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Highlights • Degree of adoption of decision support tools is improved by knowledge integration. • 5 abstraction layers based Expert systems allows local and global reasoning. • Guidelines indicate a specific decision support system for each sleep stage. • Turing-Machine like decision process reflects dynamic of sleep. • Evaluation should be conducted using inter-rater agreement rates. Abstract Automatic sleep staging is challenging since several issues need to be addressed. Traditional approaches from literature do not satisfy medical experts since they do not reflect the cognitive process they perform when scoring polysomnographic curves. We propose a new approach that is based on the implementation of medical knowledge by symbolic fusion. Medical knowledge coming from the international clinical practice guidelines for sleep medicine is formalized as a five-layer framework dedicated to data abstraction in order to deliver local and global propositions and support the interpretation of polysomnographic curves. Firstly, features are extracted from raw curves. Then these features are combined to recognize sleep events in accordance with guidelines. Sleep events are then fused into the criteria required to recognize the different sleep stages. Sleep is not homogeneous through the night. The physiological events observed during the night follow a dynamic that needs to be included into an automatic sleep staging system. In order to take this into account, decision rules are selected and applied to recognize a sleep stage according to the current context. Thereby, transitions are considered with interest. In this paper, we propose to use a Turing Machine-like decision process to handle transitions. To interpret the local observations and properly score a given state, the previous state which has been stored in a specific register is used as a context. One of the advantages of following the principles of symbolic fusion is to benefit from the full traceability of the decision. Hence, it makes possible to discuss each final — or intermediate — decision with an expert and check for relevance. The preliminary results are encouraging since agreement rates provided between decisions taken by our automatic approach and human experts are similar to those measured between human experts (average agreement rate = 54.60% / average Cohen’s kappa = 0.40) on a dataset of 131 full polysomnographic recordings. [ABSTRACT FROM AUTHOR]

Abstract: This study concerns itself with providing user support for a decision problem in logistic regression analysis: given a set of metric variables and one binary dependent variable, select the optimal subset of variables that can best predict this dependent variable. The problem requires an evaluation of competing models based on heuristic selection criteria such as goodness-of-fit and prediction accuracy. This paper documents the heuristics, formalizes the algorithms, and eventually presents an interactive decision support system to facilitate the selection of such an optimal model. This study adds to the sparsely studied domain of expert systems for social science researchers, and makes three contributions to the literature. First, the study formalizes a number of heuristics to arrive at optimal logistic regression models. Second, the study presents two computational algorithms that incorporate these formalized heuristics. Third, the paper documents an implementation of these algorithms through an interactive decision support system. The study concludes with a discussion on the risks of relying too heavily on the system and with future opportunities for research. [Copyright &y& Elsevier]

Expert systems for diabetes care need to automatically evaluate glycaemia measurements in relationship to meals to correctly determine patients’ metabolic condition and generate recommendations about therapy adjustments. Most glucose meters allow patients to manually label each measurement with a meal tag, but as this utility is not always used, a completion procedure is needed. Classification methods are usually based on predefined mealtimes and present insufficient accuracy that might affect the automatic data analysis. Expert systems in diabetes require a reliable method to manage incomplete glycaemia data so that they can determine if patients’ metabolic condition is altered due to a specific meal or due to an extended fasting period. This paper presents the design and application of a classification module to automatically assign the appropriate meal and ‘moment of measurement’ to incomplete glycaemia data. Different machine learning techniques were studied in order to design the best classification algorithm in terms of accuracy. The selected classifier was implemented with a C4.5 decision tree with 7 input features selected with a wrapper evaluator and the genetic search algorithm, which achieved 95.45% of accuracy with the training set on cross-validation. The classification module was integrated in the Sinedie expert system for gestational diabetes care and was evaluated in a clinical environment for 8 months with 42 patients. A total of 7,113 glycaemia measurements were uploaded by patients into the Sinedie system and were completed by the “classification module”. The 98.79% of the measurements were correctly classified, while patients modified the automatic classification of 1.21% of them. Classification results were improved by 21.04% compared to a classification based on predefined mealtimes. The automatic classification of glycaemia measurements minimizes the patient's intervention, allows structuring measurements in relationship to meals and makes automatic data interpretation by expert systems more reliable. [ABSTRACT FROM AUTHOR]

The rapidly growing world energy use already has concerns over the exhaustion of energy resources and heavy environmental impacts. As a result of these concerns, a trend of green and smart cities has been increasing. To respond to this increasing trend of smart cities with buildings every time more complex, in this paper we have proposed a new method to solve energy inefficiencies detection problem in smart buildings. This solution is based on a rule-based system developed through data mining techniques and applying the knowledge of energy efficiency experts. A set of useful energy efficiency indicators is also proposed to detect anomalies. The data mining system is developed through the knowledge extracted by a full set of building sensors. So, the results of this process provide a set of rules that are used as a part of a decision support system for the optimisation of energy consumption and the detection of anomalies in smart buildings. [ABSTRACT FROM AUTHOR]

Abstract: This paper analyzes the use of feedforward multilayer perceptron neural networks in the support of the decision process during the conceptual design phase of engineering systems. A user friendly software tool is proposed in order to increase the quality of the designers’ decisions by offering intuitive insights during the early design phase. The performance gain is evaluated through controlled experiments performed with non-experienced users. This paper proposes using the prediction capabilities of Artificial Neural Networks to recommend design solutions based on earlier successful designs. It implements a feedforward multilayer perceptron architecture with sigmoid activation functions, and uses the Levenberg–Marquardt training algorithm for weight matrix update. The network complexity was abstracted to ease the utilization by non-expert users. Different stopping conditions were developed including one where the training and testing error divergence is monitored to tackle the bias/variance dilemma. The advantages of this approach for decision support were measured through a set of six different case studies such as gaseous automobile emissions prediction based on institutional data, or the choice of a launcher for a specific space mission. The decision support tool generated quality performance gains between 13% and 88% for examples ranging from simple continuous single variable to complex discrete multivariate problems. [Copyright &y& Elsevier]

