Design Of Decision Support System Research Articles

Comparison and Evaluation of Learning Capabilities of Deep Learning Methods for Predicting Ship Motions

The development of intelligent ship control systems in real-world conditions relies heavily on the accurate identification and prediction of ship seakeeping and maneuvering trajectories. In this study, we comprehensively evaluate a selection of deep learning methods to assess their learning capabilities in terms of idealizing ship motion behavior in realistic operational environments. To recover real conditions, we utilize historical Automatic Identification System (AIS) data and a time domain 6 Degree of Freedom (6- DoF) grounding dynamics model to generate ship motion sequences for a Ro-Ro passenger ship operating in the Gulf of Finland. Via a rigorous evaluation process, we validate the performance of these methods using extensive data streams. The analysis includes the identification and estimation of uncertainties between two ports. The paper demonstrates the proficiency of the selected deep learning methods in capturing ship maneuvering features, their potential use in the design of ship control and intelligent decision support systems.

Design and Implementation of an Intelligent Metro Project Investment Decision Support System

The construction of subway projects involves tight engineering cycles, multiple technical challenges, and complex coordination among various stakeholders. Due to the influence of uncertain factors during the construction process, the investment in subway project construction exhibits non-linear changes over time. Investment decision-making is the process through which the investment entity determines its investment activities. For typical investment entities, project investment decision-making primarily entails analyzing and evaluating proposed engineering projects based on investigation, analysis, and argumentation, ultimately deciding whether to invest. With the widespread application of information technology (IT) across various fields, decision support systems (DSS) have emerged to enhance the decision-making capabilities of enterprise management. This article designs an intelligent subway project investment DSS, leveraging data mining (DM) technology to integrate DSS with a data warehouse (DW).

Design and Application of Decision Support System for Educational Management Based on Big Data

Decision support system (DSS) for educational management based on big data leverage advanced analytics and machine learning techniques to enhance decision-making processes within educational institutions. By collecting and analyzing large volumes of data from various sources such as student performance, administrative records, and institutional resources, the DSS provides valuable insights and predictive models to administrators and educators. These insights can inform strategic planning, resource allocation, student interventions, and curriculum development, ultimately improving student outcomes and organizational efficiency. With the ability to process vast amounts of data in real-time, the DSS enables timely and data-driven decision-making, empowering educational leaders to address challenges and capitalize on opportunities in today's complex educational landscape.The integration of big data analytics with the Recommender Ranking Decision Support System (RRDS) presents a transformative approach to enhancing educational management. This paper explores the application of big data analytics within the educational setting, focusing on the development and implementation of the RRDS. By leveraging large and diverse datasets, the RRDS enables personalized learning experiences, optimizes resource allocation, and enhances student outcomes. Through predictive modeling, the RRDS predicts student performance and identifies at-risk students, facilitating targeted interventions to support student success and retention. Additionally, the system offers personalized learning recommendations tailored to individual student needs, fostering engagement and motivation. The findings underscore the significance of data-driven decision-making in improving educational practices and institutional performance. As educational institutions increasingly embrace technology-driven solutions, the integration of big data analytics and RRDS stands poised to revolutionize educational management, ultimately leading to more effective teaching and learning methodologies and better outcomes for students.

To NIRS or not: understanding clinical decision-making of respiratory support management related to acute respiratory failure using Critical Decision Method

Clinical decisions related to noninvasive respiratory support (NIRS) for acute respiratory failure patients are complex. NIRS strategies have favorable benefits for patients when successful but NIRS failure comes with longer ICU stays, increased mortality, and more intubation-related and mechanical ventilator-associated complications. Our goal is to understand how physicians make decisions regarding NIRS and explore the differences between emergency physicians and critical care physicians—who commonly make these decisions. We used the critical decision method to conduct ten semi-structured interviews with five emergency physicians and five critical care physicians from ten different healthcare systems across the United States. We found that physicians similarly use objective and subjective clinical cues but differentially use temporal and contextual cues in their decision-making. Several insights identified in our study can be helpful in the design and development of clinical decision support systems for respiratory support decision-making. Specifically, we recommend two customized distinct clinical decision support systems for respiratory support decision-making, one for each designated clinical setting (i.e., ED and ICU) with tailored designs that support the different decision-making processes.

