Architecture Of Decision Support System Research Articles

Структура информационного хранилища системы поддержки принятия клинических решений

The construction of an information repository for a clinical decision support system (CDSS) in the weakly formalized subject area of treating bronchopulmonary diseases is considered. An overview of approaches to creating knowledge bases in this subject area is provided. A method for extracting knowledge is described, which is based on rules from clinical recommendations and the search for dependencies between words in sentences, taking into account the se-quence of rule application. The information repository of the CDSS is populated with ontological and production knowledge bases using the proposed knowledge extraction method. An ontology for the selected subject area was de-veloped, and studies of its quality were conducted through an analysis of the graph topology using cognitive ergonom-ics metrics. The effectiveness of the described knowledge extraction method is demonstrated. An original architecture for a clinical decision support system has been developed.

A Conceptual Architecture of Fuzzy Risk-Based Decision Support Systems (R+DSS)

This paper introduces a conceptual architecture for Fuzzy Risk-Based Decision Support Systems (R+DSS). This architecture is designed to provide a comprehensive and efficient approach to decision-making procedures in various domains involving assessing and controlling potential risks. The proposed architecture exhibits versatility in its applicability across multiple fields, such as finance, healthcare, engineering, and environmental management. It incorporates these components flexibly and scalable while also being user-friendly. The framework employs fuzzy logic principles such as membership functions, rule sets, and inference methods to facilitate a thorough evaluation of the risk that accommodates the inherent uncertainties and imprecisions characteristic of real-world risk scenarios. The Fuzzy Inference Engine is a versatile and resilient risk analysis tool capable of accommodating diverse data and systems, enabling effective risk mitigation strategies. The adaptability of this architecture to effectively handle complex, uncertain and dynamic environments makes it a promising tool for decision-makers looking to improve risk assessment and management protocols.

Modeling, Implementing and Evaluation a Decision Support System Used for Choosing the Best HVAC System in The Buildings, Case Study in Iraq

The life cycle cost of a building is affected by the heating, ventilation, and air conditioning (HVAC) system chosen by the Life Cycle Costs (LCC). Quality, constructability, appearance of the structure's interior and exterior, HVAC size and weight, and LCC are some of the criteria influencing the choice. Methods: To monitor a project's progress based on energy savings, standard measures such as cost variance (CV) and schedule variation have used an idea when tracking the performance of intelligent buildings. Also, as described in the article, this research compared the decision-making limits of Building Information Modelling (BIM) and (MCDM). Analysis: The conventional approach cannot reveal information regarding divergence from the expected level of performance. Based on the outcomes of the construction cost variables, the key finding was the observation of 12 efficient elements. Finding and Novelty: According to the R, a building's most valuable features are its (Energy Saving Features, Warranties, Budget, Protect Your Unit, SEER Ratings, and Home Square Footage). The findings of Actual value (AV) and planned value (PV) were significantly different, as noted by the Multi-Criteria Decision Maker (MCDM). The new method also makes it possible to track project costs and timetables more accurately. The paper will characterize the HVAC Decision Support System's architecture (HVACDSS). Also, a case study of action modeling is provided, and the preliminary findings are addressed. Six criteria characteristics are used by the HVACDSS technique by an analysis of building construction conducted using the WEKA mining tool (decision tree).

Design and Simulation of an Edge Compute Architecture for IoT-Based Clinical Decision Support System

Design and Simulation of an Edge Compute Architecture for IoT-Based Clinical Decision Support System

Intelligent Decision Support System for Building Project Management Based on Artificial Intelligence

With the deepening of research on artificial intelligence (AI) technology in the field of computer science and the improvement of its social influence, the effective combination of AI technology and engineering project management practice has become a hot topic of discussion in the field of engineering project management. In the application research of AI in engineering project management, most of them use data-driven models to solve project management problems, improve data insight and improve the speed and quality of project management decision-making. Decision support systems have been developed on the basis of management information systems, but they have rarely been successfully applied in project management. The decision support system based on data warehouse is a new level of development in decision support systems, providing an effective way for projects to achieve real-time decision-making, thereby achieving synchronous, integrated, and optimized dynamic management of project progress, cost and function, and improving the timeliness and scientificity of decision-making. Therefore, this article proposes the theoretical framework and architecture of a construction project decision support system based on data warehousing. Then, this article proposes a deep belief network (DBN) evaluation method to evaluate the performance of the system, and the results show that the proposed method has superior performance.

