Generic Decision Support System Research Articles

EviGUIDE - a tool for evidence-based decision making in image-guided adaptive brachytherapy for cervical cancer

PurposeTo develop a novel decision-support system for radiation oncology that incorporates clinical, treatment and outcome data, as well as outcome models from a large clinical trial on magnetic resonance image-guided adaptive brachytherapy (MR-IGABT) for locally advanced cervical cancer (LACC). MethodsA system, called EviGUIDE, was developed that combines dosimetric information from the treatment planning system, patient and treatment characteristics, and established tumor control probability (TCP), and normal tissue complication probability (NTCP) models, to predict clinical outcome of radiotherapy treatment of LACC. Six Cox Proportional Hazards models based on data from 1341 patients of the EMBRACE-I study have been integrated. One TCP model for local tumor control, and five NTCP models for OAR morbidities. ResultsEviGUIDE incorporates TCP-NTCP graphs to help users visualize the clinical impact of different treatment plans and provides feedback on achievable doses based on a large reference population. It enables holistic assessment of the interplay between multiple clinical endpoints and tumour and treatment variables. Retrospective analysis of 45 patients treated with MR-IGABT showed that there exists a sub-cohort of patients (20%) with increased risk factors, that could greatly benefit from the quantitative and visual feedback. ConclusionA novel digital concept was developed that can enhance clinical decision- making and facilitate personalized treatment. It serves as a proof of concept for a new generation of decision support systems in radiation oncology, which incorporate outcome models and high-quality reference data, and aids the dissemination of evidence-based knowledge about optimal treatment and serve as a blueprint for other sites in radiation oncology.

SUPPORTING DECISION MAKING WITH AN ARIZ-BASED MODEL FOR SMART MANUFACTURING

Smart manufacturing has transformed the way decisions are made. By accelerating the delivery of data to the various decision points, more rapid decision-making processes can be realized. A generic Decision Support System (DSS) utilizes an efficient technique, which integrates the algorithm for inventive problem solving (ARIZ) and supervised machine learning into a model for supporting various automated decision making processes. The proposed model is to examine the theoretical framework of ARIZ by devising an ARIZ-based DSS model. It incorporates supervised ML algorithms to assist decision making processes. Three case studies from the manufacturing sector are evaluated. The results indicate the capability of the proposed DSS in achieving a high accuracy rate and, at the same time reducing the time and resources required for decision making. Our study has simplified the data processing and extraction processes through an automated ARIZ-based DSS model; therefore enabling a non-technical user the opportunity to harvest the vast knowledge from the collected data for efficient decision making.

Classification of Gastric Lesions Using Gabor Block Local Binary Patterns

The identification of cancer tissues in Gastroenterology imaging poses novel challenges to the computer vision community in designing generic decision support systems. This generic nature demands the image descriptors to be invariant to illumination gradients, scaling, homogeneous illumination, and rotation. In this article, we devise a novel feature extraction methodology, which explores the effectiveness of Gabor filters coupled with Block Local Binary Patterns in designing such descriptors. We effectively exploit the illumination invariance properties of Block Local Binary Patterns and the inherent capability of convolutional neural networks to construct novel rotation, scale and illumination invariant features. The invariance characteristics of the proposed Gabor Block Local Binary Patterns (GBLBP) are demonstrated using a publicly available texture dataset. We use the proposed feature extraction methodology to extract texture features from Chromoendoscopy (CH) images for the classification of cancer lesions. The proposed feature set is later used in conjuncture with convolutional neural networks to classify the CH images. The proposed convolutional neural network is a shallow network comprising of fewer parameters in contrast to other state-of-the-art networks exhibiting millions of parameters required for effective training. The obtained results reveal that the proposed GBLBP performs favorably to several other state-of-the-art methods including both hand crafted and convolutional neural networks-based features.