Abstract: Safety assessment of thermal power plants (TPPs) is one of the important means to guarantee the safety of production in thermal power production enterprises. Due to various technical limitations, existing assessment approaches, such as analytic hierarchy process (AHP), Monte Carlo methods, artificial neural network (ANN), etc., are unable to meet the requirements of the complex security assessment of TPPs. Currently, most of the security assessments of TPP are completed by the means of experts’ evaluations. Accordingly, the assessment conclusions are greatly affected by the subjectivity of the experts. Essentially, the evaluation of power plant systems relies to a large extent on the knowledge and length of experience of the experts. Therefore in this domain case-based reasoning (CBR) is introduced for the security assessment of TPPs since this methodology models expertise through experience management. Taking the management system of TPPs as breakthrough point, this paper presents a case-based approach for the Safety assessment decision support of TPPs (SATPP). First, this paper reviews commonly used approaches for TPPs security assessment and the current general evaluation process of TPPs security assessment. Then a framework for the Management System Safety Assessment of Thermal Power Plants (MSSATPP) is constructed and an intelligent decision support system for MSSATPP (IDSS-MSSATPP) is functionally designed. IDSS-MSSATPP involves several key technologies and methods such as knowledge representation and case matching. A novel case matching method named Improved Gray CBR (IGCBR) has been developed in which a statistical approach (logistic regression) and Gray System theory are integrated. Instead of applying Gray System theory directly, it has been improved to integrate it better into CBR. In addition this paper describes an experimental prototype system of IDSS-MSSATPP (CBRsys-TPP) in which IGCBR is integrated. The experimental results based on a MSSATPP data set show that CBRsys-TPP has high accuracy and systematically good performance. Further comparative studies with several other common classification approaches also show its competitive power in terms of accuracy and the synergistic effects of the integrated components. [Copyright &y& Elsevier]

Abstract: This paper investigates the forecasting accuracy of fuzzy extended group decisions in the adjustment of statistical benchmark results. DELPHI is a frequently used method for implementing accurate group consensus decisions. The concept of consensus is subject to expert characteristics and it is sometimes ensured by a facilitator’s judgment. Fuzzy set theory deals with uncertain environments and has been adapted for DELPHI, called fuzzy-DELPHI (FD). The present paper extends the recent literature via an implementation of FD for the adjustment of statistical predictions. We propose a fuzzy-DELPHI adjustment process for improvement of accuracy and introduced an empirical study to illustrate its performance in the validation of adjustments of statistical forecasts in the dry bulk shipping index. [Copyright &y& Elsevier]

Abstract: The detection of non-technical losses (NTLs), in most papers, commonly deals with the utilization of the registered consumption for each customer; besides, some researchers used the economic activity, the active/reactive ratio and the contract power. Currently, utility company databases store enormous amounts of information on both installations and customers: consumption, technical information on the measure equipment, documentation, inspections results, commentaries of inspectors, etc. In this paper, an integrated expert system (IES) for the analysis and classification of all the available useful information of the customer is presented. Customer classification identifies the presence of an NTL and the problem type. This IES include several modules: text mining module for analysis of inspector commentaries and extraction of additional information on the customer, data mining module to draw up the rules that determine the customer estimate consumption, and the Rule Based Expert System module to analyze each customer using the results of the text and data mining modules. This IES is used with real data extracted from Endesa company databases. Endesa is the most important power distribution company in Spain, and one of the most significant companies of Europe. This IES is used in the test phase by human experts in the Endesa company. In this phase, the IES is used as a Decision Support System (DSS), as it contains another module which provides a report with additional information about the customer and a summarized result that the inspectors can use to reach a decision. [Copyright &y& Elsevier]

Abstract: Production quality in the food production supply chain is studied in this paper. The deficiencies of quality monitoring that exist in traceability systems are analyzed. An abnormality diagnosis algorithm, pre-warning method and structure of pre-warning system are presented. Four abnormal data types in supply chain are analyzed, they are substandard abnormality, over-range abnormality, abnormal distribution and abnormal tendency. All the detection data of the whole supply chain are monitored timely and pre-warned. The production abnormality of the logistics unit is diagnosed and automatically warned, and the decision support information is given. A standard hierarchy evaluation indicator system for abnormalities is developed in this paper. A mathematical model for abnormality detection is developed by combining radial base function (RBF) neural network, fuzzy control, and statistical analysis methods. This model is used in detecting and recognizing different types of abnormalities in the food production supply chain, especially hidden problems. The simulation results show that the proposed pre-warning system can effectively identify abnormal data types, and accurately determine whether a warning should be issued, depending on the warning level when an abnormality is detected by the system. The pre-warning system for food production supply chain performs well and effectively. [ABSTRACT FROM AUTHOR]

Abstract: Multiple criteria decision-making (MCDM) research has developed rapidly and has become a main area of research for dealing with complex decision problems. The purpose of the paper is to explore the performance evaluation model. This paper develops an evaluation model based on the fuzzy analytic hierarchy process and the technique for order performance by similarity to ideal solution, fuzzy TOPSIS, to help the industrial practitioners for the performance evaluation in a fuzzy environment where the vagueness and subjectivity are handled with linguistic values parameterized by triangular fuzzy numbers. The proposed method enables decision analysts to better understand the complete evaluation process and provide a more accurate, effective, and systematic decision support tool. [ABSTRACT FROM AUTHOR]

