Complex Decision Support Systems Research Articles

Measuring efficiency of retrieval algorithms with Schweizer-Sklar information aggregation

The task of an information retrieval system (IRS) is to retrieve relevant information from large datasets efficiently. However, selecting the best algorithm for an IRS is a complex decision support system (DSS) problem. The cubic m-polar fuzzy set (CmPFS) model is proposed to address the multi-polarity issue during algorithm selection and to express multi-polar information under m-fuzzy intervals. In this study, we introduce the Schweizer-Sklar (SS) aggregation operator based on CmPFS and develop various new aggregation operators using CmPFS and SS t-norm and t-conorm. New operations such as addition, usual multiplication, and power of two or more Cubic m-polar fuzzy Numbers (CmPFNs) based on SS t-norm and t-conorm are also defined. We also define score and accuracy functions to determine the priorities of alternatives. Four operators are proposed as follows: Cubic m-polar fuzzy Schweizer-Sklar weighted average (CmPFSSWA), Cubic m-polar fuzzy Schweizer-Sklar ordered weighted average (CmPOWA), Cubic m-polar fuzzy Schweizer-Sklar weighted geometric (CmPFSSWG), and Cubic m-polar fuzzy Schweizer-Sklar ordered weighted geometric (CmPFSSOWG). The desired qualities of these operators are explored and used to solve the DSS problem based on CmPFS. Finally, a practical application of an IRS is presented to demonstrate the effectiveness and reliability of the proposed operators.

Emergency shelter materials under a complex non-linear diophantine fuzzy decision support system

AbstractThe distribution of emergency shelter materials in emergency cases around the world is a hard task, the goal of this research is to offer a Complex Non-linear Diophantine Fuzzy (C-NLDF) decision-making model for earthquake shelter construction. Essentially, the article is divided into three sections to acquire acceptable and precise measures in emergency decision-making situations. First, we present the Complex Non-Linear Diophantine Fuzzy Set (CN-LDFS), a new generalization of the complex linear Diophantine fuzzy set (CLDFS) and q-linear Diophantine fuzzy set (q-LDFS), as well as explore its key aspects. Furthermore, aggregation operators are useful for aggregating uncertainty in decision-making issues. As a result, algebraic norms for CN-LDFSs are produced based on certain operational laws. In the second section of the work, we offer a series of averaging and geometric aggregation operators under CN-LDFS that are based on defined operating laws. In the final section of the work, under complex Non-linear Diophantine fuzzy information, the ranking algorithms based on suggested aggregation operators are present to address the case study regarding emergency situation of earthquakes. In comparison section, results of existing and proposed operators explore the effectiveness of proposed methodologies and provide accurate emergency measures to address the global uncertainty about the construction of emergency shelters in earthquakes.

INFUS Conference

INFUS (http://infus.itu.edu.tr) is an acronym for Intelligent and Fuzzy Systems. It is an international research forum to advance the foundations and applications of intelligent and fuzzy systems, computational intelligence, and soft computing for applied research in general and for complex engineering and decision support systems. The principal mission of INFUS is bridging the gap between fuzzy and intelligence systems and real complex systems via joint research between universities and international research institutions, encouraging interdisciplinary research and bringing multidiscipline researchers together.

A paradigm shift to ethical decision-making—incorporating systemic epistemology into complex socio-technical decision support systems research

ABSTRACT Current decision support systems (DSS) research is overwhelmingly positivist and limits the adequate representation of relevant stakeholders. This crisis of relevance precludes the systemic inclusion of social and ethical issues in complex socio-technical DSS designs. For their unstable boundary conditions, such systems also require explicit coverage of their dynamics. Lending a novel perspective to DSS research, this paper proposes a framework with the new decision-making paradigm and the systemic inquiry of boundary critique to explore stakeholder perspectives for more ethically-aware DSS development. Using qualitative research, the developed framework is applied to unleash ethical issues for a dynamic and complex socio-technical university timetabling scenario implemented as a web-based group decision support system. Multiple stakeholder perspectives are recognised and analysed for boundary judgements, while a dynamic stakeholder network considers boundary liquidity and emergence. This work aims to couple systemic epistemology with the socio-technical lens to encourage the inclusion of ethical dimensions in dynamic and complex DSS designs.

