for a significant proportion of deaths and the destruction of infrastructure over the last few decades. Underground mining fires deplete natural coal resources, have an adverse impact on the environment by releasing hazardous chemicals and greenhouse gases into the atmosphere, and cause subsidence due to coal depletion during the combustion process. This study aims to predict fire danger rating of underground mining production processes by using the application of state-of-the-art unsupervised and supervised machine learning techniques. The developed k-nearest-neighbors-based isometric feature mapping and fuzzy c-means clustering algorithm has shown its dependability and superiority with a higher accuracy and has been advantageous to the monitoring and prevention of fire danger in underground mining production processes. The proposed multi-criteria decision intelligence framework permits early fire detection, providing the emergency response team extra time to respond the critical situations in order to prevent the fire from spreading, hence promoting sustainable, green, climate-smart, environmentally friendly and safe mining engineering operations. [ABSTRACT FROM AUTHOR]

Clustering validity function is an important research direction in clustering problems. Its idea is to specify the number of data clusters in advance so as to judge the optimal partition result on data sets. Studies have shown that no clustering validity function can handle all types of data, and its performance is not consistently better than other indices. Therefore, a component-wise design method of the fuzzy C-means (FCM) clustering validity function based on CRITIC combination weighting is proposed by adopting components for evaluating clustering performance. The weighting method combines expert weighting and the coefficient of variation method (CRITIC), arranges and combines six validity components with weights, and generates 55 different fuzzy clustering validity functions. These clustering validity functions are then tested on six typical UCI data sets, and the function with the simplest structure and best classification performance is selected through comparison. Finally, it is compared with seven typical clustering validity functions and four common combination clustering validity evaluation methods on eight UCI data sets. The simulation results demonstrate that the proposed validity function can achieve better classification results and determine the optimal cluster number for different data sets. [ABSTRACT FROM AUTHOR]

The development of learning objects (LO) and e-pedagogical practices has significantly influenced and changed the performance of e-learning systems. This development promotes a genuine sharing of resources and creates new opportunities for learners to explore them easily. Therefore, the need for a system of categorization for these objects becomes mandatory. In this vein, classification theories combined with web mining techniques can highlight the performance of these LOs and make them very useful for learners. This study consists of two main phases. First, we extract metadata from learning objects, using the algorithm of Web exploration techniques such as feature selection techniques, which are mainly implemented to find the best set of features that allow us to build useful models. The key role of feature selection in learning object classification is to identify pertinent features and eliminate redundant features from an excessively dimensional dataset. Second, we identify learning objects according to a particular form of similarity using Multi-Label Classification (MLC) based on Fuzzy C-Means (FCM) algorithms. As a clustering algorithm, Fuzzy C-Means is used to perform classification accuracy according to Euclidean distance metrics as similarity measurement. Finally, to assess the effectiveness of LOs with FCM, a series of experimental studies using a real-world dataset were conducted. The findings of this study indicate that the proposed approach exceeds the traditional approach and leads to viable results. Doi: 10.28991/ESJ-2023-07-03-010 Full Text: PDF

In wind power generation system, studying the correlation characteristics between wind speed and power fluctuation can provide a good theoretical basis for wind power prediction, and has important reference value for enhancing the security and stability of power system. Aiming at the problem that power does not fluctuate linearly in real time with wind speed fluctuation, this paper has divided the wind speed and power fluctuation process by K-means clustering algorithm and fuzzy C-means clustering algorithm respectively, and obtained five different fluctuation processes. Then, the correlation characteristics of wind speed and power fluctuation under different fluctuation processes are analyzed by Pearson correlation analysis method and grey correlation analysis method. The results show that the maximum correlation coefficient is 0.492 5 and the minimum correlation coefficient is 0.331 8 in K-means clustering. In fuzzy C-means clustering, the maximum correlation coefficient is 0.486 8 and the minimum correlation coefficient is 0.329 3. Under all fluctuation processes, the grey correlation degree is around 0.5, indicating that the trend of power fluctuation is similar to that of wind speed fluctuation.

Gravity inversion requires much computation, and inversion results are often non-unique. The first problem is often due to the large number of grid cells. Edge detection method, i.e., tilt angle method of analytical signal amplitude (TAS), helps to identify the boundaries of underground geological anomalies at different depths, which can be used to optimize the grid and reduce the number of grid cells. The requirement of smooth inversion is that the boundaries of the meshing area should be continuous rather than jagged. In this paper, the optimized meshing strategy is improved, and the optimized meshing region obtained by the TAS is changed to a regular region to facilitate the smooth inversion. For the second problem, certain constraints can be used to improve the accuracy of inversion. The results of analytic signal amplitude (ASA) are used to delineate the central distribution of geological bodies. We propose a new method using the results of ASA to perform local constraints to reduce the non-uniqueness of inversion. The guided fuzzy c-means (FCM) clustering algorithm combined with priori petrophysical information is also used to reduce the non-uniqueness of gravity inversion. The OpenAcc technology is carried out to speed up the computation for parallelizing the serial program on GPU. In general, the TAS is used to reduce the number of grid cells. The local weighting and priori petrophysical constraint are used in conjunction with the FCM algorithm during the inversion, which improves the accuracy of inversion. The inversion is accelerated by the OpenAcc technology on GPU. The proposed method is validated using synthetic data, and the results show that the efficiency and accuracy of gravity inversion are greatly improved by using the proposed method.

