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Journal articles on the topic 'DECISION METAMODEL'

Author: Grafiati

Published: 11 September 2023

Last updated: 31 July 2025

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1

Soeteman, Djøra I., Stephen C. Resch, Hawre Jalal, et al. "Developing and Validating Metamodels of a Microsimulation Model of Infant HIV Testing and Screening Strategies Used in a Decision Support Tool for Health Policy Makers." MDM Policy & Practice 5, no. 1 (2020): 238146832093289. http://dx.doi.org/10.1177/2381468320932894.

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Background. Metamodels can simplify complex health policy models and yield instantaneous results to inform policy decisions. We investigated the predictive validity of linear regression metamodels used to support a real-time decision-making tool that compares infant HIV testing/screening strategies. Methods. We developed linear regression metamodels of the Cost-Effectiveness of Preventing AIDS Complications Pediatric (CEPAC-P) microsimulation model used to predict life expectancy and lifetime HIV-related costs/person of two infant HIV testing/screening programs in South Africa. Metamodel perfo

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Palza, Edgardo. "FACILITATING MEASUREMENT INDICATORS IN SOFTWARE IMPROVEMENTS PROJECTS." Revista de investigación de Sistemas e Informática 2, no. 2 (2005): 7–15. http://dx.doi.org/10.15381/risi.v2i2.3126.

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This paper reports on the design and development of a Measurement Metamodel to facilitate the Implementation and Monitoring of Software Improvement initiatives. The Indicators provided for the Measurement Metamodel are building on a Data Warehouse environment. The Measurement Metamodel is designed based on the International Standard ISO 15939 – Software Measurement Process [1]. The Measurement Metamodel supports Practical Software Measurement (PSM) [2] approach and Capability Maturity Model Integrated (CMMI) [3] implementations. The Measurement Metamodel is based on the principle that Software

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Bracco, A., J. D. Neelin, H. Luo, J. C. McWilliams, and J. E. Meyerson. "High dimensional decision dilemmas in climate models." Geoscientific Model Development Discussions 6, no. 2 (2013): 2731–67. http://dx.doi.org/10.5194/gmdd-6-2731-2013.

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Abstract. An important source of uncertainty in climate models is linked to the calibration of model parameters. Interest in systematic and automated parameter optimization procedures stems from the desire to improve the model climatology and to quantify the average sensitivity associated with potential changes in the climate system. Neelin et al. (2010) used a quadratic metamodel to objectively calibrate an atmospheric circulation model (AGCM) around four adjustable parameters. The metamodel accurately estimates global spatial averages of common fields of climatic interest, from precipitation

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Bracco, A., J. D. Neelin, H. Luo, J. C. McWilliams, and J. E. Meyerson. "High dimensional decision dilemmas in climate models." Geoscientific Model Development 6, no. 5 (2013): 1673–87. http://dx.doi.org/10.5194/gmd-6-1673-2013.

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Abstract. An important source of uncertainty in climate models is linked to the calibration of model parameters. Interest in systematic and automated parameter optimization procedures stems from the desire to improve the model climatology and to quantify the average sensitivity associated with potential changes in the climate system. Building upon on the smoothness of the response of an atmospheric circulation model (AGCM) to changes of four adjustable parameters, Neelin et al. (2010) used a quadratic metamodel to objectively calibrate the AGCM. The metamodel accurately estimates global spatia

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Cravero, Ania, Dante Álvarez, Samuel Sepúlveda, Maria-Isabel Valdivieso, and Lilia Muñoz. "Meta4CBC: Metamodel for Competency-Based Curriculum Design in Higher Education." Applied Sciences 14, no. 22 (2024): 10110. http://dx.doi.org/10.3390/app142210110.

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Meta4CBC is proposed as an innovative metamodel for competency-based curriculum design in higher education, addressing the critical need to adapt curricula to contemporary challenges driven by globalization, technological advancement, and climate change. Inspired by the Business Motivation Model and Semantics of Business Vocabulary and Business Rules metamodels, Meta4CBC seeks to overcome the limitations of current CBCD models by providing a coherent structure that integrates curriculum components at supra, macro, meso, micro, and nano levels and establishes a common language that facilitates

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Gayathri, Rajakumaran, Shola Usha Rani, Lenka Čepová, Murugesan Rajesh, and Kanak Kalita. "A Comparative Analysis of Machine Learning Models in Prediction of Mortar Compressive Strength." Processes 10, no. 7 (2022): 1387. http://dx.doi.org/10.3390/pr10071387.