Abstract: Fish disease and health management is becoming a key barrier and major problem that currently limits expanded production and sustainable development of aquaculture and bring out food-safety issues. There has been a marked growth of interest in developing expert system to aid fish disease/health management. The literature shows that a few have been used to any extent due to the lack of deep user requirement analysis and long-term support and maintenance. This paper presents the effort to apply evolutionary prototyping model in developing an intelligent decision support system for fish disease/health management. The paper describes in detail about the four evolution stages as the system development experienced, every stage including the system design and implementation, feedback and requirement from system evaluation, and system redevelopment until a blown system. At last the evolution process is concluded from three directions. The evolutionary prototyping benefits and challenges experienced during the development and promotion are also discussed from the project management perceptive. [Copyright &y& Elsevier]

Abstract: The need for computer-based intelligent techniques for recruitment and retention of employees in a highly competitive global market has grown significantly in the last decade. Salesperson recruitment is a critical task for most organisations. Existing approaches for salesperson recruitment primarily rely on filtering of applications based on selection criteria followed by interviews. Some organisations also include personality testing based on psychometric techniques. The high turnover of salesperson in the industry suggests limited success of these procedures. Additionally, existing approaches lack benchmarking methods. In this paper we describe design and development of an intelligent sales recruitment and benchmarking system (ISRBS) for recruitment and benchmarking of salespersons. ISRBS design represents operation of the findings and outcomes based on actual field studies and random surveys of salespersons as well as development of models for measuring independent and dependent variables related to selling behaviour. The main contributions of the paper are (i) Developing an on line selling behaviour profiling technique based on integration of intelligent system techniques like expert systems and fuzzy sets, psychology based selling behaviour model, and AHP techniques, and (ii) an objective and novel selling behaviour benchmarking technique to facilitate modelling of organisation based benchmarks and cultural fits. An earlier version of this system has been commercially used in the industry in Australia. ISRBS integrates psychology based selling behaviour model with artificial intelligence techniques and soft computing methods for selling behaviour profiling and benchmarking. [Copyright &y& Elsevier]

Abstract: In this paper, we investigate the performance of several systems based on ensemble of classifiers for bankruptcy prediction and credit scoring. The obtained results are very encouraging, our results improved the performance obtained using the stand-alone classifiers. We show that the method “Random Subspace” outperforms the other ensemble methods tested in this paper. Moreover, the best stand-alone method is the multi-layer perceptron neural net, while the best method tested in this work is the Random Subspace of Levenberg–Marquardt neural net. In this work, three financial datasets are chosen for the experiments: Australian credit, German credit, and Japanese credit. [Copyright &y& Elsevier]

Expert systems (ES) development technology has been used to build rural business applications in the past but these have usually been developed using traditional expert systems shells. This paper introduces a new architecture for the development of a design environment where the domain experts can build a knowledge base for target-specific ES for rural business operators. The system allows rural business operators to use their own knowledge in building their own, target-specific ES for tailored development to their own specific requirements. At this stage, this reusable design environment caters for the Australian dairy industry but in the long run we claim it will be useful for the other livestock based rural industries such as beef cattle and sheep. This approach of developing target-specific ES contributes new knowledge in that it provides a new way of developing decision support by allowing human domain experts to develop relevant ES for different livestock farming business. An evolutionary prototyping approach was employed for initial development of a proof of concept example and as a method of outlining the solution environment. Multiple qualitative data collection methods were engaged to facilitate knowledge acquisition in the domain of milk protein enhancement for dairy operations. This paper also describes the generic development procedure used in this project. [Copyright &y& Elsevier]

This paper presents a hybrid approach of case-based reasoning and rule-based reasoning, as an alternative to the purely rule-based method, to build a clinical decision support system for ICU. This enables the system to tackle problems like high complexity, low experienced new staff and changing medical conditions. The purely rule-based method has its limitations since it requires explicit knowledge of the details of each domain of ICU, such as cardiac domain hence takes years to build knowledge base. Case-based reasoning uses knowledge in the form of specific cases to solve a new problem, and the solution is based on the similarities between the new problem and the available cases. This paper presents a case-based reasoning and rule-based reasoning based model which can provide clinical decision support for all domains of ICU unlike rule-based inference models which are highly domain knowledge specific. Experiments with real ICU data as well as simulated data clearly demonstrate the efficacy of the proposed method. [Copyright &y& Elsevier]

Abstract: As the advent of the Internet, B2B negotiation process on the Internet has been given attention from both researchers and practitioners. Therefore, B2B ecommerce decision making will be a challenge for organizations in the foreseeable future. Some literature shows that important issues to reduce uncertainty in the development of long-term relationships among B2B commerce partners. In this sense, this paper proposes a new negotiation support system to incorporate causal relationships of negotiation terms in the process of B2B negotiation, on the basis of a cognitive map. The proposed a CAKES-NEGO (CAusal Knowledge-driven Expert System) suggests that causal relationships of negotiation terms could be explicitly represented by using the cognitive map as knowledge representation vehicle as well as inference engine. Cognitive maps can illustrate causal relationships among the factors describing a given object and/or problem, and it can also describe experts’ tacit knowledge about a certain object. A fuzzy cognitive map (FCM) is an extension of a cognitive map with the additional capability of representing feedback through weighted causal links. FCM, a fuzzy signed digraph with causal relationships between concept variables found in a specific application domain, is used for the causal knowledge acquisition. The objectives of this paper are to (1) suggest a fuzzy cognitive mapping based expert system that support decision process of decision makers and (2) apply it to the illustrative examples, which are B2B negotiation problems, to show the validity of our proposed system. In addition, statistical tests proved that the proposed negotiation mechanism could improve decision performance significantly in B2B negotiations. [Copyright &y& Elsevier]