The Minimally-Invasive Oral Glucose Minimal Model: Estimation of Gastric Retention, Glucose Rate of Appearance, and Insulin Sensitivity From Type 1 Diabetes Data Collected in Real-Life Conditions.

Modeling the effect of meal composition on glucose excursion would help in designing decision support systems (DSS) for type 1 diabetes (T1D) management. In fact, macronutrients differently affect post-prandial gastric retention (GR), rate of appearance (R[Formula: see text]), and insulin sensitivity (S[Formula: see text]). Such variables can be estimated, in inpatient settings, from plasma glucose (G) and insulin (I) data using the Oral glucose Minimal Model (OMM) coupled with a physiological model of glucose transit through the gastrointestinal tract (reference OMM, R-OMM). Here, we present a model able to estimate those quantities in daily-life conditions, using minimally-invasive (MI) technologies, and validate it against the R-OMM. Forty-seven individuals with T1D (weight =78±13 kg, age =42±10 yr) underwent three 23-hour visits, during which G and I were frequently sampled while wearing continuous glucose monitoring (CGM) and insulin pump (IP). Using a Bayesian Maximum A Posteriori estimator, R-OMM was identified from plasma G and I measurements, and MI-OMM was identified from CGM and IP data. The MI-OMM fitted the CGM data well and provided precise parameter estimates. GR and R[Formula: see text] model parameters were not significantly different using the MI-OMM and R-OMM (p 0.05) and the correlation between the two S[Formula: see text] was satisfactory ( ρ =0.77). The MI-OMM is usable to estimate GR, R[Formula: see text], and S[Formula: see text] from data collected in real-life conditions with minimally-invasive technologies. Applying MI-OMM to datasets where meal compositions are available will allow modeling the effect of each macronutrient on GR, R[Formula: see text], and S[Formula: see text]. DSS could finally exploit this information to improve diabetes management.

CNN-driven Art Design Decision Support System Based on Big Data

CNN-driven Art Design Decision Support System Based on Big Data

Conceptual design of a nationwide spatial decision support system for forest fire prevention and fighting

Conceptual design of a nationwide spatial decision support system for forest fire prevention and fighting

Initial User-Centred Design of an AI-Based Clinical Decision Support System for Primary Care.

A clinical decision support system based on different methods of artificial intelligence (AI) can support the diagnosis of patients with unclear diseases by providing tentative diagnoses as well as proposals for further steps. In a user-centred-design process, we aim to find out how general practitioners envision the user interface of an AI-based clinical decision support system for primary care. A first user-interface prototype was developed using the task model based on user requirements from preliminary work. Five general practitioners evaluated the prototype in two workshops. The discussion of the prototype resulted in categorized suggestions with key messages for further development of the AI-based clinical decision support system, such as the integration of intelligent parameter requests. The early inclusion of different user feedback facilitated the implementation of a user interface for a user-friendly decision support system.

Application of Exponential Comparison Method in Designing Decision Support Systems For Performance and Promotions Assessment in Banking Industry

This research aims to find out how to design a decision support system for evaluating employee performance as a promotion. Data collection in this research used observation, interviews, and a literature study. The population in this study were employees. Rapid Application Development (RAD) is the system development method that the author used to create this application. The tools that the author uses in system design are UML (Unified Model Language) diagrams to show the process and data flow to be designed; Adobe Dreamweaver CS6 is used to design applications; Wamp Server is used as a webserver; and Diaportable is used to illustrate UML diagrams. Based on the results of the discussion, the following were obtained: The decision support system with MPE analysis (Exponential Comparison Method) aims to find employees who are worthy of promotion. The assessment system consists of seven, including education, work performance, discipline, communicativeness, cooperation, reasoning, encouragement of achievement, and responsibility for self-assessment, which are carried out by HRD staff and supervisors, while the HRD manager is only responsible for the decision-making process. The results of this research are in the form of graphs and accumulated MPE assessments, which show the ranking order of each candidate employee.