Development of REDCap-based architecture for a clinical decision support tool linked to the electronic health record for assessment of medication appropriateness.

To develop the architecture for a clinical decision support system (CDSS) linked to the electronic health record (EHR) using the tools provided by Research Electronic Data Capture (REDCap) to assess medication appropriateness in older adults with polypharmacy. The tools available in REDCap were used to create the architecture for replicating a previously developed stand-alone system while overcoming its limitations. The architecture consists of data input forms, drug- and disease-mapper, rules engine, and report generator. The input forms integrate medication and health condition data from the EHR with patient assessment data. The rules engine evaluates medication appropriateness through rules built through a series of drop-down menus. The rules generate output, which are a set of recommendations to the clinician. This architecture successfully replicates the stand-alone CDSS while addressing its limitations. It is compatible with several EHRs, easily shared among the large community using REDCap, and readily modifiable.

Computer-Supported Smart Green-Blue Infrastructure Management

Answering climate change challenges, the paper proposes an intelligent decision support system (DSS) for the management of green-blue infrastructure (GBI). Addressing the gaps identified in other studies, the designed DSS incorporates four key elements: 1/interdisciplinary collaboration among all stakeholders 2/inclusion of practical operation and maintenance activities, 3/main components of distributed DSS, with practical examples of use, 4/consideration of conditions specific to the location. The multi-layered DSS architecture can be implemented as a unified platform that provides a comprehensive, customizable, and flexible framework based on AI tools, big data and analytics, edge computing, cloud, and mobile, IIoT, and biometric system tools. The use of cobots and digital clones alongside humans results in the implementation of hybrid human-machine units. DSS for GBI increases decision-making capacity and can serve as a foundation for the implementation of similar systems by governments and local communities to build sustainable and resilient communities.

Meeting Human and Biodiversity Needs for 30 × 30 and beyond with an Iterative Land Allocation Framework and Tool

Spatial conservation prioritization does not necessarily lead to effective conservation plans, and good plans do not necessarily lead to action. These “science-action” gaps are pernicious and need to be narrowed, especially if the international goal of conserving 30% of the planet by 2030 is to be realized. We present the Earthwise Framework, a flexible and customizable spatial decision support system (SDSS) architecture and social process to address the challenges of these science-action gaps. Utilizing case study experience from regions within California, South Africa, and British Columbia, we outline the framework and provide the Little Karoo, South Africa SDSS data, code and results to illustrate five design strategies of the framework. The first is to employ an “open science” strategy for collaborative conservation planning and action. Another is that marginal value functions allow for the continuous accounting of element (e.g., habitat) representation in prioritization algorithms, allowing for an SDSS that is more automated and saves valuable time for stakeholders and scientists. Thirdly, we program connectivity modeling integrated within the SDSS, with an algorithm that not only automatically calculates all the least cost corridors of a region, but prioritizes among them and removes the ones that do not make ecological sense. Fourth, we highlight innovations in multi-criteria decision analysis that allow for both cost-efficient plan development, like representative solution sets, but also land-use planning requirements, like site specific valuation, in what appears to be a more transparent, understandable, and usable manner than traditional approaches. Finally, strategic attention to communicating uncertainty is also advocated. The Earthwise Framework is an open science endeavor that can be implemented via a variety of software tools and languages, has several frontiers for further research and development, and shows promise in finding a better way to meet the needs of both humans and biodiversity.

Communication architecture for real-time decision support systems in railway nodes

Rail transport nodes are complex systems providing transport for goods and people. The dispatcher providing operational control is essential for the proper functioning of these nodes. His role mainly consists of decision-making connected with the allocation of limited resources such as tracks or personnel. These decisions are complicated because of many unexpected influences, e.g., train delays. Recognizing the importance of decision support for the dispatcher's decision-making process, we had designed a real-time decision support system with a simulation model at its core. Such a system requires a high-quality information source for its operation, that needs to be efficiently acquired, processed, and used. In this paper, therefore, we address the possibilities of obtaining up-to-date information about the railway system. Moreover, we designed a communication architecture that allows the distribution of information to the designed decision support system with a simulation model in the core.