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Predicting the mechanical properties of cement-based mortars is essential in understanding the life and functioning of structures. Machine learning (ML) algorithms in this regard can be especially useful in prediction scenarios. In this paper, a comprehensive comparison of nine ML algorithms, i.e., linear regression (LR), random forest regression (RFR), support vector regression (SVR), AdaBoost regression (ABR), multi-layer perceptron (MLP), gradient boosting regression (GBR), decision tree regression (DT), hist gradient boosting regression (hGBR) and XGBoost regression (XGB), is carried out.

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Mahmud, Istiak, Md Mohsin Kabir, M. F. Mridha, Sultan Alfarhood, Mejdl Safran, and Dunren Che. "Cardiac Failure Forecasting Based on Clinical Data Using a Lightweight Machine Learning Metamodel." Diagnostics 13, no. 15 (2023): 2540. http://dx.doi.org/10.3390/diagnostics13152540.

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Accurate prediction of heart failure can help prevent life-threatening situations. Several factors contribute to the risk of heart failure, including underlying heart diseases such as coronary artery disease or heart attack, diabetes, hypertension, obesity, certain medications, and lifestyle habits such as smoking and excessive alcohol intake. Machine learning approaches to predict and detect heart disease hold significant potential for clinical utility but face several challenges in their development and implementation. This research proposes a machine learning metamodel for predicting a pati

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Suljic, Mirza, Edin Osmanbegovic, and Željko Dobrović. "Common Metamodel of Questionnaire Model and Decision Tree Model." Research in Applied Economics 10, no. 3 (2018): 106. http://dx.doi.org/10.5296/rae.v10i3.13540.

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The subject of this paper is metamodeling and its application in the field of scientific research. The main goal is to explore the possibilities of integration of two methods: questionnaires and decision trees. The questionnaire method was established as one of the methods for data collecting, while the decision tree method represents an alternative way of presenting and analyzing decision making situations. These two methods are not completely independent, but on the contrary, there is a strong natural bond between them. Therefore, the result reveals a common meta-model that over common conce

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Meidt, Gregory J., and Kenneth W. Bauer. "PCRSM: A decision support system for simulation metamodel construction." SIMULATION 59, no. 3 (1992): 183–91. http://dx.doi.org/10.1177/003754979205900307.

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Rojas, Luz Andrea Rodríguez, Juan Manuel Cueva Lovelle, Giovanny Mauricio Tarazona Bermúdez, Carlos Enrique Montenegro, Elena Giménez de Ory, and Rubén Arístides González Crespo. "Metamodel to support decision-making from open government data." Journal of Ambient Intelligence and Humanized Computing 9, no. 3 (2017): 553–63. http://dx.doi.org/10.1007/s12652-016-0443-7.

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Shevchenko, Igor, Denis Vasiliev, Nataly Rylova, and Natalia Sharonova. "CONCEPTUAL MODELS OF THE DECISION SUPPORT SYSTEM FOR MANAGING SET OF THE MUNICIPAL SPHERE PROJECTS." Transactions of Kremenchuk Mykhailo Ostrohradskyi National University, no. 3(128) (June 11, 2021): 57–62. http://dx.doi.org/10.30929/1995-0519.2021.3.57-62.

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Purpose. As a rule modern organizational systems in any industry contain many elements. These elements are connected by a complex scheme of relationships. In the municipal sphere, various programs and projects are implemented simultaneously. To increase the efficiency of work with projects, it is necessary to solve operations management problems. It is necessary to coordinate the allocation of resources, assess the quality of implementation of individual stages of each project, take into account the joint use of contractors, assess risks and eliminate problem situations. The purpose of the res

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Idier, Déborah, Axel Aurouet, François Bachoc, et al. "A User-Oriented Local Coastal Flooding Early Warning System Using Metamodelling Techniques." Journal of Marine Science and Engineering 9, no. 11 (2021): 1191. http://dx.doi.org/10.3390/jmse9111191.

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Given recent scientific advances, coastal flooding events can be properly modelled. Nevertheless, such models are computationally expensive (requiring many hours), which prevents their use for forecasting and warning. In addition, there is a gap between the model outputs and information actually needed by decision makers. The present work aims to develop and test a method capable of forecasting coastal flood information adapted to users’ needs. The method must be robust and fast and must integrate the complexity of coastal flood processes. The explored solution relies on metamodels, i.e., math

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Kalita, Kanak, Ranjan Kumar Ghadai, and Ankur Bansod. "Sensitivity Analysis of GFRP Composite Drilling Parameters and Genetic Algorithm-Based Optimisation." International Journal of Applied Metaheuristic Computing 13, no. 1 (2022): 1–17. http://dx.doi.org/10.4018/ijamc.290539.