Abstract: The purpose of this paper is to develop a model selection method for time constrained decision problems. Usually there are many models in a decision support system (DSS), thus the model management system (MMS) is an important component of a DSS. In this paper, we study a DSS that offers competitive models with different levels of solution accuracy. The goodness of a model will be judged by its payoff value that involves the model solution accuracy and information costs. A Genetic Algorithm (GA) is used to find the maximum payoff value of a model, and the model with the best payoff value is selected for the target problem. We also propose a model selection system that is easy to implement with the help of web services technology. [Copyright &y& Elsevier]

Abstract: This paper describes an intelligent decision support system for evaluating state-owned enterprises (SOEs) using DEA models. This decision support system converts numerical data into information that can be used to evaluate state-owned enterprises (SOEs). In this paper, we use DEA models, including CCR, BCC and FDH, to shed new light on the operational efficiency and determinants of state-owned enterprises in Beijing. We propose an evaluation framework for evaluating state-owned enterprises (SOEs), including a database management subsystem, a model base subsystem, a knowledge acquisition subsystem, and a dialogue subsystem. Finally, we adopt the logistic regression model to analyze the effect factors of inefficient state-owned enterprises and provide optimum decision-making for state-owned enterprises (SOEs). [Copyright &y& Elsevier]

Abstract: This paper describes a knowledge-based decision support system for measuring the performance of government real estate investment using DEA models. We propose an evaluation framework for real estate investment, including a database, a model base, and a knowledge base, to create a tool that a government can use to deal with decision-making problems via the Internet. This decision support system converts numerical data into information that can be used to evaluate possible real estate investments. Particularly, rules in the rule base are explained in more detail for illustrating the process of reasoning and KDSSGREI adapts quickly and accurately to infer and generate suggestions or actions. Data envelopment analysis (DEA) is used to perform efficiency analysis in this paper. Finally, we apply China''s case to obtain strategies for reforming real estate investment. [Copyright &y& Elsevier]

This paper surveys expert systems (ES) development using a literature review and classification of articles from 1995 to 2004 with a keyword index and article abstract in order to explore how ES methodologies and applications have developed during this period. Based on the scope of 166 articles from 78 academic journals (retrieved from five online database) of ES applications, this paper surveys and classifies ES methodologies using the following eleven categories: rule-based systems, knowledge-based systems, neural networks, fuzzy ESs, object-oriented methodology, case-based reasoning, system architecture, intelligent agent systems, database methodology, modeling, and ontology together with their applications for different research and problem domains. Discussion is presented, indicating the followings future development directions for ES methodologies and applications: (1) ES methodologies are tending to develop towards expertise orientation and ES applications development is a problem-oriented domain. (2) It is suggested that different social science methodologies, such as psychology, cognitive science, and human behavior could implement ES as another kind of methodology. (3) The ability to continually change and obtain new understanding is the driving power of ES methodologies, and should be the ES application of future works. [Copyright &y& Elsevier]

Abstract: The use of computer-based clinical decision support (CDS) tools is growing significantly in recent years. These tools help reduce waiting lists, minimise patient risks and, at the same time, optimise the cost health resources. In this paper, we present a CDS application that predicts the probability of having unstable angina based on clinical data. Due to the characteristics of the variables (mostly binary) a Bayesian network model was chosen to support the system. Bayesian-network model was constructed using a population of 1164 patients, and subsequently was validated with a population of 103 patients. The validation results, with a negative predictive value (NPV) of 91%, demonstrate its applicability to help clinicians. The final model was implemented as a web application that is currently been validated by clinician specialists. [Copyright &y& Elsevier]

Abstract: The aim of this paper is to introduce a decision support system that ranks patents based on multiple expert evaluations. The presented approach starts with the creation of three value scenarios for each considered patent by each expert. These are used for the construction of individual fuzzy pay-off distribution functions for the patent value; a consensual fuzzy pay-off distribution is then determined starting from the individual distributions. Possibilistic moments are calculated from the consensus pay-off distribution for each patent and used in ranking them with TOPSIS. It is further showed how the analytic hierarchy process (AHP) can be used to include additional decision variables into the patent selection, thus allowing for a two-tier decision making process. The system is illustrated with a numerical example and the usability of the system and the combination of methods it includes for patent portfolio selection in the real world context is discussed. [Copyright &y& Elsevier]

Abstract: In this paper we present a general framework for time-aware decision support systems. The framework uses the state-of-the-art tOWL language for the representation of temporal knowledge and enables temporal reasoning over the information that is represented in a knowledge base. Our approach uses state-of-the-art Semantic Web technology for handling temporal data. Through such an approach, the designer of a system can focus on the application intelligence rather than enforcing/checking data related restrictions manually. Also, there is an increased support for reuse of temporal reasoning tools across applications. We illustrate the applicability of our framework by building a market recommendations aggregation system. This system automatically collects market recommendations from online sources and, based on the past performance of the analysts that issued a recommendation, generates an aggregated recommendation in the form of a buy, hold, or sell advice. We illustrate the flexibility of our proposed system by implementing multiple methods for the aggregation of market recommendations. [Copyright &y& Elsevier]

Abstract: An adaptive clustering procedure specifically designed for process monitoring, fault detection and isolation is presented in this paper. The key feature of the proposed procedure can be identified as its underlying capability to detect novelties in the system’s mode of operation and, thus, to identify previously unseen functioning modes of the process. Once a novelty is detected, relevant informations are used to enrich the knowledge-base of the algorithm and as a result the proposed clustering procedure evolves and learns the new features of the monitored process in accordance with the available process data. The suggested clustering procedure is theoretically illustrated and its effectiveness has been investigated experimentally. Particularly, the on-line implementation of the algorithm and its integration with a fault detection expert system have been considered by making reference to a pneumatic process. [Copyright &y& Elsevier]

