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The clustering of incomplete patterns is a very challenging task because the estimations may negatively affect the distribution of real centers and thus cause uncertainty and imprecision in the results. To address this problem, a new belief-based incomplete pattern unsupervised classification method (BPC) is proposed in this paper. Firstly, the complete patterns are grouped into a few clusters by a classical soft method like fuzzy c-means to obtain the corresponding reliable centers and thereby are partitioned into reliable patterns and unreliable ones by an optimization method. Secondly, a basic classifier trained by reliable patterns is employed to classifies unreliable patterns and the incomplete patterns edited by the neighbors. In this way, most of the edited incomplete patterns can be submitted to specific clusters. Finally, some ambiguous patterns will be carefully repartitioned again by a new distance-based rule depending on the obtained reliable centers and belief functions theory. By doing this, a few patterns that are very difficult to classify between different specific clusters will be reasonably submitted to meta-cluster which can characterize the uncertainty and imprecision of the clusters due to missing values. The simulation results show that the BPC has the potential to deal with real datasets. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

The efficient segmentation of customers of an enterprise is categorized into groups of similar behavior based on the RFM (Recency, Frequency and Monetary) values of the customers. The transactional data of a company over is analyzed over a specific period. Segmentation gives a good understanding of the need of the customers and helps in identifying the potential customers of the company. Dividing the customers into segments also increases the revenue of the company. It is believed that retaining the customers is more important than finding new customers. For instance, the company can deploy marketing strategies that are specific to an individual segment to retain the customers. This study initially performs an RFM analysis on the transactional data and then extends to cluster the same using traditional K-means and Fuzzy C- Means algorithms. In this paper, a novel idea for choosing the initial centroids in K- Means is proposed. The results obtained from the methodologies are compared with one another by their iterations, cluster compactness and execution time.

[EN] Wound rotor induction motors are used in a certain number of industrial applications due to their interesting advantages, such as the possibility of inserting external rheostats in series with the rotor winding to enhance the torque characteristics under starting and to decrease the high inrush currents. However, the more complex structure of the rotor winding, compared to cage induction motors, is a source for potential maintenance problems. In this regard, several anomalies can lead to the occurrence of asymmetries in the rotor winding that may yield terrible repercussions for the machine¿s integrity. Therefore, monitoring the levels of asymmetry in the rotor winding is of paramount importance to ensure the correct operation of the motor. This work proposes the use of Bicoherence of the stray flux signal, as an indicator to obtain an automatic classification of the rotor winding condition. For this, the Fuzzy C-Means machine learning algorithm is used, which starts with the Bicoherence calculation and generates the different clusters for grouping and classification, according to the level of winding asymmetry. In addition, an analysis regarding the influence of the flux sensor position on the automatic classification and the failure detection is carried out. The results are highly satisfactory and prove the potential of the method for its future incorporation in autonomous condition monitoring systems that can be satisfactorily applied to determine the health of these machines., This work was supported in part by Generalitat Valenciana, Conselleria de Innovacion, Universidades, Ciencia y Sociedad Digital, (project AICO/019/224) and in part by MEC under Project MTM2016-75963-P.

Clustering, which is handled by many researchers, is separating data into clusters without supervision. In clustering, the data are grouped using similarities or differences between them. Many traditional and heuristic algorithms are used in clustering problems and new techniques continue to be developed today. In this study, a new and effective clustering algorithm was developed by using the Whale Optimization Algorithm (WOA) and Levy flight (LF) strategy that imitates the hunting behavior of whales. With the developed WOA-LF algorithm, clustering was performed using ten medical datasets taken from the UCI Machine Learning Repository database. The clustering performance of the WOA-LF was compared with the performance of k-means, k-medoids, fuzzy c-means and the original WOA clustering algorithms. Application results showed that WOA-LF has more successful clustering performance in general and can be used as an alternative algorithm in clustering problems.

Wireless Sensor Networks formed by grouping of small batteries equipped sensor nodes, faces the major issue in optimization of energy when designing the routing protocols. Clustering based routing algorithm proved energy efficiency in Wireless Sensor Networks (WSNs), in which appropriate cluster formation and cluster heads (CHs) selection is crucial for energy saving. Thus, this paper proposes an approach which uses fuzzy C-Means (FCM) in cluster formation and uses the three parameters for cluster head selection: the new energy parameter considered threshold value, the residual energy of each node and the distance from node to base station (BS). FCM algorithm creates uniform clusters in the network area and reduces the distances between each node and their corresponding cluster head node. The proposed clustering protocol can give longer network lifetime when it is implemented in MATLAB simulation environment and compared to LEACH. Simulation results tell that the proposed protocol overcome the performance of LEACH protocol in terms of saving energy and reducing dead nodes to prolong the network lifetime.

Feature discretization is an important preprocessing technology for massive data in industrial control. It improves the efficiency of edge-cloud computing by transforming continuous features into discrete ones, so as to meet the requirements of high-quality cloud services. Compared with other discretization methods, the discretization based on rough set has achieved good results in many applications because it can make full use of the known knowledge base without any prior information. However, the equivalence class of rough set is an ordinary set, which is difficult to describe the fuzzy components in the data, and the accuracy is low in some complex data types in big data environment. Therefore, we propose a rough fuzzy model based discretization algorithm (RFMD). Firstly, we use fuzzyc-means clustering to get the membership of each sample to each category. Then, we fuzzify the equivalence class of rough set by the obtained membership, and establish the fitness function of genetic algorithm based on rough fuzzy model to select the optimal discrete breakpoints on the continuous features. Finally, we compare the proposed method with the discretization algorithm based on rough set, the discretization algorithm based on information entropy, and the discretization algorithm based on chi-square test on remote sensing datasets. The experimental results verify the effectiveness of our method.

Automated segmentation of histopathological images is a challenging task to detect cancerous cells in breast tissue. Recent reviews state high accuracy to segment image, but depends on user input, say window area size, time steps, level set, magnification factor and so on. To extract the region of interest effectively, the subject expert performs post-processing operations several times on the segmentation results with different input values for different parameters say, area opening, fill holes and selects most appropriate enhanced image required for further analysis. The authors proposed an automated segmentation technique followed by self-driven post-processing operations to detect cancerous cells effectively. The post-processing method itself determines the value of different parameters for different operations based on segmented results obtained. The proposed technique has the following features: (i) technique is context sensitive; (ii) no prior setting of time step, weighted area coefficient parameters is required; (iii) magnification independent; (iv) post-processing operations are self-driven which enhance segmentation results adaptively. The experimental results are compared with four state-of-the-art techniques: fuzzy C-means, spatial fuzzy C-means, spatial neutrosophic distance regularised level set and convolutional neural network-based PangNet. Experimental results obtained on two publicly available data sets show that the proposed technique outperforms effectively.

