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This paper describes the 9th Emotion Recognition in the Wild (EmotiW) challenge, which is being run as a grand challenge at the 25th ACM International Conference on Multimodal Interaction 2023. EmotiW challenge focuses on affect related benchmarking tasks and comprises of two sub-challenges: a) User Engagement Prediction in the Wild, and b) Audio-Visual Group-based Emotion Recognition. The purpose of this challenge is to provide a common platform for researchers from diverse domains. The objective is to promote the development and assessment of methods, which can predict engagement levels and/or identify perceived emotional well-being of a group of individuals in real-world circumstances. We describe the datasets, the challenge protocols and the accompanying sub-challenge.

The two-volume set CCIS 1142 and 1143 constitutes thoroughly refereed contributions presented at the 26th International Conference on Neural Information Processing, ICONIP 2019, held in Sydney, Australia, in December 2019. For ICONIP 2019 a total of 345 papers was carefully reviewed and selected for publication out of 645 submissions. The 168 papers included in this volume set were organized in topical sections as follows: adversarial networks and learning; convolutional neural networks; deep neural networks; embeddings and feature fusion; human centred computing; human centred computing and medicine; human centred computing for emotion; hybrid models; image processing by neural techniques; learning from incomplete data; model compression and optimization; neural network applications; neural network models; semantic and graph based approaches; social network computing; spiking neuron and related models; text computing using neural techniques; time-series and related models; and unsupervised neural models.

This paper introduces the Eighth Emotion Recognition in the Wild (EmotiW) challenge. EmotiW is a benchmarking effort run as a grand challenge of the 22nd ACM International Conference on Multimodal Interaction 2020. It comprises of four tasks related to automatic human behavior analysis: a) driver gaze prediction; b) audio-visual group-level emotion recognition; c) engagement prediction in the wild; and d) physiological signal based emotion recognition. The motivation of EmotiW is to bring researchers in affective computing, computer vision, speech processing and machine learning to a common platform for evaluating techniques on a test data. We discuss the challenge protocols, databases and their associated baselines.

This paper describes the Seventh Emotion Recognition in the Wild (EmotiW) Challenge. The EmotiW benchmarking platform provides researchers with an opportunity to evaluate their methods on affect labelled data. This year EmotiW 2019 encompasses three sub-challenges: a) Group-level cohesion prediction; b) Audio-Video emotion recognition; and c) Student engagement prediction. We discuss the databases used, the experimental protocols and the baselines.

An important factor in the operational success of any teleoperated human-swarm system is situation awareness (SA). A loss of SA has been associated with poor human performance, which can lead to misjudgement, errors, and life-threatening situations. One of the major factors that causes loss of SA is the degradation of data transmission. It is imperative to assess the SA of an operator before the performance of a teleoperated system has declined, in particular in situations of delayed relay and/or loss of critical information. We use electroencephalography (EEG) to predict different levels of SA. A human-swarm simulation was used to obtain subjective scores from participants. Quality of information significantly affected the perception of SA of the participants. EEG data provided objective confirmation of the resultant SA level. Theta, Alpha, and Beta band exhibited an increase during loss of SA. Frontal and occipital areas were identified to reflect changes in SA. These preliminary results offer evidence for the potential use of EEG to offer real-time indicators for the objective assessment of SA.

We analyse a large drug prescription dataset and test the hypothesis that drug prescription data can be used to predict further complications in older patients newly diagnosed with type 2 diabetes mellitus. More specifically, we focus on mortality and polypharmacy prediction. We also examine the balance between interpretability and predictive performance for both prediction tasks, and compare performance of interpretable models with models generated with automated methods. Our results show good predictive performance in the polypharmacy prediction task with AUC of 0.859 (95% CI: 0.857–0.861). On the other hand, we were only able to achieve the average predictive performance for mortality prediction task with AUC of 0.754 (0.747–0.761). It was also shown that adding additional drug related features increased the performance only in the polypharmacy prediction task, while additional information on prescribed drugs did not influence the performance in the mortality prediction. Despite the limited success in mortality prediction, this study demonstrates the added value of the systematic collection and use of Electronic Health Record (EHR) data in solving the problem of polypharmacy related complications in older age.

