The development of a mobile decision support system

The emergence of mobile computing environments brings out various changes in the requirements and applications involving distributed data and has made the traditional Intelligent Decision Support System (IDSS) architectures based on the client/server model ineffective in mobile computing environments. This paper discusses the deficiencies of the current IDSS architectures based on data warehouse, on-line analysis processing (OLAP), model base (MB) and knowledge based (KB) technologies. By adopting the agent technology, the paper extends the IDSS system architecture to the Mobile Decision Support System (MDSS) architecture. The logical structure and the application architecture of the MDSS and the mechanisms and implementation strategies of the User Access Agent System, a major component of the MDSS, are described in this paper.

An index of regional sustainability : a GIS-based multiple criteria analysis decision support system for progressing sustainability

GIS (Geographical Information Systems) based decision support tools will be useful in helping guide regions to sustainability. These tools need to be simple but effective at identifying, for regional managers, areas most in need of initiatives to progress sustainability. Multiple criteria analysis (MCA) has been used as a decision support tool for a wide number of applications, as it provides a systematic framework for evaluating various options. It has the potential to be used as a tool for sustainability assessment, because it can bring together the sustainability criteria from all pillars, social, economic and environmental, to give an integrated assessment of sustainability. Furthermore, the use of GIS and MCA together is an emerging addition to conducting sustainability assessments. This paper further develops a sustainability assessment framework developed for the Glenelg Hopkins Catchment Management Authority region of Victoria, Australia by providing a GIS-based decision support system for regional agencies. This tool uses multiple criteria analysis in a GIS framework to assess the sustainability of sub-catchments in the Glenelg Hopkins Catchment. The multiple criteria analysis based on economic, social and environmental indicators developed in previous stages of this project was used as the basis to build a model in ArcGIS1. The GIS-based multiple criteria analysis, called An Index of Regional Sustainability Spatial Decision Support System (AIRS SDSS),
produced maps showing sub-catchment sustainability, and environmental, social and economic condition. As a result, this tool is able to highlight those sub-catchments most in need of assistance with achieving sustainability. It will also be a valuable tool for evaluation and monitoring of strategies for sustainability. This paper shows the usefulness of GIS-based multiple criteria analysis to enhance the monitoring and evaluation of sustainability at the regional to sub-catchment scale.

Neurofuzzy decision support system for efficient risk allocation in public-private partnership infrastructure projects

The performance of public-private partnership (PPP) infrastructure projects is largely contingent on whether the adopted risk allocation (RA) strategy is efficient. Theoretical frameworks drawing on the transaction cost economics and the resource-based view of organizational capability are able to explain the underlying mechanism but unable to accurately forecast efficient RA strategies. In this paper, a neurofuzzy decision support system (NFDSS) was developed to assist in the RA decision-making process in PPP projects. By combining fuzzy and neural network techniques, a synthesized fuzzy inference system was established and taken as the core component of the NFDSS. Evaluation results show that the NFDSS can forecast efficient RA strategies for PPP infrastructure projects at a highly accurate and effective level. A real PPP infrastructure project is used to demonstrate the NFDSS and its practical significance.

Developing a spatial decision support system for sustainable built environments in Abu Dhabi

 The research aims at developing a set of sustainability indicators for the challenging Abu Dhabi built environment and examine the possible use of GIS. The research has illustrated the real potential of the sustainability indicators for managing built environment sustainability performance and provides a clear perspective on how the proposed indicators can be used to develop a DSS to assess and improve Abu Dhabi’s sustainability.

A multi-objective evolutionary algorithm-based decision support system: a case study on job-shop scheduling in manufacturing

In this paper, an evolutionary algorithm is used for developing a decision support tool to undertake multi-objective job-shop scheduling problems. A modified micro genetic algorithm (MmGA) is adopted to provide optimal solutions according to the Pareto optimality principle in solving multi-objective optimisation problems. MmGA operates with a very small population size to explore a wide search space of function evaluations and to improve the convergence score towards the true Pareto optimal front. To evaluate the effectiveness of the MmGA-based decision support tool, a multi-objective job-shop scheduling problem with actual information from a manufacturing company is deployed. The statistical bootstrap method is used to evaluate the experimental results, and compared with those from the enumeration method. The outcome indicates that the decision support tool is able to achieve those optimal solutions as generated by the enumeration method. In addition, the proposed decision support tool has advantage of achieving the results within a fraction of the time.