One of the strategic plans of the developing universities in obtaining new students is forming a partnership with surrounding high schools. However, partnerships made does not always behave as expected. This paper presented the segmentation technique to the previous new student admission dataset using the integration of recency, frequency, and monetary (RFM) analysis and fuzzy c-means (FCM) algorithm to evaluate the loyalty of the entire school that has bound the partnership with the institution. The dataset is converted using the RFM approach before processed with the FCM algorithm. The result reveals that the schools can be segmented, respectively, as high potential (SP), potential (P), low potential (CP), and very low potential (KP) categories with PCI value 0.86. From the analysis of SP, P, and CP, only 71 % of 52 school partners categorized as loyal partners.

This research study sought to evaluate aimed at evaluating the possible advantages of using Fuzzy Logic as opposed to Boolean Logic to assess environmental contamination and source appraisal for polycyclic aromatic hydrocarbons (PAH). Results obtained through traditional assessment tools for two different tropical coastal areas through using traditional clustering and principal components analysis were compared with those derived from the Fuzzy Logic, using the by Fuzzy C-means algorithm. The feedings achieved through Fuzzy Logic showed a greater qualitative detail than those derived from traditional tools. The abrupt and unnatural changes obtained from the usual classification methods were avoided by having membership values varying continuously in space, providing a more accurate picture of environmental contamination in complex and multiple sources environments. Furthermore, by not depending on statistic suppositions distribution of data like other methods, becomes more suitable for environmental data. Although Fuzzy Logic does not produce quantitative interpretations, its application generates adequate the data needed to avoid environmental management bias in the inference of contamination sources.

Quality of Service (QoS) is one of the key indicators to measure the overall performance of cloud services. The quantitative measurement of the QoS enables the service provider to manage its Service Level Agreement (SLA) in a viable way. It also supports a consumer in service selection and allows measuring the received services to comply with agreed services. There is much existing literature that tries to predict the QoS and assist stakeholders in their decision-making process. However, it is tricky to deal with multidimensional data in time series prediction methods. The computational complexity increases with an increase in data dimension, and it is a challenging task to give precise weights to each time interval. Existing prediction methods could not deal with the intricate reordering of input weights. To address this problem, we propose a novel Clustered Induced Ordered Weighted Averaging (IOWA) Adaptive Neuro-Fuzzy Inference System (ANFIS), (CI-ANFIS) model. This fuzzy time series prediction model reduces data dimension and handles the nonlinear relationship of the cloud QoS dataset. The proposed method uses an intelligent sorting mechanism that regulates uncertainty in prediction while incorporating a fuzzy neural network structure for optimal prediction results. The proposed method employs the IOWA operator to sort input arguments based on associated order-inducing variables and assign customised weights accordingly. The inputs are further classified using three fuzzy clustering methods - fuzzy c-means (FCM), subtractive clustering and grid partitioning. The inputs further pass to the ANFIS structure that takes the benefits of both the fuzzy and neural networks. The fuzzy structure in ANFIS builds understandable rules for cloud stakeholders and deals with uncertain occurrences of data. The model uses a real cloud QoS dataset extracted from the Amazon Elastic Compute Cloud (EC2) US-West instance and predict its behaviour every five minutes for the next 24 hours. The proposed method is further compared with the existing twelve methods. The comparative results show that the proposed CI-ANFIS model outperforms all current techniques. The proposed approach opens a new area of research in various complex prediction problems such as stock trading, big data, complex IoT sensors, and other social computing problems.

Speech Emotion Recognition (SER) plays a significant role in the field of Human–Computer Interaction (HCI) with a wide range of applications. However, there are still some issues in practical application. One of the issues is the difference between emotional expression amongst various individuals, and another is that some indistinguishable emotions may reduce the stability of the SER system. In this paper, we propose a multi-layer hybrid fuzzy support vector machine (MLHF-SVM) model, which includes three layers: feature extraction layer, pre-classification layer, and classification layer. The MLHF-SVM model solves the above-mentioned issues by fuzzy c-means (FCM) based on identification information of human and multi-layer SVM classifiers, respectively. In addition, to overcome the weakness that FCM tends to fall into local minima, an improved natural exponential inertia weight particle swarm optimization (IEPSO) algorithm is proposed and integrated with fuzzy c-means for optimization. Moreover, in the feature extraction layer, non-personalized features and personalized features are combined to improve accuracy. In order to verify the effectiveness of the proposed model, all emotions in three popular datasets are used for simulation. The results show that this model can effectively improve the success rate of classification and the maximum value of a single emotion recognition rate is 97.67% on the EmoDB dataset.

In this paper, we propose a construction of fuzzy radial basis function neural network model for diagnosing prostate cancer. A fuzzy radial basis function neural network (fuzzy RBFNN) is a hybrid model of logical fuzzy and neural network. The fuzzy membership function of the fuzzy RBFNN model input is developed using the triangle function. The fuzzy C-means method is applied to estimate the center and the width parameters of the radial basis function. The weight estimation is performed by various ways to gain the most accurate model. A singular value decomposition (SVD) is exploited to address this process. As a comparison, we perform other ways including back propagation and global ridge regression. The study also promotes image preprocessing using high frequency emphasis filter (HFEF) and histogram equalization (HE) to enhance the quality of the prostate radiograph. The features of the textural image are extracted using the gray level cooccurrence matrix (GLCM) and gray level run length matrix (GLRLM). The experiment results of fuzzy RBFNN are compared to those of RBFNN model. Generally, the performances of fuzzy RBFNN surpass the RBFNN in all accuracy calculation. In addition, the fuzzy RBFNN-SVD demonstrates the most accurate model for prostate cancer diagnosis.

Meta-analysis was introduced to aggregate the findings of different primary studies in statistical aspects. However, in the proposed study, the term "meta" is used to aggregate different models for a specific topic with the help of fuzzy c-means clustering method. One of the motivations of the proposed method is based on the concept of indices. In the literature, there are numerous proposed indices under different conditions for a specific purpose. Our assumption is that each index has some information for a given dataset. Therefore, meta fuzzy index functions, which include each index in each function with a certain degree of membership value, are introduced in the proposed method. Currency crisis and process capability indices are chosen as applications in order to show that the proposed method can be useful tool in terms of indices.

The landing gear retraction/extension(R/E) system has a critical impact on the safety of aircraft’s take-off and landing, and its health status is important to decision-making of the system’s prognostics and health management (PHM). With flight parameter data, a method to monitor the health of aircraft landing gear R/E system is proposed based on the improved fuzzy c-means algorithm (FCM). The landing gear health status are classified by the FCM cluster, and the granularity principle and density function are utilized to optimize the number of FCM algorithm clusters and the initial clustering center to better classification. At the same time, an optimized multi-dimensional scaling algorithm (MDS) is used to obtain low-dimensional features which facilitate cluster analysis. And the algorithm comparison and case analysis results show that the proposed method has a great health monitoring effect.