A behavior prediction method for complex system based on belief rule base with structural adaptive

Predicting the behavior of complex systems and taking appropriate measures for system management is of paramount importance for decision-makers. Belief rule base (BRB) is an effective method for modeling complex systems, and its construction relies on expert knowledge. However, in certain complex system prediction problems, deriving the structure and parameters of BRB from limited expert knowledge or existing models is a challenge, and there may even be a lack of available expert knowledge. Data mining plays a crucial role in obtaining the parameters for model construction in the design of decision support systems (DSSs). Therefore, this paper proposes a method for behavior prediction of complex systems called the structural adaptive BRB (SA-BRB). First, to reduce the randomness of K-means+ +, this paper introduces an error constraint and employs this algorithm to mine historical data for constructing a reference value set. Second, a model ensemble construction process is designed to create different model structures. Subsequently, the evidence reasoning (ER) algorithm is applied to derive the models, and the projection covariance matrix adaptive evolution strategy (P-CMA-ES) algorithm is used for model optimization. Finally, a model evaluation method is established, allowing adaptive adjustment of the model structure according to the needs of engineering practice and the preferences of decision-makers. Furthermore, the effectiveness of the proposed method is validated through two case studies: one focusing on predicting the health status of a flywheel system and the other on forecasting full-load power generation in a combined power plant.

Design and Development of an Intelligent Decision Support System for HR Recruitment

In the current era of globalized business environments, the effective management of human resources (HRM) plays a critical role in attracting and retaining skilled employees as companies expand into international markets. This study aims to tackle the challenges encountered in the HR recruitment process by designing and developing an intelligent decision support system based on the Mamdani fuzzy inference system. The proposed system harnesses the capabilities of fuzzy logic and advanced analytics to enhance the efficiency, accuracy, and efficacy of candidate selection. By automating tasks, the system streamlines the recruitment process and enhances candidate-job matching through the utilization of linguistic variables such as Technical Skillset, Experience, Certifications, Qualification, and Projects completed. The Mamdani fuzzy inference system facilitates adaptable decision-making based on imprecise or uncertain inputs, capturing expert knowledge and domain-specific heuristics. This approach optimizes the candidate ranking process, contributing to the successful acquisition of talented individuals and fostering organizational growth. By adopting this technology-driven HRM approach, organizations can surmount the limitations of manual methods, elevate their recruitment processes, and make more well-informed hiring decisions. The proposed Mamdani fuzzy inference system presents a robust and efficient solution that capitalizes on the potential of information technology to augment human resource management practices and gain a competitive edge in talent acquisition.

APPLICATIONS OF KNOWLEDGE DISCOVERY AND DATABASE (KDD) IN CLINICAL DECISION SUPPORT SYSTEM

Applications of Knowledge Discovery and Database (KDD) in Clinical Decision Support Systems) is a research work that aims to develop a medical system that has the ability to detect and suggest cure for an ailment with minimal effort. Healthcare system has been a major source of worry across the globe in recent time due to emergence of different types of diseases and epidemics. The number of health care personnel in various hospitals falls short of the number required most especially in terms of specialists. The medical system in Nigeria today suffers from lack of fast, accurate, reliable and intelligent software solutions that can help healthcare practitioners make decisions that would solve urgent, and in some cases, complex medical problems in real-time. Also, the cost of processing and analyzing large volumes of data in a medical environment is high most especially in terms of time consumption. So in this research design and implement clinical decision support system using knowledge discovery in database (KDD) was the major focus. The design required a computerized database for storing medical records as well as software for improved intelligence-based medical procedures that were going to be simpler to use, versatile, adaptable, savvy, and effortless at combining and evaluating medical data, allowing medical practitioners at all levels to make realistic, intelligent, and real-time decisions on critical health issues. The system was designed using the Object-Oriented Design and Analysis Methodology (OOADM).