In order to solve the defect of large error in current employment quality evaluation, an employment quality evaluation model based on grey correlation degree method and fuzzy C-means (FCM) is proposed. Firstly, it analyzes the related research work of employment quality evaluation, establishes the employment quality evaluation index system, collects the index data, and normalizes the index data; Then, the weight value of employment quality evaluation index is determined by Grey relational analysis method, and some unimportant indexes are removed; Finally, the employment quality evaluation model is established by using fuzzy cluster analysis algorithm, and compared with other employment quality evaluation models. The test results show that the employment quality evaluation accuracy of the design model exceeds 93%, the employment quality evaluation error can meet the requirements of practical application, and the employment quality evaluation effect is much better than the comparison model. The comparison test verifies the superiority of the model. [ABSTRACT FROM AUTHOR]

The business expansion installation can only simply record the most basic business information, which leads to the problems of complex power supply procedures and low efficiency. Therefore, a study on the optimal power supply parameters of the business expansion installation based on grey correlation degree and fuzzy C-means clustering algorithm is proposed. Firstly, the grey correlation degree is used to process the optimal power supply parameter data of industrial expansion and installation, and the parameters of fuzzy C-means clustering algorithm are set. On this basis, an intelligent management system for the optimal power supply process of industrial expansion and installation is constructed, and the system development conditions are set up; According to the four business links of project reserve, business acceptance, collaborative operation and performance evaluation, the customer business expansion and installation function module is constructed, so as to realize the calculation of the optimal power supply line of the business expansion and installation and complete the research on the optimal power supply parameters. The experimental results show that the output stability, output throughput performance and parameter optimization ability of this method for the line impedance characteristic control of the power supply of the industrial expansion device are good and are always on the rise. At 3 cm, the output throughput reaches 1.9%, and the parameter analysis ability can reach 350 pixels, which has certain application value. [ABSTRACT FROM AUTHOR]

Every day, the number of newly confirmed cases of coronavirus (COVID-19) rises in many countries. It is critical to adjust policies and plans in order to investigate the relationships between the distributions of the spread of this virus in other countries. During this study, the intuitionistic fuzzy c-means (IFCM) clustering method is used to compare and cluster the distributions of COVID-19 spread in 62 countries. Using the IFCM clustering algorithm, the study aims to cluster the countries that use environmental, economic, social, health, and related measurements that affect disease spread to implement policies that regulate disease spread. As a result, countries that have similar factors can take proactive measures to address the pandemic. The data are obtained for 62 countries, and six different feature variables (factors associated with the spread of COVID-19) are determined. The data are obtained for 62 countries, and six variables with different characteristics (linked to the spread of COVID-19) are identified. In this study, the IFCM clustering algorithm is used to determine the dynamic behavior of COVID-19 based on real-world data for multiple countries and Turkey around the world. Data analysis is performed through MATLAB 2018a and R programs. The clustering results revealed that the distribution of dissemination in Brazil, India, and the United States was nearly identical and distinct from that of the 59 other countries. [ABSTRACT FROM AUTHOR]

The cluster validity function is used to evaluate the quality of the cluster results, and giving the exact number of initial cluster categories will rationalize the cluster results. Most single cluster validity functions and combined cluster validity functions generally have strong subjective problems, which also increases the burden on decision analysts and have great limitations in applications. To overcome the shortcomings of these clustering validity functions and improve the accuracy of the optimal cluster category classification for the datasets, based on the clustering performance evaluation components, a validity functional component construction method based on the exponential and log form was proposed. The weighting method adopts the combination of expert empowerment and standard separation method to combine the five weights so as to obtain 52 different fuzzy clustering validity functions. Then, based on the fuzzy C-mean (FCM) clustering algorithm, the performance analysis are carried out by using multiple data sets. Experimental simulation of these functions are proceeded on six commonly used UCI datasets. A clustering validity function with the simplest structure and the best classification effect was selected by comparison. Finally, this function is compared with 8 typical single clustering validity functions and four common clustering validity combination evaluation methods on 8 UCI data sets. Through experimental simulation, the proposed validity function is compared in processing data sets, but also has strong scientific theoretical basis. Thus, the feasibility and effectiveness of the proposed clustering validity function construction method are proved. [ABSTRACT FROM AUTHOR]

