OBJECTIVE: The rising incidence and mortality in bladder cancer (BC) underscore the importance of identifying asscociated features. Current reliance on haematuria as a primary indicator for BC proves inadequate. While mining electronic health records (EHRs) offer potential of identifying BC-related signals, traditional data-driven methods struggle with high-dimensional datasets. This study aims to uncover novel BC-associated clinical signals by developing Parsimony-driven cAtegory-balaNced binary Signal extractor for Primary Care EHRs (PanSPICE) tailored to extremely high-dimensional data linked from multi-centres. METHODS: We collected BC cases and control patients (n = 64,884) linked at patient-level from Welsh nationwide databases, yielding 48,261 features in primary care settings. The PanSPICE approach begins with information gain to pre-rank features, then applies Retentive Stickiness Binary Particle Swarm Optimisation (RSBPSO) combined with C5.0 classification tree to overcome computational barriers in feature selection. A two-layer optimisation treated clinical signals in care processes (POC), diagnoses (DIAG), and medications (MED) separately to prevent feature masking. A tailored fitness function for RSBPSO to simultaneously optimise model performance and feature sparsity. Associations of the selected features were interpreted using logistic regression models adjusted for deprivation indices. RESULTS: The PanSPICE identified 38 optimal features (AUC (area under the curve) = 0.81, 95 % CI: 0.80-0.82), including urinary tract infections (OR = 2.19, 95 % CI: 2.05-2.14) and inverse associations with stroke (OR = 0.64, 95 % CI: 0.54-0.74) and dementia (OR = 0.25, 95 % CI: 0.17-0.35). Gender stratification revealed female-specific urine glucose testing association (OR = 1.24, 95 % CI: 1.08-1.43). Certain medications, such as trimethoprim, were positively associated with BC, while others, including ramipril and prednisolone, showed protective effects. CONCLUSION: The PanSPICE enables efficient high-dimensional EHR analysis, revealing under-recognised potential BC risk profiles and protective comorbidities. Gender-specific differences in BC associations highlight the importance of gender-stratified analyses, while computational advances provide a template for EHR-based clinical discovery. Findings warrant further mechanistic research into neurological protective pathways.

Gene selection is crucial for cancer classification using microarray data. In the interests of improving cancer classification accuracy, in this paper, we developed a new wrapper method called ieGENES for gene selection. First we proposed a parsimonious kernel machine regularization (PKMR) model by using ridge regularization in kernel machine driven classification to tackle multi-collinearity for the sake of stable estimates in high-dimensional settings. Then the ieGENES algorithm was developed to optimally identify relevant genes while iteratively eliminating redundant ones based on leave-one-out cross-validation accuracy. In particular, we developed a new methodology to optimally update model parameters upon gene removal. The ieGENES algorithm was evaluated on six cancer microarray datasets and compared to existing methods. Classification accuracy and number of differentially expressed genes (DEGs) identified were assessed. In terms of gene selection accuracy, the ieGENESoutperformed multiple wrapper methods on 5 out of 6 datasets (Colon, Leukemia, Hepato, Glioma, and Breast Cancers), with statistically significant improvements (p<0.001). For the Colon dataset, ieGENES achieved 96.21% accuracy with 167 DEGs. The proposed ieGENES technique demonstrated superior performance in identifying DEGs for cancer diagnosis comparing with existing techniques. It offers a promising tool for identifying biologically relevant genes in microarray data analysis and biomarker discovery for cancer research.