Research in automatic affect recognition has come a long way. This paper describes the fifth Emotion Recognition in the Wild (EmotiW) challenge 2017. EmotiW aims at providing a common benchmarking platform for researchers working on different aspects of affective computing. This year there are two sub-challenges: A) Audio-video emotion recognition and b) group-level emotion recognition. These challenges are based on the acted facial expressions in the wild and group affect databases, respectively. The particular focus of the challenge is to evaluate method in 'in the wild' settings. 'In the wild' here is used to describe the various environments represented in the images and videos, which represent real-world (not lab like) scenarios. The baseline, data, protocol of the two challenges and the challenge participation are discussed in detail in this paper.

As data is getting more complex and complicated, it is increasingly difficult to construct an effective complex decision model. Two very obvious examples where such a need emerges are in the economic and the medical fields. This paper presents the fuzzy signature and cognitive modeling approach which could improve such decision models. Fuzzy signatures are introduced to handle complex structured data and problems with interdependent features. A fuzzy signature can also be used in cases where data is missing. The proposed fuzzy signature structure will be used in problems that fall into this category. This paper also investigates a novel cognitive model to extend the usage of fuzzy signatures. This Fuzzy Cognitive Signature Modelling will enhance the usability of fuzzy theory in modelling complex systems as well as facilitating complex decision making process based on ill structured information or data.

As data is getting more complex and complicated, it is increasingly difficult to construct an effective complex decision model. Two very obvious examples where such a need emerges are in the economic and the medical fields. This paper presents the fuzzy signature and cognitive modeling approach which could improve such decision models. Fuzzy signatures are introduced to handle complex structured data and problems with interdependent features. A fuzzy signature can also be used in cases where data is missing. The proposed fuzzy signature structure will be used in problems that fall into this category. This paper also investigates a novel cognitive model to extend the usage of fuzzy signatures. This Fuzzy Cognitive Signature Modelling will enhance the usability of fuzzy theory in modelling complex systems as well as facilitating complex decision making process based on ill structured information or data.

This paper presents an approach to the prediction of mineral prospectivity that provides an assessment of uncertainty. Two feedforward backpropagation neural networks are used for the prediction. One network is used to predict degrees of favourability for deposit and another one is used to predict degrees of likelihood for barren, which is opposite to deposit. These two types of values are represented in the form of truth-membership and false-membership, respectively. Uncertainties of type error in the prediction of these two memberships are estimated using multidimensional interpolation. These two memberships and their uncertainties are combined to predict mineral deposit locations. The degree of uncertainty of type vagueness for each cell location is estimated and represented in the form of indeterminacy-membership value. The three memberships are then constituted into an interval neutrosophic set. Our approach improves classification performance compared to an existing technique applied only to the truth-membership value.

Fuzzy rule based systems have been very popular in many engineering applications. However, when generating fuzzy rules from the available information, this may result in a sparse fuzzy rule base. Fuzzy rule interpolation techniques have been established to solve the problems encountered in processing sparse fuzzy rule bases. In most engineering applications, the use of more than one input variable is common, however, the majority of the fuzzy rule interpolation techniques only present detailed analysis to one input variable case. This paper investigates characteristics of two selected fuzzy rule interpolation techniques for multidimensional input spaces and proposes an improved fuzzy rule interpolation technique to handle multidimensional input spaces. The three methods are compared by means of application examples in the field of petroleum engineering and mineral processing. The results show that the proposed fuzzy rule interpolation technique for multidimensional input spaces can be used in engineering applications.

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Fuzzy rule based systems have been very popular in many engineering applications. However, when generating fuzzy rules from the available information, this may result in a sparse fuzzy rule base. Fuzzy rule interpolation techniques have been established to solve the problems encountered in processing sparse fuzzy rule bases. In most engineering applications, the use of more than one input variable is common, however, the majority of the fuzzy rule interpolation techniques only present detailed analysis to one input variable case. This paper investigates characteristics of two selected fuzzy rule interpolation techniques for multidimensional input spaces and proposes an improved fuzzy rule interpolation technique to handle multidimensional input spaces. The three methods are compared by means of application examples in the field of petroleum engineering and mineral processing. The results show that the proposed fuzzy rule interpolation technique for multidimensional input spaces can be used in engineering applications.

