Decision Support System Framework Research Articles

Intelligent Service Scheduling Decision Technology under Artificial Intelligence Technology

The intelligent production line is a new intelligent production line resource formed by highly integrating modern sensor technology, communication technology, information technology, computer technology and control technology on the large physical real basis of the existing production line. It can finish data acquisition, information processing, dynamic virtual reconfiguration of variable batch production line, embedded algorithm, algorithm optimize the management of the production line manufacturing field, change the dynamic virtual reorganization of the mass production line, and become the choice of the development direction of the next generation of new production lines for mankind. Scheduling is the nerve center of the production line virtual reorganization system. The traditional main auxiliary decision-making tool management system can no longer fully meet the requirements of production line operation monitoring in the new environment, so the construction of an intelligent scheduling system is also one of the important contents of the construction of the production line virtual reorganization. Based on this, this article explores and researches intelligent service scheduling decision-making technology under artificial intelligence technology. The research topic of this article is intelligent scheduling of production lines, so this article first analyzes the current research status of intelligent service scheduling and decision support systems; then conducts research based on Agent decision support systems, and analyzes the framework and characteristics of decision support systems. realizes the intelligent production line scheduling DSS system; then this paper designs the system’s scheduling model with stochastic disturbance, system database and system fault diagnosis function, and realizes an intelligent service scheduling decision support system based on multi-agent technology. Finally, this article tests the data transmission speed and fault diagnosis function of the system. The test results show that the system is relatively fast in data transmission speed and the fault diagnosis function is also very reliable.

Development of a decision support system in traumatology and orthopedics. Biomechanics as a tool for preoperative planning

The main goal of preoperative planning for patients with pathologies of the musculoskeletal system is to obtain high-quality anatomical and functional results. The solution to this problem could be achieved through the selection of appropriate implants and technique of their installation, which ensure the stability of the structure and reposition of bone fragments, as well as by recreating the optimal biomechanics of the operated segment. All over the world, modern digital technologies are widely introduced into clinical practice. In traumatology, orthopedics and neurosurgery, preoperative planning systems that implement virtual treatment planning allow performing X-ray morphometric measurements on digital radiographs or computed tomography, as well as placing implant templates and selecting their optimal standard size. Typically, several treatment types can be planned for a particular patient, which are successful in terms of geometric preoperative planning. But not each of the planned types can be optimal in terms of assessing the stress-strain state of the studied segment of the spinal pelvic complex. Biomechanical modelling provides the possibility of virtual modelling of orthopedic operations, including the assessment of segment stability and implant "survival". Therefore, it is proposed to use biomechanical modelling as a tool for preoperative planning. In this regard, at the request and with the financial support of the Advanced Research Fund at Saratov University, for the first time, the development of a Russian medical decision support system in surgery of the spinal pelvic complex is being developed, which provides not only the stage of geometric planning of the operation, but also its biomechanical support, as well as predicting treatment results. The aim of this work was to show that biomechanical modelling makes it possible to choose the optimal type of surgical treatment of diseases and injuries of the spine-pelvic complex, and can also be successfully introduced into preoperative planning and successfully function within the framework of the medical decision support system. The paper presents the results of demonstration experiments, in which the optimal treatment types for degenerative diseases of the spine and hip joint were selected using the system. The results of the experiments were consistent with the established clinical practice and literature data, and a group of experts confirmed their clinical viability. It was shown that, together with geometric planning, biomechanical modelling forms a new method (algorithm) for preoperative planning.

Development of a Decision Support System Framework for Cultural Heritage Management

Decision support systems (DSSs) have been traditionally identified as useful information technology tools in a variety of fields, including the context of cultural heritage. However, to the best of our knowledge, no prior study has developed a DSS framework that incorporates all the main decision areas simultaneously in the context of cultural heritage. We fill this gap by focusing on design-science research and specifically by developing a DSS framework whose features support all the main decision areas for the sustainable management of cultural assets in a comprehensive manner. The main decision-making areas considered in our study encompass demand management, segmentation and communication, pricing, space management, and services management. For these areas, we select appropriate decision-making supporting techniques and data management solutions. The development of our framework, in the form of a web-based system, results in an architectural solution that is able to satisfy critical requirements such as ease of use and response time. We present an application of the innovative DSS framework to a museum and discuss the main managerial implications and future improvements.