Analytics with stochastic optimisation: experimental results of demand uncertainty in process industries

This study reports the test results of a two-stage stochastic linear programming (SLP) model with recourse using a user-friendly generic decision support system (DSS) in a North American steel company. This model has the flexibility to configure multiple material facilities, activities and storage areas in a multi-period and multi-scenario environment. The value of stochastic solution (VSS) with a real-world example has a potential benefit of US$ 24.61 million. Experiments were designed according to the potential joint probability distribution scenarios and the magnitude of demand variability. Overall, 144 SLP optimisation model instances were solved across four industries, namely, steel, aluminium, polymer and pharmaceuticals. The academic contribution of this research is two-fold: first, the potential contribution to profit in a steel company using an SLP model; and second, the optimisation empirical experiments confirm a pattern that the VSS and expected value of perfect information (EVPI) increase with the increase in demand variability. This study has implications for practicing managers seeking business solutions with prescriptive analytics using stochastic optimisation-based DSS. This study will attract more industry attention to business solutions, and the prescriptive analytics discipline will garner more scholarly and industry attention.

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.

A STUDY ON AGRICULTURAL SYSTEMS

Agricultural systems science generates knowledge that allows researchers to consider complex problems or take informed agricultural decisions. The rich history of this science exemplifies the diversity of systems and scales over which they operate and have been studied. Modeling, an essential tool in agricultural systems science, has been accomplished by scientists from a wide range of disciplines, who have contributed concepts and tools over more than six decades. As agricultural scientists now consider the “next generation” models, data, and knowledge products needed to meet the increasingly complex systems problems faced by society, it is important to take stock of this history and its lessons to ensure that we avoid reinvention and strive to consider all dimensions of associated challenges. To this end, we summarize here the history of agricultural systems modeling and identify lessons learned that can help guide the design and development of next generation of agricultural system tools and methods. A number of past events combined with overall technological progress in other fields have strongly contributed to the evolution of agricultural system modeling, including development of processbased bio-physical models of crops and livestock, statistical models based on historical observations, and economic optimization and simulation models at household and regional to global scales. Characteristics of agricultural systems models have varied widely depending on the systems involved, their scales, and the wide range of purposes that motivated their development and use by researchers in different disciplines. Recent trends in broader collaboration across institutions, across disciplines, and between the public and private sectors suggest that the stage is set for the major advances in agricultural systems science that are needed for the next generation of models, databases, knowledge products and decision support systems. The lessons from history should be considered to help avoid roadblocks and pitfalls as the community develops this next generation of agricultural systems models.

Effect of Database Generation on Damage Consequences’ Assessment Based on Random Forests

Recently, the application of machine learning has been explored to assess the main damage consequences without employing flooding sensors. This method can be the base of a new generation of onboard decision support systems to help the master during the progressive flooding of the ship. In particular, the application of random forests has been found suitable to assess the final fate of the ship and the damaged compartments’ set and estimate the time-to-flood. Random forests have to be trained using a database of precalculated progressive flooding simulations. In the present work, multiple options for database generation were tested and compared: three based on Monte Carlo (MC) sampling based on different probability distributions of the damage parameters and a parametric one. The methods were tested on a barge geometry to highlight the main effects on the damage consequences’ assessment in order to ease the further development of flooding-sensor-agnostic decision support systems for flooding emergencies.

Explainable Ontology-Based Intelligent Decision Support System for Business Model Design and Sustainability

Background: Case-Based Reasoning (CBR) is a problem-solving paradigm that uses knowledge of relevant past experiences (cases) to interpret or solve new problems. CBR systems allow generating explanations easily, as they typically organize and represent knowledge in a way that makes it possible to reason about and thereby generate explanations. An improvement of this paradigm is ontology-based CBR, an approach that combines, in the form of formal ontologies, case-specific knowledge with domain one in order to improve the effectiveness and explanation capability of the system. Intelligent systems make daily activities more easily, efficiently, and represent a real support for sustainable economic development. On the one hand, they improve efficiency, productivity, and quality, and, on the other hand, can reduce costs and cut waste. In this way, intelligent systems facilitate sustainable development, economic growth, societal progress, and improve efficiency. Aim: In this vision, the purpose of this paper is to propose a new generation of intelligent decision support systems for Business Model having the ability to provide explanations to increase confidence in proposed solutions. Findings/result: The performance results obtained show the benefits of the proposed solution with different requirements of an explanatory decision support system. Consequently, applying this paradigm for software tools of business model development will make a great promise for supporting business model design, sustainability, and innovation.

