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Background: Measuring non‐parametric intravoxel mean diffusivity distributions (MDDs) using magnetic resonance imaging (MRI) is a sensitive method for detecting intracellular diffusivity changes during physiological alterations. Histological and molecular glioma classifications are essential for prognosis and treatment, with distinct water diffusion dynamics among subtypes. Purpose: We developed a data‐driven approach using a fully connected network (FCN) to enhance the speed and stability of calculating MDDs across varying SNRs, enable tumor microstructural mapping, and test its reliability in identifying MIB‐1 labeling index (LI) levels and molecular status of gliomas. Methods: An FCN was trained to learn the mapping between the simulated diffusion decay curves and the ground truth MDDs. We performed 5 000 000 simulation curves with various diffusivity components and random SNR ∈[30,300]$ \in [ {30,\ 300} ]$. Eighty percent of simulation curves were used for the FCN training, 10% for validation, and the others were external tests for the FCN performance evaluation. In vivo data were collected to evaluate its clinical reliability. One hundred one patients (44 years ±$ \pm $ 14, 67 men) with gliomas and six healthy controls underwent a 3.0 T MRI examination with a spin echo–echo planar imaging (SE‐EPI) diffusion‐weighted imaging (DWI) sequence. The trained FCN was employed to calculate MDDs of each brain voxel by voxel. We used the Fuzzy C‐means algorithm to cluster the MDDs of tumor voxels, facilitating the characterization of distinct glioma tissues. Quantitative assessments were conducted through sectional integrals of the MDDs, demarcated by six bands to derive signal fractions (fn,n=1−6${{f}_n},\ n = 1 -6$) and diffusivities of the maximum peaks (Dpeak${{D}_{peak}}$). Cosine similarity scores (CSS) were used for MDD similarity. ANOVA and Mann–Whitney U test were used for difference analysis. Logistic regression and area under the receiver operator characteristic curve (AUC) were used for classification evaluation. Results: The simulation results showed that the FCN‐based MDD approach (FCN‐MDD) achieved higher CSS than non‐negative least squares‐based MDD (NNLS‐MDD). For in vivo data, the spectra of ET and NET obtained by FCN‐MDD are more distinguishable than NNLS‐MDD. Fraction maps delineate the characteristics of different tumor tissues (enhancing and non‐enhancing tumor, edema, and necrosis). f3,f4,Dpeak${{f}_3},\ {{f}_4},{{D}_{peak}}$ showed a positive and negative correlation with MIB‐1 respectively (r=0.568,r=−0.521,r=−0.654$r = 0.568,\ r = - 0.521,\ r = - 0.654$, all p<0.001$p < 0.001$). The AUC of Dpeak${{D}_{peak}}$ for predicting MIB‐1 LI levels was 0.900 (95% CI, 0.826–0.974), versus 0.781 (0.677–0.886) of ADC. The highest AUC of isocitrate dehydrogenase (IDH) mutation status, assessed by a logistic regression model (f1+f3${{f}_1} + {{f}_3}$) was 0.873 (95% CI, 0.802–0.944). Conclusion: The proposed FCN‐MDD method was more robust to variations in SNR and less reliant on empirically set regularization values than the NNLS‐MDD method. FCN‐MDD also enabled qualitative and quantitative evaluation of the composition of gliomas. [ABSTRACT FROM AUTHOR]

This article is concerned with the adaptive event‐triggered (AET) efficient output feedback model predictive control (EOFMPC) for a set of nonlinear networked interval type‐2 Takagi–Sugeno (IT2 T–S) fuzzy control systems with data loss. For the reduction in data transmission burden and required control performance, the AET scheme and a novel dynamic triggering law are introduced jointly in this article. The efficient model predictive control approach as in Kouvaritakis et al. (IEEE Trans Automat Contr. 2000;45(8):1545–1549.), which transfers the main part of online optimization work to offline stage by incorporating an extra perturbation into the fixed feedback law, is extended to networked control systems to reduce their online computational burden. This article proposes the following: (1) an unified closed‐loop model representation of data loss process and IT2 T–S fuzzy nonlinearities is established under the framework of EOFMPC, which explicitly considers the AET condition; (2) a new design method for the state observer gain is presented based on an ellipsoidal invariant set, so that a smaller bound of estimation error can be obtained through an extra optimization problem; (3) some optimization problems are formulated to design the proposed controllers, and the corresponding EOFMPC algorithm is summarized; (4) by means of the extra matrix partition technique, the online optimization problem subject to one ellipsoidal constraint is transformed into a novel convex form to solve the uncertainty of the estimation error. A simulation example and a few comparative simulation experiments illustrate the effectiveness and availability of the aforementioned approach. [ABSTRACT FROM AUTHOR]

A broad understanding of the subsoil characteristics is required to deal with uncertainty in the infrastructure planning phase or even in the diagnosis of infrastructure damage. In this work, electrical resistivity and seismic methods have been employed for lithological characterization and geomechanical parameters assessment. The study area is characterized by high variability in geotechnical characteristics due to complex lithology. For lithological discrimination, we have used a soft clustering method to combine the independently electrical‐ and seismic‐derived models. The outputs of this integration process are zonal models that help to decrease interpretation uncertainties. From single seismic profiles, both Vp and Vs information are extracted allowing geomechanical parameters to be estimated. All this information is critical for assessing the variance and inhomogeneity of the subsoil characteristics. The methodology shown in this work can be used in the infrastructure planning phase as well as to ascertain the causes of detected damage on built infrastructure. [ABSTRACT FROM AUTHOR]

Deterioration of concrete structures is closely related to their service environment. Under different environmental conditions, the durability performance of structures will be different. To perform durability design based on local conditions and extend the service life of structures, it is necessary to divide their service environment based on the prevailing deterioration mechanism. In this study, considering Guangdong Province as an example, the environmental conditions affecting the durability of concrete structures are investigated and the geographical distribution of environmental conditions is analyzed, including ambient temperature, precipitation, corrosive gas, acid rain, and salt fog. The weights of the regional durability indexes are calculated by combining fuzzy clustering analysis with information entropy using rough set theory. Based on the assessment data of 1324 existing concrete structures, the durability geographic division map of Guangdong Province, including three regions (Chloride corrosion region, Acid rain‐carbonation coupling region, and Carbonation region), is constructed. This geographic division map is divided into a total of eight subregions by considering the subordinate factors in each region. The environmental characteristics of each region and its dominating deterioration mechanism for concrete structures are analyzed, and suggestions for durability design are provided. [ABSTRACT FROM AUTHOR]

Summary: Information mining and semantic analysis have gained significant attention over recent years to obtain appropriate information from unstructured data. Several approaches have been introduced for web information mining. However, the expected accuracy is not reached by these approaches. Therefore, hybrid fuzzy clustering and enhanced latent Dirichlet allocation (ELDA) are proposed for the accuracy increment in this work. The information clustering process is performed using the hybrid fuzzy clustering algorithm called fuzzy‐C‐medoids optimized using improved whale algorithm. The clustering procedure entails grouping data points into more similar clusters than data points from other clusters. Finally, the context of the text is recognized by analyzing the semantic information with ELDA, which offers a suitable index for accurate and fast data extraction. PYTHON tool is used to develop the proposed web mining model, and the simulation analysis of the proposed model is carried out using the BibTex dataset and compared with baseline models. The performance of the proposed method is evaluated using purity, normalized mutual information, accuracy, and precision metrics. Also, it is compared with different existing algorithms. Thus, the analysis of the results proved that the proposed approach achieves better outcomes than the existing approaches. [ABSTRACT FROM AUTHOR]