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In this article, a genetic algorithm (GA) is used for optimizing a metamodel of surface roughness (R_a ) in drilling glass-fibre reinforced plastic (GFRP) composites. A response surface methodology (RSM) based three levels (-1, 0, 1) design of experiments is used for developing the metamodel. Analysis of variance (ANOVA) is undertaken to determine the importance of each process parameter in the developed metamodel. Subsequently, after detailed metamodel adequacy checks, the insignificant terms are dropped to make the established metamodel more rigorous and make accurate predictions. A sensitiv

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Sacha, Krzysztof. "On the Semantics of Architectural Decisions." International Journal of Software Engineering and Knowledge Engineering 26, no. 02 (2016): 333–46. http://dx.doi.org/10.1142/s0218194016500145.

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The architecture of a software system results from decisions made by the developers throughout the software life cycle. Any decision pertaining to software architecture is called an architectural decision. Architectural decision modelling captures the dependencies that exist between the decisions and serves as a foundation for knowledge management and reuse. Several models have been described in the literature, using natural language to explain the basic notions and class diagrams to show relations between them. However, a formal definition of an architectural decision is still missing. This p

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QUIROZ AGUILAR, FRANCISCO, and ROBERTO LEY BORRÁS. "MODELO REUSABLE PARA DECIDIR SOBRE ESTRATEGIAS DE COMERCIALIZACIÓN DE MATERIALES DE PROCESAMIENTO." Investigación Administrativa 31-2 (July 1, 2002): 1–22. https://doi.org/10.35426/iav31n91.05.

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This article presents a reusable decision model for commercialization strategies intended for firms that sell industrial processing materials. The utilization of this reusable decision model gives firms the advantages of formal decision analysis modeling at a fraction of the cost and time of building a decision model from the beginning each time the firm wants to decide on a commercialization strategy. The reusable model includes a master model (an influence diagram that expresses the relationships between the key decisions, uncertainties and parameters) and a metamodel (that guides the analys

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Kaliszewski, I., and D. Podkopaev. "Simple additive weighting—A metamodel for multiple criteria decision analysis methods." Expert Systems with Applications 54 (July 2016): 155–61. http://dx.doi.org/10.1016/j.eswa.2016.01.042.

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Lin, Li, Jeffery K. Cochran, and Joseph Sarkis. "A metamodel-based decision support system for shop floor production control." Computers in Industry 18, no. 2 (1992): 155–68. http://dx.doi.org/10.1016/0166-3615(92)90110-9.

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Di Tria, Francesco, Ezio Lefons, and Filippo Tangorra. "Metadata for Approximate Query Answering Systems." Advances in Software Engineering 2012 (September 3, 2012): 1–13. http://dx.doi.org/10.1155/2012/247592.

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In business intelligence systems, data warehouse metadata management and representation are getting more and more attention by vendors and designers. The standard language for the data warehouse metadata representation is the Common Warehouse Metamodel. However, business intelligence systems include also approximate query answering systems, since these software tools provide fast responses for decision making on the basis of approximate query processing. Currently, the standard meta-model does not allow to represent the metadata needed by approximate query answering systems. In this paper, we

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Kim, Sunghun, Youngjin Park, Seungbeom Yoo, et al. "Optimization of the Uniformity Index Performance in the Selective Catalytic Reduction System Using a Metamodel." Sustainability 15, no. 18 (2023): 13803. http://dx.doi.org/10.3390/su151813803.

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The significance of the selective catalytic reduction system in vehicles increases in line with the high standards of emission control and enhanced selective catalytic reduction efficiency. This study aims to improve the performance of the selective catalytic reduction system through an optimization method using a metamodel. The objective function is defined as the ammonia uniformity index, and the design parameters are defined in relation to the pipe length and mixer related to the chemical reaction of the urea solution. The range of design parameters has been designated by a trial-and-error

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He, Lei, Jian Yao, and Yong Lin Lei. "Air-Combat Decision Modeling Method Based on DSM." Applied Mechanics and Materials 536-537 (April 2014): 416–20. http://dx.doi.org/10.4028/www.scientific.net/amm.536-537.416.