Automatically generating an accurate and meaningful description of an image is very challenging. However, the recent scheme of generating an image caption by maximizing the likelihood of target sentences lacks the capacity of recognizing the human-object interaction (HOI) and semantic relationship between HOIs and scenes, which are the essential parts of an image caption. This article proposes a novel two-phase framework to generate an image caption by addressing the above challenges: 1) a hybrid deep learning and 2) an image description generation. In the hybrid deep-learning phase, a novel factored three-way interaction machine was proposed to learn the relational features of the human-object pairs hierarchically. In this way, the image recognition problem is transformed into a latent structured labeling task. In the image description generation phase, a lexicalized probabilistic context-free tree growing scheme is innovatively integrated with a description generator to transform the descriptions generation task into a syntactic-tree generation process. Extensively comparing state-of-the-art image captioning methods on benchmark datasets, we demonstrated that our proposed framework outperformed the existing captioning methods in different ways, such as significantly improving the performance of the HOI and relationships between HOIs and scenes (RHIS) predictions, and quality of generated image captions in a semantically and structurally coherent manner.

Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) algorithms have been widely discussed by the Explainable AI (XAI) community but their application to wider domains are rare, potentially due to the lack of easy-to-use tools built around these methods. In this paper, we present ExMed, a tool that enables XAI data analytics for domain experts without requiring explicit programming skills. It supports data analytics with multiple feature attribution algorithms for explaining machine learning classifications and regressions. We illustrate its domain of applications on two real world medical case studies, with the first one analysing COVID-19 control measure effectiveness and the second one estimating lung cancer patient life expectancy from the artificial Simulacrum health dataset. We conclude that ExMed can provide researchers and domain experts with a tool that both concatenates flexibility and transferability of medical sub-domains and reveal deep insights from data.

The type-1 ordered weighted averaging (T1OWA) operator has demonstrated the capacity for directly aggregating multiple sources of linguistic information modeled by fuzzy sets rather than crisp values. Yager's ordered weighted averaging (OWA) operators possess the properties of idempotence, monotonicity, compensativeness, and commutativity. This article aims to address whether or not T1OWA operators possess these properties when the inputs and associated weights are fuzzy sets instead of crisp numbers. To this end, a partially ordered relation of fuzzy sets is defined based on the fuzzy maximum (join) and fuzzy minimum (meet) operators of fuzzy sets, and an alpha-equivalently-ordered relation of groups of fuzzy sets is proposed. Moreover, as the extension of orness and andness of an Yager's OWA operator, joinness and meetness of a T1OWA operator are formalized, respectively. Then, based on these concepts and the representation theorem of T1OWA operators, we prove that T1OWA operators hold the same properties as Yager's OWA operators possess, i.e., idempotence, monotonicity, compensativeness, and commutativity. Various numerical examples and a case study of diabetes diagnosis are provided to validate the theoretical analyses of these properties in aggregating multiple sources of uncertain information and improving integrated diagnosis, respectively.

The non-stationary nature of electroencephalography (EEG) signals makes an EEG-based brain-computer interface (BCI) a dynamic system, thus improving its performance is a challenging task. In addition, it is well-known that due to non-stationarity based covariate shifts, the input data distributions of EEG-based BCI systems change during inter- and intra-session transitions, which poses great difficulty for developments of online adaptive data-driven systems. Ensemble learning approaches have been used previously to tackle this challenge. However, passive scheme based implementation leads to poor efficiency while increasing high computational cost. This paper presents a novel integration of covariate shift estimation and unsupervised adaptive ensemble learning (CSE-UAEL) to tackle non-stationarity in motor-imagery (MI) related EEG classification. The proposed method first employs an exponentially weighted moving average model to detect the covariate shifts in the common spatial pattern features extracted from MI related brain responses. Then, a classifier ensemble was created and updated over time to account for changes in streaming input data distribution wherein new classifiers are added to the ensemble in accordance with estimated shifts. Furthermore, using two publicly available BCI-related EEG datasets, the proposed method was extensively compared with the state-of-the-art single-classifier based passive scheme, single-classifier based active scheme and ensemble based passive schemes. The experimental results show that the proposed active scheme based ensemble learning algorithm significantly enhances the BCI performance in MI classifications.