A wide variety of natural catastrophes are induced by coal mining, with fire hazard being one of the most significant threats to underground engineering structures. In recent years, there has been an alarming rise in mine fire accidents due to the abundance of coal deposits around the world. Underground fires and explosions have continuously been the primary reason for a significant proportion of deaths and the destruction of infrastructure over the last few decades. Underground mining fires deplete natural coal resources, have an adverse impact on the environment by releasing hazardous chemicals and greenhouse gases into the atmosphere, and cause subsidence due to coal depletion during the combustion process. This study aims to predict fire danger rating of underground mining production processes by using the application of state-of-the-art unsupervised and supervised machine learning techniques. The developed k-nearest-neighbors-based isometric feature mapping and fuzzy c-means clustering algorithm has shown its dependability and superiority with a higher accuracy and has been advantageous to the monitoring and prevention of fire danger in underground mining production processes. The proposed multi-criteria decision intelligence framework permits early fire detection, providing the emergency response team extra time to respond the critical situations in order to prevent the fire from spreading, hence promoting sustainable, green, climate-smart, environmentally friendly and safe mining engineering operations.

Fuzzy clustering is an important research field in pattern recognition, machine learning and image processing. The fuzzy C-means (FCM) clustering algorithm is one of the most common fuzzy clustering algorithms. However, it requires a given number of clusters in advance for accurate clustering of data sets, so it is necessary to put forward a better clustering validity index to verify the clustering results. This paper presents a ratio component-wise design method of clustering validity function based on FCM clustering method. By permutation and combination of six clustering validity components representing different meanings in the form of ratio, 49 different clustering validity functions are formed. Then, these functions are verified experimentally under six kinds of UCI data sets, and a clustering validity function with the simplest structure and the best classification effect is selected by comparison. Finally, this function is compared with seven traditional clustering validity functions on eight UCI data sets. The simulation results show that the proposed validity function can better verify the classification results and determine the optimal clustering number of different data sets. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

At present, the walking and drilling operations of the mine kilometer directional drilling rig are all realized by the manual operation of drillers. The intelligence level is low. At present, there is no research on the correlation between the action type of kilometer directional drilling rig and the vibration state of the hydraulic pump station. Therefore, it is difficult to remotely identify the action type of the kilometer directional drilling rig. In order to solve the above problems, an action recognition method for mine kilometer directional drilling rig based on empirical wavelet transform (EWT) and fuzzy C-means (FCM) clustering algorithm is proposed. Firstly, the EWT method is used to analyze the frequency characteristic information of the three key parts (motor, hydraulic pump and coupling) of the hydraulic pump station when the kilometer directional drilling rig performs five different actions (the start of the kilometer directional drilling rig, the rotation of the power head without drill pipe, the rotation with drill pipe, the forward slow drilling with drill pipe and the forward fast drilling with drill pipe). The vibration signals in the most obvious direction of each vibration characteristic are selected to form the original signal group for action recognition. Secondly, the combination of EWT decomposition and correlation coefficient selection rules is used to extract eigenvectors containing drill action information in the original signal group for action recognition. The weight of different eigenvectors is confirmed. The standard recognition eigenvector is constructed. Finally, the membership degree between the action eigenvector to be identified and the five action recognition standard eigenvectors is obtained by using the FCM clustering algorithm. The intelligent recognition of the action types of the kilometer directional drilling rig is realized. Taking the ZYL-17000D type mine kilometer directional drilling rig as the research object, the reliability of the action recognition method based on EWT and FCM clustering algorithm for mine kilometer directional drilling rig is verified by experiments. The vibration data of the motor, hydraulic pump and coupling in the axial, horizontal and vertical radial directions under five actions are collected in the experiment. The results show that the empirical wavelet functions of the vibration signals of the motor, hydraulic pump and coupling of the drilling rig show different characteristics when it performs different actions. The clustering performance of the eigenvectors of the axial vibration signals of hydraulic pumps is the best. According to the difference of extracted eigenvectors under different actions, action types can be identified. The results of action recognition based on test data show that this method can effectively identify the action type of kilometer directional drilling rig, and the recognition accuracy is 96.8% when the membership degree is greater than 0.9.

Clustering is the process of grouping a set of physical or abstract objects into multiple similar objects. Fuzzy C-means (FCM) clustering is one of the most widely used clustering methods, whose main research goal is to find the optimal clustering number of data sets, which is related to whether the data can be effectively divided. The study of clustering validity function is the process of evaluating the clustering quality and determining the optimal clustering number. Based on the idea of components, six cluster performance evaluation components are proposed to define compactness, variation, similarity, overlap and separation of data sets, respectively. Then a new validity function based on FCM clustering algorithm is synthesized by these six components. Finally, the proposed validity function and eight typical validity functions are compared on five artificial data sets and eight UCI data sets. The simulation results show that the proposed clustering validity function can evaluate the clustering results more effectively and determine the optimal clustering number of different data sets. [ABSTRACT FROM AUTHOR]

