Decision Support System Framework Research Articles

Integrated framework of knowledge-based decision support system for user-centered residential design

A user-oriented residential environment is considered essential to providing occupants with a comfortable living experience while meeting their demands. However, design assessment in the early stage of residential design is a complex problem due to multifactor coupling, the diversity in user needs, and the incompleteness of design knowledge. This study proposes an integrated framework of knowledge-based decision support system (KBDSS) to mitigate such problems. In this framework, the user segmentation is performed in accordance with people’s particular requirements priority and, thus, enables design decisions that create better satisfaction among a wider audience. Meanwhile, the quality function deployment approach, decision support system, and fuzzy set theory are used to translate user requirements into quantifiable design specifications, thereby making the design a multicriteria decision-making problem. To illustrate and validate the proposed framework, a case study of a KBDSS for a multiunit residential building kitchen design is demonstrated to explore a user-centered residential design with such decision support. This proposed framework provides naive design practitioners with specific knowledge about decision problems and assists them in consistently making user-centered design decisions. The research results represent a further step toward a systematic methodology of user-centered built environment design.

A Multi-Attribute Decision Support System for Allocation of Humanitarian Cluster Resources Based on Decision Makers’ Perspective

The rush of the humanitarian suppliers into the disaster area proved to be counter-productive. To reduce this proliferation problem, the present research is designed to provide a technique for supplier ranking/selection in disaster response using the principles of utility theory. A resource allocation problem is solved using optimisation based on decision maker’s preferences. Due to the lack of real-time data in the first 72 h after the disaster strike, a Decision Support System (DSS) framework called EDIS is introduced to employ secondary historical data from disaster response in four humanitarian clusters (WASH: Water, Sanitation and Hygiene, Nutrition, Health, and Shelter) to estimate the demand of the affected population. A methodology based on multi-attribute decision-making (MADM), Analytical Hierarchy processing (AHP) and Multi-attribute utility theory (MAUT) provides the following results. First a need estimation technique is put forward to estimate minimum standard requirements for disaster response. Second, a method for optimization of the humanitarian partners selection is provided based on the resources they have available during the response phase. Third, an estimate of resource allocation is provided based on the preferences of the decision makers. This method does not require real-time data from the aftermath of the disasters and provides the need estimation, partner selection and resource allocation based on historical data before the MIRA report is released.

A DSS Framework for Advanced Traffic Signal Control System Investment Planning

This article presents ongoing research on the initial development of a decision support system (DSS) framework for integrated emergency vehicle preemption and transit priority system investment planning. A conceptual intelligent DSS framework provides a holistic framework to perform analytical assessments of integrated emergency vehicle preemption and transit priority systems. Three analytical tools are presented for incorporation into future DSS design: the first addresses the potential impact of transit travel time reduction on transit operating costs; the second addresses the potential impact of reduced emergency vehicle crashes at signalized intersections on fire and rescue operating costs; and the third integrates fuzzy sets concepts and multiattribute decision-making methods to rank order transit signal priority strategy alternatives at the intersection level.

Traffic Congestion Monitoring Using Smart Traffic Control Approach for Emergency Vehicle

Abstract: The efficiency and robustness increased after using embedded technology for monitoring and control applications. Due to traffic load, intensity can get stuck or they are delayed in reaching their destination. The system describes an intelligent and complex application that helps to clear traffic and to improve security while having fast response of the emergency services. By making use of embedded and wireless communication we can develop an application to clear the traffic while coming in the path. There is a system called as “Intelligent traffic light controller”. Where traffic light intelligently decides based on the total traffic on all adjacent roads. This paper presents the current framework of decision support systems for traffic management based on short and medium term predictions and includes some reflections on their likely evolution, based on current scientific research and the evolution of the availability of new types of data and their associated methodologies. When unexpected and unplanned events such as natural disasters, traffic accidents or even terrorist attacks happen, it is necessary to prepare a quick evacuation process and the provisioning of the quickest access routes for emergency services units.