Integration of solar energy by nature-inspired optimization in the context of circular economy

The purpose of this paper is to make a research on solar power plants integration in an electric power system, taking into account all costs arising from circular economy criteria. Four scenarios of solar power plant installation are analyzed. The methodology relies on nature-inspired optimization. The evolutionary multiobjective genetic algorithm is applied. The optimization is based on the costs of electricity production, encompassing not only the costs of technology, but also the costs of environmental protection and sustainable development. These costs are included in the model and the objectives are formulated accordingly. The objectives are based on maximization of electricity production up to the level of demand, minimization of total electricity costs and minimization of greenhouse gas emissions. In addition, grid losses as well as other limitations and constraints of the electric power system are included into the model. The loss distribution is calculated in proportion to the distance between the supplier and the end user. The solar power plant losses equal zero, because it is assumed that the plant is located in a local community, close to consumers. The results show that the emissions and costs are higher without an introduced solar power plant. Introduction of solar capacities reduces the costs and emissions to a certain level. The developed model may be useful as a decision-support system generator.

Exploring a Flooding-Sensors-Agnostic Prediction of the Damage Consequences Based on Machine Learning

Recently, progressive flooding simulations have been applied onboard to support decisions during emergencies based on the outcomes of flooding sensors. However, only a small part of the existing fleet of passenger ships is equipped with flooding sensors. In order to ease the installation of emergency decision support systems on older vessels, a flooding-sensor-agnostic solution is advisable to reduce retrofit cost. In this work, the machine learning algorithms trained with databases of progressive flooding simulations are employed to assess the main consequences of a damage scenario (final fate, flooded compartments, time-to-flood). Among the others, several classification techniques are here tested using as predictors only the time evolution of the ship floating position (heel, trim and sinkage). The proposed method has been applied to a box-shaped barge showing promising results. The promising results obtained applying the bagged decision trees and weighted k-nearest neighbours suggests that this new approach can be the base for a new generation of onboard decision support systems.

Performance Improvement Algorithms in Big Data Analysis

Abstract This article describes the study results of development methods and algorithms for performance improvement in the big data analysis and training of machine learning models. The data growing and complexity problem describing complex processes require new approaches and tools for the scientific and technical community. In the course of research, the algorithms for analysis of heterogeneous medical and law records were performed. The performance improvement in classification, clustering and graph calculation problems were solved. With using CUDA it was possible to get more than 95 times performance. The usage of high-performance technologies is important in the analysis of electronic records because it provides an adequate response to the process of analysis of large scale of data from information systems. This study shows how to speed up the calculations on the example of most basic and widespread machine learning tasks. The results of the study can be used to develop a new generation of decision support systems, interactive data analysis systems and methods for eScience

A Novel Case Base Reasoning and Frequent Pattern Based Decision Support System for Mitigating Software Risk Factors

Software risk management is crucial for the success of software project development. The existing literature has models for risk management, but is too complex to be used in practice. The information in the existing studies is scattered over different articles which makes it difficult to find relevant knowledge to establish relationship between risk factors and mitigations. This paper presents a novel model which identifies the relationship between risk factors and mitigations automatically by using intelligent Decision Support System (DSS). The proposed model has four steps. Firstly, the input of the system has been designed where risk factors and mitigations have been inputted into it. Secondly, rule based machine learning approach has been used for mining of associations between risks and mitigations. Thirdly, Case Based Reasoning (CBR) approach has been used to determine the previous cases as rules. Finally, automated rules have been generated to develop an intelligent DSS to mitigate the software risks. The proposed technique copes with the highly cited existing limitations of risk handling like, lack of generic DSS and intelligent relationship between software risks and mitigations. Automated rules have been discovered with a novel idea of CBR and frequent pattern. The proposed model is capable of mitigating upcoming risks in future. Star schema has been implemented to support our proposed DSS. Moreover, from highly cited literature 40 studies were identified from which 26 risk factors, 57 mitigations, 14 questions and 26 automated rules have been extracted. According to the validation of IT industry experts, the average of the effectiveness of DSS is 51-55%. The novelty of the proposed research is that it uses two state of the art methods (Rule Based Machine Learning and CBR) to identify software risk mitigations. The results of the proposed model show that the chances of risks in software development have been reduced significantly.