This study investigates the projected heatwaves for India using 0.25° × 0.25° summer mean temperature data from Coupled Model Intercomparison Project (CMIP) 5 and 6 simulations for 2016–2100. The heatwaves were delineated using count and density‐based indices. A new approach of using medians of past as threshold for computing projected variables was introduced to amplify the footprint of the warming signal on the heatwaves. Further, spatio‐climatic heterogeneity of India was accounted through optimizing climatic zones using the fuzzy c‐mean clustering technique. Temporal homogeneity, trend, intensification and attribution of the heatwave variables were computed and analysed. Additionally, the scenario uncertainty was partitioned from the internal climatic variability, and consequently the variables were modelled within a probabilistic framework. Within this probabilistic framework, a novel concept of intensification ratio was introduced to capture the cost of climate action/inaction on heatwaves in India. All the heatwave variables except heatwave count have breaks in homogeneity between 2045 and 2055 and show predominantly increasing trends. While heatwave count shows an intensification of 4–6 times over the past, with 70% of the intensification being attributable to climate change, for other variables, the figures are 35–30 times and 95%. The scenario uncertainty surpasses the internal climatic variability around 2050. The probabilistic framework yields normalized values of uncertainty mostly below 0.5, enhancing the applicability of the results. Finally, the intensification ratio shows that climate inaction will intensify the heatwaves for ~75% of India by ~50–100%. The intensification of heatwave density over its count and relative accentuation of heatwaves resulting from climate inaction are the major takeaways from this study. [ABSTRACT FROM AUTHOR]

Cluster‐scaled analysis means exploiting the cluster‐based scaling to conventional data analysis to obtain more accurate results or results that we cannot obtain by using ordinary analysis. Our target data is complex and large amounts of data. For this type of data, it is well known that ordinary statistical methods do not always work well, or theoretically, we know that we cannot obtain a correct result. As a tool of this implementation, we utilize fuzzy clustering, which is well known as a robust clustering to a complex and large amount of data. That is, we use the fuzzy clustering result as a scale of data and apply the rescaled data by the cluster‐scale to another target analysis. Our target analysis in this article is principal component analysis, which is a well‐known dimensional reduction method. A numerical example shows a better performance of the cluster‐scaled principal component analysis. This article is categorized under:Statistical Learning and Exploratory Methods of the Data Sciences > Knowledge DiscoveryStatistical Learning and Exploratory Methods of the Data Sciences > Modeling MethodsStatistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification [ABSTRACT FROM AUTHOR]

This paper presents a new fuzzy time series forecasting model based on technical analysis, affinity propagation (AP) clustering, and a support vector regression (SVR) model. Technical analysis indicators are divided into three categories to construct multivariate fuzzy logical relationships. AP clustering without specifying the number of clusters is used to obtain a suitable partition for the universe of discourse, and the representative exemplars are generated as defuzzied values. The SVR model is employed to explore the unrecognized relationships and modify the forecasts. In addition, the error‐based evaluation criteria are applied to evaluate the methods. The performance of the method is evaluated using the Taiwan Capitalization Weighted Stock Index (TAIEX), Standard & Poor's 500 Index (S&P500), and Dow Jones Industrial Average (DJIA) dataset, and the experimental results demonstrate that the proposed method outperforms some classic models. [ABSTRACT FROM AUTHOR]

Summary: For the dataset with different sample contributions and different feature importance, it is difficult to acquire a proper cluster structure that covers the entire features of the sample set. To improve the clustering result, a novel weighted fuzzy clustering algorithm based on both samples and generalized entropy features, called SGEF‐WFCM, is proposed in this article, among which a new objective function is developed on the basis of feature‐weighted generalized entropy regularization with a double‐weighting strategy of samples and features, the weighted coefficients of the features to each cluster, as well as the importance of the samples to the cluster are calculated dynamically, to obtain a better clustering result. Finally, experiments on both synthetic datasets and real‐world datasets from UCI are employed to verify the performance of the proposed SGEF‐WFCM algorithm. The results show that SGEF‐WFCM is superior to the conventional FCM algorithm in both the effectiveness and the usefulness during practices. [ABSTRACT FROM AUTHOR]

During the past decades, many studies have been carried out in an attempt to build accurate software development effort estimation techniques. However, none of the techniques proposed has proven to be successful at predicting software effort in all circumstances. Among these techniques, analogy‐based estimation has gained significant popularity within software engineering community because of its outstanding performance and ability to mimic the human problem solving approach. One of the challenges facing analogy‐based effort estimation is how to predict effort when software projects are described by a mixture of continuous and categorical features. To address this issue, the present study proposes an improvement of our former 2FA‐kprototypes technique referred to as 2FA‐cmeans. 2FA‐cmeans uses a clustering technique, called general fuzzy c‐means, which is a generalization of the fuzzy c‐means clustering technique to cluster objects with mixed attributes. The performance of 2FA‐cmeans was evaluated and compared with that of our former 2FA‐kprototypes technique as well as classical analogy over six datasets that are quite diverse and have different sizes. Empirical results showed that 2FA‐cmeans outperforms the two other analogy techniques using both all‐in and jackknife evaluation methods. This was also confirmed by the win–tie–loss statistics and the Scott–Knott test. [ABSTRACT FROM AUTHOR]

Summary: The procedure for revelation and extraction is known as the data mining (DM) where immense measure of information is included. The information mining along with the human services industry has developed solid frameworks and different social insurance that are related to the frameworks from the clinical and finding information. Numerous types of assistance have been offered by healthcare. This includes diagnosing, treatment, avoidance of maladies, sicknesses, wounds and other physical and mental issue. Enormous scopes that are appropriated for preparing applications in healthcare have essential idea that works on a lot of information. Large information application capacities are the primary piece of medicinal services activities. However, no far reaching and methodical review regarding the examining and assessing the significant systems in the field. The different researches associated with the field of healthcare are considered or different strategies, calculations and results. Enormous information object has countless traits, representing a wonderful test on fuzzy C‐means (FCM) for huge information constant grouping. An effective FCM depends on the tensor accepted polyadic deterioration for huge information clustering. The advanced and enhanced fuzzy C‐means algorithm for healthcare (AEFCH) calculation which is an introduced term is changed over to a high‐request tensor FCM calculation by means of a bijection work. Tensor authoritative polyadic deterioration is used to decrease qualities of the items for improving the grouping productivity. Outcomes obtained accomplish fundamentally higher productivity with a slight cluster exactness drop contrasted and the customary calculation, demonstrating the capability of plot that has been created for information of smart data from Internet of Things. [ABSTRACT FROM AUTHOR]