The Type-2 fuzzy set (T2 FS) is widely used for efficient control uncertainties, such as noise sensitivity in the fuzzy set. In addition, unsupervised machine learning requires a clustering parameter value in advance, and may affect clustering performance according to prior information such as the number and size of clusters. In this case, the fuzzifier value m to be applied is the most important factor in improving the accuracy of data. Therefore, in this paper, we intend to perform clustering to automatically acquire the determination of m1 and m2 values that depended on existing repeated experiments. To this end, in order to increase efficiency on deriving appropriate fuzzifier value, we used the Interval type-2 possibilistic fuzzy C-means (IT2PFCM), clustering method to classify a given pattern. In Efficient IT2PFCM method, used for clustering, we propose an algorithm that derives suitable fuzzifier values for each data. These values also extract information from each data point through the histogram approach and Gaussian Curve Fitting method. Using the extracted information, two adaptive fuzzifier value m1 and m2 are determined. Obtained values apply the new lowest and highest membership values. In addition, it is possible to form an appropriate fuzzy area on each cluster by only taking advantage of the characteristics of IT2PFCM, which reduces uncertainty. This doesn't only improve the accuracy of clustering of measured sensor data, but can also be used without additional procedures such as data labeling or the provision of prior information. It is also efficient at monitoring numerous sensors, managing and verifying sensor data collected in real time such as smart cities. Eventually, in this study, the proposed method is to improve IT2PFCM performance on accurate and quick clustering of large amount of complex data such as Internet of Things (IoT).

In the Intelligent Transportation Systems (ITS), highly accurate traffic flow prediction is considered as key technology to evaluate traffic state of the urban road network. However, due to disturbing from environment, the original traffic flow data may be influenced by noise and finally cause the decline of prediction accuracy. This study design a hybrid prediction model combining Ensemble Empirical Mode Decomposition (EEMD) denoising schemes and classifying learning algorithm based on Fuzzy C-means Neural Network (FCMNN) to improve prediction accuracy. In the model training process, several key parameters in EEMD and FCMNN are determined according to prediction errors based on traffic volume detected from highway network in the Minneapolis city. In the model validation, three widely used indicators for error evaluation are applied to estimate the prediction accuracy of four candidate models under single and multi step, including Artificial Neural Network (ANN), EEMD+ANN, FCMNN and EEMD+FCMNN. The results shown in the case study indicate that the prediction models combined with denoising methods are superior to the models without adopting denoising algorithm. Furthermore, the model using classifying learning method FCMNN can produce higher prediction accuracy than traditional ANN model. In addition, the long-term prediction performance of FCMNN is also much better than that of ANN because that sub-NN system is trained according to each classifying patterns to obtain better optimization effect. Results summarized in this study could be helpful for administration to design managing and controlling strategies according to high prediction accuracy.

With the widespread attention and research of distributed Photovoltaic (PV) systems, the state evaluation of distributed PV system, especially the evaluation of ash-covered state, has become increasingly prominent. To this end, an ash-covered state evaluation method for the distributed PV systems, which considers the full lifetime degradation, is proposed. First, the PV lifetime degradation rate is calculated based on the historical data of the PV system. Second, the fuzzy C-means (FCM) is used to cluster PV measured AC output power data into four weather types: sunny, cloudless, cloudy, and rainy. According to weather conditions, the Levenberg-Marquardt backpropagation (LMBP) Deep Neural Network (DNN) is adopted to establish four distributed PV AC output power fitting models by using measured meteorological data and measured output power data. Afterward, distributed PV output power fitted value is obtained by reducing the DNN output proportionally according to the PV degradation rate. Moreover, by analyzing the difference between the PV output power fitted value and the measured output power data, the ash-covered state of the system is evaluated, and a state alarm mechanism is established based on the system state. Finally, the validity and rationality of the proposed method are verified by the analysis of examples.

Manual inspection and harvesting of ripening tomatoes is time consuming and labor intensive. Smart agriculture can emphasize the use of digital horticultural resources for farming and can increase farm sustainability; to that end, we proposed a fuzzy Mask R-CNN model to automatically identify the ripeness levels of cherry tomatoes. First, to annotate the images automatically, a fuzzy c-means model was used to maintain the spatial information of various foreground and background elements of the image. Then, a Hough transform method was applied to locate the specific geometric edge positions of the tomatoes. Each data point of the image space was annotated to a JavaScript Object Notation file. Second, annotated images were trained with Mask R-CNN to identify each tomato precisely. Finally, to prevent preharvest abscission of tomatoes, a hue-saturation-value color model and fuzzy inference rules were used to predict the ripeness of the tomatoes. A trigonometric function with Euclidian distance was calculated from the origin of calyx and stem to the bottom of the tomato to obtain the position of the pedicle head and dissect the fruit in a timely manner. For detection of 100 tomato images, Mask R-CNN achieved an accuracy of 98.00%. The ripeness classification of tomatoes achieved overall weighted precision and recall rates of 0.9614 and 0.9591, respectively. Thus, automatic tomato harvesting applications can empower farmers to make better decisions and enhance overall production efficiency and yield.

The enhancement of the carrying capacity of high-speed railway and the acceleration of train speed lead to the proliferation of signal interference in the communication network, which leads to the degradation of network performance and user service quality. To address the issue, we first classify the cell users in high-speed railway wireless communication environment based on Fuzzy C-Means algorithm and user mobility prediction model, and divide communicating users on trains into cell center users and edge users. Then, differential power distribution schemes are implemented for center users and edge users according to the classification results. Finally, the Max-SINR based interference alignment algorithm is used to realize the interference management. Simulation results show that the proposed algorithm fully takes into account the mobility of high-speed train users and effectively manages interference, which improves the high-speed railway wireless communication network performance significantly.

Input variable selection is an essential step in the development of data-driven models. In order to establish a fuzzy model with high identification accuracy for complex nonlinear systems (such as variable load pneumatic loading system) in engineering, a novel fuzzy identification method is proposed, which is based on the selection of important input variables. Firstly, the simplified Two Stage Fuzzy Curves and Surfaces method is used to rank the original input variables according to their significance, and the variables which are most relevant to the output are selected as the input of the T-S fuzzy model. Then, the Fuzzy c-Means clustering algorithm and Particle Swarm Optimization algorithm are used to identify the antecedent parameters, and the Recursive Least Square method is used to identify the consequent parameters. The validity of the proposed fuzzy identification method is verified by two benchmark problems, and the results show that the accuracies of identified models have been improved significantly compared with the other existing models. Finally, the proposed approach is implemented to the practical data of an actual variable load pneumatic loading system, and preponderant trajectory matching performance is achieved.

The clustering which is an unsupervised classification method is very important for data processing applications. The main purpose of the clustering is to separate the data samples into different groups by using the similarity (or dissimilarity) between data samples. There are many conventional and heuristic algorithms which are used for the clustering problem. Nevertheless, in last years, it is seen that many new techniques are proposed and improved to solve the clustering problem. In this paper, grey wolf optimization (GWO) algorithm which is modelled according to the social behaviour of grey wolves is applied to partition the data samples by searching the optimal center of the clusters. The clustering performance of the GWO is compared with the performances of the three clustering algorithms: k-means, k-medoids and fuzzy c-means algorithms. The experiments show that the GWO algorithm has generally better results than the other clustering algorithms and can be alternatively applied on the clustering problem.