Abstract: In the recent years, diagnosing students’ learning problems after testing and providing learning suggestions for them are an important research issue. Many studies have been conducted to develop a method for analyzing learning barriers of students such that helpful learning suggestions or guidance can be provided based on the analysis results. In this paper, we present a new procedure for integrating test item–concept relationship opinions based on majority density of multiple experts in order to enhance a concept–effect relationship model used for generating personalized feedback. It provides a useful and practical way to decrease inconsistencies in the weighting criteria of multiple experts and to enhance the entire learning-diagnosis procedure for developing testing and diagnostic systems. [Copyright &y& Elsevier]

Abstract: As the nuclear power plants within the UK age, there is an increased requirement for condition monitoring to ensure that the plants are still be able to operate safely. This paper describes the novel application of Intelligent Systems (IS) techniques to provide decision support to the condition monitoring of Nuclear Power Plant (NPP) reactor cores within the UK. The resulting system, BETA (British Energy Trace Analysis) is deployed within the UK’s nuclear operator and provides automated decision support for the analysis of refuelling data, a lead indicator of the health of AGR (Advanced Gas-cooled Reactor) nuclear power plant cores. The key contribution of this work is the improvement of existing manual, labour-intensive analysis through the application of IS techniques to provide decision support to NPP reactor core condition monitoring. This enables an existing source of condition monitoring data to be analysed in a rapid and repeatable manner, providing additional information relating to core health on a more regular basis than routine inspection data allows. The application of IS techniques addresses two issues with the existing manual interpretation of the data, namely the limited availability of expertise and the variability of assessment between different experts. Decision support is provided by four applications of intelligent systems techniques. Two instances of a rule-based expert system are deployed, the first to automatically identify key features within the refuelling data and the second to classify specific types of anomaly. Clustering techniques are applied to support the definition of benchmark behaviour, which is used to detect the presence of anomalies within the refuelling data. Finally data mining techniques are used to track the evolution of the normal benchmark behaviour over time. This results in a system that not only provides support for analysing new refuelling events but also provides the platform to allow future events to be analysed. The BETA system has been deployed within the nuclear operator in the UK and is used at both the engineering offices and on station to support the analysis of refuelling events from two AGR stations, with a view to expanding it to the rest of the fleet in the near future. [Copyright &y& Elsevier]

Abstract: The decision-making process is one of the complicated processes involved in warehouse operation for efficiently fulfilling various specific customer orders. This is especially true if the orders require cross-border delivery activities, such as palletization of the delivery goods according to regulation requirements. Case-based reasoning is an intelligent method for complex problem solving that uses past cases to find a solution to new problems. To achieve an appropriate solution, retrieving useful prior cases effectively for the problem is essential. However, current case retrieval methods are mainly based on a fixed set of attributes for different type of orders in which specific order features for case groups are neglected. In this paper a hybrid approach called the case-genetic algorithm-based decision support model (C-GADS), is proposed in classifying new customer orders into case groups with the highest similarity value, allowing for effectively selecting the most similar cases among the group. The proposed model also suggests the types of features considered in each case group. It helps enhance the effectiveness of formulating warehouse order operations based on grouping similar cases. To validate the feasibility of the proposed model, a case study is conducted and the results show that planning effectiveness is enhanced. [Copyright &y& Elsevier]

Abstract: Members of university commissions decide on an especial sort of students’ requests, based on various criteria. Since these decisions may seriously affect the students’ future life, it is necessary to pay precise attention to the decision-making process. However, many causes like lack of mental readiness, impact of irrelevant factors, change in views and limitation in concentration power of the commission members plus some other undistinguishable facts can affect the decision process leading to many ruinous problems. The paper aims simulation of a decision-making process which is used by management or consulting teams in order to solve this usual problem. As an especial case study, it focuses on the decisions made by members of university commissions on an especial sort of students’ requests, who have faced problems with their education. After analyzing the decision process, we have designed a hybrid expert system that can regenerate the process. In this system, the criteria shape a fuzzy rule base with linguistic variables and the membership functions are adjusted by experts through interviews and/or questionnaire. Indeed, it is a composition of a fuzzy MCDM and a fuzzy inference system (FIS). Moreover, to differentiate between the criteria, we have attributed weightings to the criteria, which are computed using a fuzzy AHP method. Results show that the system output error in comparison to the historical data is less than 5%, and it can resemble the council’s behavior. This supporting system can be applied to generate consistent outputs that contribute in convergence and unification of the decisions, and hence preventing false aftermaths. [Copyright &y& Elsevier]

Abstract: Forensic entomology consists, during a criminal investigation, in studying the insects found on a cadaver to estimate the time of death. This is the only technique that can be used for a large post-mortem interval. But, because of the important system complexity, the result given by the expert are imperfect. In this paper, a decision support system (DSS) has been developed to take into account all the ecosystemic parameters and a significant quantity of biological models. The proposed DSS is based on the belief function theory to validate and calibrate agent based simulations. First results of this architecture are presented within the framework of a real forensic examination. [Copyright &y& Elsevier]