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Air-combat decision modeling in effectiveness simulation has to be concerned with the important feature of decision making, such as complexity, diversity, flexibility. So Several challenges have to be mastered, including: improving the abstract level of modeling, providing friendly modeling language, validating concept model and generated code (or executive model) automatically. In this paper, domain-specific modeling (DSM) method is applied in air-combat decision simulation modeling to cope with those challenges. A graphical and textual domain-specific modeling language (DSML) of air-combat d

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Kaptan, Kubilay. "An Organizational Metamodel for Hospital Emergency Departments." Disaster Medicine and Public Health Preparedness 8, no. 5 (2014): 436–44. http://dx.doi.org/10.1017/dmp.2014.101.

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AbstractI introduce an organizational model describing the response of the hospital emergency department. The hybrid simulation/analytical model (called a “metamodel”) can estimate a hospital’s capacity and dynamic response in real time and incorporate the influence of damage to structural and nonstructural components on the organizational ones. The waiting time is the main parameter of response and is used to evaluate the disaster resilience of health care facilities. Waiting time behavior is described by using a double exponential function and its parameters are calibrated based on simulated

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William, P., Oluwadare Joshua Oyebode, Aman Sharma, Nikhil Garg, Anurag Shrivastava, and ALN Rao. "Integrated Decision Support System for Flood Disaster Management with Sustainable Implementation." IOP Conference Series: Earth and Environmental Science 1285, no. 1 (2024): 012015. http://dx.doi.org/10.1088/1755-1315/1285/1/012015.

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Abstract Emergency management of a flood catastrophe should involve not only an immediate reaction to the flooding but also the pre-flood disaster phase, the flood disaster phase, and the post-flood disaster phase. This is in addition to the immediate response to the flooding. This research investigates whether or not a decision support system (DSS) for flood emergency management is necessary and whether or not it is feasible. Previously, we presented a model for the development of modular disaster management decision support systems. The prior work that we did on the integration of DSS model

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Abd Sahrin, Mohammad Sahrul Akmal, and Mohd Faisal Abdul Khanan. "GEOSPATIAL METAMODEL FOR LANDSLIDE DISASTER MANAGEMENT IN MALAYSIA: CURRENT PRACTICES." Journal of Information System and Technology Management 7, no. 25 (2022): 65–82. http://dx.doi.org/10.35631/jistm.725005.

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Most of time, landslide in Malaysia was triggered by heavy rainfall during monsoon season. Landslides disaster in Malaysia are managed by Public Work Department (JKR) via Slope Engineering Branch (CKC), Department of Mineral and Geosciences (JMG), and Malaysian Space Agency (Agensi Angkasa Malaysia). JKR was critically engaged in slope remediation activities and the establishment of slope management. JMG and MYSA contribution was informing the government areas prone to landslides via landslide mapping. National Institution, expert practices, and researcher in landslide management in Malaysia i

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Rulik, Sebastian, Włodzimierz Wróblewski, and Daniel Frączek. "Metamodel-Based Optimization of the Labyrinth Seal." Archive of Mechanical Engineering 64, no. 1 (2017): 75–91. http://dx.doi.org/10.1515/meceng-2017-0005.

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Abstract The presented paper concerns CFD optimization of the straight-through labyrinth seal with a smooth land. The aim of the process was to reduce the leakage flow through a labyrinth seal with two fins. Due to the complexity of the problem and for the sake of the computation time, a decision was made to modify the standard evolutionary optimization algorithm by adding an approach based on a metamodel. Five basic geometrical parameters of the labyrinth seal were taken into account: the angles of the seal’s two fins, and the fin width, height and pitch. Other parameters were constrained, in

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Menapace, Andrea, Ariele Zanfei, Manuel Herrera, and Bruno Brentan. "Graph Neural Networks for Sensor Placement: A Proof of Concept towards a Digital Twin of Water Distribution Systems." Water 16, no. 13 (2024): 1835. http://dx.doi.org/10.3390/w16131835.

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Urban water management faces new challenges due to the rise of digital solutions and abundant data, leading to the development of data-centric tools for decision-making in global water utilities, with AI technologies poised to become a key trend in the sector. This paper proposes a novel methodology for optimal sensor placement aimed at supporting the creation of a digital twin for water infrastructure. A significant innovation in this study is the creation of a metamodel to estimate pressure at consumption nodes in a water supply system. This metamodel guides the optimal sensor configuration

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Haberlandt, U. "From hydrological modelling to decision support." Advances in Geosciences 27 (August 23, 2010): 11–19. http://dx.doi.org/10.5194/adgeo-27-11-2010.