Background: Current naked-eye examination of the ultrasound images for inflamed appendix has limitations due to its intrinsic operator subjectivity problem. Objective: In this paper, we propose a fully automatic intelligent method for extracting inflamed appendix from ultrasound images. Accurate and automatic extraction of inflamed appendix from ultrasonography is a major decision making resource of the diagnosis and management of suspected appendicitis. Methods: The proposed method uses Fuzzy C-means learning algorithm in pixel clustering with semi-dynamic control of initializing the number of clusters based on the intensity contrast dispersion of the input image. Thirty percent of the prepared ultrasonography samples are classified into four different groups based on their intensity contrast distribution and then different number of clusters are assigned to the images in accordance with such groups in Fuzzy C-means learning process. Results: In the experiment, the proposed system successfully extracts the target without human intervention in 82 of 85 cases (96.47% accuracy). The proposed method also shows that it can cover the false negative cases occurred previously that used self-organizing map as the learning engine. Conclusion: Such high level reliable correct extraction of inflamed appendix encourages to use the automatic extraction software in the diagnosis procedure of suspected acute appendicitis.

Pedestrian level of service (PLOS) is an important measure of performance in the analysis of existing pedestrian crosswalk conditions. Many researchers have developed PLOS models based on pedestrian delay, turning vehicle effect, etc., using the conventional regression method. However, these factors may not effectively reflect the pedestrians’ perception of safety while crossing the crosswalk. The conventional regression method has failed to estimate accurate PLOS because of the primary assumption of an arbitrary probability distribution and vagueness in the input data. Moreover, PLOS categories in existing studies are based on rigid threshold values and the boundaries that are not well defined. Therefore, it is an important attempt to develop a PLOS model with respect to pedestrian safety, convenience, and efficiency at signalized intersections. For this purpose, a video-graphic and user perception surveys were conducted at selected nine signalized intersections in Mumbai, India. The data such as pedestrian, traffic, and geometric characteristics were extracted, and significant variables were identified using Pearson correlation analysis. A consistent and statistically calibrated PLOS model was developed using fuzzy linear regression analysis. PLOS was categorized into six levels (A–F) based on the predicted user perception score, and threshold values for each level were estimated using the fuzzy c-means clustering technique. The developed PLOS model and threshold values were validated with the field-observed data. Statistical performance tests were conducted and the results provided more accurate and reliable solutions. In conclusion, this study provides a feasible alternative to measure pedestrian perception-based level of service at signalized intersections. The developed PLOS model and threshold values would be useful for planning and designing pedestrian facilities and also in evaluating and improving the existing conditions of pedestrian facilities at signalized intersections.

Background Cervical cancer is preventable if effective screening measures are in place. Pap-smear is the commonest technique used for early screening and diagnosis of cervical cancer. However, the manual analysis of the pap-smears is error prone due to human mistake, moreover, the process is tedious and time-consuming. Hence, it is beneficial to develop a computer-assisted diagnosis tool to make the pap-smear test more accurate and reliable. This paper describes the development of a tool for automated diagnosis and classification of cervical cancer from pap-smear images. Method Scene segmentation was achieved through a Trainable Weka Segmentation classifier and a sequential elimination approach was used for debris rejection. Feature selection was achieved using simulated annealing integrated with a wrapper filter, while classification was achieved using a fuzzy C-means algorithm. Results The evaluation of the classifier was carried out on three different datasets (single cell images, multiple cell images and pap-smear slide images from a pathology lab). Overall classification accuracy, sensitivity and specificity of ‘98.88%, 99.28% and 97.47%’, ‘97.64%, 98.08% and 97.16%’ and ‘95.00%, 100% and 90.00%’ were obtained for each dataset, respectively. The higher accuracy and sensitivity of the classifier was attributed to the robustness of the feature selection method that accurately selected cell features that improved the classification performance and the number of clusters used during defuzzification and classification. Results show that the method outperforms many of the existing algorithms in sensitivity (99.28%), specificity (97.47%), and accuracy (98.88%) when applied to the Herlev benchmark pap-smear dataset. False negative rate, false positive rate and classification error of 0.00%, 10.00% and 5.00%, respectively were obtained when applied to pap-smear slides from a pathology lab. Conclusions The major contribution of this tool in a cervical cancer screening workflow is that it reduces on the time required by the cytotechnician to screen very many pap-smears by eliminating the obvious normal ones, hence more time can be put on the suspicious slides. The proposed system has the capability of analyzing a full pap-smear slide within 3 min as opposed to the 5–10 min per slide in the manual analysis. The tool presented in this paper is applicable to many pap-smear analysis systems but is particularly pertinent to low-cost systems that should be of significant benefit to developing economies.

A bean counter is defined as an accountant or economist who makes financial decisions for a company or government, especially someone who wants to severely limit the amount of money spent. The rise of the bean counter in both public and private companies has motivated us to develop a Bean Counter Profiling Scale in order to further depict this personality typology in real organizational contexts. Since there are no scales to measure such traits in personnel, we have followed the methodological steps for elaborating the scale’s items from the available qualitative literature and further employed a cognitive systems engineering approach based on statistical architecture, employing cluster, factor and items network analysis to statistically depict the best mathematical design of the scale. The statistical architecture will further employ a hierarchical clustering analysis using the unsupervised fuzzy c-means technique, an exploratory factor analysis and items network analysis technique. The network analysis which employs the use of networks and graph theory is used to depict relations among items and to analyze the structures that emerge from the recurrence of these relations. During this preliminary investigation, all statistical techniques employed yielded a six-element structural architecture of the 68 items of the Bean Counter Profiling Scale. This research represents one of the first scale validation studies employing the fuzzy c-means technique along with a factor analysis comparative design.

World is facing stress due to unpredicted pandemic of novel COVID-19. Daily growing magnitude of confirmed cases of COVID-19 put the whole world humanity at high risk and it has made a pressure on health professionals to get rid of it as soon as possible. So, it becomes necessary to predict the number of upcoming cases in future for the preparation of future plan-of-action and medical set-ups. The present manuscript proposed a hybrid fuzzy time series model for the prediction of upcoming COVID-19 infected cases and deaths in India by using modified fuzzy C-means clustering technique. Proposed model has two phases. In phase-I, modified fuzzy C-means clustering technique is used to form basic intervals with the help of clusters centroid while in phase-II, these intervals are upgraded to form sub-intervals. The proposed model is tested against available COVID-19 data for the measurement of its performance based on mean square error, root mean square error and average forecasting error rate. The novelty of the proposed model lies in the prediction of COVID-19 infected cases and deaths for next coming 31 days. Beside of this, estimation for the approximate number of isolation beds and ICU required has been carried out. The projection of the present model is to provide a base for the decision makers for making protection plan during COVID-19 pandemic.

