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Journal articles on the topic 'Meta Models'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Wheeler, Gregory. "Models, Models, and Models." Metaphilosophy 44, no. 3 (2013): 293–300. http://dx.doi.org/10.1111/meta.12036.

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Ehrig, Karsten, Jochen Malte Küster, and Gabriele Taentzer. "Generating instance models from meta models." Software & Systems Modeling 8, no. 4 (2008): 479–500. http://dx.doi.org/10.1007/s10270-008-0095-y.

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3

Shenton, Andrew K., and Naomi V. Hay‐Gibson. "Information behaviour meta‐models." Library Review 61, no. 2 (2012): 92–109. http://dx.doi.org/10.1108/00242531211220735.

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4

Elfaham, Haitham, and Ulrich Epple. "Meta models for intralogistics." at - Automatisierungstechnik 68, no. 3 (2020): 208–21. http://dx.doi.org/10.1515/auto-2019-0083.

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AbstractIn industrial automation, adaptability is a key feature that can enhance the degree of autonomy in a plant sparing engineering months and costs. With the introduction of the service oriented architecture in control automation, a topology model is required to identify the vicinity relationships between the devices. Nowadays, due to the absence of the coupling between logistic aspects and control logic, the topology models are manually constructed which consequently affects the autonomy of the procedures generation. In this contribution, we introduce a concept to couple the logistics mod

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Conn, Vicki S. "Meta-Analysis for Models." Western Journal of Nursing Research 37, no. 4 (2015): 423–24. http://dx.doi.org/10.1177/0193945914563305.

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6

Babenyshev, S., and V. Rybakov. "Logics of Kripke meta-models." Logic Journal of IGPL 18, no. 6 (2009): 823–36. http://dx.doi.org/10.1093/jigpal/jzp047.

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7

Ma, Han, Baoyu Fan, Benjamin K. Ng, and Chan-Tong Lam. "VL-Meta: Vision-Language Models for Multimodal Meta-Learning." Mathematics 12, no. 2 (2024): 286. http://dx.doi.org/10.3390/math12020286.

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Multimodal learning is a promising area in artificial intelligence (AI) that can make the model understand different kinds of data. Existing works are trying to re-train a new model based on pre-trained models that requires much data, computation power, and time. However, it is difficult to achieve in low-resource or small-sample situations. Therefore, we propose VL-Meta, Vision Language Models for Multimodal Meta Learning. It (1) presents the vision-language mapper and multimodal fusion mapper, which are light model structures, to use the existing pre-trained models to make models understand

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Patterson, Eann. "On the credibility of engineering models and meta-models." Journal of Strain Analysis for Engineering Design 50, no. 4 (2015): 218–20. http://dx.doi.org/10.1177/0309324715577930.

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Hedges, Larry V. "Meta-Analysis." Journal of Educational Statistics 17, no. 4 (1992): 279–96. http://dx.doi.org/10.3102/10769986017004279.

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The use of statistical methods to combine the results of independent empirical research studies (meta-analysis) has a long history. Meta-analytic work can be divided into two traditions: tests of the statistical significance of combined results and methods for combining estimates across studies. The principal classes of combined significance tests are reviewed, and the limitations of these tests are discussed. Fixed effects approaches treat the effect magnitude parameters to be estimated as a consequence of a model involving fixed but unknown constants. Random effects approaches treat effect m

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Sheu, Ching-Fan, and Sawako Suzuki. "Meta-analysis using linear mixed models." Behavior Research Methods, Instruments, & Computers 33, no. 2 (2001): 102–7. http://dx.doi.org/10.3758/bf03195354.

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Bruns, Stephan B. "Meta-Regression Models and Observational Research." Oxford Bulletin of Economics and Statistics 79, no. 5 (2017): 637–53. http://dx.doi.org/10.1111/obes.12172.

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Gross, Thilo, Korinna T. Allhoff, Bernd Blasius, et al. "Modern models of trophic meta-communities." Philosophical Transactions of the Royal Society B: Biological Sciences 375, no. 1814 (2020): 20190455. http://dx.doi.org/10.1098/rstb.2019.0455.