Fuzzy C-means (FCM) clustering algorithm is a method mainly applied to machine learning and data mining. It can cluster objects into a limited number of categories according to their similarity degree without much prior knowledge. However, FCM clustering algorithm must first give a predefined number of clusters. Therefore, it is crucial to use clustering effectiveness function to get the optimal cluster number. Therefore, partition coefficient, partition entropy, separation index, Bensaid clustering validity function, Xie and Beni clustering validity function, Dunn clustering validity function and the improved Dunn clustering validity function were selected. Clustering experiments were conducted on three typical UCI data sets in view of FCM clustering algorithm. Finally, different fuzzy indexes are used to evaluate the validity of clustering. [ABSTRACT FROM AUTHOR]

Clustering validity function is an index used to judge the accuracy of clustering results. At present, most studies on clustering validity are based on single clustering validity function. Research shows that no clustering validity function can handle any data and always perform better than other indexes. Therefore, a hybrid weighted combination evaluation method based on fuzzy C-means (FCM) clustering validity functions was proposed. The weighting method combines expert weighting with information entropy weighting to improve the subjective factor influence of expert weighting and the shortcoming of information entropy weighting in the value judgment of each clustering validity function. Four clustering validity function combination methods of linear, exponential, logarithm and proportion was studied. Finally, the proposed fuzzy clustering validity evaluation method is verified by experiments on artificial data sets and UCI data sets. The experimental results show that the proposed fuzzy clustering validity evaluation method can overcome the shortcoming of single clustering validity function, and can get the optimal clustering number more accurately for different data sets.

Building energy data analysis is a major branch of smart city development research. The usual back propagation neural network model for building energy prediction has problems of unclear physical significance, poor data generalization and low fitting accuracy. Therefore, a composite prediction model of building power consumption based on FCM-GWO-BP neural network was proposed. According to the similar statistical distribution characteristics of data, the fuzzy C-means clustering algorithm (FCM) was used to cluster the historical power consumption data. BP neural network prediction model was established for different categories to reduce the impact of relevant noise in the sample data on the modeling accuracy. Then, according to the train and test data sets of each category, the corresponding grey wolf algorithm was established to optimize the error back propagation neural network prediction model (GWO-BP). The experimental results showed that compared with the sample prediction accuracy index root mean square percentage error (RMSPE), the GWO-BP neural network after FCM clustering was reduced by about 0.225 compared with the BP model, and was reduced by about 0.135 compared with the GWO-BP model, so its prediction accuracy was improved by 75% at most. Respectively, the mean absolute percentage error (MAPE) was reduced by 14.41% and 6.48%. It can be seen that this model has strong generalization ability, better prediction accuracy and reliability, and absolutely can meet the needs of practical engineering.

Carbon price, to a certain extent, reflects the intensity of a national emission reduction target, whereas carbon price forecasting is the basis for improving crisis management competence and strengthening market enthusiasm. This paper advances a novel hybrid carbon price forecasting methodology consisting of the empirical wavelet transform (EWT) and the gated recurrent unit (GRU) neural network. First, the carbon price data is decomposed through the EWT approach into the more stable and regular sub-components. These sub-components are divided into trend, low-frequency and high-frequency component using the fuzzy C-means clustering algorithm. Next, the lag order of different classes of components is determined as the input variables of the GRU model by the partial auto-correlation function method. Then, all values of each component predicted by the GRU method are aggregated to produce a final combined prediction result for the original carbon price. Finally, the EWT-GRU model is compared with the individual Autoregressive Integrated Moving Average (ARIMA), Back Propagation Neural Network (BPNN), GRU and EWT-BPNN models. The simulation results demonstrate that the proposed EWT-GRU combined forecasting model is superior to other models in terms of prediction effect, prediction accuracy, etc. They also confirm the validity and accuracy of the EWT-GRU model in carbon price prediction and show it deserves popularization.

Abstract In order to improve the ability of accounting data analysis and statistics, a method of constructing modern accounting data analysis platform based on industrial cloud computing is proposed. The time series model of modern accounting data analysis is constructed by using block bit sequence analysis method, and the association rule characteristic quantity of accounting data is extracted. Combined with cloud computing technology, the modern accounting data analysis platform is constructed, and the fuzzy C-means clustering algorithm is used to realize the clustering of modern accounting data, which can improve the ability of parallel computing and statistical analysis of accounting data. The simulation results show that the intelligent data analysis platform designed makes the statistical analysis ability of accounting data better and the parallel computing efficiency higher.