A Conceptual Design of an AI-Enabled Decision Support System for Analysing Donor Behaviour in Nonprofit Organisations

Analysing and understanding donor behaviour in nonprofit organisations (NPOs) is challenging due to the lack of human and technical resources. Machine learning (ML) techniques can analyse and understand donor behaviour at a certain level; however, it remains to be seen how to build and design an artificial-intelligence-enabled decision-support system (AI-enabled DSS) to analyse donor behaviour. Thus, this paper proposes an AI-enabled DSS conceptual design to analyse donor behaviour in NPOs. A conceptual design is created following a design science research approach to evaluate an AI-enabled DSS’s initial DPs and features to analyse donor behaviour in NPOs. The evaluation process of the conceptual design applied formative assessment by conducting interviews with stakeholders from NPOs. The interviews were conducted using the Appreciative Inquiry framework to facilitate the process of interviews. The evaluation of the conceptual design results led to the recommendation for efficiency, effectiveness, flexibility, and usability in the requirements of the AI-enabled DSS. This research contributes to the design knowledge base of AI-enabled DSSs for analysing donor behaviour in NPOs. Future research will combine theoretical components to introduce a practical AI-enabled DSS for analysing donor behaviour in NPOs. This research is limited to such an analysis of donors who donate money or volunteer time for NPOs.

An Analysis of Factors Influencing Ice Management Performance in an Experimental Marine Simulator and Their Application to Decision Support System Design

Abstract Ice management is essential for maintaining the safety of offshore operations in Arctic regions. We present the combined results of three experiments conducted in a full-mission bridge simulator specially designed for ice management. From a quantitative analysis of the results, we infer the effect of three variables on performance: (1) experience, (2) training, and (3) Decision Support System (DSS). The results confirm that experience and training improve performance for untrained and inexperienced simulator participants. The DSS also improves performance, but with a smaller effect. Qualitative observations using vessel position heat-map diagrams and exit interviews suggested that novice participants using the DSS adopted expert strategies but carried out their tasks more slowly and with less precision. This has important consequences for the design of a future DSS used in training simulators or onboard ships. Potential improvements to the DSS design might include real-time feedback to the user, a redesign of the human–machine interface (HMI), and increasing user input and customization with a human factors focus.

S1493 Design of a Clinical Decision Support System for Cirrhosis Management

S1493 Design of a Clinical Decision Support System for Cirrhosis Management

Rational Unified Process as a Software Development Method in Decision Support Systems Design with Simple Additive Weighting in Determining Priority Rehabilitations of Classrooms

Rational Unified Process as a Software Development Method in Decision Support Systems Design with Simple Additive Weighting in Determining Priority Rehabilitations of Classrooms

Selective access to decision support as a function of event uncertainty

The effective design of decision support systems requires an understanding of how system outputs affect users’ preferences and behavior. In an online experiment with 171 participants, we investigated how uncertainty influences users’ tendency to access additional information when using decision support (DS) with varying levels of reliability (high or low) that exhibit the same data in different ways. We compared preferences across three DS designs: binary independent output, likelihood independent output, and integrated likelihood output. We also examined the impact of DS displays and reliability on users’ performance and subjective trust. Users were more likely to seek additional information when presented with events incorporated with uncertain information. Moreover, the presentation of the information had a significant effect on performance and subjective trust.

Toward potential applications of the nitrogen nutrition index in Sweet basil (Ocimum basilicum L.) production under greenhouse conditions