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Dispersal and foodweb dynamics have long been studied in separate models. However, over the past decades, it has become abundantly clear that there are intricate interactions between local dynamics and spatial patterns. Trophic meta-communities, i.e. meta-foodwebs, are very complex systems that exhibit complex and often counterintuitive dynamics. Over the past decade, a broad range of modelling approaches have been used to study these systems. In this paper, we review these approaches and the insights that they have revealed. We focus particularly on recent papers that study trophic interactio

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13

Gallegos, Arnold. "Meta-evaluation of school evaluation models." Studies in Educational Evaluation 20, no. 1 (1994): 41–54. http://dx.doi.org/10.1016/s0191-491x(00)80004-8.

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14

Harald, Baumeister, and Parker Gordon. "Meta-review of depressive subtyping models." Journal of Affective Disorders 139, no. 2 (2012): 126–40. http://dx.doi.org/10.1016/j.jad.2011.07.015.

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15

Allen, Theodore T., Mikhail A. Bernshteyn, and Khalil Kabiri-Bamoradian. "Constructing Meta-Models for Computer Experiments." Journal of Quality Technology 35, no. 3 (2003): 264–74. http://dx.doi.org/10.1080/00224065.2003.11980220.

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16

Dumouchel, William. "Meta-analysis for dose—response models." Statistics in Medicine 14, no. 5-7 (1995): 679–85. http://dx.doi.org/10.1002/sim.4780140524.

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Han, Xinyu, Xinya Wang, Xiaotong Chen, et al. "Intervention Efficacy of Slightly Processed Allergen/Meat in Oral Immunotherapy for Seafood Allergy: A Systematic Review, Meta-Analysis, and Meta-Regression Analysis in Mouse Models and Clinical Patients." Nutrients 16, no. 5 (2024): 667. http://dx.doi.org/10.3390/nu16050667.

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Background: Seafood allergy is a significant global health concern that greatly impacts a patient’s quality of life. The intervention efficacy of oral immunotherapy (OIT), an emerging intervention strategy, for seafood allergy remains controversial. This study aimed to perform a systematic review and meta-analysis to evaluate the efficacy of slightly processed allergen/meat from fish and crustacea in OIT, both in mouse models and clinical patients. Methods: A comprehensive literature search was performed in four mainstream databases and the EBSCOhost database to identify all relevant case–cont

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18

Forgione, Marco, Filippo Pura, and Dario PIGA. "From System Models to Class Models: An In-Context Learning Paradigm." IEEE Control Systems Letters 7 (November 21, 2023): 3513–18. https://doi.org/10.1109/LCSYS.2023.3335036.

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Is it possible to understand the intricacies of a dynamical system not solely from its input/output pattern, but also by observing the behavior of other systems within the same class? This central question drives the study presented in this letter. In response to this query, we introduce a novel paradigm for system identification, addressing two primary tasks: one-step-ahead prediction and multi-step simulation. Unlike conventional methods, we do not directly estimate a model for the specific system. Instead, we learn a meta model that represents a class of dynamical systems. This meta model i

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Ehrig, Hartmut, Karsten Ehrig, Claudia Ermel, and Ulrike Prange. "Consistent integration of models based on views of meta models." Formal Aspects of Computing 22, no. 3 (2009): 327–44. http://dx.doi.org/10.1007/s00165-009-0127-6.

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20

Bonissone, Piero P., Feng Xue, and Raj Subbu. "Fast meta-models for local fusion of multiple predictive models." Applied Soft Computing 11, no. 2 (2011): 1529–39. http://dx.doi.org/10.1016/j.asoc.2008.03.006.

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21

Palmer, Kent D. "9.3.2 Vajra Logic and Mathematical Meta-models for Meta-systems Engineering." INCOSE International Symposium 12, no. 1 (2002): 475–86. http://dx.doi.org/10.1002/j.2334-5837.2002.tb02499.x.

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Madden, L. V., H. P. Piepho, and P. A. Paul. "Statistical Models and Methods for Network Meta-Analysis." Phytopathology® 106, no. 8 (2016): 792–806. http://dx.doi.org/10.1094/phyto-12-15-0342-rvw.