Evaluation Framework for Successful Artificial Intelligence-Enabled Clinical Decision Support Systems: Mixed Methods Study.

BackgroundClinical decision support systems are designed to utilize medical data, knowledge, and analysis engines and to generate patient-specific assessments or recommendations to health professionals in order to assist decision making. Artificial intelligence–enabled clinical decision support systems aid the decision-making process through an intelligent component. Well-defined evaluation methods are essential to ensure the seamless integration and contribution of these systems to clinical practice.ObjectiveThe purpose of this study was to develop and validate a measurement instrument and test the interrelationships of evaluation variables for an artificial intelligence–enabled clinical decision support system evaluation framework.MethodsAn artificial intelligence–enabled clinical decision support system evaluation framework consisting of 6 variables was developed. A Delphi process was conducted to develop the measurement instrument items. Cognitive interviews and pretesting were performed to refine the questions. Web-based survey response data were analyzed to remove irrelevant questions from the measurement instrument, to test dimensional structure, and to assess reliability and validity. The interrelationships of relevant variables were tested and verified using path analysis, and a 28-item measurement instrument was developed. Measurement instrument survey responses were collected from 156 respondents.ResultsThe Cronbach α of the measurement instrument was 0.963, and its content validity was 0.943. Values of average variance extracted ranged from 0.582 to 0.756, and values of the heterotrait-monotrait ratio ranged from 0.376 to 0.896. The final model had a good fit (χ262=36.984; P=.08; comparative fit index 0.991; goodness-of-fit index 0.957; root mean square error of approximation 0.052; standardized root mean square residual 0.028). Variables in the final model accounted for 89% of the variance in the user acceptance dimension.ConclusionsUser acceptance is the central dimension of artificial intelligence–enabled clinical decision support system success. Acceptance was directly influenced by perceived ease of use, information quality, service quality, and perceived benefit. Acceptance was also indirectly influenced by system quality and information quality through perceived ease of use. User acceptance and perceived benefit were interrelated.

An interdisciplinary assessment model for supporting decision making in urban regeneration plans: A case study of the Maspero Triangle, Cairo City, Egypt

Urban regeneration plays a key role in achieving urban sustainability at several levels via multidisciplinary activities. Regeneration plans in Egypt have policy and investment devoted to them, but they often fail to create sustainable urban environments that can meet community needs. Stakeholders and financiers involved in regeneration should consider the social sustainability angle to a far greater degree. The aim of this paper is to close the gap between regeneration top-down planning policies and the bottom-up initiatives of community associations through an interdisciplinary assessment model. This model combines different evaluation tools and provides a decision support system framework that can improve the quality and transparency of decisions in regeneration plans. This paper provides a theoretical background which clarifies the problem, outlines the objectives and explains the fundamental scientific terms of research variables for better understanding the complexity of regeneration processes. It conducts multiple quantitative assessment attributes with comparative analysis between different studies. Finally, the paper proposes an interdisciplinary assessment model with a detailed analysis of a pilot case study of the Maspero Triangle area located in the centre of Cairo, Egypt.

Towards a Knowledge Graph-Based Explainable Decision Support System in Healthcare.

The decisions derived from AI-based clinical decision support systems should be explainable and transparent so that the healthcare professionals can understand the rationale behind the predictions. To improve the explanations, knowledge graphs are a well-suited choice to be integrated into eXplainable AI. In this paper, we introduce a knowledge graph-based explainable framework for AI-based clinical decision support systems to increase their level of explainability.

A hierarchical expert-guided machine learning framework for clinical decision support systems: an application to traumatic brain injury prognostication

Prognosis of the long-term functional outcome of traumatic brain injury is essential for personalized management of that injury. Nonetheless, accurate prediction remains unavailable. Although machine learning has shown promise in many fields, including medical diagnosis and prognosis, such models are rarely deployed in real-world settings due to a lack of transparency and trustworthiness. To address these drawbacks, we propose a machine learning-based framework that is explainable and aligns with clinical domain knowledge. To build such a framework, additional layers of statistical inference and human expert validation are added to the model, which ensures the predicted risk score’s trustworthiness. Using 831 patients with moderate or severe traumatic brain injury to build a model using the proposed framework, an area under the receiver operating characteristic curve (AUC) and accuracy of 0.8085 and 0.7488 were achieved, respectively, in determining which patients will experience poor functional outcomes. The performance of the machine learning classifier is not adversely affected by the imposition of statistical and domain knowledge “checks and balances”. Finally, through a case study, we demonstrate how the decision made by a model might be biased if it is not audited carefully.