Machine Learning Based Logistic Decision Support System for Intelligent Vehicles and Transportation Systems

One-third of the freight expense is spent on transportation and transport networks, thus significantly impacting the logistics sector's efficiency. Artificial Intelligent logistics strategies are designed to alleviate the impact on metropolitan areas exacerbated by increased freight transport. The city of logistics is widely employed as a modern study area, has a significant social and economic influence, and has extensively explored the key elements and components. Most recent experiments concentrate on traffic management and logistics monitoring, even though no research studies have tried to detect drivers' distractions. Since drivers are one of the major parts of logistic service, this study incorporates existing logistics DSS with the cognitive model for predicting driver distraction. This paper presents a Machine Learning Integrated Decision Support System (MLIDSS) architecture and core components based on simulationoptimization modules to assist the smart logistics DSS with an eye to service delivery driver distraction level. The ML algorithms perform real-time driver distraction predictions for smooth logistics transportation. The viability of such a transportation system is often illustrated in the real world. The findings result in the highest-performing intelligent decision support system compared to existing models.

A Real-Time Decision Support Approach for Managing Disruptions in Line-Haul Freight Transport Networks

Unexpected disruptions in road freight transport caused by poor weather conditions, traffic accidents, etc., are quite frequent and have negative effects on the whole supply chain. Therefore, an intelligent disruption management system is necessary to revise the transport plan directly after disruptions have occurred. The literature presents several approaches for managing disruptions in road freight transport. However, most of them focus on urban freight distributions where disruptions are often handled by vehicle rerouting. The current work, in contrast, addresses disruption management in a line-haul freight transport network that connects urban distribution systems. We present a novel hybrid approach combining a simulation model, optimization algorithms, and a cost-effectiveness analysis. When a disruption occurs, the proposed approach can be used to analyze the impacts of the disruption, identify the affected trips, and revise their plan quickly in real time. Six re-planning strategies are proposed to handle the disruptions and are evaluated in terms of cost, reliability (expressed in time delays), and CO2 emissions. Cost-effectiveness analyses are conducted to rank the obtained solutions and identify the best strategy. Moreover, we suggest a decision support system architecture, based on the proposed approach, to enable disruption management in real-time settings. Real data is used to evaluate the proposed approach in different disruption scenarios. The results provide transport planners with useful insights into possible re-planning strategies and how to identify the best cost-effective strategy to minimize the disruptions’ effects and be more economically sustainable. This work also supports carriers in the transition towards intelligent disruption management.

An Intelligent System for Medical Decision Support in Differential Diagnosis and Treatment of COVID-19.

The development of electronic services to help doctors in the diagnosis and treatment of COVID-19 is discussed. The existing systems for such purposes are analyzed, and requirements for them are formulated. The architecture of an intelligent medical decision support system and the basic principles of its development using an ontology-oriented approach are given. The unique capabilities of the system are shown, and its information and software components are described.

Statistical analysis of e-commerce delivery demand modeling framework

The e-commerce delivery demand has developed quickly in the past two decades and such trend has intensified tremendously due to the continuing coronavirus pandemic. Given the situation, the requirement for forecasting e-commerce delivery demand and evaluating applicable logistics solutions is expanding. However, the present simulation models for e-commerce delivery demand are still limited and do not address the delivery alternatives and their properties that customers experience on ecommerce order placements. The inability to construct and monitor agent systems that have been deployed throughout a network is one of the primary challenges, along with ensuring that software agents are installed in a manner that is both secure and efficient. The field of multi-agent technology is one of the areas of information and communication technology that is advancing at one of the quickest rates. Virtually every day, new agent-based services, products, and applications are being developed. The coordination, control, and simulation of the architecture of decision support systems that are used in e-commerce are all performed with the use of an agent-based simulation model. The design that was recommended improves the decision support systems that are already in place and achieves a competitive advantage.