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Meta-analysis, the methodology for analyzing the results from multiple independent studies, has grown tremendously in popularity over the last four decades. Although most meta-analyses involve a single effect size (summary result, such as a treatment difference) from each study, there are often multiple treatments of interest across the network of studies in the analysis. Multi-treatment (or network) meta-analysis can be used for simultaneously analyzing the results from all the treatments. However, the methodology is considerably more complicated than for the analysis of a single effect size,

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Alicea, B., and J. Parent. "Meta-brain Models: biologically-inspired cognitive agents." IOP Conference Series: Materials Science and Engineering 1261, no. 1 (2022): 012019. http://dx.doi.org/10.1088/1757-899x/1261/1/012019.

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Abstract Artificial Intelligence (AI) systems based solely on neural networks or symbolic computation present a representational complexity challenge. While minimal representations can produce behavioral outputs like locomotion or simple decision-making, more elaborate internal representations might offer a richer variety of behaviors. We propose that these issues can be addressed with a computational approach we call meta-brain models. Meta-brain models are embodied hybrid models that include layered components featuring varying degrees of representational complexity. We will propose combinat

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Riley, Richard D., Joie Ensor, Dan Jackson, and Danielle L. Burke. "Deriving percentage study weights in multi-parameter meta-analysis models: with application to meta-regression, network meta-analysis and one-stage individual participant data models." Statistical Methods in Medical Research 27, no. 10 (2017): 2885–905. http://dx.doi.org/10.1177/0962280216688033.

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Many meta-analysis models contain multiple parameters, for example due to multiple outcomes, multiple treatments or multiple regression coefficients. In particular, meta-regression models may contain multiple study-level covariates, and one-stage individual participant data meta-analysis models may contain multiple patient-level covariates and interactions. Here, we propose how to derive percentage study weights for such situations, in order to reveal the (otherwise hidden) contribution of each study toward the parameter estimates of interest. We assume that studies are independent, and utilis

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25

Copas, J. "What works?: selectivity models and meta‐analysis." Journal of the Royal Statistical Society: Series A (Statistics in Society) 162, no. 1 (1999): 95–109. http://dx.doi.org/10.1111/1467-985x.00123.

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26

Snowdon, Bob, and Peter Kawalek. "Active meta-process models: a conceptual exposition." Information and Software Technology 45, no. 15 (2003): 1021–29. http://dx.doi.org/10.1016/s0950-5849(03)00130-7.

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27

Ristic, Sonja, Slavica Aleksic, Milan Celikovic, and Ivan Lukovic. "Generic and standard database constraint meta-models." Computer Science and Information Systems 11, no. 2 (2014): 679–96. http://dx.doi.org/10.2298/csis140216037r.

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Many software engineering activities entail dealing with legacy information systems. When these systems become too costly to maintain, or when new technologies need to be incorporated, they need to be replaced or somehow reengineered. This can be done with significantly reduced amount of effort and cost if the conceptual models of these systems are available. Reverse engineering is the process of analyzing a subject system to create representations of the system at a higher level of abstraction. Relational databases are a common source of reverse engineering. Starting from a physical database

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28

Thiry, Laurent, and Michel Hassenforder. "A Calculus for (Meta)Models and Transformations." International Journal of Software Engineering and Knowledge Engineering 24, no. 05 (2014): 715–30. http://dx.doi.org/10.1142/s0218194014500272.

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This paper proposes a formal representation of modeling languages based on category theory. These languages are generally described by "metamodels", i.e. structures composed by classes and relations, and related by "transformations". Thus, this paper studies how the key categorical concepts such as functors and relations between functors (called natural transformations) can be used for equational reasoning about modeling artifacts (models, metamodels, transformations). As a result, this paper proposes a formal point of view of models usable to specify/prove equivalence between models or transf

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29

Cheung, Mike W. L. "Multivariate Meta-Analysis as Structural Equation Models." Structural Equation Modeling: A Multidisciplinary Journal 20, no. 3 (2013): 429–54. http://dx.doi.org/10.1080/10705511.2013.797827.