A Scalable Earth Observations‐Based Decision Support System for Hydropower Planning in Africa

AbstractHydropower is a key part of the increasing shift in power production from nonrenewables to renewable energy. In regions such as Africa, hydropower reservoirs are vital for achieving several sustainable development goals, including clean water, energy, and poverty elimination. However, the operations of hydropower reservoirs are often suboptimal due to the lack of hydrologic data for generating reliable inflow forecasts. Here, we present a decision support system (DSS) framework for hydropower planning at daily to seasonal time scales by combining data from earth observation satellites (EOS) with ensemble climate forecasts from dynamical models and hydrologic modeling. The large uncertainty inherent in satellite‐based datasets is overcome by using a data validation framework which does not require ground‐based measurements. In addition, an EOS evapotranspiration product is used as a proxy for streamflow in calibrating hydrologic models. Compared to a DSS forced with a climatological forecast (zero‐skill), the hydropower production with the new DSS increased by 20%. The study highlights the advantage of using data from EOS in overcoming the issue of data scarcity in water resources applications, particularly in developing regions of the world such as Africa.

RV-DSS: Towards a resilience and vulnerability- informed decision support system framework for interdependent infrastructure systems

The common challenge currently faced by critical infrastructure (CI) asset owners and operators is the lack of an integrated and robust resilience-informed business planning and management approach in response to interdependent assets’ failures, in particular due to low-probability/high-impact environmental hazards.Interdependencies among CI can cause cascading failures and hence, amplify impacts due to these failures. This can also affect CI’s service restoration rate and consequently, reducing their resilience in coping with these hazardous events. As infrastructures are becoming more interdependent in some sectors, there is an increasing need for better management of the interactions and interdependencies.To reduce these impacts, an integrated resilience and vulnerability- informed Decision Support System (DSS) is required to identify interdependent network’s vulnerable components and introduce adaptive capacities accordingly. This is of particular importance given ever growing investments in asset management across different sectors in order to improve the resilience of the networks in response to extreme environmental hazards.This study presents a novel framework for building a resilience and vulnerability-informed decision support system (RV-DSS). This framework provides potential means of communicating challenges induced due to interdependencies and quantifies benefits of considering interdependencies in streamlining intervention strategies for systems. It also proposes a measure of network resilience in response to hazardous events, in addition to the commonly used measures of vulnerability for assessment of the network performance. The framework can be used in initiating the interdependency-based communications among different CI network owners and managers, leading to shared knowledge and common understanding of their connected assets, hidden failure propagation mechanisms and collective recovery process. The application of the framework is then demonstrated using a case study in North Argyll, Scotland. It is quantitatively demonstrated that although infrastructures with a higher level of interdependency, can impose the network to higher vulnerability, it provides a greater opportunity for an integrated recovery process.

Optimal Fuzzy Cluster Partitioning by Crow Search Meta-Heuristic for Biomedical Data Analysis

Biomedical data is often more unstructured in nature, and biomedical data processing task is becoming more complex day by day. Thus, biomedical informatics requires competent data analysis and data mining techniques for designing decision support system's framework to solve clinical and heathcare-related issues. Due to increasingly large and complex data sets and demand of biomedical informatics research, researchers are attracted towards automated machine learning models. This paper is proposed to design an efficient machine learning model based on fuzzy c-means with meta-heuristic optimizations for biomedical data analysis and clustering. The main contributions of this paper are 1) projecting an efficient machine learning model based on fuzzy c-means and meta-heuristic optimization for biomedical data classification, 2) employing benchmark validation techniques and critical hypothesises testing, and 3) providing a background for biomedical data processing with a view of data processing and mining.

A decision support system for Taiwan’s forest resource management using Remote Sensing Big Data

ABSTRACT This study aims to incorporate the application of RS technology with management strategies and proposes the Remote Sensing Knowledge-Based Decision Support System (RS-KBDSS) framework. It is not only focusing on considering RS technology for discussing the accuracy of estimation but also conducting strategic thinking that is needed for keeping the inventory and monitoring task running by obtaining knowledge and decision support. This study offers more flexible and accurate estimation models for different resolution images and different kinds of models. The results obtained by the study is satisfied with the practical application.