A semiotic-inspired machine for personalized multi-criteria intelligent decision support

The need for appropriate decisions to tackle complex problems increases every day. Selecting destinations for vacation, comparing and optimizing resources to create valuable products, or purchasing a suitable car are just a few examples of puzzling situations in which there is no standard form to find an appropriate solution. Such scenarios become arduous when the number of possibilities, restrictions, and factors affecting the decision rise, thereby turning decision makers into almost mere spectators. In such circumstances, decision support systems (DSS) can play an important role in guiding people and organizations towards more accurate decision making. However, conventional DSS lack the necessary adaptability to account for dynamic changes and are frequently inadequate to tackle the subjectivity inherent in decision-maker's preferences and intention. We argue that these shortcomings can be addressed by a suitable combination of Semiotic Theory and Computational Intelligence algorithms, which together can make up a new generation of DSS. In this article, a formal description of an Intelligent Semiotic Machine is provided and tried out in practical decision contexts. The results obtained show that our approach can provide well-suited decisions based on user preferences, achieving appropriateness while fanning out subjective options without losing decision context, objectivity, or accuracy.

Recognition of business risks when purchasing goods on the Internet using GIS: experience from Slovakia

For operators of Internet shops and their investors on the one hand, and suppliers on the other hand, knowledge of the consumer market is becoming critical in terms of the risk of non-payment for purchased goods, as most small e-shops maintain their stocks according to current demand. The aim of this paper is to identify customers who come from different districts of Slovakia and display a certain type of consumer behavior regarding the risk connected with willingness to pay for goods purchased via the Internet. To solve this problem we used data from a specialized e-retailer and Geographic Information Systems (GIS) as a Decision Support System generator for constructing maps of consumers to investigate the operation of an e-shop. In the article, we used data from the years 2012–2015 concerning 489 buyers, including their addresses and other geographical data about the consumers and their purchases, and integrated them into the GIS environment. Subsequently, the data were analyzed and documented by means of GIS and maps of consumers were generated. The result of this study is to show that GIS can play a significant role in the decision-making process of e-shops in support of a manager’s experience. The geographical results were evaluated statistically in order to offer a better information capability.

A DECISION SUPPORT SYSTEM FRAMEWORK FOR LOCALISATION INVESTMENT SELECTION

Formulating localisation strategies is crucial to ensuring the development of local suppliers, which in turn results in job creation, increased export earnings, and local and national economic growth. The aim of this article is to present a generic decision support system (DSS) to guide localisation decision-making. Multi-criteria decision analysis (MCDA) is used to develop the model, which is then applied to a biometric identity management system (BIMS) case study that places the study within the context of the tool, die and mould-making (TDM) industry of South Africa.

Generic decision support system to leverage supply chain performance (GLE) for SMEs in Thailand

PurposeThis paper explains the development stages of a generic decision support system to leverage supply chain performance (GLE). The purpose of this paper is to identify and trade off the critical supply chain measures which are interrelated and in contradiction with each other.Design/methodology/approachThe GLE was developed as an extension of the supply chain performance assessment tool proposed by Banomyong and Supatn (2011). It contained nine measures covering key activities along the supply chain under dimensions of cost, time and reliability. Their interrelations were figured out by causal linkages, whereas their contradictions were traded off as multi-objective optimization. It is solved using fuzzy goal programming along with a weighted max-min operator in order to acquire the Pareto-optimal solution.FindingsThe results from the GLE showed there were two critical supply chain measures including supply chain cost per sales and average order cycle time. They contradictorily influenced by a root-cause, namely product lot size. Its Pareto-optimal value was provided to achieve the minimized values of supply chain cost per sales and average order cycle time which were consistent with their relative weights.Research limitations/implicationsAs generic features, the GLE needs further validation in several industries under various supply chain strategies. The further validation may contribute the GLE to include multiple decision variables, multiple types of product and multiple periods of time. In addition, the GLE may consider a dimensional measure of environmental impact along the supply chain activities.Originality/valueThe GLE is a unique decision support system to identify and trade off the critical, interrelated and contradicting supply chain measures. More uniqueness is obtained when the GLE offers an option of inputting a set of relative weights for the interrelated supply chain measures.