The study of the kinematics and stress field related to seismicity makes an important contribution to the understanding of tectonic processes. In this kind of analysis, a crucial issue is identifying seismically homogeneous areas, which implies data classification and cluster creation. We present an approach that combines unsupervised learning techniques in order to reveal patterns in the focal mechanisms data set. In particular, a combination of two popular clustering algorithms, that is, self‐organizing maps and Fuzzy C‐means, was applied to focal mechanisms of events located in the Central Mediterranean region, characterized by a complex geodynamic framework. The analysis allowed identifying eight groups of focal mechanisms and their spatial distribution in the crust, and revealing the tectonic style of key sectors of southern Italy and of the neighboring offshore areas. A compressive regime was found between the lower Tyrrhenian Sea and southeastern Sicily, whereas extension prevails along the Calabrian Arc and the southern Apennines. A NW‐SE transcurrent faulting between the Aeolian Islands and the Ionian Sea forms a transfer zone between these two domains. Plain Language Summary: The study of mechanisms describing the movement of the crustal blocks in a fault and of the forces causing earthquakes, provides precious information on the ongoing tectonic processes in a region. An important aspect in such an analysis is distinguishing the type of fault mechanism and identifying seismically homogeneous areas. To this end, in this work we have used a two‐step approach that combines unsupervised learning—where objects are classified by the computer on the basis of their characteristics—and an analysis of the spatial distribution of the objects. This approach has been applied to a data set selected from the earthquakes recorded in the past 40 years in the Central Mediterranean region. The first step allowed distinguishing the earthquake mechanisms into fairly homogeneous groups; the final step was the analysis of their distribution in space, aiming at identifying prevailing regimes of deformation within the crust. In this way, we were able to characterize the different tectonic processes acting in key areas of the studied region. Key Points: Underlying patterns in focal mechanisms can be revealed by an approach that combines unsupervised learning techniquesThe prevailing kinematics and stress regime are inferred by a spatial analysis and focal mechanisms categorizationThe tectonic regime characterizing key sectors of the studied region is identified [ABSTRACT FROM AUTHOR]

Accurate estimation of ET0, and determination of its trend/variability and sensitivity to changes in climate variables is essential at regional scale for different applications. These are attempted for India by considering 18 homogeneous ET0 regions formed in companion paper. Food and Agriculture Organization (FAO) recommends Penman–Monteith (PM) equation for ET0 estimation, which requires information on several climate variables. The equation cannot be used in data‐sparse areas where information/forecasts on one or more required climate variables is unavailable/unreliable. To address this, relevance vector regression (RVR) relationships are developed in this paper for the 18 homogeneous regions to arrive at FAO‐PM estimate of ET0 from subsets of its predictor climate variables, which could be typically expected in data‐sparse areas. The developed relationships are shown to be better in arriving at ET0 estimates when compared to multiple linear regression (MLR) relationships and three widely used empirical equations (Hargreaves, Mcguinness–Bordne, Priestly–Taylor). Regional trend analysis revealed that ET0 is significantly decreasing (increasing) in most regions located in north (south) India. Sensitivity of ET0 and surface runoff to changes in their predictor climate variables is determined for each region by considering third‐order Taylor series approximation of their functional relationships. Key climate variable(s) that govern ET0 changes in each region are identified. Results indicate that at annual scale solar radiation and relative humidity govern ET0 changes in regions located in south and north‐east India, whereas wind speed followed by relative humidity mostly influence the ET0 changes over other regions. Existence of evaporation paradox in four regions and validity of Bouchet's complementary relationship between ET0, actual evapotranspiration (ETa), and precipitation in 11 regions is also established. Significant divergence in trend of ET0 and ETa was evident in north‐east India. [ABSTRACT FROM AUTHOR]

In the i‐vector/probabilistic linear discriminant analysis (PLDA) technique, the PLDA backend classifier is modelled on i‐vectors. PLDA defines an i‐vector subspace that compensates the unwanted variability and helps to discriminate among speaker‐phrase pairs. The channel or session variability manifested in i‐vectors are known to be nonlinear in nature. PLDA training, however, assumes the variability to be linearly separable, thereby causing loss of important discriminating information. Besides, the i‐vector estimation, itself, is known to be poor in case of short utterances. This paper attempts to address these issues using a simple hierarchy‐based system. A modified fuzzy‐clustering technique is employed to divide the feature space into more characteristic feature subspaces using vocal source features. Thereafter, a separate i‐vector/PLDA model is trained for each of the subspaces. The sparser alignment owing to subspace‐specific universal background model and the relatively reduced dimensions of variability in individual subspaces help to train more effective i‐vector/PLDA models. Also, vocal source features are complementary to mel frequency cepstral coefficients, which are transformed into i‐vectors using mixture model technique. As a consequence, vocal source features and i‐vectors tend to have complementary information. Thus using vocal source features for classification in a hierarchy tree may help to differentiate some of the speaker‐phrase classes, which otherwise are not easily discriminable based on i‐vectors. The proposed technique has been validated on Part 1 of RSR2015 database, and it shows a relative equal error rate reduction of up to 37.41% with respect to the baseline i‐vector/PLDA system. [ABSTRACT FROM AUTHOR]

Summary: The business models of banks are often seen as the result of a variety of simultaneously determined managerial choices, such as those regarding the types of activities, funding sources, level of diversification, and size. Moreover, owing to the fuzziness of data and the possibility that some banks may combine features of different business models, the use of hard clustering methods has often led to poorly identified business models. In this paper we propose a framework to deal with these challenges based on an ensemble of three unsupervised clustering methods to identify banking business models: fuzzy c‐means (which allows us to handle fuzzy clustering), self‐organizing maps (which yield intuitive visual representations of the clusters), and partitioning around medoids (which circumvents the presence of data outliers). We set up our analysis in the context of the European banking sector, which has seen its regulators increasingly focused on examining the business models of supervised entities in the aftermath of the twin financial crises. In our empirical application, we find evidence of four distinct banking business models and further distinguish between banks with a clearly defined business model (core banks) and others (non‐core banks), as well as banks with a stable business model over time (persistent banks) and others (non‐persistent banks). Our proposed framework performs well under several robustness checks related with the sample, clustering methods, and variables used. [ABSTRACT FROM AUTHOR]

The seismic reflection method provides high‐resolution data that are especially useful for discovering mineral deposits under deep cover. A hindrance to the wider adoption of the seismic reflection method in mineral exploration is that the data are often interpreted differently and independently of other geophysical data unless common earth models are used to link the methods during geological interpretation. Model‐based inversion of post‐stack seismic data allows rock units with common petrophysical properties to be identified and permits increased bandwidth to enhance the spatial resolution of the acoustic‐impedance model. However, as seismic reflection data are naturally bandlimited, any inversion scheme depends upon an initial model, and must deal with non‐unique solutions for the inversion. Both issues can be largely overcome by using constraints and integrating prior information. We exploit the abilities of fuzzy c‐means clustering to constrain and to include prior information in the inversion. The use of a clustering constraint for petrophysical values pushes the inversion process to select models that are primarily composed of several discrete rock units and the fuzzy c‐means algorithm allows some properties to overlap by varying degrees. Imposing the fuzzy clustering techniques in the inversion process allows solutions that are similar to the natural geologic patterns that often have a few rock units represented by distinct combinations of petrophysical characteristics. Our tests on synthetic models, with clear and distinct boundaries, show that our methodology effectively recovers the true model. Accurate model recovery can be obtained even when the data are highly contaminated by random noise, where the initial model is homogeneous, or there is minimal prior petrophysical information available. We demonstrate the abilities of fuzzy c‐means clustering to constrain and to include prior information in the acoustic‐impedance inversion of a challenging magnetotelluric/seismic data set from the Carlin Gold District, USA. Using fuzzy c‐means guided inversion of magnetotelluric data to create a starting model for acoustic‐impedance proved important in obtaining the best result. Our inversion results correlate with borehole data and provided a better basis for geological interpretation than the seismic reflection images alone. Low values of the acoustic impedance in the basement rocks were shown to be prospective by geochemical analysis of rock cores, as would be predicted for later gold mineralization in weak, decalcified rocks. [ABSTRACT FROM AUTHOR]