An intelligent decision support system for leukaemia diagnosis using microscopic blood images

This research proposes an intelligent decision support system for acute lymphoblastic leukaemia diagnosis from microscopic blood images. A novel clustering algorithm with stimulating discriminant measures (SDM) of both within- and between-cluster scatter variances is proposed to produce robust segmentation of nucleus and cytoplasm of lymphocytes/lymphoblasts. Specifically, the proposed between-cluster evaluation is formulated based on the trade-off of several between-cluster measures of well-known feature extraction methods. The SDM measures are used in conjuction with Genetic Algorithm for clustering nucleus, cytoplasm, and background regions. Subsequently, a total of eighty features consisting of shape, texture, and colour information of the nucleus and cytoplasm sub-images are extracted. A number of classifiers (multi-layer perceptron, Support Vector Machine (SVM) and Dempster-Shafer ensemble) are employed for lymphocyte/lymphoblast classification. Evaluated with the ALL-IDB2 database, the proposed SDM-based clustering overcomes the shortcomings of Fuzzy C-means which focuses purely on within-cluster scatter variance. It also outperforms Linear Discriminant Analysis and Fuzzy Compactness and Separation for nucleus-cytoplasm separation. The overall system achieves superior recognition rates of 96.72% and 96.67% accuracies using bootstrapping and 10-fold cross validation with Dempster-Shafer and SVM, respectively. The results also compare favourably with those reported in the literature, indicating the usefulness of the proposed SDM-based clustering method.

An Intelligent Human-Computer Interaction System for Decision Support

This paper proposes a novel architecture for
developing decision support systems. Unlike conventional decision support systems, the proposed architecture endeavors to reveal the decision-making process such that humans' subjectivity can be
incorporated into a computerized system and, at the same time, to
preserve the capability of the computerized system in processing information objectively. A number of techniques used in developing the decision support system are elaborated to make the decisionmarking
process transparent. These include procedures for high dimensional data visualization, pattern classification, prediction, and evolutionary computational search. An artificial data set is first
employed to compare the proposed approach with other methods. A simulated handwritten data set and a real data set on liver disease diagnosis are then employed to evaluate the efficacy of the proposed
approach. The results are analyzed and discussed. The potentials of the proposed architecture as a useful decision support system are demonstrated.

Application of an Adaptive Neural Network to Medical Decision Support

Medical diagnostic and prognostic problems are prime examples of decision making in the face of uncertainty. In this paper, we investigate the applicability of the Fuzzy ARTMAP neural network as an intelligent decision support system in clinical medicine. In particular, Fuzzy ARTMAP is employed as a predictive model for prognosis of complications in patients admitted to the Coronary Care Units. A number of off-line and on-line experiments have been conducted with various network parameter settings, training methods, and learning rules. The results are compared with those from other systems including the logistic regression model. In addition, the outcomes of a set of on-line learning experiments revealed the potential of employing Fuzzy ARTMAP as an autono-mously learning system that is able to learn perpetually and, at the same time, to provide useful decision support.

Stakeholder participation in estuary management: the development of Victoria's Estuary Entrance Management Support System

Stakeholder involvement in the management of estuaries is a necessary element of good environmental governance. In Victoria, Australia, a key challenge for estuary managers is whether or not estuaries should be artificially opened since many river mouths close ‘naturally’ from time to time. Estuary closure resulting in raised estuarine water levels leads to economic and social impacts on local communities. In the past these effects have been addressed by artificial river mouth openings, often without reference to associated environmental impacts. This article discusses the development and features of an Estuary Entrance Management Support System and considers its performance against principles of effective environmental management. It concludes that, in bringing together technical information with stakeholder input through a structured process, such a system makes a useful contribution to improving estuary entrance management.

Categorization of disaster decision support needs for the development of an integrated model for DMDSS

The wide variety of disasters and the large number of activities involved have resulted in the demand for separate Decision Support System (DSS) models to manage different requirements. The modular approach to model management is to provide a framework in which to focus multidisciplinary research and model integration. A broader view of our approach is to provide the flexibility to organize and adapt a tailored DSS model (or existing modular subroutines) according to the dynamic needs of a disaster. For this purpose, the existing modular subroutines of DSS models are selected and integrated to produce a dynamic integrated model focussed on a given disaster scenario. In order to facilitate the effective integration of these subroutines, it is necessary to select the appropriate modular subroutine beforehand. Therefore, subroutine selection is an important preliminary step towards model integration in developing Disaster Management Decision Support Systems (DMDSS). The ability to identify a modular subroutine for a problem is an important feature before performing model integration. Generally, decision support needs are combined, and encapsulate different requirements of decision-making in the disaster management area. Categorization of decision support needs can provide the basis for such model selection to facilitate effective and efficient decision-making in disaster management. Therefore, our focus in this paper is on developing a methodology to help identify subroutines from existing DSS models developed for disaster management on the basis of needs categorization. The problem of the formulation and execution of such modular subroutines are not addressed here. Since the focus is on the selection of the modular subroutines from the existing DMDSS models on basis of a proposed needs classification scheme.

Decision support in interest based negotiation support systems: the AssetDivider system

Negotiation Support Systems (NSS) model the process of negotiation from basic template support to more sophisticated decision making support. The authors attempt to develop systems capable of decision support by suggesting possible solutions for the given dispute. Current Negotiation Support Systems primarily rely upon mathematical optimisation techniques and often ignore heuristics and other methods derived from practice. This chapter discusses the technology of several negotiation support systems in family law developed in their laboratory based on data collected and methods derived from practise. The chapter explores similarities and differences between systems the authors have created and demonstrates their latest development, AssetDivider.