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30

Eusebi, P., J. B. Reitsma, and J. K. Vermunt. "On mixture models for diagnostic meta-analyses." Journal of Clinical Epidemiology 68, no. 12 (2015): 1523. http://dx.doi.org/10.1016/j.jclinepi.2015.01.020.

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31

Platt, Robert W., Brian G. Leroux, and Norman Breslow. "Generalized linear mixed models for meta-analysis." Statistics in Medicine 18, no. 6 (1999): 643–54. http://dx.doi.org/10.1002/(sici)1097-0258(19990330)18:6<643::aid-sim76>3.0.co;2-m.

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32

Simmonds, Mark C., and Julian PT Higgins. "A general framework for the use of logistic regression models in meta-analysis." Statistical Methods in Medical Research 25, no. 6 (2016): 2858–77. http://dx.doi.org/10.1177/0962280214534409.

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Where individual participant data are available for every randomised trial in a meta-analysis of dichotomous event outcomes, “one-stage” random-effects logistic regression models have been proposed as a way to analyse these data. Such models can also be used even when individual participant data are not available and we have only summary contingency table data. One benefit of this one-stage regression model over conventional meta-analysis methods is that it maximises the correct binomial likelihood for the data and so does not require the common assumption that effect estimates are normally di

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33

Eriksson, Henrik, and Mark A. Musen. "Conceptual models for automatic generation of knowledge-acquisition tools." Knowledge Engineering Review 8, no. 1 (1993): 27–47. http://dx.doi.org/10.1017/s0269888900000059.

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AbstractInteractive knowledge-acquisition (KA) programs allow users to enter relevant domain knowledge according to a model predefined by the tool developers. KA tools are designed to provide conceptual models of the knowledge to their users. Many different classes of models are possible, resulting in different categories of tools. Whenever it is possible to describe KA tools according to explicit conceptual models, it is also possible to edit the models and to instantiate new KA tools automatically for specialized purposes. Several meta-tools that address this task have been implemented. Meta

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34

Kim, Ye-Jung, and Ji-Young Lim. "Predictive Models for Nurses’ Entrepreneurial Intentions Using Comparison of Competing Models." International Journal of Environmental Research and Public Health 19, no. 10 (2022): 6027. http://dx.doi.org/10.3390/ijerph19106027.

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There is a need to introduce predictive models of nurses’ entrepreneurial intentions that can identify which variables will promote entrepreneurship among nurses. This study aimed to explore the factors influencing nurses’ entrepreneurial intentions. We performed a systematic review and developed prediction models using factors identified and validated in a meta-analysis. Moreover, we individually tested and compared three models based on: (1) the Theory of Planned Behavior, (2) a meta-analysis, and (3) a combination of the two. Data from 386 nurses were analyzed using SPSS 23.0 for Windows an

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Ventura Martins, Paula, and Marielba Zacarias. "Applying the Business Process and Practice Alignment Meta-model: Daily Practices and Process Modelling." Business Systems Research Journal 8, no. 1 (2017): 1–16. http://dx.doi.org/10.1515/bsrj-2017-0001.

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AbstractBackground: Business Process Modelling (BPM) is one of the most important phases of information system design. Business Process (BP) meta-models allow capturing informational and behavioural aspects of business processes. Unfortunately, standard BP meta-modelling approaches focus just on process description, providing different BP models. It is not possible to compare and identify related daily practices in order to improve BP models. This lack of information implies that further research in BP meta-models is needed to reflect the evolution/change in BP. Considering this limitation, th

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de-Miguel, Sergio, Lauri Mehtätalo, and Ali Durkaya. "Developing generalized, calibratable, mixed-effects meta-models for large-scale biomass prediction." Canadian Journal of Forest Research 44, no. 6 (2014): 648–56. http://dx.doi.org/10.1139/cjfr-2013-0385.

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Large-scale prediction of forest biomass is of interest for forest science, ecology, and issues related to climate change. Previous research has attempted to provide allometric models suitable for large-scale biomass prediction using different methods. We present a new approach for meta-analysis of existing biomass equations using mixed-effects modelling on simulated data. The resulting generalized meta-models can be calibrated for local conditions. This meta-analytical approach allows for directly benefiting from previous research to minimize data collection and properly take into account the

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Siau, Keng, and Yuan Long. "Synthesizing e‐government stage models – a meta‐synthesis based on meta‐ethnography approach." Industrial Management & Data Systems 105, no. 4 (2005): 443–58. http://dx.doi.org/10.1108/02635570510592352.