A statistical model for typhoon‐induced accumulated rainfall (TAR) prediction over the Korean Peninsula using track, intensity and rainfall data of 91 typhoons affecting the peninsula during the period 1977–2014 is developed. The statistical estimation of the TAR consists of three steps: (1) estimating the TAR at 56 observational weather stations for the 91 typhoons; (2) selecting typhoons whose tracks are similar to that of the target typhoon within the area of 32–40 ° N and 120–138 ° E using a fuzzy C‐mean clustering method; and (3) calculating the mean TAR for the 16 selected typhoons based on track similarity after an intensity correction of the TAR using a linear regression between the TAR and intensity anomaly. To validate the model, real case predictions were performed on typhoons Chan‐hom and Goni in 2015 and compared with the observed TARs as well as with those from local and global operational models. The result showed that when the best‐track data are used, the present statistical model can predict the TAR with the accuracy of mean root mean square errors (RMSEs) of 33 mm (Chan‐hom) and 29 mm (Goni) at the 56 stations, which were much less than the results of the local model. With the predicted track and intensity data for the two typhoons, the present model also showed an overall good performance with RMSEs of 30–34 mm (Chan‐hom) and 29–49 mm (Goni), depending on the accuracy of the predicted track and intensity, which were generally less than the results of the global model. [ABSTRACT FROM AUTHOR]

In smart power system, effective methods have to be chosen to interact with consumers. Demand response programmes (DRPs) as the most effective way to interact with consumers have a variety of types that should be decided to be chosen and implemented according to consumer's behaviour. Power consumers are classified according to their behaviour and consumption. This classification consists of various clusters including residential, commercial, agricultural, industrial and public consumption. The consumers' behaviour approach is classified by weighed fuzzy average K-means clustering method. An accurate DRPs non-linear model considering coefficient of participation is presented and applied to each cluster. The decision-making indicators are determined and weighed using the entropy method. The aim is to prioritise the DRPs implementation in each cluster, which is done by the technique for order preference by similarity to ideal-solution method. Finally, a case study is investigated on a real network and the impact of variables and factors on the priority of DRPs is discussed. [ABSTRACT FROM AUTHOR]

An improved cluster validity index for fuzzy clustering that is able to overcome three intrinsic drawbacks in conventional cluster validity indexes is proposed. The effectiveness of the proposed index is demonstrated by the analysis of its limiting behaviour and comparison with the performance of the Xie‐Beni index, Kwon index, and Tang index on twelve data sets. [ABSTRACT FROM AUTHOR]

Image clustering is considered amongst the most important tasks in medical image analysis and it is regularly required as a starter and vital stage in the computer-aided medical image process. In brain magnetic resonance imaging (MRI) analysis, image clustering is regularly used for estimating and visualising the brain anatomical structures, to detect pathological regions and to guide surgical procedures. This study presents a new method for MRI brain images clustering based on the farthest point first algorithm and fuzzy clustering techniques without using any a priori information about the clusters number. The algorithm has been approved against both simulated and clinical magnetic resonance images and it has been compared with the fourth clustered algorithms. Results demonstrate that the proposed algorithm has given reasonable segmentation of white matter, grey matter and cerebrospinal fluid from MRI data, which is superior in preserving image details and segmentation accuracy compared with the other four algorithms giving more than 91% in Jaccard similarity. [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. [ABSTRACT FROM AUTHOR]

This study proposes an adaptive boosting (AdaBoost) algorithm for precise and accurate prediction of transient security assessment of power systems using synchronised measurements. The proposed approach (PRPA) also determines the generator coherent state as well as the synchronism status of each generating unit. The PRPA consists of three classifier models, in which classifier I determines the transient security status and classifier II is used to determine the generator coherency. Classifier III is a hybrid classifier, which determines the individual generator synchronism state for a given operating condition. This hybrid classifier consists of an array of parallel classifiers, where one classifier is assigned to each generating unit of the power system. For this assessment, the measured rotor angles of the generators are used as inputs to the proposed classifier models. In classification stage, several weak classifiers are combined in a linear manner to construct a strong classifier. The performance of the AdaBoost algorithm is further improved by a new weight updation strategy using fuzzy clustering thresholding technique. Simulation results obtained from IEEE 14-bus, IEEE 30-bus and Indian 246-bus systems reveal that the PRPA can enhance the overall monitoring and assessment of power system using synchronised measurements. [ABSTRACT FROM AUTHOR]

During the color design process, it is critical to create diversified color schemes for various consumer groups. It is also important to allow members in a design team to brainstorm for creative ideas. A theory of inspecting natural color elements was proposed in this study based on the CIE 1976 (L*,a*,b*) (CIE 1976) color space and the fuzzy c‐means clustering method. A calculation model was built to determine people's preference for colors based on a serialized system. To determine which factors affect the prediction of consumers' preference for a series of products, a gray relational color preference system was used in combination with gray relational grade so that a designer can obtain reasonable prediction results. The proposed system recommends not only the optimal colors for a product but also the optimal color scheme for a series of products. [ABSTRACT FROM AUTHOR]

In order to transfer the color imagery from an image to another object to generate color schemes, a color network model is proposed. It consists of two subnets: source net for describing the color information of the reference image; target net for demonstrating the target object to be colored. Thus, the problem of reusing features of color patterns is translated into the mapping process from the source net to target net. Four indicators are designed to measure the conformity between the two nets: color sequence, adjacency, concentration, and the subspace size. In the meantime, a comprehensive aesthetic evaluation given by the designer is introduced to search the optimum combination of the four indicators to help generate imagery matching schemes. A prototype system is developed based on CorelDraw as a design tool. The feasibility of the color network model is then verified through a color combination of graphic design task. [ABSTRACT FROM AUTHOR]

In order to obtain the parameters of excitation system accurately, and improve the accuracy and efficiency of parameter identification much further, an improved niche genetic algorithm was adopted, which can overcome local convergence of genetic algorithm by the fitness sharing mechanism that could increase population diversity. Moreover, the proposed algorithm could avoid the disadvantage of usual niche genetic algorithm that is difficult in determining the niches. By using the method of fuzzy clustering, niche groups can be created dynamically. In addition, regarding the problem that the partial parameters of excitation system cannot identify stably because of the relevance among these parameters, this study analysed the identifiability of excitation system model primarily, separated associated parameters into several parameter sets then. This type of relevance would be removed by assigning typical values to representative parameters, which belong to different sets. Identifiability analysis can not only avoid identifying parameters blindly, but also increase the credibility of the results. The test study shows that the proposed method can acquire accurate and reliable parameter values. [ABSTRACT FROM AUTHOR]