In the mining industry, identifying new geographic locations that are favourable for mineral exploration is very important. However definitive prediction of such locations is not an easy task. In the recent years artificial neural networks have received much attention in this area. This paper uses a class of neural networks known as the Polynomial Neural Network (PNN) to construct a model to correctly classify given location into deposit and barren areas. This model uses the Geographic Information Systems (GIS) data of the location. The method is tested on the GIS data for the Kalgoorlie region of Western Australia.

Use of GIS layers, in which the cell values represent fuzzy membership variables, is an effective method of combining subjective geological knowledge with empirical data in a neural network approach to mineral-prospectivity mapping. In this study, multilayer perceptron (MLP), neural networks are used to combine up to 17 regional exploration variables to predict the potential for orogenic gold deposits in the form of prospectivity maps in the Archean Kalgoorlie Terrane of Western Australia. Two types of fuzzy membership layers are used. In the first type of layer, the statistical relationships between known gold deposits and variables in the GIS thematic layer are used to determine fuzzy membership values. For example, GIS layers depicting solid geology and rock-type combinations of categorical data at the nearest lithological boundary for each cell are converted to fuzzy membership layers representing favorable lithologies and favorable lithological boundaries, respectively. This type of fuzzy-membership input is a useful alternative to the 1-of-N coding used for categorical inputs, particularly if there are a large number of classes. Rheological contrast at lithological boundaries is modeled using a second type of fuzzy membership layer, in which the assignment of fuzzy membership value, although based on geological field data, is subjective. The methods used here could be applied to a large range of subjective data (e.g., favorability of tectonic environment, host stratigraphy, or reactivation along major faults) currently used in regional exploration programs, but which normally would not be included as inputs in an empirical neural network approach.

Rainfall prediction in this paper is a spatial interpolation problem that makes use of the daily rainfall information to predict volume of rainfall at unknown locations within area covered by existing observations. This paper proposed the use of self-organising map (SOM), backpropagation neural networks (BPNN) and fuzzy rule systems to perform rainfall spatial interpolation based on local method. The SOM is first used to separate the whole data space into some local surface automatically without any knowledge from the analyst. In each sub-surface, the complexity of the whole data space is reduced to something more homogeneous. After classification, BPNNs are then use to learn the generalization characteristics from the data within each cluster. Fuzzy rules for each cluster are then extracted. The fuzzy rule base is then used for rainfall prediction. This method is used to compare with an established method, which uses radial basis function networks and orographic effect. Results show that this method could provide similar results from the established method. However, this method has the advantage of allowing analyst to understand and interact with the model using fuzzy rules.

Spatial interpolation is an important feature of a Geographic Information System, which is the procedure used to estimate values at unknown locations within the area covered by existing observations. In this paper, we describe a conservative spatial interpolation technique that incorporates the advantages of local interpolation, Euclidean interpolation, and conservative fuzzy reasoning, and a dynamic fuzzy-reasoning-based function estimator with parameters optimised by a genetic algorithm. The main objective of this paper is to formulate a computationally efficient spatial interpolation technique similar to the IDWA technique that can be used in real time application. The main feature of our spatial interpolation technique is a capability for spatial interpolation and extrapolation in a higher-dimensional space. Examples from a rainfall spatial interpolation problem are used to illustrate the applicability of the proposed technique.

The application of new mathematics using fuzzy logic has been successful in several areas of petroleum engineering. This paper reviews the state-of–the–art of fuzzy logic applied to reservoir evaluation, especially in the area of petrophysical properties prediction and lithofacies prediction from well logs. In this paper, we will also review some fuzzy methods that have been successfully applied to case studies. Besides using fuzzy logic in establishing the model itself, fuzzy logic is also used in some cases as pre–processing or post–processing tools. This paper will act as a guide for petroleum engineers to take advantage of these advanced technologies as well as those undertaking research in this field.