A data-driven approach to clinical decision support in tinnitus retraining therapy.

BackgroundTinnitus, known as “ringing in the ears”, is a widespread and frequently disabling hearing disorder. No pharmacological treatment exists, but clinical management techniques, such as tinnitus retraining therapy (TRT), prove effective in helping patients. Although effective, TRT is not widely offered, due to scarcity of expertise and complexity because of a high level of personalization. Within this study, a data-driven clinical decision support tool is proposed to guide clinicians in the delivery of TRT.MethodsThis research proposes the formulation of data analytics models, based on supervised machine learning (ML) techniques, such as classification models and decision rules for diagnosis, and action rules for treatment to support the delivery of TRT. A knowledge-based framework for clinical decision support system (CDSS) is proposed as a UI-based Java application with embedded WEKA predictive models and Java Expert System Shell (JESS) rule engine with a pattern-matching algorithm for inference (Rete). The knowledge base is evaluated by the accuracy, coverage, and explainability of diagnostics predictions and treatment recommendations.ResultsThe ML methods were applied to a clinical dataset of tinnitus patients from the Tinnitus and Hyperacusis Center at Emory University School of Medicine, which describes 555 patients and 3,000 visits. The validated ML classification models for diagnosis and rules: association and actionable treatment patterns were embedded into the knowledge base of CDSS. The CDSS prototype was tested for accuracy and explainability of the decision support, with preliminary testing resulting in an average of 80% accuracy, satisfactory coverage, and explainability.ConclusionsThe outcome is a validated prototype CDS system that is expected to facilitate the TRT practice.

A Framework to Classify Clinical Data Using a Genetic Algorithm and Artificial Flora-Optimized Neural Network

A new classification framework for a Clinical Decision Support System, utilizing a Genetic algorithm and an Artificial Flora Optimized Neural Network is presented in this paper. GAFON is an artificial neural network whose topology is optimized with Genetic Algorithm and the learnable parameters are optimized with Artificial Flora Optimization algorithm. Drop out technique is used in the topology optimization phase and weight regularization is used in the parameter optimization phase. The proposed method minimizes the co-adaptation problem, reduces over-fitting of training data and improves the generalization of a feed forward neural network. The classification framework developed has been tested for classifying both multi class and binary class clinical datasets. The proposed method attained accuracy values of 86.82% for Hepatitis C Virus (HCV) for Egyptian patients, 84.91% for Vertebral Column 95.65% for Statlog Heart Disease (SHD), SHD and 93.79% for Early Stage Diabetes Risk Prediction (ESDRP), all datasets obtained from UCI repository

Social influence-based contrast language analysis framework for clinical decision support systems

Depression is a leading mental health problem affecting 300 million people globally. Recent studies show that social networks provide a tremendous potential for mental health professionals as a source of supplemental information about their patients. This study presents a methodological framework for clinical decision support systems (CDSSs) through analysis of social network data to distinguish the language usage of individuals with early signs of depression (i.e., contrast language analysis). By analyzing the contrast language patterns of different user groups, we are able to uncover constructive and actionable insights into the pain points and characteristics of users with signs of depression as decision support mechanisms for clinicians during intervention, (early) diagnosis and treatment plans. First, we discover terms that represent contrasting language by analyzing the percentage difference of terms in two user groups, labeled as”depressed” and”non-depressed” for ease of reference. Second, by building topic models based on social network contents, the topic-level contrast features are discovered. Finally, we consider the structure of the social network to discover the network-level contrast features. To illustrate the effectiveness of the proposed framework, we present a case study on early depression detection using a real-world dataset. The proposed framework has methodological contributions in enhancing the features and functionalities of CDSS for clinicians. It also contributes to evidence-based health research through cost-effective data and analytical insights that can supplement or improve the traditional survey and time-consuming interview methods.

A Survey Towards Decision Support System on Smart Irrigation Scheduling Using Machine Learning approaches.