As a popular clustering algorithms, fuzzy c-means (FCM) algorithm has been used in various fields, including fault diagnosis, machine learning. To overcome the sensitivity to outliers problem and the local minimum problem of the fuzzy c-means new algorithm is proposed based on the simulated annealing (SA) algorithm and the genetic algorithm (GA). The combined algorithm utilizes the simulated annealing algorithm due to its local search abilities. Thereby, problems associated with the genetic algorithm, such as its tendency to prematurely select optimal values, can be overcome, and genetic algorithm can be applied in fuzzy clustering analysis. Moreover, the new algorithm can solve other problems associated with the fuzzy clustering algorithm, which include initial clustering center value sensitivity and convergence to a local minimum. Furthermore, the simulation results can be used as classification criteria for identifying several types of bearing faults. Compare with the dimensionless indexes, it shows that the mutual dimensionless indexes are more suitable for clustering algorithms. Finally, the experimental results show that the method adopted in this paper can improve the accuracy of clustering and accurately classify the bearing faults of rotating machinery.

Background & Objective: To evaluate the application value of dynamic enhanced magnetic resonance imaging (DCE-MRI) in the clinical diagnosis in hospital based on the fuzzy C-means clustering algorithm and conduct its cost-benefit analysis in practice. Methods: This study was conducted from October 11th 2020 to November 25, 2020. Eighteen patients with lung cancer confirmed by pathology were collected and examined by DCE-MRI one day before and one month after surgery using a fuzzy C-means clustering algorithm. And then, taking Guang'an District People's Hospital as an example, the present paper conducts the cost-benefit analysis with the utilization of net present value (NPV). Results: After 1-month review, eleven patients with effective treatment, four numerical Ktrans, Kep, Ve, Vp compared with preoperative decreased, which Ktrans, Kep, the change of the Ve was statistically significant (P < 0.05), the Vp of decline has no statistical significance (P > 0.05), the experimental and ineffective group after an operation, compared to four numerical Ktrans and Vp difference was statistically significant (P < 0.05), the corresponding AUC (area under the curve) to 1 and 0.818, the sensitivity of 100% and 85.7%, a specific degree of 100% and 72.7%. The NPV analysis results showed that the hospital could achieve profit and loss balance in the third year after purchasing MRI equipment and then gradually obtain positive cash inflow. Conclusion: The application of MRI in the Clinical Diagnosis helps hospital promote the therapeutic effect, thus having great practical value, and also contributes to increasing benefits in the long run. [ABSTRACT FROM AUTHOR]

In recent years, with the change of lifestyle in Europe and America, the incidence of breast cancer in Chinese women is increasing. In order to find the model of breast cancer image screening and diagnosis with higher accuracy and better classification performance, this paper mainly constructs the breast cancer CT image detection model and the breast cancer screening model based on the convolution and deconvolution neural network (CDNN) through the convolution neural network (CNN). In this paper, the fuzzy C-means clustering algorithm (FCM) is used to improve and optimize the image of breast cancer, and the experimental results are analyzed. The optimized kernel fuzzy C-means clustering algorithm was tested on a common dataset to segment the region of interest more accurately. Our experiments show that the new deep learning model of this paper improves the automatic classification performance of breast cancer. In this paper, the research results of deep learning are applied to the medical field, and a new method based on CNN model for breast cancer screening and diagnosis is proposed, which provides a new idea for improving the artificial intelligence assisted medical diagnosis method. [ABSTRACT FROM AUTHOR]

This paper addressed the robust fuzzy C-Means design for a class of clustering algorithm that are robust against both the plant parameter perturbations with nonlinearity and controller gain variations. Based on the description of Takagi–Sugeno (TS) fuzzy model, the stability and control of nonlinear systems are studied. The recently proposed integral inequality is selected based on the free weight matrix, and the minimum conservative stability criterion is given in the form of linear matrix inequality (LMI). Assuming that the controller and the system have the same premise, this method does not require the number and membership function rules. In addition, the improved control is used as the stability criterion of the closed-loop TS fuzzy system obtained from LMI in large-scale nonlinear systems, and is reorganized for machine learning. The novelty of this paper is to develop a simplified and robust controller design for a class of nonlinear perturbed systems. Moreover, the proposed control process was also ensured by the control criterion derived from the energy function for the stability of the nonlinear system. Finally, a simulation is given and demonstrated the feasibility of the practical application motivated by certain concrete-real problem in vibrated structures. [ABSTRACT FROM AUTHOR]

This paper addresses the criterion of the robust controller design for the solution of a number of fuzzy C-means clustering algorithms, which are robust to plant parameter disturbances and controller gain variations. The control and stability problems in the present nonlinear systems are studied based on a Takagi–Sugeno (T–S) fuzzy model. A lately and important proposed integral inequality is considered and selected according to the method of the free weight matrix, with these comparatively flexible stability criteria which are determined in the numerical form of linear matrix inequalities (LMIs). Under the condition of the premise in which the controller and the control system partake the same rules, the method does not inquire the same number of membership functions and mathematical rules. In addition, the improved control is used for large-scale nonlinear systems, where the stability criterion of the closed T–S fuzzy system is obtained through LMI and rearranged through the membership function for machine learning. The close-loop controller criteria are derived by using the Lyapunov energy functions to guarantee the stability of the system. Eventually, an instance is presented to reveal the efficacy of evolution. [ABSTRACT FROM AUTHOR]