Brain computer interface (BCI) is about the communication channel between the brain of a human subject and a computerized device. Electroencephalography (EEG) signals are the primary choice as the sources of interpreting the intention of the human subject. EEG signals have a long history of being used in human health for the purposes of studying brain activities and medical diagnosis. EEG signals are very weak and are subject to the contamination from many artifact signals. For the applications in human health, true EEG signals, without the contamination, is highly desirable. However, for the purposes of BCI, where stable patterns from the source signals are critical, the origins of the signals are of less concern. In this paper, we propose a BCI, which is simple to implement and easy to use, by taking the advantage of EEG artifacts, generated by a number of purposely designed voluntary facial muscle movements.

Due to advances of the "omics" technologies, rich sources of clinical, biomedical, contextual, and environmental data about each patient have been available in medical and health sciences. However, an enormous amount of electronic health records is actually generated as textual data, such as descriptive terms/concepts. No doubt, efficiently harnessing these valuable textual data would allow doctors and nurses to identify the most appropriate treatments and the predicted outcomes for a given patient in real time. We used textual data to identify patient phenotypes from UK primary care records that were managed by Read codes (a clinical classification system). The fine granularity level of Read codes leads to a huge number of clinical terms to be handled. Unfortunately, traditional medical statistics methods have struggled to process this sort of data effectively. In this paper, we described how the problem of patient phenotype identification can be transformed into document classification task, a text mining scheme is addressed to integrate feature ranking methods and genetic algorithm to identify the most parsimonious subset of features that still holds the capacity of characterizing the distinction of patient phenotypes. The experimental results have demonstrated that compact feature sets with 2 or 3 important terms describing clinical events were effectively identified from 16852 Read codes while their classification accuracy remained high level of agreements with specialists from secondary care in classifying testing samples.

For general type-2 fuzzy sets, the defuzzification process is very complex and the exhaustive direct method of implementing type-reduction is computationally expensive and turns out to be impractical. This has inevitably hindered the development of type-2 fuzzy inferencing systems in real-world applications. The present situation will not be expected to change, unless an efficient and fast method of deffuzzifying general type-2 fuzzy sets emerges. Type-1 ordered weighted averaging (OWA) operators have been proposed to aggregate expert uncertain knowledge expressed by type-1 fuzzy sets in decision making. In particular, the recently developed alpha-level approach to type-1 OWA operations has proven to be an effective tool for aggregating uncertain information with uncertain weights in real-time applications because its complexity is of linear order. In this paper, we prove that the mathematical representation of the type-reduced set (TRS) of a general type-2 fuzzy set is equivalent to that of a special case of type-1 OWA operator. This relationship opens up a new way of performing type reduction of general type-2 fuzzy sets, allowing the use of the alpha-level approach to type-1 OWA operations to compute the TRS of a general type-2 fuzzy set. As a result, a fast and efficient method of computing the centroid of general type-2 fuzzy sets is realized. The experimental results presented here illustrate the effectiveness of this method in conducting type reduction of different general type-2 fuzzy sets.

The six papers in this special section are devoted to the topic of enterprise systems (ES) or enterprise information systems (EIS). ES has emerged as a promising tool used for integrating and extending business processes across the boundaries of business functions at both intra and interorganizational levels.

The use of integrated information systems for healthcare has been started more than a decade ago. In recent years, rapid advances in information integration methods have spurred tremendous growth in the use of integrated information systems in healthcare delivery. Various techniques have been used for probing such integrated systems. These techniques include service-oriented architecture (SOA), EAI, workflow management, grid computing, and others. Many applications require a combination of these techniques, which gives rise to the emergence of enterprise systems in healthcare. Development of the techniques originated from different disciplines has the potential to significantly improve the performance of enterprise systems in healthcare. This editorial paper briefly introduces the enterprise systems in the perspective of healthcare informatics.