Abstract: The aim of this paper is to develop a generic version of the conventional fuzzy-analytic hierarchy process (FAHP) method and investigate the shipping asset management (SAM) problem in the dry bulk shipping market. The recent literature has various applications of the FAHP, but these studies lack consistency control, use identical decision support rather than weighted expert choices, and lack measurable criteria. The proposed model, generic fuzzy-AHP (here after GF-AHP), provides a standard control of consistency on the decision matrix for the expert group. GF-AHP also improves the capabilities of the FAHP by executing direct numerical inputs without expert consultation. In practical business, some of the criteria can be easily calculated and expert consultation is a redundant process. Therefore, GF-AHP presents how to transform such numerical inputs to a priority scale. Finally, expertise differences on the decision group are reflected in the GF-AHP process by an expert weighting algorithm. [Copyright &y& Elsevier]

Abstract: Despite its usefulness, design knowledge is not often captured or documented, and is therefore lost or damaged after a product design is completed. As a way to address this issue, two major formalisms can be used for modeling, representing, and reasoning about causal design knowledge: fuzzy cognitive map (FCM) and Bayesian belief network (BBN). Although FCM has been used extensively in knowledge engineering, few methodologies exist for systematically constructing it. In this paper, we present a methodology and application—FCM Constructor—to systematically acquire design knowledge from domain experts, and to construct a corresponding BBN. To show the system’s usability, we use three realistic product design cases to compare BBNs that are directly generated by domain experts, with BBNs that are generated using the FCM Constructor. We find that the BBN constructed through the FCM Constructor is similar, based on reasoning results, to the BBN constructed directly by specifying conditional probability tables of BBNs. [Copyright &y& Elsevier]

Abstract: Modern particle physics experiments observing collisions of particle beams generate large amounts of data. Complex trigger and data acquisition systems are built to select on-line the most interesting events and write them to persistent storage. The final stage of this selection process nowadays often happens on large computer clusters. The stable and reliable operation of such event filter clusters is critical for the success of these experiments. Operating the event filter cluster must ensure dead time free processing of large amount of data, requiring 24-h continuous status monitoring of each processing node, and fast detection and problem solving. Ideally, problems should be recognized before they deteriorate the system performance. The process control of the event filter cluster is performed exclusively by a human operator, placing high demands difficult to accomplish. In this paper, a hybrid system based on expert systems technology and statistical tools and methods is proposed to address this issue. The system is built upon a scalable modular architecture and a design overview is given. The proposed hybrid system is designed and tested in a real environment, with an event filter cluster prototype based on the architecture of the Compact Muon Solenoid experiment at CERN. The system test results with an analysis are provided. Finally, the future possibilities are discussed. [Copyright &y& Elsevier]

Abstract: In this paper, a new method in classifier fusion is introduced for decision making based on internal structure of base classifiers. Amongst methods used in combining classifiers, there are some methods which work on decision template as a tool for modeling behavior of base classifiers in order to label data. This tool models their behavior only based on their final outputs. Our new method, introduces a special structure for decision template such that internal behavior of a neural network base classifier can be modeled in a proper manner suitable for classifiers fusion. The new method builds decision template for each layer of the neural network including all hidden layers. Therefore, the process of making decision in each base classifier is also available for classifiers fusion. Efficiency of the new method is compared with some known benchmark datasets to show how it can improve efficiency of classifiers fusion. [Copyright &y& Elsevier]

Abstract: Prescription is an important element in the medical practice. An appropriate drug therapy is complex in which the decision of prescribing is influenced by many factors. Any discrepancy in the prescription making process can lead to serious consequences. In particular, the General Practitioners (GPs), who need to diagnose and treat a wide range of health conditions and diseases, must be knowledgeable enough in deciding what type of medicines should be given to the patients. With the widespread computerization of medical records, GPs now can make use of accumulated historic clinical data in retrieving similar decisions in therapeutic treatment for treating the new situation. However, the applications of decision support tools are rarely found in the prescription domain due to the complex nature of the domain and limitations of the existing tools. It was argued that existing tools can only solve a small amount of the cases on the real world dataset. This paper proposes a new revised Case-based Reasoning (CBR) mechanism, named Rule-Associated CasE-based Reasoning (RACER), which integrates CBR and association rules mining for supporting GPs prescription. It aims at leveraging the two most common techniques in the field and dealing with the complex multiple values solution. Eight hundred real cases from a medical organization are collected and used for evaluating the performance of RACER. The proposed method was also compared with CBR and association rules mining for testing. The results demonstrate that the combination leads to increased in both recall and precision in various settings of parameters. The performance of RACER remains stable by using different sets of parameters, which shows that the most important element of the mechanism is self-determined. [ABSTRACT FROM AUTHOR]

Abstract: The exploration for oil and gas requires real-time petrophysical expertise to interpret measurement data acquired in boreholes and to recommend further steps. High time pressure and the far reaching nature of these decisions, as well as the limited opportunity to gain in depth petrophysical experience suggests that a decision support system that can aid the petrophysicist will be very useful. In this paper we describe a Bayesian approach for obtaining compositional estimates that combines expert knowledge with information obtained from measurements. We define a prior model for the compositional volume fractions and observation models for each of the measurement tools. Both prior and observation models are based on domain expertise. These models are combined in a joint probability model. To deal with the nonlinearities in the model, Bayesian inference is implemented by using the hybrid Monte Carlo algorithm. In the system, tool measurement values can entered and the posterior probability distribution of the compositional fractions can be obtained by applying Bayes’ rule. Bayesian inference is also used for optimal tool selection, using conditional entropy to select the most informative tool to obtain better estimates of the reservoir. Reliability and consistency of the method is demonstrated by inference on synthetically generated data. [ABSTRACT FROM AUTHOR]

Abstract: Engineering work is a complex task mainly supported by computer aids. This paper highlights its deficiencies when the designer cannot obtain any expert recommendations or guidelines from the computer’s program regarding a decision-making process such as material selection. Particular product’s development design process, within a well-known enterprise from the white goods industry is presented and supported by a case study. The advantages and disadvantages are set out, and any potential benefits gained from using an intelligent decision support system for plastic products’ design are introduced. The proposed decision support system contains human cognition within the field of design knowledge and special domain knowledge expertise regarding plastics supported by design methodology called ‘Design for Manufacturing’ (DFM). [Copyright &y& Elsevier]