Usability, User Comprehension, and Perceptions of Explanations for Complex Decision Support Systems in Finance: A Robo-Advisory Use Case

Robo-financial advisors are complex algorithmic decision-making systems with a high potential for mass adoption due to their low operating costs and multitasking abilities. The quantitative aspects of our study measure the efficacy and usability of explanations and qualitative aspects determine the effect of explanations on users and system usability.

Clinician acceptance of complex clinical decision support systems for treatment allocation of patients with chronic low back pain

BackgroundThe uptake of complex clinical decision support systems (CDSS) in daily practice remains low, despite the proven potential to reduce medical errors and to improve the quality of care. To improve successful implementation of a complex CDSS this study aims to identify the factors that hinder, or alleviate the acceptance of, clinicians toward the use of a complex CDSS for treatment allocation of patients with chronic low back pain.MethodsWe tested a research model in which the intention to use a CDSS by clinicians is influenced by the perceived usefulness; this usefulness, in turn is influenced by the perceived service benefits and perceived service risks. An online survey was created to test our research model and the data was analysed using Partial Least Squares Structural Equation Modelling. The study population consisted of clinicians. The online questionnaire started with demographic questions and continued with a video animation of the complex CDSS followed by the set of measurement items. The online questionnaire ended with two open questions enquiring the reasons to use and not use, a complex CDSS.ResultsNinety-eight participants (46% general practitioners, 25% primary care physical therapists, and 29% clinicians at a rehabilitation centre) fully completed the questionnaire. Fifty-two percent of the respondents were male. The average age was 48 years (SD ± 12.2). The causal model suggests that perceived usefulness is the main factor contributing to the intention to use a complex CDSS. Perceived service benefits and risks are both significant antecedents of perceived usefulness and perceived service risks are affected by the perceived threat to autonomy and trusting beliefs, particularly benevolence and competence.ConclusionsTo improve the acceptance of complex CDSSs it is important to address the risks, but the main focus during the implementation phase should be on the expected improvements in patient outcomes and the overall gain for clinicians. Our results will help the development of complex CDSSs that fit more into the daily clinical practice of clinicians.

Management information system (mis) as a part of the complex decision support system in business

Management information system (mis) as a part of the complex decision support system in business

Multiple attribute group decision making using J-divergence and evidential reasoning theory under intuitionistic fuzzy environment

The theory of intuitionistic fuzzy sets has been proved to be an effective and convenient tool in the construction of fuzzy multiple attribute group decision-making models to deal with the uncertainty in developing complex decision support systems. Concerning this topic, the current studies mainly focus on their attention on two aspects including aggregation operators on intuitionistic fuzzy sets and determining the weights of both decision makers and attributes. However, some challenges have not been fully considered including existing aggregation operators which may induce unreasonable results in some situations and how to objectively determine the weights of both attributes and decision makers to meet different decision-making demands. To overcome the challenges of existing decision-making models and to satisfy much more decision-making situations, a novel intuitionistic fuzzy multiple attribute group decision-making method via J-divergence and evidential reasoning theory is proposed in this paper as a supplement of conventional models. On the one hand, a weighted J-divergence of intuitionistic fuzzy sets and a J-divergence between two intuitionistic fuzzy matrices are introduced. Following the two concepts, two consensus-based approaches are proposed to determine the weights of both decision makers and attributes. The weights obtained from the proposed method can more accurately reflect the importance levels of both attributes and decision makers from the perspective of consensus by comparison with existing models. On the other hand, an evidential reasoning theory-based operator is established to replace conventional operators for aggregating intuitionistic fuzzy information. The fusion result via this operator is consistent with most of intuitionistic fuzzy numbers. With these works, the proposed method can provide more accurate and reasonable decision results than existing algorithms.