Fuzzy rule-based systems have been very popular in many engineering applications. In mineral engineering, fuzzy rules are normally constructed using some fuzzy rule extraction techniques to establish the determination model in predicting the d50c of hydrocyclones. However, when generating fuzzy rules from the available information, it may result in a sparse fuzzy rule base. The use of more than one input variable is also common in hydrocyclone data analysis. This paper examines the application of fuzzy interpolation to resolve the problems using sparse fuzzy rule bases, and to perform analysis of fuzzy interpolation in multidimensional input spaces.

Information overloading has become a serious problem due to the exponential growth of the use of the Internet, emails and other online information resources. One of the solutions to this problem is the deployment of an automated classification system so as to provide an efficient means to manage the ever increasing amount of information and documents. A hybrid neural network approach for the automated classification of text –based articles is reported in this paper. In this study, the research has centered on the classification of newsgroup documents (postings) in accordance to the relevant newsgroups. The classification was initially based on the original documents. The documents are then reclassified with replacement of words from a domanin nonspecific thesaurus. Experiments based on over 40,000 news articles have been carried out and the results are found to be compatible in both cases. The technique can be extended to other online documents such as email articles and web pages.

Accurate short term load forecasting is crucial to the efficient and economic operation of modern electrical power systems. With the recent effort by many governments in the development of open and deregulated power markets, research in forecasting methods is getting renewed attention, Although long term and short term electric load forecasting has been of interest to the practicing engineers and researchers for many years, spot-price prediction is a relatively new research area. This paper examines the use of a neural-fuzzy inference method for the prediction of 24 hourly load and spot price for the next day. Publicly available data of the electricity market of the state of New South Wales, Australia is used in a case study.

In this paper, we propose a fast feature selection technique for clustering-based fuzzy modeling. The technique involves the creation of 'rough' fuzzy systems quickly from a set of data and chooses the one with the lowest error. The set of features used by the chosen fuzzy system is accepted as the optimal set of features. The effectiveness and efficiency of the proposed technique is validated using artificially generated data.

Spatial interpolation is an important feature of a Geographic Information System, which is the procedure used to estimate values at unknown locations within the area covered by existing observations. In this paper, we describe a conservative spatial interpolation technique that incorporates the advantages of local interpolation, Euclidean interpolation, and conservative fuzzy reasoning, and a dynamic fuzzy-reasoning-based function estimator with parameters optimised by a genetic algorithm. The main objective of this paper is to formulate a computationally efficient spatial interpolation technique similar to the IDWA technique that can be used in real time application. The main feature of our spatial interpolation technique is a capability for spatial interpolation and extrapolation in a higher-dimensional space. Examples from a rainfall spatial interpolation problem are used to illustrate the applicability of the proposed technique.

Rainfall prediction in this paper is a spatial interpolation problem that makes use of the daily rainfall information to predict volume of rainfall at unknown locations within area covered by existing observations. This paper proposed the use of self-organising map (SOM), backpropagation neural networks (BPNN) and fuzzy rule systems to perform rainfall spatial interpolation based on local method. The SOM is first used to separate the whole data space into some local surface automatically without any knowledge from the analyst. In each sub-surface, the complexity of the whole data space is reduced to something more homogeneous. After classification, BPNNs are then use to learn the generalization characteristics from the data within each cluster. Fuzzy rules for each cluster are then extracted. The fuzzy rule base is then used for rainfall prediction. This method is used to compare with an established method, which uses radial basis function networks and orographic effect. Results show that this method could provide similar results from the established method. However, this method has the advantage of allowing analyst to understand and interact with the model using fuzzy rules.

The application of new mathematics using fuzzy logic has been successful in several areas of petroleum engineering. This paper reviews the state-of–the–art of fuzzy logic applied to reservoir evaluation, especially in the area of petrophysical properties prediction and lithofacies prediction from well logs. In this paper, we will also review some fuzzy methods that have been successfully applied to case studies. Besides using fuzzy logic in establishing the model itself, fuzzy logic is also used in some cases as pre–processing or post–processing tools. This paper will act as a guide for petroleum engineers to take advantage of these advanced technologies as well as those undertaking research in this field.