An approach to the development of decision support systems with knowledge-driven automated service composition

The development of a decision support system typically requires a significant effort from both domain modelling and technical perspectives. The paper proposes an approach for reducing the complexity of decision support system development leveraging automated service composition. The rationale behind the approach is that today many software systems (including decision support systems) are based on service-oriented architecture and to some extent the development of such systems can be represented as building composition of services satisfying the specified requirements. The problem of building such compositions can be successfully addressed by automated planning algorithms. Particularly, the paper presents the functional framework of decision support systems, requirements analysis for configurable service-oriented decision support systems and their main components, and a conceptual model of a configurable service-oriented decision support system.

Framework design of decision support system: Improving decision making in fishery supply chain for coastal communities

The focus of researchers in the field of decision making which related to designing a decision support system to assist in the decision-making process, so far is very limited to the fisheries sector, especially at the small and micro business scale. On the other hand, the SME-scale fisheries sector is a major driver of regional and country performance because it has a very significant multiplier effect on the economic development of the region. Specifically, for coastal areas, the main problem faced is the consistency of the results obtained, one of the main causes is a distorted supply chain. In this study, we are trying to design a conceptual framework for decision support systems in order to assist the decision-making process of fisheries business operators in coastal areas in the Kei Islands, Indonesia. With the main objective of expediting the supply chain of fisheries in this region. The design of the DSS that was built was adjusted to the needs of every fishery businessman in this region. So, in addition to web-based DSS framework design, we also conducted surveys on fisheries supply chain processes from capture fishermen and aquaculture farmers to adjust the conditions of needs and empirical problems to design DSS concepts that can help overcome supply chain problems in the fisheries sector in the region.

Knowledge-Based Decision Support System for Emergency Management: The Pandemic Framework

Emergency management systems (EMS) assist emergency managers to resolve emergencies on hand, through analyzing the emergency characteristics and consolidating data from different departments that are involved in resolving the emergency. Many countries have adopted various forms of EMS that are specialized in resolving one type of emergency, and studies demonstrate their effectiveness in producing better decisions. However, the COVID-19 pandemic uncovered the lack of a comprehensive framework that could deal with different emergencies. It also revealed the inability of the current systems to communicate with each other to retrieve the needed data. The aim of this study is to show the current state of EMS in emergency departments by constructing a framework for a knowledge-based decision support system for emergency management focusing on resolving pandemics. Qualitative approach was adopted in this research, where the authors reviewed emergency management in general and pandemics in specific. Existing EMS systems were investigated, and knowledgebased decision support systems were explored. Approaches for integration, communication, and collaboration were also studied. As a result of this study, a comprehensive framework, i.e., a knowledgebased decision support system for emergency departments, focusing on resolving pandemics was introduced. The framework was validated by a domain expert who provided insights and suggestions for future research. While the primary research focus is to assist emergency managers in resolving the COVID-19 pandemic, the proposed framework is unique by adopting different approaches and techniques that enable the system to deal with various emergencies not limited to the current pandemic.

System Design for a Data-Driven and Explainable Customer Sentiment Monitor Using IoT and Enterprise Data

The most important goal of customer services is to keep the customer satisfied. However, service resources are always limited and must be prioritized. Therefore, it is important to identify customers who potentially become unsatisfied and might lead to escalations. Today this prioritization of customers is often done manually. Data science on IoT data (esp. log data) for machine health monitoring, as well as analytics on enterprise data for customer relationship management (CRM) have mainly been researched and applied independently. In this paper, we present a framework for a data-driven decision support system which combines IoT and enterprise data to model customer sentiment. Such decision support systems can help to prioritize customers and service resources to effectively troubleshoot problems or even avoid them. The framework is applied in a real-world case study with a major medical device manufacturer. This includes a fully automated and interpretable machine learning pipeline designed to meet the requirements defined with domain experts and end users. The overall framework is currently deployed, learns and evaluates predictive models from terabytes of IoT and enterprise data to actively monitor the customer sentiment for a fleet of thousands of high-end medical devices. Furthermore, we provide an anonymized industrial benchmark dataset for the research community.