Interval-Valued Intuitionistic Fuzzy Multiple Attribute Group Decision Making with Uncertain Weights

The theory of interval-valued intuitionistic fuzzy sets (IVIFSs) has been an impactful and convenient tool in the construction of advanced multiple attribute group decision making (MAGDM) models to counter the uncertainty in the developing complex decision support system. To satisfy much more demands from fuzzy decision making problems, we propose a method to solve the MAGDM problem in which all the information supplied by the decision makers is expressed as interval-valued intuitionistic fuzzy decision matrices where each of the elements is characterized by an interval-valued intuitionistic fuzzy number, and the information about the weights of both decision makers and attributes may be completely unknown or partially known. Firstly, we introduce a consensus-based method to quantify the weights of all decision makers based on all interval-valued intuitionistic fuzzy decision matrices. Secondly, we utilize the interval-valued intuitionistic fuzzy weighted arithmetic (IVIFWA) operator to aggregate all interval-valued intuitionistic fuzzy decision matrices into the collective one. Thirdly, we establish an optimization model to determine the weights of attributes depending on the collective decision matrix and the given attribute weight information. Fourthly, we adopt the weighted correlation coefficient of IVIFSs to rank all the alternatives from the perspective of TOPSIS via the collective decision matrix and the obtained weights of attributes. Finally, some examples are used to illustrate the validity and feasibility of our proposed approach by comparison with some existing models.

Energy, land and water nexus in Sri Lanka's Mahaweli basin

The Mahaweli Water Security Investment Program, financed by the Asian Development Bank and the Government of Sri Lanka, aims to complete the Mahaweli Development Program, started in the 1970s to improve farmer incomes, food security, equity between different parts of the country, public health, and domestic, municipal and industrial water supplies. Up to 900 million m3 of water will be transferred annually through canals, reservoirs and tunnels from the Mahaweli River to the water-scarce north and north-west, where smallholder farmers have traditionally practised single-season rice cultivation. The transfer will facilitate cultivation of a second, diversified, crop and must be shared with competing consumptive demands, as well as meeting daily peak energy demands through releases for hydropower. The paper comprises a briefing exploring how the nexus is to be managed with the help of strengthened integrated water resources management, complex decision-support systems, institutional capacity development, strategic environmental analysis and consensus winning.

Method for determining the reliability parameters of software for complex decision support systems

The testing stage, creating great opportunities to verify and shape software reliability, significantly increases the cost of its production. The effectiveness of the work related to testing, expressed by the interdependence of the level of program product reliability and the cost of testing it, strongly depends on the adopted testing strategy, specifying the organization and scope of the work performed. In this situation, therefore, there is a need to define the conditions for a compromise in terms of reliability and cost requirements set for the software. The practical finding of this compromise can be greatly facilitated if there are possibilities to formally assess the level of software quality and the cost of testing it using appropriate indicators. The paper attempts to describe a method of determining a program testing strategy as a result of solving a two-criteria optimization problem, with the program reliability coefficient and the cost of testing as component criteria. The paper consists of description of the program testing process and mathematical model of this process, formulation of the problem of two-criteria optimization of the program testing strategy, remarks on method of solving the problem that has been formulated. proposed. To illustrate the method of finding an optimal testing strategy that has been proposed a numerical example is considered.

Management of information protection based on the integrated implementation of decision support systems

We developed the method and the model for managing protection of objects of informatization, based on the integrated implementation of decision support systems for the tasks on cybersecurity. The proposed solutions differ from the existing ones by the possibility to automate the procedure of generating variants for controlling actions using the decision support system, designed as a web application. The described model for the coordination of experts' opinions is based on the Delphi method. The approach proposed makes it possible to coordinate expert opinions, including to take into account different interval estimates of the degree of protection and information security metrics of the objects of informatization. Results are presented of testing under actual conditions at the enterprises of Ukraine the software complex Decision support system for managing cyber security of an enterprise ‒ DMSSCSE. The DSS is adapted for the on-line work of experts. It was established that the DSS DMSSCSE makes it possible to improve effectiveness of the applied organizational and technical measures to protect objects of informatization. The proposed solutions enabled bringing down the cost of organizing comprehensive information protection systems by 12−15 % compared to the existing methods

Agent-based Decision Support System for Technology Recommendation

This paper presents an idea of a multi-agent decision support system. Agent-based technology allows for decentralized problem solving and creating complex decision support systems, mixing various processing techniques, such as simulation, reasoning and machine learning and allows for distributed knowledge. Our main contribution is an agent-based architecture for decision support systems which is an agent-based implementation of a labeled deductive system. Such approach allows to decompose an inference algorithm into separate modules and distribute knowledge base into parts. The system is tested on a domain of material choice support for casting.