Type-1 Ordered Weighted Averaging (OWA) operator provides us with a new technique for directly aggregating uncertain information with uncertain weights via OWA mechanism in soft decision making and data mining, in which uncertain objects are modeled by fuzzy sets. The Direct Approach to performing type-1 OWA operation involves high computational overhead. In this paper, we define a type-1 OWA operator based on the \alpha-cuts of fuzzy sets. Then, we prove a Representation Theorem of type-1 OWA operators, by which type-1 OWA operators can be decomposed into a series of \alpha-level type-1 OWA operators. Furthermore, we suggest a fast approach, called Alpha-Level Approach, to implementing the type-1 OWA operator. A practical application of type-1 OWA operators to breast cancer treatments is addressed. Experimental results and theoretical analyses show that: 1) the Alpha-Level Approach with linear order complexity can achieve much higher computing efficiency in performing type-1 OWA operation than the existing Direct Approach, 2) the type-1 OWA operators exhibit different aggregation behaviors from the existing fuzzy weighted averaging (FWA) operators, and 3) the type-1 OWA operators demonstrate the ability to efficiently aggregate uncertain information with uncertain weights in solving real-world soft decision-making problems.

System modeling and data mining techniques have considerable potential for use in public health informatics. In this paper, we propose a methodology of constructing a parsimonious Takagi-Sugeno (TS) rule model from data in predicting the impacts of child deprivation on education according to the ones of deprivations on health and socioeconomic conditions at a small-area level. In order to identify the most influential TS fuzzy rules, some novel indices for TS fuzzy rule ranking are suggested and named as L-values and ω-values of TS rules respectively. The L-values of TS rules are suggested to identify important rules based on the effects of the local linear models in rule consequent parts. The ω-values of TS rules are defined by considering both the rule base structure and the contribution of the local linear models. The experimental results have shown that by using the proposed indices, the most influential TS fuzzy rules can be effectively selected for a parsimonious TS fuzzy model in predicting the impacts of child deprivation on education at a satisfactory level.

Linking large disparate database systems at individual person based level for medical informatics and e-health research is a challenging task. In the interests of identifying influential determinants of effects of children's health and socioeconomic status on educational attainment, this paper links child health data records including birth records, school key stage attainment records, deprivation index scores etc. from multiple sources via an e-health infrastructure SAIL databank. Furthermore, a novel scheme of automatically identifying influential attributes from high dimensional data is presented. The proposed scheme applies the entropy regularisation and particle swarm optimisation (PSO) techniques to the construction of an optimal support vector machine (SVM) model. The novelty of the proposed scheme lies in that during learning process the importance of less influential attributes automatically approaches to zero, whilst the importance of the most important attributes turns to one, so that only the most influential attributes turn up in the final SVM model. What's more, the model selection, feature identification and dimensionality reduction are performed simultaneously in an integrated manner in one model structure. The experimental results have shown that the proposed method is efficient in performing dimensionality reduction and identifying the important determinants of the effects of children's health and socioeconomic status on educational attainment.

Yager's ordered weighted averaging (OWA) operator has been widely used in soft decision making to aggregate experts' individual opinions or preferences for achieving an overall decision. The traditional Yager's OWA operator focuses exclusively on the aggregation of crisp numbers. However, human experts usually tend to express their opinions or preferences in a very natural way via linguistic terms. Type-2 fuzzy sets provide an efficient way of knowledge representation for modeling linguistic terms. In order to aggregate linguistic opinions via OWA mechanism, we propose a new type of OWA operator, termed type-2 OWA operator, to aggregate the linguistic opinions or preferences in human decision making modeled by type-2 fuzzy sets. A Direct Approach to aggregating interval type-2 fuzzy sets by type-2 OWA operator is suggested in this paper. Some examples are provided to delineate the proposed technique. © 2010 Wiley Periodicals, Inc.