[EN] Accurate glucose prediction along a long-enough time horizon is a key component for technology to improve type 1 diabetes treatment. Subjects with diabetes might benefit from supervision and control systems that accurately predict risks and trigger corrective actions early enough with improved mitigation. However, large intra-patient variability poses big challenges to glucose prediction. In previous works by the authors, clustering and local modeling techniques with seasonal stochastic models proved to be efficient, allowing for good glucose prediction accuracy for long prediction horizons. Continuous glucose monitoring (CGM) data were partitioned into fixed-length postprandial time subseries and clustered with Fuzzy C-Means to collect similar behaviors, enforcing seasonality at each cluster after subseries concatenation. Then, seasonal stochastic models were identified for each cluster and local predictions were integrated into a global prediction. However, free-living conditions do not support the fixed-length partition of CGM data since daily events duration is variable. In this work, a new algorithm is provided to overcome this constraint, allowing better coping with patient's variability under variable-length time-stamped daily events in supervision and control applications. Besides predicted glucose, two real-time indices are additionally provided-a crispness index, indicating good representation of current glucose behavior by a single model, and a normality index, allowing for the detection of an abnormal glucose behavior (unusual according to registered historical data). The framework is tested in a proof-of-concept in silico study with ten patients over four month training data and two independent two month validation datasets, with and without abnormal behaviors, from the distribution version of the UVA/Padova simulator extended with diverse sources of intra-patient variability., This work was supported by the Ministerio de Economia, Industria y Competitividad (MINECO), Grant Number DPI2016-78831-C2-1-R, the Agencia Estatal de Investigacion PID2019107722RB-C21/AEI/10.13039/501100011033, and the European Union (FEDER funds).

The banking sector as one of the economic drivers plays an important role in society. Over time, bank operations did not only raise funds from the public but were more complex. The development of the banking industry can be seen from the number of banks in Indonesia that have spurred the level of competition. Of course, the bank must pay attention to its health. The use of bank soundness level parameters or RGEC combined with clusters is interesting to study. By using the cluster method, banks can be classified based on the parameters of their health level. This study aims to analyze the RGEC-based bank grouping classification generated by the Fuzzy C-Means and Fuzzy Gustafson Kessel clustering analysis using financial ratio data on 80 conventional banks in Indonesia. The software used in this study is Matlab r2015b. The results showed that the FCM clustering had a smaller standard deviation than FGK so that the first cluster in the FCM showed that the banks were in good condition compared to the other clusters even though the overall condition of banks in Indonesia was good when viewed from their financial performance.

Presented at the 4th XoveTIC Conference, A Coruña, Spain, 7–8 October 2021. [Abstract] Three robust algorithms for clustering multidimensional time series from the perspective of underlying processes are proposed. The methods are robust extensions of a fuzzy C-means model based on estimates of the quantile cross-spectral density. Robustness to the presence of anomalous elements is achieved by using the so-called metric, noise and trimmed approaches. Analyses from a wide simulation study indicate that the algorithms are substantially effective in coping with the presence of outlying series, clearly outperforming alternative procedures. The usefulness of the suggested methods is also highlighted by means of a specific application. This research has been supported by MINECO (MTM2017-82724-R and PID2020-113578RB-100), the Xunta de Galicia (ED431C-2020-14), and “CITIC” (ED431G 2019/01). Xunta de Galicia; ED431C-2020-14 Xunta de Galicia; ED431G 2019/01

Difficulties faced by STMIK Pontianak students while choosing an Android smartphone are the diversity of brands, types, series and specifications makes it hard to choose which is the best. Determine that matches to needs along with an appropriate budget also difficult. This research aims to design a reference system for selecting an Android smartphone to resolve the problem. This system was developed using the Fuzzy C-Means algorithm and TOPSIS. The research method used is survey. Software design uses agile with extreme programming models also White-Box Testing, cluster center testing, and acceptance testing. This research found a method to get an alternative group that matches to the user from existing cluster by Euclidean Distance. This research produces a system that can clustering smartphone data and provide references in the form of alternatives that matches to the user. The test results using White-Box Testing produce all functions running well. Testing the cluster center using MSE gets the central error values ​​C1: 1.8481, C2: 2.5316, and C3: 1.8214. Acceptance tesing results above 70%. Weaknesses in this system do not discuss lifestyle needs in choosing an Android smartphone. The criteria used in this research is still technical and not use non-technical criteria., Kesulitan yang dihadapi mahasiswa STMIK Pontianak dalam memilih smartphone android yaitu beragamnya merek, tipe, seri dan spesifikasi menimbulkan kebingungan untuk menentukan mana yang terbaik. Kesulitan lain yang dihadapi adalah menentukan yang sesuai dengan kebutuhan beserta dengan budget yang sesuai. Penelitian ini bertujuan untuk merancang sistem referensi pemilihan smartphone android agar permasalahan terselesaikan. Sistem ini dikembangkan menggunakan algoritma Fuzzy C-Means dan TOPSIS. Metode penelitian yang digunakan adalah survei. Perancangan perangkat lunak menggunakan model agile dengan teknik extreme programming serta pengujian perangkat lunak White-Box Testing, pengujian pusat cluster, dan pengujian acceptance. Pada penelitian ini ditemukan metode untuk mendapatkan satu kelompok alternatif yang cocok dengan pengguna dari cluster yang ada yaitu Euclidean Distance. Penelitian ini menghasilkan sistem yang dapat mengelompokkan data smartphone dan memberikan referensi berupa alternatif yang cocok dengan pengguna, Hasil pengujian menggunakan White-Box Testing menghasilkan seluruh function berjalan dengan baik. Pengujian pusat cluster menggunakan MSE mendapatkan nilai error pusat C1: 1,8481, C2: 2,5316, dan C3: 1,8214. Pengujian acceptance yang dilakukan mendapatkan hasil penerimaan di atas 70%. Kelemahan yang ditemukan pada sistem ini tidak membahas mengenai kebutuhan lifestyle dalam memilih smartphone android. Kriteria yang digunakan masih bersifat teknis dan belum dapat menggunakan kriteria bersifat non-teknis.

Solubility data is one of the essential basic data for CO2 capture by ionic liquids. A selective ensemble modeling method, proposed to overcome the shortcomings of current methods, was developed and applied to the prediction of the solubility of CO2 in imidazolium ionic liquids. Firstly, multiple different sub&ndash, models were established based on the diversities of data, structural, and parameter design philosophy. Secondly, the fuzzy C&ndash, means algorithm was used to cluster the sub&ndash, models, and the collinearity detection method was adopted to eliminate the sub&ndash, models with high collinearity. Finally, the information entropy method integrated the sub&ndash, models into the selective ensemble model. The validation of the CO2 solubility predictions against experimental data showed that the proposed ensemble model had better performance than its previous alternative, because more effective information was extracted from different angles, and the diversity and accuracy among the sub&ndash, models were fully integrated. This work not only provided an effective modeling method for the prediction of the solubility of CO2 in ionic liquids, but also provided an effective method for the discrimination of ionic liquids for CO2 capture.