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Abstract. Decision support for planning and management of water resources needs to consider many target criteria simultaneously like water availability, water quality, flood protection, agriculture, ecology, etc. Hydrologic models provide information about the water balance components and are fundamental for the simulation of ecological processes. Objective of this contribution is to discuss the suitability of classical hydrologic models on one hand and of complex eco-hydrologic models on the other hand to be used as part of decision support systems. The discussion is based on results from two

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Yousefi, Milad, and Moslem Yousefi. "Human resource allocation in an emergency department." Kybernetes 49, no. 3 (2019): 779–96. http://dx.doi.org/10.1108/k-12-2018-0675.

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Purpose The complexity and interdisciplinarity of healthcare industry problems make this industry one of the attention centers of computer-based simulation studies to provide a proper tool for interaction between decision-makers and experts. The purpose of this study is to present a metamodel-based simulation optimization in an emergency department (ED) to allocate human resources in the best way to minimize door to doctor time subject to the problem constraints which are capacity and budget. Design/methodology/approach To obtain the objective of this research, first the data are collected fro

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Chattopadhyay, Ritwika, Partha Protim Das, and Shankar Chakraborty. "Development of a Rough-MABAC-DoE-based Metamodel for Supplier Selection in an Iron and Steel Industry." Operational Research in Engineering Sciences: Theory and Applications 5, no. 1 (2022): 20–40. http://dx.doi.org/10.31181/oresta190222046c.

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In the context of supply chain management, supplier selection can be defined as the process by which organizations score and evaluate a range of alternative suppliers to choose the best possible one who can provide superior quality of raw materials at cheaper rate and lesser lead time. It is a decision making process with multiple trade-offs between various conflicting criteria which in turn helps the organizations identify the suitable suppliers that would establish a robust supply chain assisting in maintaining a competitive edge. The main objective of supplier selection is thus focused on r

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SIMONSSON, MÅRTEN, PONTUS JOHNSON, MATHIAS EKSTEDT, and WALDO ROCHA FLORES. "IT GOVERNANCE DECISION SUPPORT USING THE IT ORGANIZATION MODELING AND ASSESMENT TOOL." International Journal of Innovation and Technology Management 08, no. 02 (2011): 167–89. http://dx.doi.org/10.1142/s0219877011002325.

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This paper describes the information technology (IT) organization modeling and assessment tool (ITOMAT) and how it can be used for IT governance decision making. The ITOMAT consists of an enterprise architecture metamodel that describes IT organizations. Further, ITOMAT contains a Bayesian network for making predictions on how changes to IT organization models will affect the IT governance performance as perceived by business stakeholders. Thorough case studies at 20 different companies have been conducted in order to calibrate the network. Finally, the paper describes a case study where ITOMA

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Lima, Gustavo Meirelles, Bruno Melo Brentan, Daniel Manzi, and Edevar Luvizotto. "Metamodel for nodal pressure estimation at near real-time in water distribution systems using artificial neural networks." Journal of Hydroinformatics 20, no. 2 (2017): 486–96. http://dx.doi.org/10.2166/hydro.2017.036.

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Abstract The development of computational models for analysis of the operation of water supply systems requires the calibration of pipes' roughness, among other parameters. Inadequate values of this parameter can result in inaccurate solutions, compromising the applicability of the model as a decision-making tool. This paper presents a metamodel to estimate the pressure at all nodes of a distribution network based on artificial neural networks (ANNs), using a set of field data obtained from strategically located pressure sensors. This approach aims to increase the available pressure data, redu

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Nishat, Fariha Ahmed, M. F. Mridha, Istiak Mahmud, Meshal Alfarhood, Mejdl Safran, and Dunren Che. "Enhancing Typhoid Fever Diagnosis Based on Clinical Data Using a Lightweight Machine Learning Metamodel." Diagnostics 15, no. 5 (2025): 562. https://doi.org/10.3390/diagnostics15050562.

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Background: Typhoid fever remains a significant public health challenge, especially in developing countries where diagnostic resources are limited. Accurate and timely diagnosis is crucial for effective treatment and disease containment. Traditional diagnostic methods, while effective, can be time-consuming and resource-intensive. This study aims to develop a lightweight machine learning-based diagnostic tool for the early and efficient detection of typhoid fever using clinical data. Methods: A custom dataset comprising 14 clinical and demographic parameters—including age, gender, headache, mu

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Gilliams, S., J. Van Orshoven, B. Muys, H. Kros, G. W. Heil, and W. Van Deursen. "AFFOREST sDSS: a metamodel based spatial decision support system for afforestation of agricultural land." New Forests 30, no. 1 (2005): 33–53. http://dx.doi.org/10.1007/s11056-004-0761-z.