Recommending appropriate follow-up treatment options to patients after diagnosis and primary (usually surgical) treatment of breast cancer is a complex decision making problem. Often, the decision is reached by consensus from a multi-disciplinary team of oncologists, radiologists, surgeons and pathologists. Non-stationary fuzzy sets have been proposed as a mechanism to represent and reason with the knowledge of such multiple experts. In this paper, we briefly describe the creation of a non-stationary fuzzy inference system to provide decision support in this context, and examine a number of alternative methods for interpreting the output of such a non-stationary inference system. The alternative interpretation methodologies and the experiments carried out to compare these methods are detailed. Results are presented which shown that using majority voting ensemble decision making from a non-stationary fuzzy system improves accuracy of the decision making. We conclude that non-stationary systems coupled with ensemble interpretation methods are worthy of further exploration.

In this paper, a novel type-1 OWA based non-stationary fuzzy system is proposed, in which the type-1 OWA operator is used in the fuzzy inference process to aggregate the non-stationary fuzzy outputs. The advantage of non-stationary fuzzy sets lies in their ability to model expert's variations in automated decision support systems. The proposed scheme offers an opportunity to combine different uncertain objects with uncertain weights into an overall decision in the fuzzy inference process. The agreement achieved between the proposed fuzzy system and clinical expert decision in selecting optimal treatment plans is used to evaluate the performance of the method. The experimental results on post-operative breast cancer treatments have demonstrated that the proposed fuzzy system can effectively diagnose breast cancer treatment in decision supports.

Type-2 fuzzy systems are increasing in popularity, and there are many examples of successful applications. While many techniques have been proposed for creating parsimonious type-1 fuzzy systems, there is a lack of such techniques for type-2 systems. The essential problem is to reduce the number of rules, while maintaining the system's approximation performance. In this paper, four novel indexes for ranking the relative contribution of type-2 fuzzy rules are proposed, which are termedR-values,c-values, omega1-values, and omega2-values. TheR-values of type-2 fuzzy rules are obtained by applying a QR decomposition pivoting algorithm to the firing strength matrices of the trained fuzzy model. Thec-values rank rules based on the effects of rule consequents, while the omega1-values and omega2-values consider both the rule-base structure (via firing strength matrices) and the output contribution of fuzzy rule consequents. Two procedures for utilizing these indexes in fuzzy rule selection (termed ldquoforward selectionrdquo and ldquobackward eliminationrdquo) are described. Experiments are presented which demonstrate that by using the proposed methodology, the most influential type-2 fuzzy rules can be effectively retained in order to construct parsimonious type-2 fuzzy models.

In this paper, a method for constructing Takagi-Sugeno (TS) fuzzy system from data is proposed with the objective of preserving TS submodel comprehensibility, in which linguistic modifiers are suggested to characterize the fuzzy sets. A good property held by the proposed linguistic modifiers is that they can broaden the cores of fuzzy sets while contracting the overlaps of adjoining membership functions (MFs) during identification of fuzzy systems from data. As a result, the TS submodels identified tend to dominate the system behaviors by automatically matching the global model (GM) in corresponding subareas, which leads to good TS model interpretability while producing distinguishable input space partitioning. However, the GM accuracy and model interpretability are two conflicting modeling objectives, improving interpretability of fuzzy models generally degrades the GM performance of fuzzy models, and vice versa. Hence, one challenging problem is how to construct a TS fuzzy model with not only good global performance but also good submodel interpretability. In order to achieve a good tradeoff between GM performance and submodel interpretability, a regularization learning algorithm is presented in which the GM objective function is combined with a local model objective function defined in terms of an extended index of fuzziness of identified MFs. Moreover, a parsimonious rule base is obtained by adopting a QR decomposition method to select the important fuzzy rules and reduce the redundant ones. Experimental studies have shown that the TS models identified by the suggested method possess good submodel interpretability and satisfactory GM performance with parsimonious rule bases.