Fuzzy C-means (FCM) clustering algorithm is a widely used method in data mining. However, there is a big limitation that the predefined number of clustering must be given. So it is very important to find an optimal number of clusters. Therefore, a new validity function of FCM clustering algorithm is proposed to verify the validity of the clustering results. This function is defined based on the intra-class compactness and inter-class separation from the fuzzy membership matrix, the data similarity between classes and the geometric structure of the data set, whose minimum value represents the optimal clustering partition result. The proposed clustering validity function and seven traditional clustering validity functions are experimentally verified on four artificial data sets and six UCI data sets. The simulation results show that the proposed validity function can obtain the optimal clustering number of the data set more accurately, and can still find the more accurate clustering number under the condition of changing the fuzzy weighted index, which has strong adaptability and robustness. [ABSTRACT FROM AUTHOR]

This paper presents a fuzzy c-means clustering interval type-2 cerebellar model articulation neural network (FCM-IT2CMANN) method to help physicians improve diagnostic accuracy. The proposed method combines two classifiers, in which the IT2CMANN is the primary classifier and the fuzzy c-means algorithm is the pre-classifier. First, the data are divided into nc groups using the pre-classifier, and then, the main classifier is applied to determine whether the sample is in a healthy or diseased state. Implementing the gradient descent method, the adaptive laws for updating the FCM-IT2CMANN parameters are derived. Furthermore, the system convergence is proven by the Lyapunov stability theory. Finally, the classification of breast cancer and liver disease datasets from the University of California at Irvine is conducted to illustrate the effectiveness of the proposed classifier.

Cluster analysis refers to the process of grouping a collection of physical or abstract objects into multiple classes of similar objects. Determining the optimal classification number of a data set is the key to the clustering problem, that is to say whether the data set can be effectively partitioned. Cluster validity study is a process of establishing clustering effectiveness indicators, evaluating clustering quality and determining the optimal number of clusters. A validity function of fuzzy C-means (FCM) clustering algorithm is proposed by adopting the division of intra-class compactness and inter-class separation, whose minimum represents the best clustering. Then, the proposed validity function on FCM clustering algorithm is compared with the known typical validity functions by carrying out simulation experiments to compare the related clustering performance. Three data sets are adopted to carry out FCM clustering, which includes three classical data sets, two artificial data sets and six real data sets in UCI database. Simulation experimental results show that the proposed validity function can effectively partition the data set.

Abstract To improve spectrum sensing performance, a cooperative spectrum sensing method based on information geometry and fuzzy c-means clustering algorithm is proposed in this paper. In the process of signal feature extraction, a feature extraction method combining decomposition, recombination, and information geometry is proposed. First, to improve the spectrum sensing performance when the number of cooperative secondary users is small, the signals collected by the secondary users are split and reorganized, thereby logically increasing the number of cooperative secondary users. Then, in order to visually analyze the signal detection problem, the information geometry theory is used to map the split and recombine signals onto the manifold, thereby transforming the signal detection problem into a geometric problem. Further, use geometric tools to extract the corresponding statistical characteristics of the signal. Finally, according to the extracted features, the appropriate classifier is trained by the fuzzy c-means clustering algorithm and used for spectrum sensing, thus avoiding complex threshold derivation. In the simulation results and performance analysis section, the experimental results were further analyzed, and the results show that the proposed method can effectively improve the spectrum sensing performance.

Copyright of Journal of South China Normal University (Natural Science Edition) / Huanan Shifan Daxue Xuebao (Ziran Kexue Ban) is the property of Journal of South China Normal University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Currently, the complexity of mechanical equipment is increasing rapidly together with the poor working environment. If a fault occurs, how to find the fault in time becomes a poser. Motivated by this existing problem, based on the analysis of the fault characteristics of electric fans, a fault diagnosis algorithm model based on Least Square Support Vector Machine (LSSVM) and Kd-Tree was proposed. This algorithm was based on the LSSVM optimized by the Cuckoo Search (CS). This paper used the "one-to-many" principle and the sigma threshold method to introduce k-Nearest Neighbor (kNN) which was implemented by Kd-Tree as a secondary classifier to optimize the model. In data preprocessing, the data based on time series was first processed by Empirical Mode Decomposition (EMD) and the energy ratios were calculated, and the the above results were degraded by Principal Component Analysis (PCA) and normalized. On top of that, in case of the uncertain fault types, the Fuzzy C-Means clustering algorithm (FCM) optimized by Particle Swarm Optimization (PSO) was proposed to provide a priori knowledge for the model. In this paper, the algorithm model, FCM and other parts were verified to prove that the performance and generality of the algorithm were better than those of general classification algorithms, and relevant experiments were conducted for different data processing methods to expand the universality of the algorithm. [ABSTRACT FROM AUTHOR]