Maternal Mortality Rate (MMR) is an indicator used to assess maternal health as well as the health status of a country. MMR is a target that must be achieved by Indonesian Government in Sustainable Development Goals (SDGs) in 2030. The Government of Indonesia has made various efforts to reduce MMR. This study aims to determine the distribution pattern of indicators for improving maternal health by grouping provinces in Indonesia based on the characteristics of maternal health indicators. The variables used are indicators that affect maternal mortality, namely K4 coverage (x1), Td2+ immunization coverage (x2), maternity assisted by health workers in health facilities coverage (x3), post-partum check up coverage (x4), Puskesmas implementing pregnant classes (x5), Puskesmas implementing P4K (x6), participant of KB coverage (x7) in Indonesia in 2017. The grouping methods are Variable Weighting K-Means (VWKM) and Fuzzy C-Means (FCM). The selection of the best grouping results uses the Internal Cluster Dispersion Rate (icdrate). Based on the analysis results, the best grouping is generated by the FCM method. The icdrate value generated by FCM is 0.325 while the icdrate value generated by VWKM is 0.552. FCM produces five groups which can be categorized as groups with maternal health indicator characteristics with very low, low, medium, high, and very high scores. Provinces in a group tend to be geographically close. East Java and Bali are provinces included in the indicator group of very high maternal health. Papua and West Papua fall into the group for maternal health which is very low.

This paper proposes an alterable weight minimum spanning tree (AW-MST) method to plan electrical collector systems (ECSs) in tidal current generation farms (TCGFs) toward economic objectives. First, a sector-division-based fuzzy c-means (FCM) grouping algorithm is proposed. The tidal current turbines (TCTs) in the TCGF are divided into several sectors by the improved FCM algorithm to relieve the computational burden. Meanwhile, trans-region crossings and overloads of submarine cables can be simultaneously avoided. Second, an ECS planning model is established which fully considers the tidal current velocity (TCV) characteristics and ECS investment and operating costs. Since the variable factors cannot be considered by the common minimum spanning tree algorithms, alterable weights are used in the AW-MST to optimize the variable factors, including power losses and cable types. Finally, the two different TCGFs and the measured TCV datasets collected from North of Orkney, Scotland, were used to verify the effectiveness and adaptability of the proposed method. Published version

The increased usage demand for digital multimedia has induced significant challenges regarding copyright protection, which is the copy control and proof of ownership. Digital watermarking serves as a solution to these kinds of problems. Among different types of digital watermarking, visible watermarking protects the copyrights effectively, since the approach not only prevents pirates but also visually proves the copyright of the broadcasted video. A visible watermark could be in any location on the frame (corner, center, diagonal, etc.). In addition, it could either completely or partially disappear for some frames. The same video also might have multiple watermarks. In order to strengthen the techniques of adding visible watermarks, there is a need to discover the weakness of the used watermarks. Since the major step of attacking a visible watermark is to locate it accurately, in this paper, a Fuzzy C-Means (FCM)-based approach is proposed to locate visible watermarks in video. Broadcasting channels are used to utilize video logos, which can be considered as a form of visible watermark that represents a trademark or symbol to declare the intellectual property ownership. In general, a high-standard video watermark has such properties as a clear background with distinctive shape without additional texture obscuring the watermark area. In addition, the probability of the logo appearing in the four corners of the video frames is higher than in the center. Based on these common properties of the video watermark, the proposed scheme locates the visible watermark using the Fuzzy C-Means technique without any prior information. The proposed technique has two stages: the first stage is positioning, and the second is masking (extracting the watermark mask). Due to real-world limitations such as noise, shadowing, and variations in cameras, the positioning stage is developed by employing gradient and Fuzzy C-Means classifier techniques. By using the dilation and erosion operators, the masking stage is developed to extract the watermark mask. Using a set of trademark videos, the proposed algorithm is tested and evaluated. A comparative study shows that the proposed FCM-based technique is able to achieve higher accuracy at a reasonable computational cost in comparison to the most related and recent published work. The proposed technique could locate different watermarks with high symmetry in their pattern, even if they appeared mutually in the same location. Still, it will be a challenge if the symmetry is low between used watermarks in the same location.

Based on the Alzheimer's Charter, 2-3 million cases of dementia by Alzheimer's disease occur every year. People with Alzheimer's disease experience memory and cognitive disorders progressively for 3 to 9 years. Patients experience confusion in understanding the question and have a chaotic sequence of memory, which can interfere with daily activities and unchecked well, it cause death. The classification system is based on Alzheimer's and non-Alzheimer's disease Magnetic Resonance Imaging (MRI) using Support Vector Machine (SVM). The feature data segmentation using Fuzzy C-Means (FCM) and feature extraction using Gray Level Co-Occurrence Matrix (GLCM) and give accuracy result of 93.33%.

A novel two-stage fraud detection system in mobile telecom networks has been presented in this paper that identifies the malicious calls among the normal ones in two stages. Initially, a genetic algorithm-based optimized fuzzy c-means clustering is applied to the user’s historical call records for constructing the calling profile. Thereafter, the identification of the fraudulent calls occurs in two stages. In the first stage, each incoming call is passed to the clustering module that identifies the call as genuine, malicious or suspicious. This is done by comparing the distance value of the new calling instance from the profile cluster centers against two predefined threshold values. The calls detected as genuine or malicious are not further processed. However, the call records that are found to be suspicious are additionally scrutinized in the second stage by a previously trained group method of data handling model for final decision making. The legitimate and forged labeled call records generated out of the clustering module are utilized for training the supervised classifier. Experimentation is done on a real-world call dataset to exhibit the effectiveness of the proposed model. A comparative analysis of the current approach with one of our earlier propositions and another recent fraud detection system clearly illustrates the efficacy of the developed model.

Eye exam can be as efficacious as physical one in determining health concerns. Retina screening can be the very first clue for detecting a variety of hidden health issues including pre-diabetes and diabetes. Through the process of clinical diagnosis and prognosis; ophthalmologists rely heavily on the binary segmented version of retina fundus image; where the accuracy of segmented vessels, optic disc, and abnormal lesions extremely affects the diagnosis accuracy which in turn affect the subsequent clinical treatment steps. This paper proposes an automated retinal fundus image segmentation system composed of three segmentation subsystems follow same core segmentation algorithm. Despite of broad difference in features and characteristics; retinal vessels, optic disc, and exudate lesions are extracted by each subsystem without the need for texture analysis or synthesis. For sake of compact diagnosis and complete clinical insight, our proposed system can detect these anatomical structures in one session with high accuracy even in pathological retina images. The proposed system uses a robust hybrid segmentation algorithm combines adaptive fuzzy thresholding and mathematical morphology. The proposed system is validated using four benchmark datasets: DRIVE and STARE (vessels), DRISHTI-GS (optic disc), and DIARETDB1 (exudates lesions). Competitive segmentation performance is achieved, outperforming a variety of up-to-date systems and demonstrating the capacity to deal with other heterogeneous anatomical structures.