From last decade, Big data analytics and machine learning is a hotspot research area in the domain of agriculture. Agriculture analytics is a data intensive multidisciplinary problem. Big data analytics becomes a key technology to perform analysis of voluminous data. Irrigation water management is a challenging task for sustainable agriculture. It depends on various parameters related to climate, soil and weather conditions. For accurate estimation of requirement of water for a crop a strong modeling is required. This paper aims to review the application of big data based decision support system framework for sustainable water irrigation management using intelligent learning approaches. We examined how such developments can be leveraged to design and implement the next generation of data, models, analytics and decision support tools for agriculture irrigation water system. Moreover, water irrigation management need to rapidly adapt state-of-the-art using big data technologies and ICT information technologies with the focus of developing application based on analytical modeling approach. This study introduces the area of research, including a irrigation water management in smart agriculture, the crop water model requirement, and the methods of irrigation scheduling, decision support system, and research motivation.

Safe food for infants: An EU-China project to enhance the control of safety risks raised by microbial and chemical hazards all along the infant food chains

The EU-project SAFFI targets food for EU's 15 million and China's 45 million children under the age of three. It aims at developing an integrated approach to enhance the identification, assessment, detection and mitigation of health risks raised by microbial and chemical hazards along EU and China infant food chains.SAFFI will benchmark the main risks through an extensive hazard identification system based on multiple data sources and a risk ranking procedure. It will also develop procedures to enhance top-down and bottom-up hazard control by combining management options with a panel of technologies for the detection and mitigation of priority hazards. Furthermore, it will explore unexpected contaminants by predictive toxicology and improve risk-based food safety management of biohazards by omics and predictive microbiology. SAFFI will co-develop with and deliver to stakeholders a decision-support system (DSS) to enhance safety control all along the food chain. This DSS will integrate the databases, procedures and methods described above and will be a framework for a generic DSS dedicated to other food.This overall methodology will also be implemented in a complementary Chinese side of the project, and exemplified for each side, with four case studies that were selected to cover priority hazards, main ingredients, processes and control steps of the infant food chain. Resulting databases, tools and procedures will be shared, cross-validated, concatenated, benchmarked and finally harmonized for further use in the EU and China.This EU-China multi-actor consortium of 20 partners involves academia, food safety authorities, infant food companies, a paediatrics association and technological and data-science SMEs.

Multi-Criteria Decision Support System for Recommendation of PhD Supervisor

A decision support system (DSS) is a computerized system used to discover determinations, judgments, and courses of action within a business or an organization. In every arena of life, DSS play an important role. In higher education, DSSs are recognized for diverse reasons, for example to conjugate data and intelligence, to pull off the unrivaled and likely interpretations, and to fine-tune decisions under hesitancy. The PhD supervisorselection is itself a very difficult and mind tickling task which makes the student very nervous and sometime make him desperate because he is unable to make the right decision. This work will facilitate the scholars to get proper guidance to make it successful. For the selection of particular supervisor there are some set of criterions which a supervisor should also follow while selecting a particular scholar. Though, the lack of information about the supervisor can hamper scholars in making the selection of the supervisor. Correspondingly, the identification of thought-provoking criteria might be stimulating for potential scholars due to their level of immaturity. Therefore, a system is required which can facilitate scholars in selecting the research work advisors in accordance with the research topic based on multi-criteria like relevancy of the supervisor’s research area of interest and the relevance of the publications. In this research effort, a user-friendly conceptual DSS framework has been suggested to recommend the Ph.D. supervisor to scholars in academics. A multi-criteria DSS framework has been proposed for the facilitation of the scholars while selecting their Ph.D. supervisor. This recommendation is based on several criteria of selecting potential supervisor including the area of research interest along with the publications i.e., in journals and conferences in addition to number of Ph.Ds. produced so far. Finally, the publications of the Ph.D. produced so far and the research projects involved. Preliminary results of the proposed work has been discussed along with the future directions.