This paper devises a new detector based on singular value decomposition (SVD) and improved fuzzy c -means (FCM) clustering for automatically detecting high-frequency oscillations (HFOs) that are used for localizing seizure onset zones (SOZs) in epilepsy. First, HFO candidates (HFOCs) are obtained by the root mean square method. Next, a time-frequency analysis method is applied to eliminate spikes from HFOCs, which consists of the Stockwell transform, SVD combined with the k -medoids clustering algorithm, Stockwell inverse transform, and threshold method. Then, four kinds of distinctive features, i.e. mean singular values, line lengths, power ratios and spectral centroid of the rest of HFOCs, are extracted and augmented as feature vectors. These vectors are used as the input of the improved FCM clustering algorithm optimized by the simulated annealing algorithm combined with the genetic algorithm. Finally, the localization of SOZs is accomplished based on the concentrations of the detected HFOs. The superiority of the devised detector over other five existing ones is demonstrated by comparing their localization performance. [ABSTRACT FROM AUTHOR]

With the development of modern remote sensing technology, remote sensing images have become one of the powerful tools for people to understand the Earth and its surroundings. However, there is currently no good classification algorithm that can accurately classify images. In order to accurately classify remote sensing images, this paper studies the content of the article by using fuzzy C-means clustering algorithm and radial basis neural network (RBF). The classification accuracy of SIRI-WHU dataset was analyzed by using the classification accuracy evaluation index such as overall accuracy and Kappa coefficient. The Kappa coefficient of vegetation classification in SIRI-WHU dataset was 0.9678, and the overall accuracy reached 97.18%. According to the classification problem of remote sensing image, according to the characteristics of remote sensing image, the improved model Alex Net-10-FCM is used to classify the remote sensing image dataset, and very high classification accuracy is obtained. [ABSTRACT FROM AUTHOR]

Carbon price, to a certain extent, reflects the intensity of a national emission reduction target, whereas carbon price forecasting is the basis for improving crisis management competence and strengthening market enthusiasm. This paper advances a novel hybrid carbon price forecasting methodology consisting of the empirical wavelet transform (EWT) and the gated recurrent unit (GRU) neural network. First, the carbon price data is decomposed through the EWT approach into the more stable and regular sub-components. These sub-components are divided into trend, low-frequency and high-frequency component using the fuzzy C-means clustering algorithm. Next, the lag order of different classes of components is determined as the input variables of the GRU model by the partial auto-correlation function method. Then, all values of each component predicted by the GRU method are aggregated to produce a final combined prediction result for the original carbon price. Finally, the EWT-GRU model is compared with the individual Autoregressive Integrated Moving Average (ARIMA), Back Propagation Neural Network (BPNN), GRU and EWT-BPNN models. The simulation results demonstrate that the proposed EWT-GRU combined forecasting model is superior to other models in terms of prediction effect, prediction accuracy, etc. They also confirm the validity and accuracy of the EWT-GRU model in carbon price prediction and show it deserves popularization. [ABSTRACT FROM AUTHOR]

In order to improve the ability of accounting data analysis and statistics, a method of constructing modern accounting data analysis platform based on industrial cloud computing is proposed. The time series model of modern accounting data analysis is constructed by using block bit sequence analysis method, and the association rule characteristic quantity of accounting data is extracted. Combined with cloud computing technology, the modern accounting data analysis platform is constructed, and the fuzzy C-means clustering algorithm is used to realize the clustering of modern accounting data, which can improve the ability of parallel computing and statistical analysis of accounting data. The simulation results show that the intelligent data analysis platform designed makes the statistical analysis ability of accounting data better and the parallel computing efficiency higher. [ABSTRACT FROM AUTHOR]

Education greatly aids in the process of students' growth; therefore, education institutions try to provide high-quality education to their students. A possible remedy to provide high-quality education is by discovering knowledge from educational data. However, accurately evaluating students' performance is very challenging due to different sources and structures of educational data. In addition, different teaching strategies are required because students' learning ability are different. One way to discover the hidden knowledge from educational data is the use of clustering algorithms, which are capable of mining interesting patterns from educational data. Thus, this study presents a fuzzy C-means clustering algorithm using 2D and 3D clustering to evaluate students' performance based on their examination results (the examination grades from College of Computer Science and Technology, Huaqiao University for students enrolled in year 2014). Based on the experimental results from 2D and 3D clustering for evaluating students' performance, the educators can better understand the students' performance so as to build a pedagogical basis for decisions. Students can also receive some recommendations from the mining results about their performance.