A Wushu Referee’s Decision Support System Using Error Recognition Theory

To realize the recognition and scoring of Wushu Athletes’ wrong actions by electronic referees, reduce the difficulty of referees’ recognition, help reduce man-made errors and influence in the process of competition judgment, and make the referee work fairer and more scientific, therefore, this paper aims to make an in-depth study on the Wushu referee decision support system based on the theory and method of error recognition. Firstly, this paper discusses the overall architecture of the Wushu decision support system, analyzes the functions and database design of each system in the system, constructs a digital and information-based Wushu referee decision support system, and uses the error matrix logic equation to solve the recognition state and goal. The Nrej3d technology is introduced for collecting the characteristics of martial arts wrong actions, and matching recognition is completed by using the sports space, to complete the research on the decision support system of martial arts referees based on the theory and method of wrong recognition. The simulation results show that the method proposed in this paper is superior to other methods in the recognition accuracy and effective measurement of martial arts wrong movements and has a certain application value.

Integration of Data Flows of the Construction Project Life Cycle to Create a Digital Enterprise Based on Building Information Modeling

This study is devoted to the problem of digital transformation in the construction industry. To solve the proposed emerging approach for integrating information flows based on Building information modeling (BIM). This research describes the advanced methodology for creating a unified information model in construction, which combines architectural, design, engineering, cost and depreciation models of a construction object, raises a number of problems of reorganizing business processes at enterprises. The novelty of the research lies in the model of adapted common data environment based on life cycle of a construction project by stages of implementation and emergence of information. The proposed architecture is a platform for intelligent parametric modeling as intelligent CAD, which allows linking all elements of the model "Digital Enterprise".The result of the study is a conceptual mechanism for improving the efficiency of enterprise management processes based on the architectural approach in terms of digitalization of construction. Emerging technology in the study is the architecture of the intelligent decision support system in construction based on 5D BIMtechnology and Digital Enterprise. The main advantage of the proposed approach is the possibility of multiple reuse of information by all participants in the development and implementation of an investment and construction project without changes and distortions of data about the construction object. Keywords — Building Information Modeling, BIM, Digital Enterprise, Common Data Environment, life cycle of construction project, digitalization of construction

ІНФОРМАЦІЙНА СИСТЕМА ВИБОРУ ОПТИМАЛЬНИХ СТРАТЕГІЙ ЛІКУВАННЯ ВІРУСНИХ ЗАХВОРЮВАНЬ

Recently, the importance of information support of medical technologies has increased. Widely used modern information technology, development and application of information technology is one of the urgent tasks. Most personal computers are used only for processing text documents, storing and processing databases, statistics, financial reporting. The article is devoted to the problems of choosing the optimal strategies for the treatment of viral diseases, which is quite an acute and urgent issue today. The aim of this study is to create an information system based on the architecture of a clinical diagnostic decision support system (DSS) that uses a knowledge base to assist the physician in his personal decision-making process. The developed software product will consist of several modules with characteristic functions: categorization of parameters and indicators according to the initial data of patients and division of the sample of the corresponding categories of parameters, selection of analysis and display of results. This system processes data and displays the results in the form of graphs showing the dynamics of changes in temperature of patients with prognosis. The system will provide information to the user, not the exact "answer", which was the purpose of their development. The user must analyze this information and discard erroneous or unnecessary information. He must be active and interact with the system, not just a passive recipient of information. User interaction with the system plays an important role in choosing how to use DSS. Modeling of health indicators is based on the mathematical apparatus of Markov chains. This technique allows you to calculate the sets of unconditional probabilities from the initial data and to make a probabilistic forecast of the health status of an individual or groups of the population for future periods of life.

A Specialized Shell for Intelligent Systems of Prescribing Medication

This paper analyzes the existing decision support systems for prescription of drug therapy. The main principles of development and architecture of an intelligent clinical decision support system that is implemented as a specialized shell are described. The unique features of the system, as well as information and software components that are part of it, are shown. The presented examples demonstrate all the proposed solutions.

Ontological Modelling of the Decision-making Support Intellectual System in Risks Analysis in the Innovation and Investment Sphere

The purpose of the article is to study, analyze and consider general problems and prospects of using ontologies when modelling decision-making processes in intelligent systems when analyzing the risks of the innovation and investment sphere. The research methodology lies in methods of the basic concepts semantic analysis of a given subject area (innovation and investment projects and management processes for the implementation of these projects). The article discusses the existing approaches to modelling decision-making processes in the analysis of risks in the innovation and investment sphere. The novelty of the research is the intellectualization problems solving of processes in the innovation and investment sphere on the basis of formal ontological models. Conclusions. The practical value of the presented results lies in the development and use of knowledge management system components for identifying and predicting problem situations in innovation and investment projects. The proposed integrated ontology can be used in the management of innovation and investment projects in various subject areas since it contains classes of concepts that have the status of a project activity standard. The developed ontological model makes it possible to develop software architecture for an intelligent decision support system, develop metadata and build a set of interrelated thesauri to support the semantics of end-user requests.