More and more ensemble models are used to forecast business failure. It is generally known that the performance of an ensemble relies heavily on the diversity between each base classifier. To achieve diversity, this study uses kernel‐based fuzzy c‐means (KFCM) to organize firm samples and designs a hierarchical selective ensemble model for business failure prediction (BFP). First, three KFCM methods—Gaussian KFCM (GFCM), polynomial KFCM (PFCM), and Hyper‐tangent KFCM (HFCM)—are employed to partition the financial data set into three data sets. A neural network (NN) is then adopted as a basis classifier for BFP, and three sets, which are derived from three KFCM methods, are used to build three classifier pools. Next, classifiers are fused by the two‐layer hierarchical selective ensemble method. In the first layer, classifiers are ranked based on their prediction accuracy. The stepwise forward selection method is employed to selectively integrate classifiers according to their accuracy. In the second layer, three selective ensembles in the first layer are integrated again to acquire the final verdict. This study employs financial data from Chinese listed companies to conduct empirical research, and makes a comparative analysis with other ensemble models and all its component models. It is the conclusion that the two‐layer hierarchical selective ensemble is good at forecasting business failure. [ABSTRACT FROM AUTHOR]

SUMMARY: In this paper, a hybrid model of fuzzy clustering and ANN (Artificial Neural Network) is proposed for electricity price forecasting. Due to the complicated behavior of electricity price in power markets, market players are interested in maximizing profits while minimizing risks. As a result, more accurate models are required to deal with electricity price forecasting. This paper proposes a new method that makes use of fuzzy clustering preconditioned GRBFN (Generalized Radial Basis Function Network) to provide more accurate predicted prices. Fuzzy clustering plays a key role to prevent the number of learning data from decreasing at each cluster. GRBFN is one of efficient ANNs to approximate nonlinear systems. Furthermore, a modified GRBFN model is developed to improve the performance of GRBFN with the use of DA (Deterministic Annealing) clustering for the parameters initialization and EPSO (Evolutionary Particle Swarm Optimization) for optimizing the parameters of GRBFN. The proposed method is successfully applied to real data of ISO New England, USA. [ABSTRACT FROM AUTHOR]

The method used for feature selection or feature weighting in regionalization of watersheds may affect the results of regionalization methods considerably. It can play a key role in forming hydrologically homogeneous regions for regional flood frequency analysis. In this study, a method based on exploring the nearest and farthest neighbours of data points is introduced for identifying salient features for regionalization of watersheds. The method includes options to relate watershed features to flood data records in order to increase the homogeneity of the regions. The nearest and farthest neighbours are identified based on the criteria such as the mutual information criterion and Spearman's rank correlation coefficient. Then, the watershed features more able to explain the relationships between the nearest and farthest neighbours are identified as salient features to form homogeneous features for regional flood frequency analysis. The results show that the optimum option of the proposed method improves the performances of the hard and fuzzy clustering algorithms in more than half of the cases based on the cluster validity indices. Furthermore, the results reveal that the optimum option can increase the number of the homogeneous regions formed by clustering algorithms to a great extent. By using the optimum option with 5 nearest and 5 farthest neighbours, longitude, drainage area, and run‐off coefficient are identified as the salient features to regionalize Sefidrud basin. The results show that the proposed method can be considered as an efficient method to form homogeneous regions for regional flood frequency analysis. [ABSTRACT FROM AUTHOR]

This article aims at developing an automated hybrid algorithm using Cuckoo Based Search (CBS) and interval type-2 fuzzy based clustering, so as to exhibit efficient magnetic resonance (MR) brain image segmentation. An automatic MR brain image segmentation facilitates and enables a radiologist to have a brief review and easy analysis of complicated tumor regions of imprecise gray level regions with minimal user interface. The tumor region having severe intensity variations and suffering from poor boundaries are to be detected by the proposed hybrid technique that could ease the process of clinical diagnosis and this tends to be the core subject of this article. The ability of the proposed technique is compared using standard comparison parameters such as mean squared error, peak signal to noise ratio, computational time, Dice Overlap Index, and Jaccard Tanimoto Coefficient Index. The proposed CBS combined with interval type-2 fuzzy based clustering produces a sensitivity of 0.7143 and specificity of 0.9375, which are far better than the conventional techniques such as kernel based, entropy based, graph-cut based, and self-organizing maps based clustering. Appreciable segmentation results of tumor region that enhances clinical diagnosis is made available through this article and two of the radiologists who have hands on experience in the field of radiology have extended their support in validating the efficiency of the proposed methodology and have given their consent in utilizing the proposed methodology in the processes of clinical oncology. [ABSTRACT FROM AUTHOR]

ABSTRACT Correlations between wet/dry variations and El Niño-Southern Oscillation ( ENSO), North Atlantic Oscillation ( NAO), Indian Ocean Dipole ( IOD), and Pacific Decadal Oscillation ( PDO) across the Pearl River basin were analysed at annual and seasonal time scales using the Pearson correlation and moving correlation coefficient. The basin was divided into homogeneous climate zones using the fuzzy C-means method. The influences of ENSO, NAO, IOD, and PDO on annual and seasonal wet/dry variations and impacts of warm and cold episodes of these indices on Standardized Precipitation Index-based dry and wet conditions were investigated. Furthermore, the mechanisms of the influences were tried to be analysed from the perspective of moisture transmission. The results indicated that: (1) IOD, NAO, and ENSO are the principle factors driving annual, wet, and dry seasonal precipitation variations, respectively. Moreover, opposite correlations can be identified between precipitation and IOD, NAO, and ENSO in the same/previous year; (2) Correlations between precipitation variations at different time scales and climate indices are strongly stable. Furthermore, climate indices tend to have more significant and stable impacts on precipitation variations during the dry season than on annual precipitation and precipitation variations during the wet season; (3) There exist different moisture convergence and divergence phenomena in the Pearl River basin under anomalous year of climate indices, so the annual, wet, and dry seasonal precipitation characteristic will be affected; (4) ENSO exhibits remarkable impacts on the variations of dry conditions; and meanwhile ENSO and IOD have significant impacts on the variations of wet conditions. In general, the cold phase of climate indices tends to trigger a higher risk of droughts, and the warm phase of climate indices tends to cause higher rates of extreme wet conditions. Taking different climate indices as predictors, this study may help forecast precipitation variations at different time scales. [ABSTRACT FROM AUTHOR]

This research proposed a novel fuzzy clustering model, called fuzzy discriminant c-means (FDCM) clustering, to classify apple varieties couple with near infrared spectroscopy. FDCM can not only partition samples but also extract discriminant information from them by introducing fuzzy linear discriminant analysis (FLDA) into fuzzy c-means (FCM) clustering. The 200 apple samples of four apple varieties were made experiments to collect the near infrared reflectance (NIR) spectra. Principal component analysis (PCA) was used to reduce the high dimensionality of NIR spectra data. FDCM and FCM were performed as classifiers to cluster the data, respectively. The clustering accuracy of FDCM achieved 97% which was higher than FCM, possibilistic c-means and Gustafson-Kessel (GK) clustering. Furthermore, soluble solids content and total acidity of apples were measured, and the quantitative results showed that different types of apples contain different content of the nutritional composition. This makes it feasible for classification of apple varieties via NIR technology. The clustering results obtained in experiments showed that NIR spectroscopy combined with PCA and FDCM clustering could successfully discriminate apple varieties. Practical Applications Postharvest processing of apple is very important for the agricultural industry. For this purpose, the proposed fuzzy discriminant c-means clustering model coupled with near infrared spectroscopy can be used to select higher quality of apples for grading and sorting machines. [ABSTRACT FROM AUTHOR]