Education greatly aids in the process of students' growth; therefore, education institutions try to provide high-quality education to their students. A possible remedy to provide high-quality education is by discovering knowledge from educational data. However, accurately evaluating students' performance is very challenging due to different sources and structures of educational data. In addition, different teaching strategies are required because students' learning ability are different. One way to discover the hidden knowledge from educational data is the use of clustering algorithms, which are capable of mining interesting patterns from educational data. Thus, this study presents a fuzzy C-means clustering algorithm using 2D and 3D clustering to evaluate students' performance based on their examination results (the examination grades from College of Computer Science and Technology, Huaqiao University for students enrolled in year 2014). Based on the experimental results from 2D and 3D clustering for evaluating students' performance, the educators can better understand the students' performance so as to build a pedagogical basis for decisions. Students can also receive some recommendations from the mining results about their performance. [ABSTRACT FROM AUTHOR]

The early and accurate detection of brain tumors is key to improve the quality of life and the survival of cancer patients. Magnetic resonance imaging (MRI) is a widely used imaging technique to assess these tumors, but the large amount of data produced by MRI prevents manual segmentation in a reasonable time, limiting the use of precise quantitative measurements in the clinical practice. Consequently, automatic and reliable segmentation methods are required. However, the large spatial and structural variability among brain tumors make automatic segmentation a challenging problem. In this contribution, we present a new model of segmentation of brain magnetic resonance images. In order to obtain the region of interest, we propose a hybrid approach that carries out both fuzzy c-mean algorithm and multiobjective optimization taking into account both compactness and separation in the clusters with the purpose of improving the cluster center detection and speed up the convergence time. This new segmentation approach is a key component of the proposed magnetic resonance image-based classification system for brain tumors. Experimental results are presented to demonstrate the effectiveness and efficiency of the proposed approach using the DICOM MRI database. [ABSTRACT FROM AUTHOR]

In order to pre-warning the product quality risk of the e-commerce platform, this paper studies the machine learning algorithm for the products quality risk assessment, which propose the Fuzzy C-Means clustering algorithm for the feature extraction and the Cost Sensitive Leaning (CSL)-Naive Bayesian algorithm to construct the assessment model for E-commerce product quality risk form the massive and unbalanced data. The experimental results show that the Machine Learning algorithm based on Spark has better scalability and superiority in the large-scale data environment, which can accurately identify e-commerce product quality risk. [ABSTRACT FROM AUTHOR]

This study develops a new comprehensive pattern recognition modeling framework that leverages activity data to derive clusters of homogeneous daily activity patterns, for use in activity-based travel demand modeling. The pattern recognition model is applied to time use data from the large Halifax STAR household travel diary survey. Several machine learning techniques not previously employed in travel behavior analysis are used within the pattern recognition modeling framework. Pattern complexity of activity sequences in the dataset was recognized using the FCM algorithm, and resulted in identification of twelve unique clusters of homogeneous daily activity patterns. We then analysed inter-dependencies in each identified cluster and characterized the cluster memberships through their socio-demographic attributes using the CART classifier. Based on the socio-demographic characteristics of individuals we were able to correctly identify which cluster individuals belonged to, and also predict various information related to their activities, such as start time, duration, travel distance, and travel mode, for use in activity-based travel demand modeling. To execute the pattern recognition model, the 24-h activity patterns are split into 288 three dimensional 5 min intervals. Each interval includes information on activity types, duration, start time, location, and travel mode if applicable. Results from aggregated statistical evaluation and Kolmogorov–Smirnov tests indicate that there is heterogeneous diversity among identified clusters in terms of temporal distribution, and substantial differences in a variety of socio-demographic variables. The homogeneous clusters identified in this study may be used to more accurately predict the scheduling behavior of specific population groups in activity-based modeling, and hence to improve prediction of the times and locations of their travel demands. Finally, the results of this study are expected to be implemented within the activity-based travel demand model, Scheduler for Activities, Locations, and Travel (SALT). [ABSTRACT FROM AUTHOR]

Cloud computing is used to connect several number of remote servers through Internet to accumulate and recover large data anywhere and anytime. As of the conventional privacy defending process, there is a possibility for malevolent assault on the sensitive information accumulated in the cloud. In this research, the authors have proposed a competent large data convert among privacy defending by Hadoop map reduce in the cloud. The procedure exploits fuzzy C‐means clustering (FCM) algorithm grouping the data. For dimensionality reduction, map reduce framework will be used. In evaluation module, the recommended technique performed with the aid of K‐nearest neighbour (KNN) classification algorithm in this phase using KNN technique to check the convolution process based on the threshold value, which is improving the utility of the privacy data. The consequence acquired illustrates that authors' proposed scheme has enhanced the clustering exactness and also accomplishes the effectual convolution procedure to improve the privacy. From the experimental results, the proposed research achieved an effective clustering accuracy 76.07% and the existing K‐means approach gets the clustering accuracy of 73.5% which is minimum value when compared to the proposed researches. The suggested technique is implemented in JAVA with Cloud Sim platform. [ABSTRACT FROM AUTHOR]