Fuzzy rule-based systems have been very popular in many engineering applications. In mineral engineering, fuzzy rules are normally constructed using some fuzzy rule extraction techniques to establish the determination model in predicting the d50c of hydrocyclones. However, when generating fuzzy rules from the available information, it may result in a sparse fuzzy rule base. The use of more than one input variable is also common in hydrocyclone data analysis. This paper examines the application of fuzzy interpolation to resolve the problems using sparse fuzzy rule bases, and to perform analysis of fuzzy interpolation in multidimensional input spaces.

Use of GIS layers, in which the cell values represent fuzzy membership variables, is an effective method of combining subjective geological knowledge with empirical data in a neural network approach to mineral-prospectivity mapping. In this study, multilayer perceptron (MLP), neural networks are used to combine up to 17 regional exploration variables to predict the potential for orogenic gold deposits in the form of prospectivity maps in the Archean Kalgoorlie Terrane of Western Australia. Two types of fuzzy membership layers are used. In the first type of layer, the statistical relationships between known gold deposits and variables in the GIS thematic layer are used to determine fuzzy membership values. For example, GIS layers depicting solid geology and rock-type combinations of categorical data at the nearest lithological boundary for each cell are converted to fuzzy membership layers representing favorable lithologies and favorable lithological boundaries, respectively. This type of fuzzy-membership input is a useful alternative to the 1-of-N coding used for categorical inputs, particularly if there are a large number of classes. Rheological contrast at lithological boundaries is modeled using a second type of fuzzy membership layer, in which the assignment of fuzzy membership value, although based on geological field data, is subjective. The methods used here could be applied to a large range of subjective data (e.g., favorability of tectonic environment, host stratigraphy, or reactivation along major faults) currently used in regional exploration programs, but which normally would not be included as inputs in an empirical neural network approach.

With the increase in information on the Internet, the strive to find more effective tools for distinguishing between interesting and non-interesting material is increasing. Drawing analogies from the biological immune system, this paper presents an immune-inspired algorithm called AISEC that is capable of continuously classifying electronic mail as interesting and non-interesting without the need for re-training. Comparisons are drawn with a nave Bayesian classifier and it is shown that the proposed system performs as well as the nave Bayesian system and has a great potential for augmentation

Both simple and hybrid genetic algorithms encounter difficulties when presented with a function which has multiple values. Similarly, changing environments or functions which change rapidly present other problems. This paper presents an algorithm that is capable of coping with both of these scenarios: it can accommodate multiple solutions simultaneously and can track changes in optima efficiently. The proposed B-cell algorithm is inspired by the natural immune system, which itself displays similar capabilities of tracking multiple, moving targets in the form of infectious agents. This paper employs two nonlinear mappings which display chaotic behaviour to demonstrate the effectiveness of the B-cell algorithm in tracking multiple, moving targets. A number of experiments are conducted and results reported from the B-cell algorithm and standard hybrid genetic algorithm approaches. These results show the benefit of the B-cell algorithm approach when compared against these heuristic approaches.

The uses of soft computing techniques comprising artificial neural networks, fuzzy logic and genetic algorithms are emerging for the building of permeability interpretation models in well log data analysis. Regardless of which soft computing techniques are used, they rely on a set of core permeability data to give a better understanding of the formation. However uncertainties and errors with the core permeability data may undetermined the accuracy of permeability determination. This paper examines the problems that could possibly appear in the core permeability data. In most cases, data preprocessing and postprocessing are required to ensure that the permeability determination is successful. In this paper, soft computing techniques that are mainly based on fuzzy and neural networks approaches are used to assist the preprocessing and post processing stages thereby improving the overall accuracy.

Customer relationship management (CRM) initiatives have gained much attention in recent years. With the aid of data mining technology, businesses can formulate specific strategies for different customer bases more precisely. Additionally, personalisation is another important issue in CRM - especially when a company has a huge product range. This paper presents a case model and investigates the use of computational intelligent techniques for CRM. These techniques allow the complex functions of relating customer behaviour to internal business processes to be learned more easily and the industry expertise and experience from business managers to be integrated into the modelling framework directly. Hence, they can be used in the CRM framework to enhance the creation of targeted strategies for specific customer bases.