ABSTRACT Tumor and Edema region present in Magnetic Resonance (MR) brain image can be segmented using Optimization and Clustering merged with seed-based region growing algorithm. The proposed algorithm shows effectiveness in tumor detection in T1 - w, T2 - w, Fluid Attenuated Inversion Recovery and Multiplanar Reconstruction type MR brain images. After an initial level segmentation exhibited by Modified Particle Swarm Optimization (MPSO) and Fuzzy C - Means (FCM) algorithm, the seed points are initialized using the region growing algorithm and based on these seed points; tumor detection in MR brain images is done. The parameters taken for comparison with the conventional techniques are Mean Square Error, Peak Signal to Noise Ratio, Jaccard (Tanimoto) index, Dice Overlap indices and Computational Time. These parameters prove the efficacy of the proposed algorithm. Heterogeneous type tumor regions present in the input MR brain images are segmented using the proposed algorithm. Furthermore, the algorithm shows augmentation in the process of brain tumor identification. Availability of gold standard images has led to the comparison of the suggested algorithm with MPSO-based FCM and conventional Region Growing algorithm. Also, the algorithm recommended through this research is capable of producing Similarity Index value of 0.96, Overlap Fraction value of 0.97 and Extra Fraction value of 0.05, which are far better than the values articulated by MPSO-based FCM and Region Growing algorithm. The proposed algorithm favors the segmentation of contrast enhanced images. © 2017 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 27, 33-45, 2017 [ABSTRACT FROM AUTHOR]

Effective conservation requires an understanding not only of contemporary vegetation distributions in the landscape, but also cognizance of vegetation transitions over time with the goal of maintaining persistence of all states within the landscape. Using a state and transition model framework, we investigated temporal transitions over 31 years in species composition among five upland swamp vegetation communities in southeastern Australia. We applied fuzzy clustering to document transitions across communities; evaluated the resilience and resistance of communities to change; and explored the relationship between ecosystem states and major environmental factors posited to structure the system. We also evaluated the predictive ability of an established vegetation dynamics model. We found that community composition remained stable or underwent reversible or directional transitions depending on the vegetation type. Wetter communities (Ti-tree thicket and Cyperoid heath) were more stable (i.e., resistant) while drier communities showed a greater propensity to transition (i.e., had lower resistance) under the observed disturbance regime (low variance fire intervals). The resilience of drier communities differed under this regime, with Banksia thicket showing reversible compositional change, while Restioid heath and Sedgeland showed directional change. In accord with an established conceptual model, we found that communities were distributed along a hydrological gradient. In addition, vegetation structure, along with light penetration to ground level, differentiated communities. However, internal dynamics of drier communities were complex: differences in fire regime (penultimate fire interval in 2014 and number of fires since 1965) were unable to predict differences in community membership among sites. Aspects of the fire regime are expected to be more important predictors if fire intervals vary more strongly among sites in the future. Fuzzy clustering of compositional data allows managers to track community transitions over time and facilitates planned interventions for conservation purposes. [ABSTRACT FROM AUTHOR]

The analysis of lignocellulosic materials is crucial to optimizing the conversion efficiencies in biorefineries and to studying crop residue input to soil nutrient cycles. Mid-infrared (MIR) and near-infrared (NIR) spectroscopies are rapid, simple, and nondestructive methods for the determination of biomass compositions. However, the analysis of a small set of plant biomass is not generally possible with conventional methods of data processing, such as partial least squares. Additionally, IR spectra do not distribute spherically in the data space. To overcome these problems, we propose a weighted-covariance factor fuzzy C-means clustering method combined with bootstrapping. The algorithm can classify spherical and nonspherical clusters, in contrast to classic fuzzy C-means, which is only adapted to spherical clusters. Bootstrapping enables resampling of the available spectra to generate several datasets on which the classification is performed. This unsupervised clustering methodology was tested to classify a small set of maize roots in soil according to genotype or period of their biodegradation process based on their NIR and MIR spectra. This methodology is applied to determine the optimal pretreatment of IR spectra, to study the contribution of the combination of MIR and NIR spectra and to compare the results on spectral and chemical data. The results show that the best methods of pretreatment are the first-order Savitzky-Golay derivative followed by standard normal variate. The MIR spectra produce a better result than NIR spectra for the initial characterization and for dynamic samples, while MIR spectra acquired on raw samples, without soluble extraction, provided better classification than wet chemistry. [ABSTRACT FROM AUTHOR]

Summary: Glaucoma became a leading reason for losing vision as it progresses in a gradual manner without any symptoms. The discovery of glaucoma at an earlier phase is imperative as it can help to accelerate the progress. The fundus images are an option for screening glaucoma and help to enable the observation of optic disc. The classical techniques are less accurate and time‐consuming. In this way, a highly accurate automatic glaucoma diagnosis is developed in this research. The noise in the images is eliminated during pre‐processing. Additionally, the DeepJoint model and improved fuzzy clustering technique are combined to develop the proposed Enhanced DeepJoint fuzzy clustering algorithm, which is used to segment blood vessels. The optic disc is additionally divided using black hole entropic fuzzy clustering (BHEFC). In order to identify glaucoma, the acquired segments are fed into a deep Maxout network that has been trained using the multi‐verse water rider optimization (MVRWO) algorithm. By combining the rider optimization algorithm, multi‐verse optimizer, and water wave optimization (WWO), the MVRWO is produced (ROA). The developed model has the greatest accuracy, sensitivity, and specificity scores, coming in at 93.91%, 95.61%, and 92.57% respectively. [ABSTRACT FROM AUTHOR]

ABSTRACT Fully automatic brain tumor segmentation is one of the critical tasks in magnetic resonance imaging (MRI) images. This proposed work is aimed to develop an automatic method for brain tumor segmentation process by wavelet transformation and clustering technique. The proposed method using discrete wavelet transform (DWT) for pre- and post-processing, fuzzy c-means (FCM) for brain tissues segmentation. Initially, MRI images are preprocessed by DWT to sharpen the images and enhance the tumor region. It assists to quicken the FCM clustering technique and classified into four major classes: gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), and background (BG). Then check the abnormality detection using Fuzzy symmetric measure for GM, WM, and CSF classes. Finally, DWT method is applied in segmented abnormal region of images respectively and extracts the tumor portion. The proposed method used 30 multimodal MRI training datasets from BraTS2012 database. Several quantitative measures were calculated and compared with the existing. The proposed method yielded the mean value of similarity index as 0.73 for complete tumor, 0.53 for core tumor, and 0.35 for enhancing tumor. The proposed method gives better results than the existing challenging methods over the publicly available training dataset from MICCAI multimodal brain tumor segmentation challenge and a minimum processing time for tumor segmentation. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 305-314, 2016 [ABSTRACT FROM AUTHOR]