Developing a treatment plan for breast cancer patient is a very complex process. In this paper, we propose a scheme of inducing fuzzy rules that characterise breast caner treatment knowledge from data. These fuzzy rules can augment the human experts in the process of medical diagnosis to select optimal treatment for patients. The proposed machine learning scheme applies the particle swarm optimisation technique (PSO) to the construction of an optimal support vector machine (SVM) model for the sake of inducing accurate and parsimonious fuzzy rules and simultaneously reducing input space dimensions, in which a new fittness function that regularises the importance ranks of features with misclassification rate is suggested. The SVM-based fuzzy classifier evades the curse of dimensionality in high-dimensional breast cancer data space in the sense that the number of support vectors, which equals the number of induced fuzzy rules, is not related to the dimensionality. The experiments have shown that not only the classification performance achieved by the proposed fuzzy classifier outperforms the ones achieved by other methods in the literature, but also the input space dimension has been reduced greatly.

Type-1 OWA operators provide an efficient way of aggregating linguistic opinions in the form of type-1 fuzzy sets for decision-makers. Like in Yager’s OWA operations, the identification of an appropriate type-1 OWA operator, i.e., to determine the linguistic weights in the form of type-1 fuzzy sets, is crucial in type-1 OWA based aggregation. Indeed, these weights reflect the decision-makers’ desired agenda for aggregating the preferences or criteria. In this paper, for the sake of identifying linguistic weights used in the type-1 OWA operators, type-2 linguistic quantifiers are proposed, in which the higher level of uncertainty of linguistic quantifiers is modelled by type-2 fuzzy sets. Some examples are provided to illustrate the proposed concepts.

Yager’s ordered weighted averaging (OWA) operator has been widely used in soft decision making to aggregate experts’ individual opinions or preferences for achieving an overall decision. The traditional Yager’s OWA operator focuses exclusively on the aggregation of crisp numbers. However, human experts usually tend to express their opinions or preferences in a very natural way via linguistic terms, like “important” , “very important”, “good” etc.. Type-2 fuzzy sets provide an efficient way of knowledge representation for modelling linguistic terms. In order to aggregate linguistic opinions via the OWA mechanism, we propose a new type of OWA operator, termed type-2 OWA operator that is able to aggregate type-2 fuzzy sets, and therefore to aggregate the linguistic opinions or preferences in human decision making. The necessary equations for performing type-2 OWA operations on aggregating interval type-2 fuzzy sets are derived in this paper. Some examples are provided to illustrate the proposed technique.

In 2003, Coupland and John proposed an approach to type-2 fuzzy arithmetic using the representation theorem. While this approach was sound, it was conceded to be computationally expensive and required considerable preliminary calculations. We present a more computationally viable approach which we believe will make type-2 fuzzy logic more useful in applied scenarios.

In this paper, we propose two novel indices for type-2 fuzzy rule ranking to identify the most influential fuzzy rules in designing type-2 fuzzy logic systems, and name them as R-values and c-values of fuzzy rules separately. The R-values of type-2 fuzzy rules are obtained by applying QR decomposition in which there is no need to estimate a rank as required in the SVD-QR with column pivoting algorithm. The c-values of type-2 fuzzy rules are suggested to rank rules based on the effects of rule consequents. Experimental results on a signal recovery problem have shown that by using the proposed indices the most influential type-2 fuzzy rules can be effectively selected to construct parsimonious type-2 fuzzy models while the system performances are kept at a satisfied level.

In this paper, a new scheme for constructing parsimonious fuzzy classifiers is proposed based on the L2-support vector machine (L2-SVM) technique with model selection and feature ranking performed simultaneously in an integrated manner, in which fuzzy rules are optimally generated from data by L2-SVM learning. In order to identify the most influential fuzzy rules induced from the SVM learning, two novel indexes for fuzzy rule ranking are proposed and named as alpha-values and omega-values of fuzzy rules in this paper. The alpha-values are defined as the Lagrangian multipliers of the L2-SVM and adopted to evaluate the output contribution of fuzzy rules, while the omega-values are developed by considering both the rule base structure and the output contribution of fuzzy rules. As a prototype-based classifier, the L2-SVM-based fuzzy classifier evades the curse of dimensionality in high-dimensional space in the sense that the number of support vectors, which equals the number of induced fuzzy rules, is not related to the dimensionality. Experimental results on high-dimensional benchmark problems have shown that by using the proposed scheme the most influential fuzzy rules can be effectively induced and selected, and at the same time feature ranking results can also be obtained to construct parsimonious fuzzy classifiers with better generalization performance than the well-known algorithms in literature.