The recommendation architecture has been proposed as a system architecture which can enable a system to learn to perform a complex combination of interrelated functions. The capability of a system with the recommendation architecture to learn to manage complex telecommunication backbone networks has been investigated. A network model with a number of nodes and links and carrying realistic but randomly generated traffic was used as the target for the management system. Traffic data taken from the model was used as input to the recommendation architecture system. The traffic data was organized into inputs once every 5 minutes, and the management system organized these inputs into a hierarchy of repetition similarity. It was demonstrated that the outputs of this hierarchy provided information on the condition of the network. This output information was a compressed version of the inputs which correlated with major network conditions.

Spatial interpolation is an important feature of a Geographic Information System, which is the procedure used to estimate values at unknown locations within the area covered by existing observations. This paper constructs fuzzy rule bases with the aid of a Self-organising Map (SOM) and Backpropagation Neural Networks (BPNNs). These fuzzy rule bases are then used to perform spatial interpolation. A case based on the 467 rainfall data in Switzerland is used to test the neural fuzzy technique. The SOM is first used to classify the data. After classification, BPNNs are then use to learn the generalization characteristics from the data within each cluster. Fuzzy rules for each cluster are then extracted. The fuzzy rules base are then used for rainfall prediction.

Spatial interpolation is an important feature of a Geographic Information System, which is the procedure used to estimate values at unknown locations within the area covered by existing observations. In this paper, we propose a conservative spatial interpolation technique that incorporates the advantages of local interpolation, Euclidean interpolation, and conservative fuzzy reasoning. The main objective of this paper is to formulate a computationally efficient spatial interpolation technique similar to the IDWA technique that can be used in real time application. The main feature of our spatial interpolation technique is inherited from the concept used in conservative fuzzy interpolation reasoning for interpolating fuzzy rules in sparse fuzzy rule bases. Illustration examples from a rainfall spatial interpolation problem are also used to illustrate the applicability of the proposed technique.

This paper investigates the Sugeno's and Yasukawa's qualitative fuzzy modelling approach. We propose some easily implementable solution for the unclear details of the original paper. These details are crucial concerning the method's performance

Fuzzy rule based systems have been very popular in many control applications. However, when fuzzy control systems are used in real problems, many rules may be required. A hierarchical fuzzy system that partitions a problem for more efficient computation may be the answer. When creating a hierarchical fuzzy system, the level identification stage is crucial and time-consuming. This has a direct effect on how efficient the hierarchical fuzzy system is. This paper reports the use of an input data mining technique to efficiently perform the level identification stage. Without the use of input data mining, k*(k-1) ways of building the hierarchical fuzzy system must be tried.

Fuzzy logic is becoming popular in dealing with data analysis problems that are normally handled by statistical approaches or ANNs. The major limitation is the difficulty in building the fuzzy rules from a given set of input-output data. This paper proposed a technique to extract fuzzy rules directly from input-output pairs. It uses a self-organising neural network and association rules to construct the fuzzy rule base. The self-organising neural network is first used to classify the output data by realising the probability distribution of the output space. Association rules are then used to find the relationships between the input space and the output classification, which are subsequently converted to fuzzy rules. This technique is fast and efficient. The results of an illustrative example show that the fuzzy rules extracted are promising and useful for domain experts.

In most control and engineering applications, the use of fuzzy system as a way to improve the human-computer interaction has becoming popular. This paper reports on the use of fuzzy system in mineral processing specifically in determining the parameter d50c of hydrocyclone. However, with the input-output data provided to build the fuzzy rule base, it normally results in a sparse fuzzy rule base. This paper examines the use of the improved multidimensional alpha-cut based (IMUL) fuzzy interpolation technique to improve the prediction capability of the sparse fuzzy rule base.