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Nikolakopoulou, Adriani, Dimitris Mavridis, and Georgia Salanti. "How to interpret meta-analysis models: fixed effect and random effects meta-analyses." Evidence Based Mental Health 17, no. 2 (2014): 64. http://dx.doi.org/10.1136/eb-2014-101794.

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Ramachandra Rao, Rakesh Rao, Steve Göring, and Alexander Raake. "Enhancement of Pixel-based Video Quality Models using Meta-data." Electronic Imaging 2021, no. 9 (2021): 264–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.9.iqsp-264.

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Current state-of-the-art pixel-based video quality models for 4K resolution do not have access to explicit meta information such as resolution and framerate and may not include implicit or explicit features that model the related effects on perceived video quality. In this paper, we propose a meta concept to extend state-of-the-art pixel-based models and develop hybrid models incorporating meta-data such as framerate and resolution. Our general approach uses machine learning to incooperate the meta-data to the overall video quality prediction. To this aim, in our study, we evaluate various mac

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Hong, Seungpyo, Dongseok Shin, and Euysik Jeon. "Inverse Approach of Parameter Optimization for Nonlinear Meta-Model Using Finite Element Simulation." Applied Sciences 11, no. 24 (2021): 12026. http://dx.doi.org/10.3390/app112412026.

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Accurate and efficient estimation and prediction of the nonlinear behavior of materials during plastic working is a major issue in academic and industrial settings. Studies on property meta-models are being conducted to estimate and predict plastic working results. However, accurately representing strong nonlinear properties using power-law and exponential models, which are typical meta-models, is difficult. The combination meta-model can be used to solve this problem, but the possible number of parameters increases. This causes a cost problem when using FE simulation. In this study, the accur

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41

Mirfenderesgi, Golnazalsadat, and S. Jamshid Mousavi. "Adaptive meta-modeling-based simulation optimization in basin-scale optimum water allocation: a comparative analysis of meta-models." Journal of Hydroinformatics 18, no. 3 (2015): 446–65. http://dx.doi.org/10.2166/hydro.2015.157.

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Incorporating river basin simulation models in heuristic optimization algorithms can help modelers address complex, basin-scale water resource problems. We have developed a hybrid optimization-simulation model by linking a stretching particle swarm optimization (SPSO) algorithm and the MODSIM river basin decision support system (DSS), and have used the SPSO-MODSIM model to optimize water allocation at basin scale. Due to high computational cost of the SPSO-MODSIM model, we have, subsequently, used four meta-model types of artificial neural networks (ANN), support vector machines (SVM), kriging

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Ibrahim, Joseph G., Sungduk Kim, Ming-Hui Chen, Arvind K. Shah, and Jianxin Lin. "Bayesian multivariate skew meta-regression models for individual patient data." Statistical Methods in Medical Research 28, no. 10-11 (2018): 3415–36. http://dx.doi.org/10.1177/0962280218801147.

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We examine a class of multivariate meta-regression models in the presence of individual patient data. The methodology is well motivated from several studies of cholesterol-lowering drugs where the goal is to jointly analyze the multivariate outcomes, low density lipoprotein cholesterol, high density lipoprotein cholesterol, and triglycerides. These three continuous outcome measures are correlated and shed much light on a subject's lipid status. One of the main goals in lipid research is the joint analysis of these three outcome measures in a meta-regression setting. Since these outcome measure

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43

Negrín-Hernández, Miguel-Angel, María Martel-Escobar, and Francisco-José Vázquez-Polo. "Bayesian Meta-Analysis for Binary Data and Prior Distribution on Models." International Journal of Environmental Research and Public Health 18, no. 2 (2021): 809. http://dx.doi.org/10.3390/ijerph18020809.