Abstract: Urinary incontinence is one of the largest diseases affecting between 10% and 30% of the adult population and an increase is expected in the next decade with rising treatment costs as a consequence. There are many types of urological dysfunctions causing urinary incontinence, which makes cheap and accurate diagnosing an important issue. This paper proposes a support vector machine (SVM) based method for diagnosing urological dysfunctions. 381 registers collected from patients suffering from a variety of urological dysfunctions have been used to ensure the (generalization) performance of the decision support system. Moreover, the robustness of the proposed system is examined by fivefold cross-validation and the results show that the SVM-based method can achieve an average classification accuracy at 84.25%. [Copyright &y& Elsevier]

Abstract: Expert systems, decision support systems, and knowledge management systems are computer constructs that are designed to service human decision makers. There are however certain sorts of decision situations where, for some reason, responsibility might better be invested in a computer program, rather than a person. This paper points out several such situations that can be found in the contemporary financial management sector, and suggests how Directive Decision Devices might be equipped to deal with them on a stand-alone basis. [Copyright &y& Elsevier]

Abstract: Designing a JEE (Java Enterprise Edition)-based enterprise application capable of achieving its performance objectives is rather hard. Predicting the performance of this type of systems at the design level is difficult and sometimes not viable, because this requires having precise knowledge of the expected load conditions and the underlying software infrastructure. Besides, the requirement for rapid time-to-market leads to postpone performance tuning until systems are developed, packaged and running. In this paper we present a novel approach for automatically detecting performance problems in JEE-based applications and, in turn, suggesting courses of actions to correct them. The idea is to allow developers to smoothly identify and eradicate performance anti-patterns by automatically analyzing execution traces. The approach has been implemented as a tool called JEETuningExpert, and validated using three well-known JEE reference applications. Specifically, we evaluated the effectiveness of JEETuningExpert for detecting performance problems, measured the overhead imposed by online monitoring each application and the improvements were achieved after following the suggested corrective actions. These results empirically showed that the refactored applications are 40.08%, 76.94% and 61.13% faster, on average. [Copyright &y& Elsevier]

Abstract: Demand forecasting is considered a key factor for balancing risk of over-stocking and out-of-stock. It is the main input to supply chain processes affecting their performance. Even with much effort and funds spent to improve supply chain processes, they still lack reliability and efficiency if the demand forecast accuracy is poor. This paper presents a proposal of an integrated model of statistical methods and improved genetic algorithm to generate better demand forecast accuracy. An improved genetic algorithm is used to choose the best weights among the statistical methods and to optimize the forecasted activities combinations that maximize profit. A case study is presented using different product types. And, a comparison is conducted between results obtained from the proposed model and from traditional statistical methods, which demonstrates improved forecast accuracy using the proposed model for all time series types. [Copyright &y& Elsevier]

Wear and corrosion are the most important factors that the surface of the engineering parts must confront. The need for protection and improvement of the mechanical characteristics of the surface of engineering parts can be to some extent satisfied by coatings. Coatings are considered as an excellent solution when resistance to corrosion, oxidation or low friction is demanded, but due the complexity of selecting the appropriate one, engineers often avoid them. The need for simultaneous consideration of qualitative and quantitative properties, render the use of classic material selection theories inadequate. An expert system for coating selection is presented in this paper, which can handle both qualitative and quantitative variables. The mathematical model used combines the multi-criteria decision making theories (MCDM) together with the fuzzy sets theory. The ¿Max-Min set¿ method is applied to calculate the ordering value of the alternatives while the TOPSIS method is used to rank them. A numerical example is provided to illustrate the method. Finally, the process presented can be easily computerized, to create the relative software. [Copyright &y& Elsevier]

Any decision process deals with two different concerns as its cornerstones, evaluating the alternatives and ranking them based on their performances. In any decision process, the former phase is usually the premise of the latter one. Alternatives¿ evaluation is the concept that largely depends on the experts and their expertise, which increase uncertainty in the decision-making process. In addition to all proposed methods for having the experts¿ knowledge as evaluations of the alternatives, utilizing expert decision support systems (EDSS) can be a sensible response to such a need. Having evaluated the alternatives in the first phase of a decision-making process, the second phase of the process deals with the ranking the alternatives based on their performances obtained from the first phase. In this paper, we discuss the architecture of a fuzzy system including both modules, utilizing fuzzy concept for dealing with the uncertainty of the problem. Concerning the problem we had been dealt with, our system comprises a fuzzy evaluation module, which is a fuzzy expert system and an appropriate tool for evaluating the existing alternatives promptly and smoothly, without the imposed time delays by the experts to propose their comments and the uncertainty of such expertise-based comments, and a fuzzy ranking module, which is a fuzzy version of ELECTRE III method ranking the alternatives based on their outranking relations and by considering the existing uncertainty in their performances. This way the final ranking is resulted from an independent fuzzy system, which has considered the existing uncertainty in the evaluations not once but twice. Our proposed system has been applied to a real case of vendor selection process in one of the greatest and the most famous companies in the Iranian oil industry, OIEC, and the results are discussed. [Copyright &y& Elsevier]