The nitrogen nutrition index (NNI) as an effective indicator for monitoring crop nitrogen (N) status has multiple applications in agriculture, such as estimating in-season crop N requirement (NR), predicting crop biomass/yield, and improving N use efficiency. The present study was aimed to develop basil (Ocimum basilicum L.) NR and relative biomass (RB) prediction models based on the concept of critical N concentration. Two experiments were carried out with different N application rates in the research greenhouse of University of Tehran, Iran. Basil NNI values were calculated using the previously developed critical N dilution curve. Subsequently, the in-season basil NR estimation model was developed as a function of NNI, N recovery efficiency (NRE) and plant age. Additionally, basil RB was simulated as a function of the averaged and integrated NNI based on a linear-plateau model. Moreover, a theoretical relationship between N utilization efficiency (NUtE) and the NNI was established. The validation results of the developed NR-NNI model demonstrated a good performance with a root mean square error (RMSE) and a normalized root mean square error (NRMSE) less than 5.28 kg ha−1 and 4.72%, respectively. The results showed that crop biomass and NNI were the most significant explanatory factors for the variations in basil NUtE. Therefore, the crop NNI can serve as a practical in-season diagnostic tool for basil NR and RB prediction, while also contributing to the design of decision support systems and sustainable agro-ecosystems.

Barriers and Enablers for Implementation of an Artificial Intelligence-Based Decision Support Tool to Reduce the Risk of Readmission of Patients With Heart Failure: Stakeholder Interviews.

Artificial intelligence (AI) applications in health care are expected to provide value for health care organizations, professionals, and patients. However, the implementation of such systems should be carefully planned and organized in order to ensure quality, safety, and acceptance. The gathered view of different stakeholders is a great source of information to understand the barriers and enablers for implementation in a specific context. This study aimed to understand the context and stakeholder perspectives related to the future implementation of a clinical decision support system for predicting readmissions of patients with heart failure. The study was part of a larger project involving model development, interface design, and implementation planning of the system. Interviews were held with 12 stakeholders from the regional and municipal health care organizations to gather their views on the potential effects implementation of such a decision support system could have as well as barriers and enablers for implementation. Data were analyzed based on the categories defined in the nonadoption, abandonment, scale-up, spread, sustainability (NASSS) framework. Stakeholders had in general a positive attitude and curiosity toward AI-based decision support systems, and mentioned several barriers and enablers based on the experiences of previous implementations of information technology systems. Central aspects to consider for the proposed clinical decision support system were design aspects, access to information throughout the care process, and integration into the clinical workflow. The implementation of such a system could lead to a number of effects related to both clinical outcomes as well as resource allocation, which are all important to address in the planning of implementation. Stakeholders saw, however, value in several aspects of implementing such system, emphasizing the increased quality of life for those patients who can avoid being hospitalized. Several ideas were put forward on how the proposed AI system would potentially affect and provide value for patients, professionals, and the organization, and implementation aspects were important parts of that. A successful system can help clinicians to prioritize the need for different types of treatments but also be used for planning purposes within the hospital. However, the system needs not only technological and clinical precision but also a carefully planned implementation process. Such a process should take into consideration the aspects related to all the categories in the NASSS framework. This study further highlighted the importance to study stakeholder needs early in the process of development, design, and implementation of decision support systems, as the data revealed new information on the potential use of the system and the placement of the application in the care process.

The aerodynamic force estimation of a swept-wing UAV using ANFIS based on metaheuristic algorithms

Abstract In this paper, a new approach to modeling and controlling the problems associated with a morphing unmanned aerial vehicle (UAV) is proposed. Within the scope of the study, a dataset was created by obtaining a wide range of aerodynamic parameters for the UAV with Ansys Fluent under variable conditions using the computational fluid dynamics approach. For this, a large dataset was created that considered 5 different angles of attack, 14 different swept angles, and 5 different velocities. While creating the dataset, the analyses were verified by considering studies that have been experimentally validated in the literature. Then, an artificial intelligence-based model was created using the dataset obtained. Metaheuristic algorithms such as the artificial bee colony algorithm, ant colony algorithm and genetic algorithms are used to increase the modeling success of the adaptive neuro-fuzzy inference system (ANFIS) approach. A novel modeling approach is proposed that constitutes a new decision support system for real-time flight. According to the results obtained, all the ANFIS models based on metaheuristic algorithms were more successful than the traditional approach, the multilinear regression model. The swept angle that meets the minimum lift needed by the UAV for different flight conditions was estimated with the help of the designed decision support system. Thus, the drag force is minimised while obtaining the required lift force. The performance of the UAV was compared with the nonmorphing configuration, and the results are presented in tables and graphs.