Publikationer från Malmö universitet
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  • 1.
    Amouzad Mahdiraji, Saeid
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Holmgren, Johan
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Alshaban, Ala’a
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Mihailescu, Radu-Casian
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Petersson, Jesper
    Lund University; Region Skåne.
    Al Fatah, Jabir
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    A Framework for Constructing Discrete Event Simulation Models for Emergency Medical Service Policy Analysis2022Ingår i: Procedia Computer Science, E-ISSN 1877-0509, Vol. 210, s. 133-140Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Constructing simulation models can be a complex and time-consuming task, in particular if the models are constructed from scratch or if a general-purpose simulation modeling tool is used. In this paper, we propose a model construction framework, which aims to simplify the process of constructing discrete event simulation models for emergency medical service (EMS) policy analysis. The main building blocks used in the framework are a set of general activities that can be used to represent different EMS care chains modeled as flowcharts. The framework allows to build models only by specifying input data, including demographic and statistical data, and providing a care chain of activities and decisions. In a case study, we evaluated the framework by using it to construct a model for the simulation of the EMS activities related to acute stroke. Our evaluation shows that the predefined activities included in the framework are sufficient to build a simulation model for the rather complex case of acute stroke.

    Ladda ner fulltext (pdf)
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  • 2.
    Boiko, Olha
    et al.
    Sumy State University,Department of Information Technologies,Sumy,Ukraine.
    Shepeliev, Dmytro
    Sumy State University,Department of Information Technologies,Sumy,Ukraine.
    Shendryk, Vira
    Sumy State University,Department of Information Technologies,Sumy,Ukraine.
    Malekian, Reza
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Davidsson, Paul
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    A Comparison of Machine Learning Prediction Models to Estimate the Future Heat Demand2023Ingår i: 2023 IEEE 13th International Conference on Consumer Electronics - Berlin (ICCE-Berlin), Institute of Electrical and Electronics Engineers (IEEE), 2023Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper compares machine learning models for short-term heat demand forecasting in residential and multi-family buildings, evaluating model suitability, data impact on accuracy, computation time, and accuracy improvement methods. The findings are relevant for energy suppliers, researchers, and decision-makers in optimizing energy management and improving heat demand forecasting. The included models in the study are k-NN, Polynomial Regression, and LSTM with weather data, building type, and time index as input variables. Single-dimensional models (Autoregression, SARIMA, and Prophet) based on historical consumption are also studied. LSTM consistently outperforms other models in accuracy across different input variable combinations, measured using mean absolute percentage error (MAPE). The incorporation of historical consumption data improved the performance of k-NN and Polynomial Regression models. The paper also explores dataset volume impact on accuracy and compares training and prediction times. k-NN has the least prediction times, Polynomial Regression takes longer, and LSTM requires more time. All models exhibit acceptable prediction times for heat consumption. LSTM outperforms single-dimensional models in accuracy and has lower prediction times compared to AR, SARIMA, and Prophet models.

  • 3.
    Liu, Yuchu
    et al.
    Volvo Cars, Gothenburg, Sweden.
    Mattos, David Issa
    Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden.
    Bosch, Jan
    Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden.
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Lantz, Jonn
    Volvo Cars, Gothenburg, Sweden.
    Bayesian propensity score matching in automotive embedded software engineering2021Ingår i: 2021 28th Asia-Pacific Software Engineering Conference (APSEC), IEEE, 2021Konferensbidrag (Refereegranskat)
    Abstract [en]

    Randomised field experiments, such as A/B testing, have long been the gold standard for evaluating the value that new software brings to customers. However, running randomised field experiments is not always desired, possible or even ethical in the development of automotive embedded software. In the face of such restrictions, we propose the use of the Bayesian propensity score matching technique for causal inference of observational studies in the automotive domain. In this paper, we present a method based on the Bayesian propensity score matching framework, applied in the unique setting of automotive software engineering. This method is used to generate balanced control and treatment groups from an observational online evaluation and estimate causal treatment effects from the software changes, even with limited samples in the treatment group. We exemplify the method with a proof-of-concept in the automotive domain. In the example, we have a larger control (Nc = 1100) fleet of cars using the current software and a small treatment fleet (Nt = 38), in which we introduce a new software variant. We demonstrate a scenario that shipping of a new software to all users is restricted, as a result, a fully randomised experiment could not be conducted. Therefore, we utilised the Bayesian propensity score matching method with 14 observed covariates as inputs. The results show more balanced groups, suitable for estimating causal treatment effects from the collected observational data. We describe the method in detail and share our configuration. Furthermore, we discuss how can such a method be used for online evaluation of new software utilising small groups of samples.

  • 4.
    Mattos, D. I.
    et al.
    Chalmers University of Technology, Gothenburg, Sweden.
    Bosch, J.
    Chalmers University of Technology, Gothenburg, Sweden.
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Statistical Models for the Analysis of Optimization Algorithms with Benchmark Functions2021Ingår i: IEEE Transactions on Evolutionary Computation, ISSN 1089-778X, E-ISSN 1941-0026, Vol. 25, nr 6, s. 1163-1177Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Frequentist statistical methods, such as hypothesis testing, are standard practices in studies that provide benchmark comparisons. Unfortunately, these methods have often been misused, e.g., without testing for their statistical test assumptions or without controlling for familywise errors in multiple group comparisons, among several other problems. Bayesian data analysis (BDA) addresses many of the previously mentioned shortcomings but its use is not widely spread in the analysis of empirical data in the evolutionary computing community. This article provides three main contributions. First, we motivate the need for utilizing BDA and provide an overview of this topic. Second, we discuss the practical aspects of BDA to ensure that our models are valid and the results are transparent. Finally, we provide five statistical models that can be used to answer multiple research questions. The online Appendix provides a step-by-step guide on how to perform the analysis of the models discussed in this article, including the code for the statistical models, the data transformations, and the discussed tables and figures. 

  • 5.
    Yang, Xiuxiang
    et al.
    Department of Mathematics, Weinan Normal University, Weinan 714000 Shaanxi, China.
    Li, Feng
    Department of Mathematics, Weinan Normal University, Weinan 714000 Shaanxi, China.
    Cheng, Yuanji
    Malmö högskola, Teknik och samhälle (TS).
    Global Stability Analysis on the Dynamics of an SIQ Model with Nonlinear Incidence Rate2012Ingår i: Advances in Future Computer and Control Systems;2, Springer, 2012, s. 561-566Konferensbidrag (Refereegranskat)
    Abstract [en]

    An SIQ epidemic model with isolation and nonlinear incidence rate is studied. We have obtained a threshold value R and shown that there is only a disease free equilibrium point when R < 1, and there is also an endemic equilibrium point if R > 1. With the help of Liapunov function, we have shown that disease free- and endemic equilibrium point is globally stable.

    Ladda ner fulltext (pdf)
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