In this paper, an intelligent agent-based communication support platform for multimodal transport is developed. The rationale for doing so is found in the potential of such a system to increase cost efficiency, service and safety for different transport-related actors. Although, at present several comparable systems exist, their current implementation is far from successful because of technological and economic obstacles. The new expert communication platform put forward here (called MamMoeT) addresses these two issues by using a software agent-based approach. Software agents are pieces of software representing a single user. They are autonomous, communicative and intelligent. The MamMoeT system developed can be described as a real-time decision support system in which intelligent software agents handle communicative tasks, exchange desired amounts of information among different users using common exchange protocols which act as translators between different systems. [Copyright &y& Elsevier]

This paper presents the development process of an expert decision support system for pre-filtering and analysis of data from the carbon dioxide (CO2) capture process. Chemical absorption has become one of the dominant CO2 capture technologies because of its efficiency and low cost. Since the chemical absorption process consists of dozens of components, it generates more than a 100 different types of data. Monitoring the vast amount of data can be complex, and data filtering and analysis processes are desirable. Specifically, invalid data captured as the equipment is started and shut down need to be filtered, and the filtered data need to be analyzed for different purposes. The expert decision support system for data pre-filtering and analysis not only filters out invalid data using different expert rules, but it can also modify or reuse filtering settings, and export the filtered data to various file formats for further analysis. During development of the expert decision support system, knowledge acquisition was emphasized. The system development process incorporated various technologies including the model-view-control (MVC) design pattern, the embedded database technology, the Java event delivery techniques and the eXtensible Markup Language (XML). Some sample sessions from system executions and some results generated from pre-filtering the data will also be discussed. [Copyright &y& Elsevier]

Abstract: In fund raising management the employment of a decision support system is crucial for optimising the results of fund raising campaigns. However, the current tools based on database technology are not able to suggest suitable fund raising strategies and the support to the fund raiser is limited to giving general indications in relation to specific claims. In this paper, we present a fuzzy knowledge based decision support system, which is able to individuate promising fund raising strategies for a given campaign. The system uses a knowledge based description of the campaign features and of the donors profiles and integrates these data with two models for strategies and historical information evaluation, rules of thumb suggested by experts in the field, and recent econometric studies. The system provides a ranking of suitable strategies using fuzzy evaluations. Experimental results confirm the applicability of the proposed approach in real contexts, providing significant insight into the improvement of campaigns performances. [Copyright &y& Elsevier]

As the service quality has been reconsidered in the public sector as well as private enterprises, the need for public sectors to adopt principle and practices of private sectors is concerned with customer-focused approach. However, the different business culture of public service organizations makes it difficult to improve service quality. It is required to establish a structured framework that leads employees to make efforts to improve their service delivery processes and supports continuous improvement of service delivery processes based on the data about the process performance from the customer-perceived value-oriented viewpoint. In this paper, we propose a structured framework that identifies the key service processes, validates from customer perspectives and establishes the measurements to monitor based on the data about the process performance. It uses periodic customer satisfaction index (CSI) surveys (S.C. Park) for understanding customer-perceived values. The proposed framework consists of three phases; the questionnaire design, the key process (KP) identification from the integrated viewpoints of importance and contribution, and the key process indicator (KPI) derivation and management. For the application, we established a web-based decision support system for a public service organization for tourism in Korea. [Copyright &y& Elsevier]

Abstract: With the rapid growth of e-commerce, there are a great number of customer reviews on the e-commerce websites. Generally, potential customers usually wade through a lot of on-line reviews in order to make an informed decision. However, retrieving sentiment information relevant to customer’s interest still remains challenging. Developing a sentiment mining and retrieval system is a good way to overcome the problem of overloaded information in customer reviews. In this paper, we propose a sentiment mining and retrieval system which mines useful knowledge from consumer product reviews by utilizing data mining and information retrieval technology. A novel ranking mechanism taking temporal opinion quality (TOQ) and relevance into account is developed to meet customers’ information need. Besides the trend movement of customer reviews and the comparison between positive and negative evaluation are presented visually in the system. Experimental results on a real-world data set show the system is feasible and effective. [Copyright &y& Elsevier]

Abstract: Design concept evaluation plays a critical role in the early phases of product development as it has significant impact on the downstream development processes as well as on the success of the product developed. Essentially, design concept evaluation is a complex multi-criteria decision-making process involving large amount of data and expert knowledge which are usually imprecise and subjective. Aiming to improve the effectiveness and objectivity of the design concept evaluation process, this paper proposes a novel method based on grey relation analysis and rough set theory. By integrating the strength of rough sets in handling vagueness and the merit of grey relation analysis in modeling multi-criteria decision-making, a rough number enabled grey relation analysis (called rough–grey analysis) is proposed to evaluate design concepts. The result of an example shows that the proposed rough–grey analysis has provided a novel alternative to perform design concept evaluation, in which the vague design information and expert knowledge can be modeled and analyzed more effectively and objectively. [Copyright &y& Elsevier]

Technology management centres provide technological and customer support services for private or public organisations. Commonly, these centres offer support by using a helpdesk software that facilitates the work of their operators. In this paper, a CBR module that acts as a solution recommender for customer support environments is presented. The CBR module is flexible and multi-domain, in order to be easily integrable with any existing helpdesk software in the company. [Copyright &y& Elsevier]

Abstract: Traditional case-based reasoning uses a table/frame or scenario to represent a case. It assumed that similar input/event results in similar output/event state. However, similar cases may not have similar output/event states since problem solver may have different way to break down the problem. Thus, authors previously proposed problem-based case reasoning to overcome the limitation of the traditional approaches and used clustered ontology to represent the problem spaces of a case. However, synonym problem causes the mismatch of similar sub-problems of historical cases for new case. Thus, this paper proposed ontology-based similarity measurement to retrieve the similar sub-problems that overcomes the synonym problems on case retrieval. The recall and precise of ontology-based similarity measurement were higher than that of the traditional similarity measurement. [Copyright &y& Elsevier]