Knowledge-Driven Automated Service Composition as a Method for Developing Decision Support Systems

A decision support system often necessitates a large amount of effort from both a domain modeling and technological standpoint. Using automated service composition, the study provides a method for minimizing the complexity of decision support system development. The justification for the approach is that many software systems (including decision support systems) today are based on service-oriented architecture, and the development of such systems may be described to some extent as a building composition of services that meet the required requirements. Automated planning algorithms can successfully address the difficulty of creating such compositions. The functional framework of decision support systems, requirements analysis for configurable service-oriented decision support systems and its primary components, and a conceptual model of a configurable service-oriented decision support system are all presented in the article.

System dynamics modelling coupled with multi-criteria decision-making (MCDM) for sustainability-related policy analysis and decision-making in the built environment

PurposeSystem dynamics has several applications in the built environment, and few studies indicate that it has potential in evaluating sustainability. Sustainability in the built environment involves numerous entities and multiple trade-offs. Hence, a Multi-Criteria Decision-Making (MCDM) method is ideal for promoting sustainability-based decision-making in the built environment. Therefore, this study integrates system dynamics with an MCDM method to enable the sustainability assessment by capturing the time-induced dynamic changes affecting long time sustainability performance of buildings.Design/methodology/approachConventional sustainability assessment tools in the built environment lack a comprehensive evaluation that balances the needs of the society, economy and environment. This study develops a system dynamics-based framework to enable sustainability-conscious decision-making and policy analysis in the built environment.FindingsVarious material, technology and water-related policies specific to the buildings are investigated for a case study building. It is found that the effect of penetration of renewable energy technology to the tune of 80% and above in the energy mix is a much superior policy in sustainability improvement in comparison to material and water-related policies. The study also demonstrates the effect of weights assigned for the different indicators on sustainability-based decisions.Originality/valueThe study provides a methodological framework for a sustainability-based decision support system for the built environment that enables dynamic performance evaluation by coupling system dynamics with the MCDM. This coupling further strengthens system dynamics as a decision-making and policy analysis for sustainability evaluation in the built environment.

Universal Decision Support System for Selection of MSW Route Optimization Method

Municipal Solid Waste (MSW) collection utilizes the highest percentage of the MSW management budget. Additionally, choosing a vehicle route optimization method is complex, difficult and does not always yield the most practical approach. There is limited published information about a decision support system (DSS) that assists in selecting the appropriate route optimization algorithm. This study aims to design and develop a universal DSS framework that suggests effective route optimization method(s). The system consists of 21 optimization data items and four criteria that assess the available constraints and recommends the most suitable optimization method(s). The DSS prototype was validated by testing it on the available literature and observing if the suggested method by the system complies with that utilized by the researchers. It was found that the system was able to predict the method which is used in 73% of studies. Moreover, the system suggested an enhanced version of the methods used in 18% of studies. It could be concluded that the proposed framework can help to select the best algorithms in almost all existing scenarios that have been used during development. Therefore, it is recommended to use the framework for selecting the appropriate route optimization algorithm for MSW collection.

Integrated Framework of Knowledge-Based Decision Support System for User-Centered Residential Design

Integrated Framework of Knowledge-Based Decision Support System for User-Centered Residential Design

Decision Support System for Adaptive Restoration Control of Distribution System

Aiming at the high-dimensional uncertainties of restoration process, an optimization model for distribution system restoration control is proposed considering expected restoration benefits, expected restoration costs, and security risks of the overall restoration scheme. In the proposed model, the effect of security control on restoration process is actively analyzed considering the security control costs of preventive, emergency, and correction controls. A two-layer decision support framework for distribution system restoration decision support system (DRDSS) is also designed. The upper layer of the proposed framework generates the pre-adjustment schemes of operation mode for energized power grid by load transfer and selects the optimal pre-adjustment scheme and the corresponding partitioning scheme based on the partition adjustment results of each pre-adjustment scheme. In addition, it optimizes the spatial-temporal decision-making of the inter-partition connectivity. For each partition, the lower layer of the proposed framework pre-selects the units and loads to be restored according to the pre-evaluated restoration income, generates the table of alternative restoration scheme for coping with uncertain events through simulation and deduction, and evaluates the risk and benefit of each scheme. For the uncertain events in the actual restoration process, the current restoration scheme can be adaptively switched to a sub-optimal scheme or re-optimized if necessary. Meanwhile, the proposed framework provides an information interaction interface for collaborative restoration with the related transmission system. A 123-node test system is built to evaluate the effectiveness and adaptability of the proposed model and framework.