This letter reports on the water quality conditions of Lake Hachiroko estimated by fuzzy c-means clustering (FCM). Growth of blue-green algae in the summer significantly degrades the water quality of the lake. On the basis of experimental results using ALOS AVNIR data from the months of August and September, and a comparison with results obtained from fuzzy regression analysis, it is found that the fuzzy c-means technique is effective in determining the water surface conditions of the lake, especially in relation to blue-green algae growth. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Gender classification is one of the most challenging problems in computer vision. Facial gender detection of neonates and children is also known as a highly demanding issue for human observers. This study proposes a novel gender classification method using frontal facial images of people. The proposed approach employs principal component analysis (PCA) and fuzzy clustering technique, respectively, for feature extraction and classification steps. In other words, PCA is applied to extract the most appropriate features from images as well as reducing the dimensionality of data. The extracted features are then used to assign the new images to appropriate classes - male or female - based on fuzzy clustering. The computational time and accuracy of the proposed method are examined together and the prominence of the proposed approach compared to most of the other well-known competing methods is proved, especially for younger faces. Experimental results indicate the considerable classification accuracies which have been acquired for FG-Net, Stanford and FERET databases. Meanwhile, since the proposed algorithm is relatively straightforward, its computational time is reasonable and often less than the other state-of-theart gender classification methods. [ABSTRACT FROM AUTHOR]

This work introduces an algorithm combining self-organizing maps (SOMs) with graph filtering procedures and discusses how to employ it for analysing financial markets. This method defines graphs resulting from SOM leader units and the calculation of the correlation among them. The technique is applied on financial datasets, with an eye to markets at different levels of (in)stability; they were analysed both on a period of 1 year and with a moving window along a time span of 10 years, comparing graph topologies. In both cases, enhancing SOMs with graphs made it possible to emphasize relations among enterprises and to check for the emergence of critical patterns. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Cloud computing came into being and is currently an essential infrastructure of many commerce facilities. To achieve the promising potentials of cloud computing, effective and efficient scheduling algorithms are fundamentally important. However, conventional scheduling methodology encounters a number of challenges. During the tasks scheduling in cloud systems, how to make full use of resources and how to effectively select resources are also important factors. At the same time, communication delay also plays an important role in cloud scheduling, which not only leads to waiting between tasks but also results in much idle interval time between processing units. In this paper, a fuzzy clustering method is used to effectively preprocess the cloud resources. Combining the list scheduling with the task duplication scheduling scheme, a new directed acyclic graph based scheduling algorithm called earliest finish time duplication algorithm for heterogeneous cloud systems is presented. Earliest finish time duplication attempts to insert suitable immediate parent nodes of the current selected node in order to reduce its waiting time on the processor. The case study and experimental results illustrate that the algorithm proposed in this paper is better than the popular heterogeneous earliest finish time algorithms. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Climate change has been identified as a major driver of habitat change, particularly for sea ice-dependent species such as the polar bear ( Ursus maritimus). Population structure and space use of polar bears have been challenging to quantify because of their circumpolar distribution and tendency to range over large areas. Knowledge of movement patterns, home range, and habitat is needed for conservation and management. This is the first study to examine the spatial ecology of polar bears in the Foxe Basin management unit of Nunavut, Canada. Foxe Basin is in the mid-Arctic, part of the seasonal sea ice ecoregion and it is being negatively affected by climate change. Our objectives were to examine intrapopulation spatial structure, to determine movement patterns, and to consider how polar bear movements may respond to changing sea ice habitat conditions. Hierarchical and fuzzy cluster analyses were used to assess intrapopulation spatial structure of geographic position system satellite-collared female polar bears. Seasonal and annual movement metrics (home range, movement rates, time on ice) and home-range fidelity (static and dynamic overlap) were compared to examine the influence of regional sea ice on movements. The polar bears were distributed in three spatial clusters, and there were differences in the movement metrics between clusters that may reflect sea ice habitat conditions. Within the clusters, bears moved independently of each other. Annual and seasonal home-range fidelity was observed, and the bears used two movement patterns: on-ice range residency and annual migration. We predict that home-range fidelity may decline as the spatial and temporal predictability of sea ice changes. These new findings also provide baseline information for managing and monitoring this polar bear population. [ABSTRACT FROM AUTHOR]

Among the diverse frameworks that have been proposed for regression analysis of angular data, the projected multivariate linear model provides a particularly appealing and tractable methodology. In this model, the observed directional responses are assumed to correspond to the angles formed by latent bivariate normal random vectors that are assumed to depend upon covariates through a linear model. This implies an angular normal distribution for the observed angles, and incorporates a regression structure through a familiar and convenient relationship. In this paper we extend this methodology to accommodate clustered data ( e.g., longitudinal or repeated measures data) by formulating a marginal version of the model and basing estimation on an EM-like algorithm in which correlation among within-cluster responses is taken into account by incorporating a working correlation matrix into the M step. A sandwich estimator is used for the parameter estimates' covariance matrix. The methodology is motivated and illustrated using an example involving clustered measurements of microbril angle on loblolly pine ( Pinus taeda L.) Simulation studies are presented that evaluate the finite sample properties of the proposed fitting method. In addition, the relationship between within-cluster correlation on the latent Euclidean vectors and the corresponding correlation structure for the observed angles is explored. [ABSTRACT FROM AUTHOR]

Cloud computing is the key and frontier field of the current domestic and international computer technology, workflow task scheduling plays an important part of cloud computing, which is a policy that maps tasks to appropriate resources to execute. Effective task scheduling is essential for obtaining high performance in cloud environment. In this paper, we present a workflow task scheduling algorithm based on the resources' fuzzy clustering named FCBWTS. The major objective of scheduling is to minimize makespan of the precedence constrained applications, which can be modeled as a directed acyclic graph. In FCBWTS, the resource characteristics of cloud computing are considered, a group of characteristics, which describe the synthetic performance of processing units in the resource system, are defined in this paper. With these characteristics and the execution time influence of the ready task in the critical path, processing unit network is pretreated by fuzzy clustering method in order to realize the reasonable partition of processor network. Therefore, it largely reduces the cost in deciding which processor to execute the current task. Comparison on performance evaluation using both the case data in the recent literature and randomly generated directed acyclic graphs shows that this algorithm has outperformed the HEFT, DLS algorithms both in makespan and scheduling time consumed. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The spatial heterogeneity of hydraulic conductivity ( K) exerts a major control on groundwater flow and solute transport. The heterogeneous spatial distribution of K can be imaged using indirect geophysical data as long as reliable relations exist to link geophysical data to K. This paper presents a nonparametric learning machine approach to predict aquifer K from cone penetrometer tests (CPT) coupled with a soil moisture and resistivity probe (SMR) using relevance vector machines (RVMs). The learning machine approach is demonstrated with an application to a heterogeneous unconsolidated littoral aquifer in a 12 km2 subwatershed, where relations between K and multiparameters CPT/SMR soundings appear complex. Our approach involved fuzzy clustering to define hydrofacies (HF) on the basis of CPT/SMR and K data prior to the training of RVMs for HFs recognition and K prediction on the basis of CPT/SMR data alone. The learning machine was built from a colocated training data set representative of the study area that includes K data from slug tests and CPT/SMR data up-scaled at a common vertical resolution of 15 cm with K data. After training, the predictive capabilities of the learning machine were assessed through cross validation with data withheld from the training data set and with K data from flowmeter tests not used during the training process. Results show that HF and K predictions from the learning machine are consistent with hydraulic tests. The combined use of CPT/SMR data and RVM-based learning machine proved to be powerful and efficient for the characterization of high-resolution K heterogeneity for unconsolidated aquifers. [ABSTRACT FROM AUTHOR]