Fuzzy logic is an approach that reflects human thinking and decision making by handling uncertainty and vagueness using fuzzy membership functions. When a human is engaged in the design of a fuzzy system, symmetric properties are naturally preferred. Fuzzy c-means clustering is a clustering algorithm that can cluster datasets to produce membership matrix and cluster centers, which results in generating type-1 fuzzy membership functions. However, fuzzy c-means algorithm has a limitation of producing only a single membership function type, Gaussian MF. Generation of multiple fuzzy membership functions is of immense importance as it provides more efficient and optimal solutions to a problem. Therefore, an approach to generate multiple type-1 fuzzy membership functions through fuzzy c-means is required for the optimal and improved results of classification datasets. Hence, to overcome the limitation of the fuzzy c-means algorithm, an approach for the generation of type-1 fuzzy triangular and trapezoidal membership function through fuzzy c-means is considered in this study. The approach is used to calculate and enhance the accuracy of classification datasets called iris, banknote authentication, blood transfusion, and Haberman’s survival. The proposed approach of generating MFs using FCM produce asymmetric MFs, whose results are compared with the MFs produced from grid partitioning (GP), which are symmetric MFs. The results show that the proposed approach of generating type-1 fuzzy membership function through fuzzy c-means is effective and can be adopted.

A new robust 3D watermarking algorithm utilizing Fuzzy C-Means (FCM) clustering technique is presented. FCM clusters 3D mesh vertices into suitable and unsuitable choices to insert the watermark without occasioning visible deformation, and also it is tough for the attacker to determine places of the watermark insertion. Two watermarking processes are offered to insert the watermark into 3D mesh models. The first process utilizes topical statistical measurements like average and standard deviation in order to alter the values of vertices to secret watermark data into 3D mesh models, however, the second process utilizes a jumbled insertion planning to insert the watermark inside 3D mesh models utilizing the topical statistical measurements and altering 3D mesh vertices together. Simulation results show that the proposed algorithm is robust. The watermarked 3D mesh models are resistant to several attacks like similarity transforms, noise addition, cropping and mesh smoothing.

A novel neuro fuzzy classifier Hybrid Kohonen Fuzzy C-Means-σ (HKFCM-σ) is proposed in this paper. The proposed classifier is a hybridization of Kohonen Clustering Network (KCN) with FCM-σ clustering algorithm. The network architecture of HKFCM-σ is similar to simple KCN network having only two layers, i.e., input and output layer. However, the selection of winner neuron is done based on FCM-σ algorithm. Thus, embedding the features of both, a neural network and a fuzzy clustering algorithm in the classifier. This hybridization results in a more efficient, less complex and faster classifier for classifying satellite images. HKFCM-σ is used to identify the flooding that occurred in Kashmir area in September 2014. The HKFCM-σ classifier is applied on pre and post flooding Landsat 8 OLI images of Kashmir to detect the areas that were flooded due to the heavy rainfalls of September, 2014. The classifier is trained using the mean values of the various spectral indices like NDVI, NDWI, NDBI and first component of Principal Component Analysis. The error matrix was computed to test the performance of the method. The method yields high producer’s accuracy, consumer’s accuracy and kappa coefficient value indicating that the proposed classifier is highly effective and efficient.

Missing values are a huge constraint in microarray technologies towards improving and identifying disease-causing genes. Estimating missing values is an undeniable scenario faced by field experts. The imputation method is an effective way to impute the proper values to proceed with the next process in microarray technology. Missing value imputation methods may increase the classification accuracy. Although these methods might predict the values, classification accuracy rates prove the ability of the methods to identify the missing values in gene expression data. In this study, a novel method, Optimised Hybrid of Fuzzy C-Means and Majority Vote (opt-FCMMV), was proposed to identify the missing values in the data. Using the Majority Vote (MV) and optimisation through Particle Swarm Optimisation (PSO), this study predicted missing values in the data to form more informative and solid data. In order to verify the effectiveness of opt-FCMMV, several experiments were carried out on two publicly available microarray datasets (i.e. Ovary and Lung Cancer) under three missing value mechanisms with five different percentage values in the biomedical domain using Support Vector Machine (SVM) classifier. The experimental results showed that the proposed method functioned efficiently by showcasing the highest accuracy rate as compared to the one without imputations, with imputation by Fuzzy C-Means (FCM), and imputation by Fuzzy C-Means with Majority Vote (FCMMV). For example, the accuracy rates for Ovary Cancer data with 5% missing values were 64.0% for no imputation, 81.8% (FCM), 90.0% (FCMMV), and 93.7% (opt-FCMMV). Such an outcome indicates that the opt-FCMMV may also be applied in different domains in order to prepare the dataset for various data mining tasks.

This paper aims to promote the importance and advantages that the clustering method brings to the world of industry, making it possible to increase production efficiency and to manage the energy resources available better. The purpose of this paper is to group the consumption profiles of a task, in order to be able to determine which is the typical load profile of the task through the Fuzzy C-Means clustering method. The case study of this paper focuses on a task performed by three machines that make up a textile production line that makes several products. Each product, when going through a task performed by a specific machine, has a specific consumption and duration. Thus, by machine, it is determined which is the typical profile of ideal consumption to perform the designated task. In the same way, the general consumption profile of the task is highlighted, that is, the possible consumption profile to be expected when executing this task on one of the three machines., This work has received funding from Portugal 2020 under SPEAR project (NORTE-01-0247-FEDER-040224) and from FEDER Funds through COMPETE program and from National Funds through (FCT), Portugal under the project UIDB/00760/2020, and CEECIND/02887/2017.

Linear empirical dynamic models have been widely used for blood glucose prediction and risks prevention in people with type 1 diabetes. More accurate blood glucose prediction models with longer prediction horizon (PH) are desirable to enable warnings to patients about imminent blood glucose changes with enough time to take corrective actions. In this study, a blood glucose prediction method is developed by integrating the predictions of a set of seasonal local models (each of them corresponding to different glucose profiles observed along historical data). In the modeling step, the number of sets and their corresponding glucose profiles characteristics are obtained by clustering techniques (Fuzzy C-Means). Then, Box-Jenkins methodology is used to identify a seasonal model for each set. Finally, blood glucose predictions of local models are integrated using different techniques. The proposed method is tested by using 18 60-h closed-loop experiments (including different exercise types and artificial pancreas strategies) and achieving mean absolute percentage error (MAPE) of 2.94%, 3.89%, 5.41%, 6.29% and 8.66% for 15-, 30-, 45-, 60-, and 90-min PHs, respectively.

Accurate prediction of material feeding before production for a printed circuit board (PCB) template can reduce the comprehensive cost caused by surplus and supplemental feeding. In this study, a novel hybrid approach combining fuzzy c-means (FCM), feature selection algorithm, and genetic algorithm (GA) with back-propagation networks (BPN) was developed for the prediction of material feeding of a PCB template. In the proposed FCM&ndash, GABPN, input templates were firstly clustered by FCM, and seven feature selection mechanisms were utilized to select critical attributes related to scrap rate for each category of templates before they are fed into the GABPN. Then, templates belonging to different categories were trained with different GABPNs, in which the separately selected attributes were taken as their inputs and the initial parameter for BPNs were optimized by GA. After training, an ensemble predictor formed with all GABPNs can be taken to predict the scrap rate. Finally, another BPN was adopted to conduct nonlinear aggregation of the outputs from the component BPNs and determine the predicted feeding panel of the PCB template with a transformation. To validate the effectiveness and superiority of the proposed approach, the experiment and comparison with other approaches were conducted based on the actual records collected from a PCB template production company. The results indicated that the prediction accuracy of the proposed approach was better than those of the other methods. Besides, the proposed FCM&ndash, GABPN exhibited superiority to reduce the surplus and/or supplemental feeding in most of the case in simulation, as compared to other methods. Both contributed to the superiority of the proposed approach.