Medical Image Retrieval Using Vector Quantization and Fuzzy S-tree

The aim of the article is to present a novel method for fuzzy medical image retrieval (FMIR) using vector quantization (VQ) with fuzzy signatures in conjunction with fuzzy S-trees. In past times, a task of similar pictures searching was not based on searching for similar content (e.g. shapes, colour) of the pictures but on the picture name. There exist some methods for the same purpose, but there is still some space for development of more efficient methods. The proposed image retrieval system is used for finding similar images, in our case in the medical area – in mammography, in addition to the creation of the list of similar images – cases. The created list is used for assessing the nature of the finding – whether the medical finding is malignant or benign. The suggested method is compared to the method using Normalized Compression Distance (NCD) instead of fuzzy signatures and fuzzy S-tree. The method with NCD is useful for the creation of the list of similar cases for malignancy assessment, but it is not able to capture the area of interest in the image. The proposed method is going to be added to the complex decision support system to help to determine appropriate healthcare according to the experiences of similar, previous cases.

A fuzzy framework for encoding uncertainty in clinical decision-making

In recent decades, technological advances coupled with research efforts have made possible to develop very complex Decision Support Systems (DSSs) able to exhibit highly sophisticated reasoning capabilities in order to improve clinical decision-making, and, thus, promote more efficient care practices. One of the most significant factors influencing, and in particular limiting, the adoption of clinical DSSs is represented by the modality of representation and computerization of clinical guidelines in form of patient-specific recommendations. Until now, many knowledge representation formalisms have been developed, mainly focused on time-oriented guidelines. However, they can generate an unrealistic over-simplification of reality, since they are not able to completely handle uncertainty and imprecision typically affecting clinical guidelines. In this respect, this paper proposes a novel fuzzy framework expressly thought for building guideline-based DSSs, by efficiently modelling and handling the peculiarities of clinical knowledge affected by uncertainty and imprecision and encoded in the form of guidelines. This framework has been devised with the aim of: (i) offering a set of patterns for easily inserting and editing clinical recommendations belonging to a guideline as a group of one or more fuzzy rules expressing positive evidence and one fuzzy ELSE rule including negative evidence; (ii) defining a set of Fuzzy Guideline Systems (FGSs), one for each guideline encoded, characterized by ad-hoc configurations for the mathematical operators necessary to evaluate rules and generate the outcome expected; (iii) implementing a multi-level inference scheme able to treat different FGSs as a whole and efficiently enable their interconnection, i.e. the chaining among the groups of fuzzy rules belonging to each FGS; (iv) exposing a set of graphical facilities for guiding the definition of fuzzy rules to be embedded into a clinical DSS and enabling their automatic encoding and execution by using an XML-based machine executable language. A usability evaluation has been performed, showing a good satisfaction of medical users with respect to the framework implemented, and, thus, proving both its feasibility and usefulness.

Complex Data-driven Predictive Modeling in Personalized Clinical Decision Support for Acute Coronary Syndrome Episodes

The objective of this paper is to demonstrate the development of complex model of clinical episode, based on data-driven approach, for decision support in treatment of ACS (Acute Coronary Syndrome). The idea is aimed at improving predictive capability of a data-driven model by combining different models within a composite data-driven model. It can be implemented either hierarchical or alternative combination of models. Three examples of data-driven models are described: simple classifier, outcome prediction based on reanimation time and states-based prediction model, to be used as part of complex model of episodes. To implement the proposed approach, a generalized architecture of data-driven clinical decision support systems was developed. The solution is developed as a part of complex clinical decision support system for cardiac diseases for Federal Almazov North-West Medical Research Centre in Saint Petersburg, Russia.