Hybrid Decision Support System Framework for Leaf Image Analysis to Improve Crop Productivity

Crop disease is one of the major problems with agriculture in India. Identifying the disease and classifying the type of disease is most important which can be made possible using the deep learning technique. To perform this verified dataset is required which consists of healthy and disease leaf images of all crops. The proposed model uses a hybrid approach which integrates VGG16 classifier with an attention mechanism, transfer learning approach and dropout operation. The proposed model uses a rice disease dataset and using the proposed approach it achieves an train accuracy of 96.45 percent and train loss 0.09 and validation loss of 0.44. The dataset is collected from the plant village project for rice leaf which consists of 4955 images which include Brown Spot, Healthy, Hipsa, and Leaf Blast type of images. The proposed model use attention mechanism that focuses mainly on the part of the image rather than the whole part of the image using a glimpse ratio of 3:1. The traditional method of detecting crop diseases needs high experience and knowledge of experts in the field which is time consuming, ineffective, and high cost. In this study, Deep Convolutional Neural Networks (DCNN) and Transfer Learning with Attention models are used to detect diseases associated with rice plants without overfitting the model.

Techno-economic assessment of bioenergy options using crop and forest residues for non-electrified rural growth centres in Zambia

Improving access to modern energy in rural areas through the electrification of rural growth centres (RGCs) mainly by grid extension remains a challenge in Zambia. During the last 15 years, electrification rates have marginally increased from 3 to 11% for rural areas. This does not only represent the status quo of access to modern energy in these areas but also shows the unlikelihood of attaining the 2030 rural electrification target of 51%. Biomass-based systems offer viable alternatives/complements of supplying modern energy. Sustainable development of these technologies begins with identifying viable bioenergy systems from a wide range of options. Accordingly, this paper focused on a first-level techno-economic assessment of different bioenergy options that employed lignocellulosic residues as feedstock to provide heating/cooking and electricity-enabled energy services in non-electrified RGCs of Zambia. The results indicate that fuels to provide cooking/heating energy services could be supplied by pellets and briquettes in an economically viable manner. Gasification and combustion technologies were more financially attractive compared to diesel generators for electricity production. Therefore, they are viable alternatives for providing electricity, an important enabler for the much-needed rural development in the country. However, at the moment they cannot provide electricity at costs competitive with grid-electricity, partly due to subsidized electricity tariffs in Zambia. It is recommended that a decision support system framework for biomass exploitation using such technologies should be established. Such a framework should guide policy makers to identify optimal means of using biomass to provide modern energy services in the RGCs of the country.

A Collaborative Decision Support System Framework for Vertical Farming Business Developments

The emerging industry of vertical farming (VF) faces three key challenges: standardisation, environmental sustainability, and profitability. High failure rates are costly and can stem from premature business decisions about location choice, pricing strategy, system design, and other critical issues. Improving knowledge transfer and developing adaptable economic analysis for VF is necessary for profitable business models to satisfy investors and policy makers. A review of current horticultural software identifies a need for a decision support system (DSS) that facilitates risk-empowered business planning for vertical farmers. Data from the literature alongside lessons learned from industry practitioners are centralised in the proposed DSS, using imprecise data techniques to accommodate for partial information. The DSS evaluates business sustainability using financial risk assessment. This is necessary for complex/new sectors such as VF with scarce data.

Research on decision support system of ship planning management based on data warehouse

This paper discusses the basic principle, model construction and theoretical framework of a new decision support system based on data warehouse, and applies this method to the data extraction process of shipyard plan management. The practice shows that the method is applicable and reliable in complex engineering systems such as shipyard planning management.

Online video channel management: An integrative decision support system framework

In the current fragmented media landscape, online video is becoming an important outlet for content dissemination. Online video channels provide content creators a way of organizing content and building an online following through subscriptions and social sharing. This paper describes a decision support systems (DSS) based framework for online video channel management and content creation. The relevant DSS literature was reviewed along with both modeling and behavioral aspects of online videos and video channels. An empirical case study was run on a dataset consisting of views, shares, and subscriptions from over 1000 videos from nine YouTube channels. This paper contributes to DSS theory by proposing a flexible framework for incorporating both behavioral and empirical work into an integrative process for online video content creation. This framework builds on existing data-driven DSS theory, but includes specific entities and processes for online content creation.