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In meta-analysis, the structure of the between-sample heterogeneity plays a crucial role in estimating the meta-parameter. A Bayesian meta-analysis for binary data has recently been proposed that measures this heterogeneity by clustering the samples and then determining the posterior probability of the cluster models through model selection. The meta-parameter is then estimated using Bayesian model averaging techniques. Although an objective Bayesian meta-analysis is proposed for each type of heterogeneity, we concentrate the attention of this paper on priors over the models. We consider four

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BATOUTA Zouhair, IBN, HAJOUI Omar, and TALEA Mohamed. "Automatic Lexical Alignment between Relational Database and Heterogeneous Big Data Based on NOSQL Systems." International Journal of Engineering & Technology 7, no. 4.32 (2018): 1–6. http://dx.doi.org/10.14419/ijet.v7i4.32.23234.

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In the absence of a universal consensus governing the use of models, the IDM has led to the creation of a large number of heterogeneous (distinct) meta-model systems with similar or complementary uses and objectives. To solve this problem of increasing heterogeneity of meta-models, we proposed an approach that we named generative automatic matching GAM. In this approach, we have dealt with the problem of the heterogeneity of meta-models in a new way that uses automatic matching, the alignments found are then took into profit to facilitate generation between source and target models which are c

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Randall, Laura, and Sally Ubnoske. "Manuscript Exchange Common Approach and Journal Article Tag Suite (JATS) compatibility: a case study utilizing the JATS Compatibility Meta Model." Science Editing 8, no. 1 (2021): 93–97. http://dx.doi.org/10.6087/kcse.235.

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The National Information Standards Organization (NISO) Manuscript Exchange Common Approach (MECA) project is a cross-organization industry initiative to develop a common approach to manuscript transfer that can be adopted across the scholarly publishing industry. MECA establishes a vocabulary set that includes transfer, review, and manifest models. These models are designed to work with different article XML schemas, including the latest NISO Journal Article Tag Suite (JATS) standard (v1.2). In order to avoid conflicts between these project vocabularies and the JATS, we reviewed the MECA vocab

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Arcelli Fontana, Francesca, Claudia Raibulet, and Marco Zanoni. "Alternatives to the Knowledge Discovery Metamodel: An Investigation." International Journal of Software Engineering and Knowledge Engineering 27, no. 07 (2017): 1097–128. http://dx.doi.org/10.1142/s0218194017500413.

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To better understand and exploit the knowledge necessary to comprehend and evolve an existing system, different models can be extracted from it. Models represent the extracted information at various abstraction levels, and are useful to document, maintain, and reengineer the system. The Knowledge Discovery Metamodel (KDM) has been defined by the object management group as a meta-model supporting a large share of reverse engineering activities. Its specification has also been adopted by the ISO in 2012. This paper explores and describes alternative meta-models proposed in the literature to supp

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Ruiz, Diana Madrigal, M. Tim Tinker, Bernie R. Tershy, Kelly M. Zilliacus, and Donald A. Croll. "Using meta-population models to guide conservation action." Global Ecology and Conservation 28 (August 2021): e01644. http://dx.doi.org/10.1016/j.gecco.2021.e01644.

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48

Kelley, George A. "Statistical models for meta-analysis: A brief tutorial." World Journal of Methodology 2, no. 4 (2012): 27. http://dx.doi.org/10.5662/wjm.v2.i4.27.

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Sawyer, Alan G., John U. Farley, and Donald R. Lehmann. "Meta-Analysis in Marketing: Generalization of Response Models." Journal of Marketing Research 24, no. 4 (1987): 451. http://dx.doi.org/10.2307/3151395.

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Siau, Keng, Fiona F. H. Nah, and Qing Cao. "A Meta-Analysis Comparing Relational and Semantic Models." Journal of Database Management 22, no. 4 (2011): 57–72. http://dx.doi.org/10.4018/jdm.2011100103.

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Data modeling is the sine quo non of systems development and one of the most widely researched topics in the database literature. In the past three decades, semantic data modeling has emerged as an alternative to traditional relational modeling. The majority of the research in data modeling suggests that the use of semantic data models leads to better performance; however, the findings are not conclusive and are sometimes inconsistent. The discrepancies that exist in the data modeling literature and the relatively low statistical power in the studies make meta-analysis a viable choice in analy

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