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García-Segura, Tatiana, Vicent Penadés-Plà, and Víctor Yepes. "Sustainable bridge design by metamodel-assisted multi-objective optimization and decision-making under uncertainty." Journal of Cleaner Production 202 (November 2018): 904–15. http://dx.doi.org/10.1016/j.jclepro.2018.08.177.

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Doroshenko, Olga V., Alex G. Kuchumov, Mikhail V. Golub, et al. "Investigation of Relationship between Hemodynamic and Morphometric Characteristics of Aortas in Pediatric Patients." Journal of Clinical Medicine 13, no. 17 (2024): 5141. http://dx.doi.org/10.3390/jcm13175141.

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Background: The utilization of hemodynamic parameters, whose estimation is often cumbersome, can fasten diagnostics and decision-making related to congenital heart diseases. The main goal of this study is to investigate the relationship between hemodynamic and morphometric features of the thoracic aorta and to construct corresponding predictive models. Methods: Multi-slice spiral computed tomography images of the aortas of patients with coarctation diagnoses and patients without cardiac or vascular diseases were evaluated to obtain numerical models of the aorta and branches of the aortic arch.

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Walter, Maximilian, Sebastian Hahner, Tomáš Bureš, Petr Hnětynka, Robert Heinrich, and Ralf Reussner. "Architecture-based attack propagation and variation analysis for identifying confidentiality issues in Industry 4.0." at - Automatisierungstechnik 71, no. 6 (2023): 443–52. http://dx.doi.org/10.1515/auto-2022-0135.

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Abstract Exchanging data between entities is an essential part of Industry 4.0. However, the data exchange should not affect the confidentiality. Therefore, data should only be shared with the intended entities. In exceptional scenarios, it is unclear whether data should be shared or not and what the impact of the access decision is. Runtime access control systems such as role-based access control often do not consider the impact on the overall confidentiality. Static design-time analyses often provide this information. We use architectural design-time analyses together with an uncertainty var

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Mauro, Francesco, Luca Braidotti, and Giorgio Trincas. "A Model for Intact and Damage Stability Evaluation of CNG Ships during the Concept Design Stage." Journal of Marine Science and Engineering 7, no. 12 (2019): 450. http://dx.doi.org/10.3390/jmse7120450.

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To face the design of a new ship concept, the evaluation of multiple feasible solutions concerning several aspects of naval architecture and marine engineering is necessary. Compressed natural gas technologies are in continuous development; therefore, there are no available databases for existing ships to use as a basis for the design process of a new unit. In this sense, the adoption of a modern multi-attribute decision-based method can help the designer for the study of a completely new ship prototype. A database of compressed natural gas ships was generated starting from a baseline hull, va

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Gumienny, Grzegorz, Barbara Kacprzyk, Barbara Mrzygłód, and Krzysztof Regulski. "Data-Driven Model Selection for Compacted Graphite Iron Microstructure Prediction." Coatings 12, no. 11 (2022): 1676. http://dx.doi.org/10.3390/coatings12111676.

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Compacted graphite iron (CGI), having a specific graphite form with a large matrix contact surface, is a unique casting material. This type of cast iron tends to favor direct ferritization and is characterized by a complex of very interesting properties. Intelligent computing tools such as artificial neural networks (ANNs) are used as predictive modeling tools, allowing their users to forecast the microstructure of the tested cast iron at the level of computer simulation. This paper presents the process of the development of a metamodel for the selection of a neural network appropriate for a s

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Kaz, M. S., and E. A. Akerman. "The Method of Real Options and the Business Model «Lean Canvas» in the Practice of Performance Evaluation of it Projects." Vestnik NSUEM, no. 4 (January 1, 2022): 80–92. http://dx.doi.org/10.34020/2073-6495-2021-4-080-092.