Brief history of agricultural systems modeling

Agricultural systems science generates knowledge that allows researchers to consider complex problems or take informed agricultural decisions. The rich history of this science exemplifies the diversity of systems and scales over which they operate and have been studied. Modeling, an essential tool in agricultural systems science, has been accomplished by scientists from a wide range of disciplines, who have contributed concepts and tools over more than six decades. As agricultural scientists now consider the “next generation” models, data, and knowledge products needed to meet the increasingly complex systems problems faced by society, it is important to take stock of this history and its lessons to ensure that we avoid re-invention and strive to consider all dimensions of associated challenges. To this end, we summarize here the history of agricultural systems modeling and identify lessons learned that can help guide the design and development of next generation of agricultural system tools and methods. A number of past events combined with overall technological progress in other fields have strongly contributed to the evolution of agricultural system modeling, including development of process-based bio-physical models of crops and livestock, statistical models based on historical observations, and economic optimization and simulation models at household and regional to global scales. Characteristics of agricultural systems models have varied widely depending on the systems involved, their scales, and the wide range of purposes that motivated their development and use by researchers in different disciplines. Recent trends in broader collaboration across institutions, across disciplines, and between the public and private sectors suggest that the stage is set for the major advances in agricultural systems science that are needed for the next generation of models, databases, knowledge products and decision support systems. The lessons from history should be considered to help avoid roadblocks and pitfalls as the community develops this next generation of agricultural systems models.

Knowledge-Driven User Behavior Pattern Discovery for System Security Enhancement

Insider threads posed by authorized users have caused significant security and privacy risks to IT systems. The behavior of authorized users in using system services must be monitored and controlled. However, the administrators in large distributed systems are overwhelmed by the number of system users, the complexity and changing nature of user activities. This paper presents a new generation of intelligent decision support systems that effectively assist system administrators to get deep insight into the system users’ dynamic behavior patterns. With these patterns, the system administrators are capable of constructing dynamic refinement to the existing security policies. We explore the method of interactively and incrementally extracting user’s behavior patterns by combining data mining techniques with domain and system knowledge, and applying such knowledge to provide recommendations throughout the whole process. A prototype tool has been developed to analyze the audit logs from distributed medical imaging systems to validate the proposed approach.

Toward generalized decision support systems for flood risk management

Despite the emergence of a large number of specialized decision-support systems (DSS) in the last decades, currently there are fewer efforts made for integrating the flood risk management relevant sciences with information and communication technologies into generalized DSS. Such systems are expected to formulate decision options for prevention, mitigation, preparation, response, and recovery from flood impacts with consideration of climate change, socio-economic evolution, and stakeholders’ input. Currently, there is no unified vision on the architecture, components, and the needed computer and communications technologies for attaining generic DSS for flood mitigation and resilience. Moreover, there is no guidance of what components should be developed first and in what order and how to efficiently include human-computer interfaces for efficient stakeholder engagement and consensus. This paper calls for the formation of a strategic global partnership for framing and subsequently assisting in the development of a generalized flood DSS (FLOODSS) that can overcome the current flood DSS limitations. The call is preceded by a review of the flood decision-support terminology and context. Subsequently, an initial vision on the FLOODSS is outlined and the steps for transitioning such a system from vision to practice are proposed.

A decision support system for water supply emergency management with multiple sources

The construction of water transfer projects brings new challenges to water resources management, especially in water supply security with the use of multiple water sources. However, the operations of water diversion and water supply have been considered in separation in practice. In particular, there are few decision support systems (DSS) designed to tackle the challenges in emergency planning of water transfer operation. This paper describes a generic DSS which is designed for the operation of multiple water sources supply and complex water supply targets. Emergency management is incorporated in the DSS to provide more effective water supply under extreme conditions. Specifically, the emergency scheduling plans regenerate the water supply strategy of Biliuhe Reservoir and Yingnahe Reservoir, and then reduce the impact of severe drought year on Dalian. The scheduling results of the case study reveal that this DSS makes good use of mutual compensation functions among multiple water sources when it is applied to the water supply emergency management of Dalian in severe drought year. The results indicate this developed DSS can be easily generalized and further applied to similar cities in China and other countries in the world.