A Web-Based Decision Support Systems Framework for Ill-Structured Decision Situations

Decisions and actions in an ill-structured situation often include high-time constraints, lack of information, and poor cognitive efforts. Obtaining the necessary information through an information systems tool is supposed to be the best solution in such situations. To expose the decision situation, this study has taken the fire and civil defense service as the field of study. In exploring the required information resources, elements of the system architecture, and suitability of the proposed system in the current field, this study has resorted to the qualitative approach. To assess the dependability and performance of the systems, this study has used the RAS metrics and a black-box test. The result showed that the reliability stood within 62.70–70.00%, and its availability stood at 99.00% with a downtime of 3.65 days/year from a three-month study. As per the black-box test with standard 4G network connection, the system takes an average loading time of 1.00s for alphanumeric contents, 3.50s for images and graphics, and 5.50s for loading maps and navigations. This research evidenced that, the local emergency response and rescue units in developing countries like Bangladesh might want to use a well-designed response support system for improved acquisition, dissemination, and utilization of response information.

A decision support system framework for strategic water resources planning and management under projected climate scenarios for a reservoir complex

The climate uncertainties, data scarcity, and socio-techno challenges pose significant threats and options respectively to the future planning of complex water projects in developing countries. This paper attempts to develop a decision support system (DSS) framework to classify uncertainties and management options using participatory scoping for scenarios analysis with the aid of six interrelated modules namely (i) water balance of reservoirs for computation of runoff (ii) statistical downscaling for climate projection (iii) hydrological modelling (iv) sediment assessment (v) water management and (vi) decision support for the assessment of the performance of multiple management plans designed for three future assessment periods of 2020-35, 2046-64 and 2081-99. The proposed framework was applied to the Tandula complex system of Chhattisgarh state of India consists of three reservoirs namely Tandula, Kharkhara, and Gondli having water transfer in their commands and for industrial use. The developed DSS framework provides the opportunity to analyze the management plans with climate uncertainties for the complex water resources projects having data scarcity. Here, in the present paper, the developed DSS framework incorporates eighty-four diverse management plans devised from constant interaction with stakeholders and technocrats resulting to deal with climate uncertainties using possible improvement in efficiencies and consumptive use for three future assessment periods. Furthermore, the performance of the developed plans was evaluated using multi-criteria decision support in the three-layer hierarchal process. The decision support analysis suggested that the second assessment period (FP-2: 2046-64) will be more crucial from the management point of view where 54 out of 81 plans could not perform as par with the present performance of the system. The developed DSS framework provides a solution for strategic water resources planning and management under projected climate scenarios in a basin having problems of scarcity, surplus, and non-uniform distribution of surface water using the participation of farmers, technocrats, and water resource managers.

Distributed Probabilistic Fuzzy Rule Mining for Clinical Decision Making

INTRODUCTION: With the growing size, complexity, and distributivity of databases, efficiency and scalability have become highly desirable attributes of data mining algorithms in decision support systems. OBJECTIVES: This study aims for a computational framework for clinical decision support systems that can handle inconsistent dataset while also being interpretable and scalable. METHODS: This paper proposes a Distributed Probabilistic Fuzzy Rule Mining (DPFRM) algorithm that extracts probabilistic fuzzy rules from numerical data using a self-organizing multi-agent approach. This agent-based method provides better scalability and fewer rules through agent interactions and rule-sharing. RESULTS: The performance of the proposed approach is investigated on several UCI medical datasets. The DPFRM is also used for predicting the mortality rate of burn patients. Statistical analysis confirms that the DPFRM significantly improves burn mortality prediction by at least 3%. Also, the training time is improved by 17% if implemented by a parallel computer. However, this speedup decreases with increased distributivity, due to the added communication overhead. CONCLUSION: The proposed approach can improve the accuracy of decision making by better handling of inconsistencies within the datasets. Furthermore, noise sensitivity analysis demonstrates that the DPFRM deteriorates more robustly as the noise levels increase.