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The relevance of the study is due to the active implementation of IT technologies in various aspects of companies, which gives special importance to the development of a methodology for assessing the effectiveness of projects in a highly uncertain environment. The paper presents the methodology and assesses the effectiveness of IT projects using binomial «decision tree» model and iterative risk assessment metamodel «Lean Canvas». The comparative assessment of IT project efficiency using discounted cash flow method, binomial «decision tree» model and Black–Scholes model was carried out. The res

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Gomolka, Zbigniew, Ewa Dudek-Dyduch, and Ewa Zeslawska. "Generalization of ALMM Based Learning Method for Planning and Scheduling." Applied Sciences 12, no. 24 (2022): 12766. http://dx.doi.org/10.3390/app122412766.

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This paper refers to a machine learning method for solving NP-hard discrete optimization problems, especially planning and scheduling. The method utilizes a special multistage decision process modeling paradigm referred to as the Algebraic Logical Metamodel based learning methods of Multistage Decision Processes (ALMM). Hence, the name of the presented method is the ALMM Based Learning method. This learning method utilizes a specifically built local multicriterion optimization problem that is solved by means of scalarization. This paper describes both the development of such local optimization

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Jiang, Tao, and Weihong Zhou. "An Approach of Defining Domain Constraints for Domain-Specific Modeling Language." International Journal of Pattern Recognition and Artificial Intelligence 35, no. 09 (2021): 2153002. http://dx.doi.org/10.1142/s0218001421530025.

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Many Domain-Specific Modeling Languages (DSML) cannot formally define their semantics, leading to difficulties in identifying user-defined domain constraints. In this study, we propose a user-defined mechanism of domain constraints based on the formalization of structural semantics of DSML. First, we formally define concepts and decision methods of consistency and validity of domain constraints. Subsequently, we establish concepts and reasoning methods of domain-based model consistency. Thus, several domain constraint instances are defined and different models instances’ consistency are reason

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Nguyen, Duc Nam, Thanh-Phong Dao, Ngoc Le Chau, and Van Anh Dang. "Hybrid Approach of Finite Element Method, Kigring Metamodel, and Multiobjective Genetic Algorithm for Computational Optimization of a Flexure Elbow Joint for Upper-Limb Assistive Device." Complexity 2019 (January 27, 2019): 1–13. http://dx.doi.org/10.1155/2019/3231914.

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Modeling for robotic joints is actually complex and may lead to wrong Pareto-optimal solutions. Hence, this paper develops a new hybrid approach for multiobjective optimization design of a flexure elbow joint. The joint is designed for the upper-limb assistive device for physically disable people. The optimization problem considers three design variables and two objective functions. An efficient hybrid optimization approach of central composite design (CDD), finite element method (FEM), Kigring metamodel, and multiobjective genetic algorithm (MOGA) is developed. The CDD is used to establish th

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Chacón, Lorena, Miguel Chen Austin, and Carmen Castaño. "A Multiobjective Optimization Approach for Retrofitting Decision-Making towards Achieving Net-Zero Energy Districts: A Numerical Case Study in a Tropical Climate." Smart Cities 5, no. 2 (2022): 405–32. http://dx.doi.org/10.3390/smartcities5020023.

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Buildings are among the main reasons for the deterioration of the world environment as they are responsible for a large percentage of CO2 emissions related to energy. For this reason, it is necessary to find solutions to this problem. This research project consists of constructing the metamodel of an urbanization located in Panama, Herrera province. The classification and systematization of its main elements, using the software DesignBuilder and SysML diagrams, were carried out for its subsequent implementation in an optimization analysis that seeks to approach the NZED standard. The main obje

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Vovchok, Ivan. "Mathematical models of individualised learning based on decision theory." Information Technology and Computer Engineering 21, no. 3 (2024): 96–107. https://doi.org/10.63341/vitce/3.2024.96.

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The study provided theoretical substantiation and development of a system of mathematical models for the individualisation of the educational process based on the integration of decision theory methods. The developed system of mathematical models is based on a metamodel that combines four mathematical paradigms through an interaction matrix, the elements of which are determined by the function of cognitive compatibility, temporal consistency and interaction efficiency. The introduction of the method of optimising partial trajectories, based on recursive updating of model parameters through the

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Blanco, Carlos, Guzmán de, Eduardo Fernández-Medina, and Juan Trujillo. "An MDA approach for developing secure OLAP applications: Metamodels and transformations." Computer Science and Information Systems 12, no. 2 (2015): 541–65. http://dx.doi.org/10.2298/csis140617007b.