International audience; In this paper, a novel local threshold binarization method using fast Fuzzy C-Means clustering is proposed. Historical document images with non-uniform background, stains, faded ink are first processed by removing the background using inpainting based method. Then using Fuzzy C-Means clustering is used to cluster out the pixels into three main clusters : sure text pixels, sure background pixels and confused pixels which may or may not be labeled as text. Based on the structural symmetry of pixels (SSP), these confused pixels are then classified into text or background pixels. The SSP is defined as those pixels around strokes whose gradient magnitudes are big enough and whose directions are opposite. As the gradient map is our basis for computing the SSP, we further propose to estimate the background surface first and to extract potential SSP in the compensated image so as to deal with degradations of document images such as uneven illumination, low contrast and stain. To prove the effectiveness of our method, tests on eight public document image datasets are preformed and the experimental results show that our method outperforms other local threshold binarization approaches on both F-measure and PSNR.

The rolling bearings are considered as the heart of rotating machinery and early fault diagnosis is one of the biggest challenges during operation. Due to complicated mechanical assemblies, detection of the advancing fault and faults at the incipient stage is very difficult and tedious. This work presents a fuzzy rule based classification of bearing faults using Fuzzy C-means clustering method using vibration measurements. Experiments were conducted to collect the vibration signals of a normal bearing and bearings with faults in the inner race, outer race and ball fault. Discrete Wavelet Transform (DWT) technique is used to decompose the vibration signals into different frequency bands. In order to detect the early faults in the bearings, various statistical features were extracted from this decomposed signal of each frequency band. Based on the extracted features, Fuzzy C-means clustering method (FCM) is developed to classify the faults using suitable membership functions and fuzzy rule base is developed for each class of the bearing fault using labeled data. The experimental results show that the proposed method is able to classify the condition of the bearing using the extracted features. The proposed FCM based clustering and classification model provides easier interpretation and implementation for monitoring the condition of the rolling bearings at an early stage and it will be helpful to take the preventive action before a large-scale failure.

ObjectivesThe aim of this study was to identify, with soft clustering methods, multimorbidity patterns in the electronic health records of a population ≥65 years, and to analyse such patterns in accordance with the different prevalence cut-off points applied. Fuzzy cluster analysis allows individuals to be linked simultaneously to multiple clusters and is more consistent with clinical experience than other approaches frequently found in the literature.DesignA cross-sectional study was conducted based on data from electronic health records.Setting284 primary healthcare centres in Catalonia, Spain (2012).Participants916 619 eligible individuals were included (women: 57.7%).Primary and secondary outcome measuresWe extracted data on demographics, International Classification of Diseases version 10 chronic diagnoses, prescribed drugs and socioeconomic status for patients aged ≥65. Following principal component analysis of categorical and continuous variables for dimensionality reduction, machine learning techniques were applied for the identification of disease clusters in a fuzzy c-means analysis. Sensitivity analyses, with different prevalence cut-off points for chronic diseases, were also conducted. Solutions were evaluated from clinical consistency and significance criteria.ResultsMultimorbidity was present in 93.1%. Eight clusters were identified with a varying number of disease values: nervous and digestive; respiratory, circulatory and nervous; circulatory and digestive; mental, nervous and digestive, female dominant; mental, digestive and blood, female oldest-old dominant; nervous, musculoskeletal and circulatory, female dominant; genitourinary, mental and musculoskeletal, male dominant; and non-specified, youngest-old dominant. Nuclear diseases were identified for each cluster independently of the prevalence cut-off point considered.ConclusionsMultimorbidity patterns were obtained using fuzzy c-means cluster analysis. They are clinically meaningful clusters which support the development of tailored approaches to multimorbidity management and further research.

Recent clinical research studies in forensic identification have highlighted the interest in sphenoid sinus anatomical characterization. Their pneumatization, well known as extremely variable in degrees and directions, could contribute to the radiologic identification, especially if dental records, fingerPrints, or DNA samples are not available. In this paper, we present a new approach for automatic person identification based on sphenoid sinus features extracted from computed tomography (CT) images of the skull. First, we present a new approach for fully automatic 3D reconstruction of the sphenoid hemisinuses which combines the fuzzy c-means method and mathematical morphology operations to detect and segment the object of interest. Second, deep shape features are extracted from both hemisinuses using a dilated residual version of a stacked convolutional auto-encoder. The obtained binary segmentation masks are thus hierarchically mapped into a compact and low-dimensional space preserving their semantic similarity. We finally employ the l2 distance to recognize the sphenoid sinus and therefore identify the person. This novel sphenoid sinus recognition method obtained 100% of identification accuracy when applied on a dataset composed of 85 CT scans stemming from 72 individuals. Automatic Forensic Identification using 3D Sphenoid Sinus Segmentation and Deep Characterization from Dilated Residual Auto-Encoders.

For resource-constrained IoT systems, data collection is one of the fundamental operations to reduce the energy dissipation of sensor nodes and improve the network lifetime. However, an anomaly or deviation will exert a great influence on the quality of data collected, especially for a data aggregation scheme. By taking into account data-aware clustering and detection of anomalous events, a similarity-aware data aggregation using a fuzzy c-means approach for wireless sensor networks is proposed. Firstly, by using a fuzzy c-means approach, the clustering process can be performed to organize sensors into clusters based on data similarity. Next, an effective support degree function is defined for further outlier diagnosis. Afterwards, the appropriate weight of valid data can be obtained by taking advantage of the probability distribution characteristics of normal samples within a certain period. Finally, the aggregation result in the cluster can be estimated. Practical database-based simulations have confirmed that the proposed data aggregation method can achieve better performance than traditional methods in terms of data outlier detection accuracy and relative recovery error.

Clustering analysis of massive data in wireless multimedia sensor networks (WMSN) has become a hot topic. However, most data clustering algorithms have difficulty in obtaining latent nonlinear correlations of data features, resulting in a low clustering accuracy. In addition, it is difficult to extract features from missing or corrupted data, so incomplete data are widely used in practical work. In this paper, the optimally designed variational autoencoder networks is proposed for extracting features of incomplete data and using high-order fuzzy c-means algorithm (HOFCM) to improve cluster performance of incomplete data. Specifically, the feature extraction model is improved by using variational autoencoder to learn the feature of incomplete data. To capture nonlinear correlations in different heterogeneous data patterns, tensor based fuzzy c-means algorithm is used to cluster low-dimensional features. The tensor distance is used as the distance measure to capture the unknown correlations of data as much as possible. Finally, in the case that the clustering results are obtained, the missing data can be restored by using the low-dimensional features. Experiments on real datasets show that the proposed algorithm not only can improve the clustering performance of incomplete data effectively, but also can fill in missing features and get better data reconstruction results.