The recommendation architecture has been proposed as a system architecture which can enable a system to learn to perform a complex combination of interrelated functions. The capability of a system with the recommendation architecture to learn to manage complex telecommunication backbone networks has been investigated. A network model with a number of nodes and links and carrying realistic but randomly generated traffic was used as the target for the management system. Traffic data taken from the model was used as input to the recommendation architecture system. The traffic data was organized into inputs once every 5 minutes, and the management system organized these inputs into a hierarchy of repetition similarity. It was demonstrated that the outputs of this hierarchy provided information on the condition of the network. This output information was a compressed version of the inputs which correlated with major network conditions.

Customer relationship management (CRM) initiatives have gained much attention in recent years. With the aid of data mining technology, businesses can formulate specific strategies for different customer bases more precisely. Additionally, personalisation is another important issue in CRM - especially when a company has a huge product range. This paper presents a case model and investigates the use of computational intelligent techniques for CRM. These techniques allow the complex functions of relating customer behaviour to internal business processes to be learned more easily and the industry expertise and experience from business managers to be integrated into the modelling framework directly. Hence, they can be used in the CRM framework to enhance the creation of targeted strategies for specific customer bases.

Autonomy of operations combined with decentralized management of data gives rise to a number of heterogeneous databases or information systems within an enterprise. These systems are often incompatible in structure as well as content and, hence, difficult to integrate. Depsite heterogeneity, the unity of overall purpose within a common application domain, nevertheless, provides a degree of semantic similarity that manifests itself in the form of similar data structures and common usage patterns of existing information systems. This article introduces a conceptual integration approach that exploits the similarity in metalevel information in existing systems and performs metadata mining on database objects to discover a set of concepts that serve as a domain abstraction and provide a conceptual layer above existing legacy systems. This conceptual layer is further utilized by an information reengineering framework that customizes and packages information to reflect the unique needs of different user groups within the application domain. The architecture of the information reengineering framework is based on an object-oriented model that represents the discovered concepts as customized application objects for each distinct user group.

This paper reports on the use of a fuzzy rule interpolation technique for the modelling of hydrocyclones. Hydrocyclones are important equipment used for particle separation in mineral processing industry . Fuzzy rule based systems are useful in this application domains where direct control of the hydrocyclone parameters is desired. It has been reported that a rule extracting technique has been used to extract fuzzy rules from the input- output data. However, it is not uncommon that the available input-output data set does not cover the universe of discourse. This results in the generation of sparse fuzzy rule bases. This paper examines the use of an improved multidimensional fuzzy rule interpolation technique to enhance the prediction ability of the sparse fuzzy hydrocyclone model. Fuzzy rule interpolation is normally used to provide interpretations from observations for which there are no overlaps with the supports of existing rules in the rule base.

The use of Artificial Neural Network (ANN) especially Backpropagation Neural Network (BPNN) has been a promising tool for well log analysis in predicting permeability. However, due to the range of permeability data, it is normally converted using logarithmic transform before being used for data analysis by the BPNN. This has an impact on the accuracy of the permeability prediction. This paper suggests a model for improving the permeability prediction. It first divides the whole sample space of the permeability values according to their logarithmic region, and then generates individual BPNNs for each logarithmic region. In this initial study, Learning Vector Quantization (LVQ) is used for this Purpose for separating the data. After that, each region is then handled by each BPNN. This method not only preserves the resolution of the permeability, but at the same time, increase the prediction accuracy. The contributions of this paper are to identify the problems in the signal processing of permeability prediction, and exploit new direction of improving permeability prediction using well logs.

Autonomy of operations combined with decentralized management of data gives rise to a number of heterogeneous databases or information systems within an enterprise. These systems are often incompatible in structure as well as content and, hence, difficult to integrate. Depsite heterogeneity, the unity of overall purpose within a common application domain, nevertheless, provides a degree of semantic similarity that manifests itself in the form of similar data structures and common usage patterns of existing information systems. This article introduces a conceptual integration approach that exploits the similarity in metalevel information in existing systems and performs metadata mining on database objects to discover a set of concepts that serve as a domain abstraction and provide a conceptual layer above existing legacy systems. This conceptual layer is further utilized by an information reengineering framework that customizes and packages information to reflect the unique needs of different user groups within the application domain. The architecture of the information reengineering framework is based on an object-oriented model that represents the discovered concepts as customized application objects for each distinct user group.