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Decision makers query enterprise information stored in Data Warehouses (DW) by using tools (such as On-Line Analytical Processing (OLAP) tools) which employ specific views or cubes from the corporate DW or Data Marts, based on multidimensional modelling. Since the information managed is critical, security constraints have to be correctly established in order to avoid unauthorized access. In previous work we defined a Model-Driven based approach for developing a secure DW repository by following a relational approach. Nevertheless, it is also important to define security constraints in the meta

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Geyer, Philipp, and Arno Schlüter. "Automated metamodel generation for Design Space Exploration and decision-making – A novel method supporting performance-oriented building design and retrofitting." Applied Energy 119 (April 2014): 537–56. http://dx.doi.org/10.1016/j.apenergy.2013.12.064.

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Hariri-Ardebili, Mohammad Amin, S. Mahdi Seyed-Kolbadi, and Mohammad Noori. "Response Surface Method for Material Uncertainty Quantification of Infrastructures." Shock and Vibration 2018 (July 5, 2018): 1–14. http://dx.doi.org/10.1155/2018/1784203.

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Recently, probabilistic simulations became an inseparable part of risk analysis. Managers and stakeholders prefer to make their decision knowing the existing uncertainties in the system. Nonlinear dynamic analysis and design of infrastructures are affected by two main uncertainty sources, i.e., epistemic and aleatory. In the present paper, the epistemic uncertainty is addressed in the context of material randomness. An old ultra-high arch dam is selected as a vehicle for numerical analyses. Four material properties are selected as random variables in the coupled dam-reservoir-foundation system

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Ekstedt, Mathias, Zeeshan Afzal, Preetam Mukherjee, Simon Hacks, and Robert Lagerström. "Yet Another Cybersecurity Risk Assessment Framework." International Journal of Comparative Studies in International Relations and Development 10, no. 1 (2024): 84–93. http://dx.doi.org/10.48028/iiprds/ijcsird.v10.i1.07.

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IT systems pervade our society more and more, and we become heavily dependent on them. At the same time, these systems are increasingly targeted in cyberattacks, making us vulnerable. Enterprise and cybersecurity responsible face the problem of defining techniques that raise the level of security. They need to decide which mechanism provides the most efficient defense with limited resources. Basically, the risks need to be assessed to determine the best cost-to-benefit ratio. One way to achieve this is through threat modeling; however, threat modeling is not commonly used in the enterprise IT

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Dintén, Ricardo, and Marta Zorrilla. "Design, Building and Deployment of Smart Applications for Anomaly Detection and Failure Prediction in Industrial Use Cases." Information 15, no. 9 (2024): 557. http://dx.doi.org/10.3390/info15090557.

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This paper presents a comparative analysis of deep learning techniques for anomaly detection and failure prediction. We explore various deep learning architectures on an IoT dataset, including recurrent neural networks (RNNs, LSTMs and GRUs), convolutional neural networks (CNNs) and transformers, to assess their effectiveness in anomaly detection and failure prediction. It was found that the hybrid transformer-GRU configuration delivers the highest accuracy, albeit at the cost of requiring the longest computational time for training. Furthermore, we employ explainability techniques to elucidat

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Zhang, Haochuan, Jie Han, Xiaojun Zhou, and Yuxuan Zheng. "Robust Optimization with Interval Uncertainties Using Hybrid State Transition Algorithm." Electronics 12, no. 14 (2023): 3035. http://dx.doi.org/10.3390/electronics12143035.

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Robust optimization is concerned with finding an optimal solution that is insensitive to uncertainties and has been widely used in solving real-world optimization problems. However, most robust optimization methods suffer from high computational costs and poor convergence. To alleviate the above problems, an improved robust optimization algorithm is proposed. First, to reduce the computational cost, the second-order Taylor series surrogate model is used to approximate the robustness indices. Second, to strengthen the convergence, the state transition algorithm is studied to explore the whole s

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Al-Kazee, Mohamed Faisal, Samand Negin Taji, Tahereh Nasr, Reza Mansoori, and Mohammadjavad Mahdavinejad. "Reframing the early-stage design process of residential buildings based on an energy-efficient, designerly decision support system (DDSS)." Future Technology 4, no. 2 (2025): 30–40. https://doi.org/10.55670/fpll.futech.4.2.4.

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The literature emphasizes the role of the early-stage design process, particularly early design decisions related to mid-rise residential buildings. On the other hand, the futuristic concepts of high-performance architecture represent a paradigm shift that requires a data-conscious approach to climate change mitigation. This research adopts a designer approach to address the complex and ill-defined sci-tech problems within the architectural field. The study aims to develop a framework for a user-friendly, data-driven Designerly Decision Support System (DDSS) to categorize and automate the arch

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