Bayesian decision support for complex systems with many distributed experts

Complex decision support systems often consist of component modules which, encoding the judgements of panels of domain experts, describe a particular sub-domain of the overall system. Ideally these modules need to be pasted together to provide a comprehensive picture of the whole process. The challenge of building such an integrated system is that, whilst the overall qualitative features are common knowledge to all, the explicit forecasts and their associated uncertainties are only expressed individually by each panel, resulting from its own analysis. The structure of the integrated system therefore needs to facilitate the coherent piecing together of these separate evaluations. If such a system is not available there is a serious danger that this might drive decision makers to incoherent and so indefensible policy choices. In this paper we develop a graphically based framework which embeds a set of conditions, consisting of the agreement usually made in practice of certain probability and utility models, that, if satisfied in a given context, are sufficient to ensure the composite system is truly coherent. Furthermore, we develop new message passing algorithms entailing the transmission of expected utility scores between the panels, that enable the uncertainties within each module to be fully accounted for in the evaluation of the available alternatives in these composite systems.

Multithreshold Entropy Linear Classifier: Theory and applications

This paper proposes a new multithreshold linear classifier (MELC) based on the Renyi’s quadratic entropy and Cauchy–Schwarz divergence, combined with the adaptive kernel density estimation in the one dimensional projections space. Due to its nature MELC is especially well adapted to deal with unbalanced data. As the consequence of both used model and the applied density regularization technique, it shows strong regularization properties and therefore is almost unable to overfit. Moreover, contrary to SVM, in its basic form it has no free parameters, however, at the cost of being a non-convex optimization problem which results in the existence of local optima and the possible need for multiple initializations.In practice, MELC obtained similar or higher scores than the ones given by SVM on both synthetic and real data from the UCI repository. We also perform experimental evaluation of proposed method as a part of expert system designed for drug discovery problem. It appears that not only MELC achieves better results than SVM but also gives some additional insights into data structure, resulting in more complex decision support system.

SABER and MyDispense: Building better pharmacy education through innovation and sharing

Information technology (IT) is increasingly a part of everyday life for health professionals. Technology such as decision support systems (DSS) and electronic health records (EHR) have great potential in improving practice efficiency and standard, and improve patient outcomes; however, like most health IT, available programs are often lacking in usability and efficiency. To improve the quality of IT available, health professionals need to be actively involved in the developmental process, from early planning stages to final testing and feedback. Aims: To gain insight into the role of IT in the health sector and pharmacy. To explore what the general requirements are of complex DSS used by health professionals. To discuss if and how these requirements change depending on practice setting. To gain insight into what skills are needed to be involved in different stages of health IT development. Learning objectives: By the end of the session participants should be able to: Describe the different types of IT used in the health sector and pharmacy. Describe the role of EHR, computer physician order entry systems dispensing systems, DSS and how these systems are related. Involved in the development of a usable health IT. Description of Workshop Activities: This workshop is aimed at health practitioners, researchers, policy makers, and other parties interested in health IT. During this workshop participants will be shown a short presentation describing a) different types of health IT, and b) the development process of a prototype DSS to illustrate how health professionals can be involved in health IT development. This will be followed by small group discussions on the requirements for DSS in clinical practice and various environments, a demonstration of a DSS developed by the authors, and feedback. Additional information (if required): N/A

Health Professional Involvement in Health Information Technology Development: What Can Your Do to Improve Them?

Information technology (IT) is increasingly a part of everyday life for health professionals. Technology such as decision support systems (DSS) and electronic health records (EHR) have great potential in improving practice efficiency and standard, and improve patient outcomes; however, like most health IT, available programs are often lacking in usability and efficiency. To improve the quality of IT available, health professionals need to be actively involved in the developmental process, from early planning stages to final testing and feedback. Aims: To gain insight into the role of IT in the health sector and pharmacy. To explore what the general requirements are of complex DSS used by health professionals. To discuss if and how these requirements change depending on practice setting. To gain insight into what skills are needed to be involved in different stages of health IT development. Learning objectives: By the end of the session participants should be able to: Describe the different types of IT used in the health sector and pharmacy. Describe the role of EHR, computer physician order entry systems dispensing systems, DSS and how these systems are related. Involved in the development of a usable health IT. Description of Workshop Activities: This workshop is aimed at health practitioners, researchers, policy makers, and other parties interested in health IT. During this workshop participants will be shown a short presentation describing a) different types of health IT, and b) the development process of a prototype DSS to illustrate how health professionals can be involved in health IT development. This will be followed by small group discussions on the requirements for DSS in clinical practice and various environments, a demonstration of a DSS developed by the authors, and feedback. Additional information (if required): N/A