AUTOMATED MODELING AND FORECASTING SYSTEM OF ENTERPRISE ACTIVITIES

Introduction. Mathematical methods and models are an effective tool for studying complex economic systems at different levels of enterprise management. Economic mathematical modelling is actively developing not only as a scien-tific field but also as a means of substantiating management decisions in business, in the analysis and forecasting of socio-economic processes and phenomena. In the arsenal of economic and mathematical modelling now are using- modern computing methods and computer technology. Libraries of economic and mathematical models are an integral part of the architecture of decision support systems in specific areas of the economy. The rapid development of computer technology stimulates the emergence and formation of new theoretical volumes and applied areas of modelling.Purpose. Like any large and complex field of knowledge, mathematical modelling is evolving in different direc-tions, acquiring new flexible research methods. Therefore, based on new hardware, technology and software platforms it is necessary to create new information systems using economic and mathematical models in particular for forecasting of enterprise activities.Results. The article analyses the methods of modelling and forecasting the enterprise, considers the principles of software design. Using systems analysis, the design problem was analytically divided into parts. Also were investigated the connections and relationships between these parts, in particular, were implemented the problem tree and the goal tree. Implemented business process modelling performs based on created structural-logical diagrams, namely the IDEF0 dia-gram, which helps to visually display data and information that affect software development, a server part, input data and users. Using the results of research, the authors developed an automated information system for modelling and forecasting the activities of the enterprise, which uses models of Holt, Brown, exponential smoothing and Box-Jenkins for modelling. Part of the developed system is a designed software product that implements the objectives of the research. The obtained program results allow creating a clear forecast of the future activities of the enterprise.Conclusions. Based on the built graphs of modelling and forecasting of the Cisco Systems company financial activity with using of the developed automated system, we established that the Brown model is the best for providing educational sampling and a forecast of activity. The development of the automated system in the future involves the expansion of functionality, improvement and increasing of quality, as well as the creation of powerful analytics for more detailed forecasting.

Decision support system for street-road network objects repair

The article analyzes factors influencing managerial decisions adoption on repair of road network facilities (RN). Road repair is a challenge that must take into account a number of conflicting criteria. It is shown that decision-making process is a complex mental activity that consists in choosing the most effective strategy and, at present, repair work planning should be carried out using decision support systems (DSS), that is, a class of knowledge-based systems. DSS developing necessity for repair planning of RN has been substantiated. DSS architecture is described and it is shown that DSS allows increasing knowledge base by adding new knowledge without changing the structure, as well as encapsulating new knowledge into the system, increasing intellectual resource used in decision-making process. The parameters are determined, on the basis of which rules in DSS are formulated, which are necessary for making decisions on determining scope, timing, cost and sequence of repair and restoration work. Production rules examples are given. The general scheme of algorithm for planning repair and restoration works at RNF, as well as algorithm scheme for complex indicator calculating of transport and operational state-ensuring movement design speed dare described. Planning process formalized description and control actions development has been completed. It is shown that intelligent decision-making is an iterative, multi-level procedure, within which decisions table is analyzed to assess technical condition of RN facilities based on regulatory and technical documentation analysis. Situation description is consistently expanded by taking into account the quality of RN road marking, traffic intensity, road construction materials volumes, repair and restoration work cost, traffic management, seasonality and weather conditions, etc. (the number of taken into account parameters and levels of their description is not limited). The advantages of proposed approach to DSS implementation are described. It is shown that proposed DSS version provides situational adaptive intelligent control with ability to issue justified recommendations for carrying out repairs. The prospects for work development in this direction were considered and conclusion was made that management digitalization of repair activities, the digital transformation of management model and business processes in the field of RN repair will increase profits, increase operational efficiency, reduce repair costs and improve its quality.