Medical decision support system for cancer treatment in precision medicine in developing countries

In many developing countries and regions, there are medical problems such as dense populations, lack of medical resources, and shortage of doctors, making it impossible to provide patients with more convenient full-cycle services. Non-small cell lung cancer is a malignant tumor with the highest morbidity and mortality in the world. Research on the auxiliary treatment of intelligent systems is helpful in understanding and evaluate the disease. The system can help doctors provide patients with effective drug treatments and personalized medical services by actively learning the experience of outstanding experts. According to expert knowledge, through quantitative efficacy scores and specific analysis of evaluation indicators, based on the efficacy evaluation matrix and the extraction of key features of patients and drugs, a predictive model of drug efficacy evaluation for adjuvant therapy is established. The model divides into latent feature extraction and curative effect collaborative prediction modules. In the feature extraction module, adding noise to the original data in model training process helps reduce the impact of the sparseness of the patient's medication data. By considering the uncertainty of experts in drug efficacy evaluation modeling, based on probability analysis and efficacy prediction, the proposed method demonstrates the potential options in the face of hesitating choices. According to the predicted efficacy score, candidate drugs are selected to assist doctors in disease analysis and secondary diagnosis. Experiments have shown that drug efficacy prediction methods can provide adjuvant treatments for diseases and quantify the therapeutic effects of targeted drugs. The efficacy information and detection information of patient-drug pairs are helpful to improve decision-making ability, and the proposed medical decision support system framework is superior to other deep learning methods. By adding data, the performance can be significantly improved.

Forest Management of Pinus pinaster Ait. in Unbalanced Forest Structures Arising from Disturbances—A Framework Proposal of Decision Support Systems (DSS)

Forests assume a great socioeconomic and environmental importance, requiring good management decisions to value and care for these natural resources. In Portugal, forest land use accounts for 34.5% of the continental area. The softwood species with the highest representation is maritime pine (Pinus pinaster Ait.). Traditionally, the species is managed as pure and even-aged stands for timber production, with a rotation age of 45 to 50 years. Depending on the initial stand density, the stands are thinned 2 to 4 times during the rotation period. Disturbances associated with forest fires have a negative impact on the age structure of stands over time, as they result in a narrow range of stand ages. This age homogenization over large forest areas increases with the recurrence and size of forest fires, bringing new challenges to forest management, namely the difficulty in ensuring the long-term sustainability of the wood supply. The problem aggravates with the increasing demand pressure on pine wood. This article aims to suggest a framework of DSS for Pinus pinaster that can effectively support the management of forest areas under these circumstances, i.e., narrow age ranges and high demand of harvested timber volume. A communal woodland area in the Northern region of Portugal affected by forest fires was selected as a study case. The Modispinaster model was used as the basis of the DSS, to simulate growth scenarios and interventions along the optional rotation period. Two clear-cut ages were considered: 25 and 40 years. The results obtained were the input data for an integer linear programming (ILP) model to obtain the plan that maximizes the volume of timber harvested in the study area, during the planning horizon. The ILP model has constraints bounding the area of clearings, and sustainability, operational and forestry restrictions. The computational results are a powerful tool for guidance in the decision-making of scheduling and forecasting the execution of interventions determining the set of stands that are exploited according to the different scenarios and the period in which the clear-cut is made throughout the planning horizon. Considering all constraints, the solution allows a balanced extraction of a total of 685 m3·ha−1, over the 50-year horizon, as well as the representation of all age classes at the end of the planning period.