SUMMARY Energy is an extremely critical resource for battery-powered wireless sensor networks (WSNs), thus making energy-efficient protocol design a key challenging problem. However, uneven energy consumption is an inherent problem in WSNs caused by multi-hop routing and many-to-one traffic pattern among sensors. In this paper, we therefore propose a new clustering method called fuzzy chessboard clustering (FFC), which is capable to overcome the bottleneck problem and addressing the uneven energy consumption problem in heterogeneous WSNs. We also propose an energy-efficient routing method called artificial bee colony routing method (ABCRM) to find the optimal routing path for the heterogeneous WSNs. ABCRM seeks to investigate the problems of balancing energy consumption and maximization of network lifetime. To demonstrate the effectiveness of FCC-ABCRM in terms of lessening end-to-end delay, balancing energy consumption, and maximization of heterogeneous network lifetime, we compare our method with three approaches namely, chessboard clustering approach, PEGASIS, and LEACH. Simulation results show that the network lifetime achieved by FCC-ABCRM could be increased by nearly 25%, 45%, and 60% more than that obtained by chessboard clustering, PEGASIS, and LEACH, respectively. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Most hyper-ellipsoidal clustering (HEC) approaches use the Mahalanobis distance as a distance metric. It has been proven that HEC, under this condition, cannot be realized since the cost function of partitional clustering is a constant. We demonstrate that HEC with a modified Gaussian kernel metric can be interpreted as a problem of finding condensed ellipsoidal clusters (with respect to the volumes and densities of the clusters) and propose a practical HEC algorithm that is able to efficiently handle clusters that are ellipsoidal in shape and that are of different size and density. We then try to refine the HEC algorithm by utilizing ellipsoids defined on the kernel feature space to deal with more complex-shaped clusters. The proposed methods lead to a significant improvement in the clustering results over K-means algorithm, fuzzy Cmeans algorithm, GMM-EM algorithm, and HEC algorithm based on minimum-volume ellipsoids using Mahalanobis distance. [ABSTRACT FROM AUTHOR]

SUMMARY The global spread of wireless devices with mobile Internet access and the increasing demand of multimedia-based applications are fueling the need for wireless broadband networks. IEEE 802.16 and 802.20 are standards for a broadband wireless access with promising cognitive radio features to support mobile Internet access. However, because of the fast changing radio environment and the demand for dynamic spectrum allocation mechanisms, these standards must continuously readjust different radio parameters. The cognitive radio makes decisions based on its built-in inference engine, which also in time can adapt itself to different situations through the process of learning from experience. In this paper we present an automated opportunistic decision making and learning process for cognitive radio based on uncertainty reasoning algorithms. This novel approach is well suited in fast changing wireless environments with vague, incomplete, and heterogeneous information. Theory and simulations prove that decision making and learning of the cognitive radio based on the proposed approach cope with the changes in the radio environment. In this work we use fuzzy logic for the learning and decision making of the cognitive radio. Simulation also show that our approach provides accurate and precise decisions on allocating spectrum to mobile Internet users even in fast varying radio conditions. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Chemoinformatics applications of cluster analysis over the past 35 years include chemical diversity for compound acquisition, analysis of HTS results for lead discovery, 2D and 3D chemical similarity searching for virtual screening, and hypothesis generation for lead hopping using molecular shape and pharmacophore descriptors. These applications still provide the majority of cluster analysis usage, but the advent of greater and greater computational resources has allowed researchers to tackle applications of ever increasing scale and complexity. In the past few years, a far broader array of clustering methods is now used-some entirely new, some common to other disciplines, and others modified to specific chemoinformatic applications. The chemoinformatic applications have also broadened to include greater biological information more commonly associated with bioinformatics. Indeed, clustering techniques, such as coclustering or self-organizing trees, commonly found in bioinformatics, are beginning to find chemoinformatic application uses. Issues such as visualization and validation of clustering results continue to present challenging problems, especially given that the scale of many problems now attempted has increased enormously. Some new validation techniques have been introduced in the chemoinformatics literature that now allow for both a better understanding of the clustering results and help point to methods of greater efficacy. Effective validation and visualization of clustering results of large data sets has proven to be more problematic. WIREs Comput Mol Sci 2014, 4:34-48. doi: 10.1002/wcms.1152 The authors have declared no conflicts of interest in relation to this article. For further resources related to this article, please visit the WIREs website. [ABSTRACT FROM AUTHOR]

To replace costly and time-consuming experimentation in laboratory, a novel solubility prediction model based on chaos theory, self-adaptive particle swarm optimization (PSO), fuzzy c-means clustering method, and radial basis function artificial neural network (RBF ANN) is proposed to predict CO2 solubility in polymers, hereafter called CSPSO-FC RBF ANN. The premature convergence problem is overcome by modifying the conventional PSO using chaos theory and self-adaptive inertia weight factor. Fuzzy c-means clustering method is used to tune the hidden centers and radial basis function spreads. The modified PSO algorithm is employed to optimize the RBF ANN connection weights. Then, the proposed CSPSO-FC RBF ANN is used to investigate solubility of CO2 in polystyrene (PS), polypropylene (PP), poly(butylene succinate) (PBS) and poly(butylene succinate-co-adipate) (PBSA), respectively. Results indicate that CSPSO-FC RBF ANN is an effective method for gas solubility in polymers. In addition, compared with conventional RBF ANN and PSO ANN, CSPSO-FC RBF ANN shows better performance. The values of average relative deviation (ARD), squared correlation coefficient ( R2) and standard deviation (SD) are 0.1071, 0.9973 and 0.0108, respectively. Statistical data demonstrate that CSPSO-FC RBF ANN has excellent prediction capability and high-accuracy, and the correlation between prediction values and experimental data is good. [ABSTRACT FROM AUTHOR]

According to the color yarns in the fabric, the fabrics can be divided into three categories: solid color fabrics, single-system-mélange color fabrics, and double-system-mélange color fabrics. The density of solid fabrics can be inspected with gray-projection method or Fourier analysis method. But the methods cannot be applied to yarn-dyed fabrics directly. A method for detecting the density of single-system-mélange color fabrics will be discussed in this article. By analyzing the pattern and color characters of single-system-mélange color fabrics, fuzzy C-means algorithm is proposed to classify the colors in the fabric image based on CIELAB color space first. With the color segmentation results, the fabric can be divided into different blocks. The yarns can be located in different blocks with different average gray-levels, and then the number of yarns can be counted in each block. The linear density of threads can be obtained by counting the yarns in a unit length finally. The experiment proved that the algorithm proposed in this study can inspect the density of single-system-mélange color fabric successfully. © 2012 Wiley Periodicals, Inc. Col Res Appl, 38, 456-462, 2013 [ABSTRACT FROM AUTHOR]