A fuzzy set merging (FSM) algorithm is proposed in order to generate distinguishable fuzzy sets. A relative compactness measure is defined to characterize the homogenous information that one pattern shares with its neighbors, and a so-called "local" entropy is employed to evaluate the distinguishability of fuzzy sets. By maximizing this entropy measure the optimal number of merged fuzzy sets with good distinguishability can be obtained, which preserve the information of original fuzzy sets as much as possible. Furthermore, we propose a scheme to optimize the input space partitioning for a Takagi-Sugeno (TS) fuzzy model by using the FSM algorithm. As a result, a good trade-off between global approximation ability and interpretability in input space partitioning is achieved in the TS model.

We present a new Tagaki-Sugeno (TS) type model whose membership functions (MFs) are characterized by linguistic modifiers. As a result, during adaptation, the trained local models tend to become the tangents of the global model, leading to good model interpretability. In order to prevent the global approximation ability from being degraded, an index of fuzziness is proposed to evaluate linguistic modification for MFs with adjustable crossover points. A new learning scheme is also developed, which uses the combination of global approximation error and the fuzziness index as its objective function. By minimizing the multiple objective performance measure, a tradeoff between the global approximation and local model interpretation can be achieved. Experimental results show that by the proposed method, good interpretation of local models and transparency of input space partitioning can be obtained for the TS model, while at the same time the global approximation ability is still preserved.

In this paper, a novel procedure for normalising Mercer kernel is suggested firstly. Then, the normalised Mercer kernel techniques are applied to the fuzzy c-means (FCM) algorithm, which leads to a normalised kernel based FCM (NKFCM) clustering algorithm. In the NKFCM algorithm, implicit assumptions about the shapes of clusters in the FCM algorithm is removed so that the new algorithm possesses strong adaptability to cluster structures within data samples. Moreover, a new method for calculating the prototypes of clusters in input space is also proposed, which is essential for data clustering applications. Experimental results on several benchmark datasets have demonstrated the promising performance of the NKFCM algorithm in different scenarios.

Abstract: In remote sensing image processing, image approximation, or obtaining a high‐resolution image from a corresponding low‐resolution image, is an ill‐posed inverse problem. In this paper, the regularization method is used to convert the image approximation problem into a solvable variational problem. In regularization, the constraints on smoothness and discontinuity are considered, and the original ill‐posed problem is thereby converted to a well‐posed optimization problem. In order to solve the variational problem, a Hopfield‐type dynamic neural network is developed. This neural network possesses two states that describe the discrepancy between a pixel and adjacent pixels, the intensity evolution of a pixel and two kinds of corresponding weights. Based on the experiment in this study with a Landsat TM image free of added noise and a noisy image, the proposed approach provides better results than other methods. The comparison shows the feasibility of the proposed approach.

Knowledge‐based modeling and implementation of the various manufacturing processes represent an intensive research area. It is known that it is difficult to analyze the mechanisms of many industrial production processes and build dynamic models by employing classical methods for intelligent systems in manufacturing. This paper describes how to use dynamic recurrent neural networks to provide the model base of a hybrid intelligent system for the metallurgical industry with a quality control model. The hybrid system extracts the features of image sequences obtained through the vision detection subsystem and employs a dynamic recurrent neural network to assess and predict the product qualities to further coordinate the entire production process.