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Mihailescu, Radu-Casian
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Gabelaia, D., Kuznetsov, E., Mihailescu, R.-C., Razmadze, K. & Uridia, L. (2024). Temporal logic of surjective bounded morphisms between finite linear processes. Journal of Applied Non-Classical Logics, 34(1), 1-30
Öppna denna publikation i ny flik eller fönster >>Temporal logic of surjective bounded morphisms between finite linear processes
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2024 (Engelska)Ingår i: Journal of Applied Non-Classical Logics, ISSN 1166-3081, E-ISSN 1958-5780, Vol. 34, nr 1, s. 1-30Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

In this paper, we study temporal logic for finite linear structures and surjective bounded morphisms between them. We give a characterisation of such structures by modal formulas and show that every pair of linear structures with a bounded morphism between them can be uniquely characterised by a temporal formula up to an isomorphism. As the main result, we prove Kripke completeness of the logic with respect to the class of finite linear structures with bounded morphisms between them. 

Ort, förlag, år, upplaga, sidor
Taylor & Francis, 2024
Nyckelord
Temporal logic, modal definability, Kripke completeness
Nationell ämneskategori
Geometri
Identifikatorer
urn:nbn:se:mau:diva-64269 (URN)10.1080/11663081.2023.2269432 (DOI)2-s2.0-85174929514 (Scopus ID)
Tillgänglig från: 2023-12-12 Skapad: 2023-12-12 Senast uppdaterad: 2024-03-28Bibliografiskt granskad
Abid, M. A., Amouzad Mahdiraji, S., Lorig, F., Holmgren, J., Mihailescu, R.-C. & Petersson, J. (2023). A Genetic Algorithm for Optimizing Mobile Stroke Unit Deployment. Paper presented at 27th International Conference on Knowledge Based and Intelligent Information and Engineering Systems (KES 2023), Athens, Greece, 6-8 September 2023. Procedia Computer Science, 225, 3536-3545
Öppna denna publikation i ny flik eller fönster >>A Genetic Algorithm for Optimizing Mobile Stroke Unit Deployment
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2023 (Engelska)Ingår i: Procedia Computer Science, ISSN 1877-0509, Vol. 225, s. 3536-3545Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

A mobile stroke unit (MSU) is an advanced ambulance equipped with specialized technology and trained healthcare personnel to provide on-site diagnosis and treatment for stroke patients. Providing efficient access to healthcare (in a viable way) requires optimizing the placement of MSUs. In this study, we propose a time-efficient method based on a genetic algorithm (GA) to find the most suitable ambulance sites for the placement of MSUs (given the number of MSUs and a set of potential sites). We designed an efficient encoding scheme for the input data (the number of MSUs and potential sites) and developed custom selection, crossover, and mutation operators that are tailored according to the characteristics of the MSU allocation problem. We present a case study on the Southern Healthcare Region in Sweden to demonstrate the generality and robustness of our proposed GA method. Particularly, we demonstrate our method's flexibility and adaptability through a series of experiments across multiple settings. For the considered scenario, our proposed method outperforms the exhaustive search method by finding the best locations within 0.16, 1.44, and 10.09 minutes in the deployment of three MSUs, four MSUs, and five MSUs, resulting in 8.75x, 16.36x, and 24.77x faster performance, respectively. Furthermore, we validate the method's robustness by iterating GA multiple times and reporting its average fitness score (performance convergence). In addition, we show the effectiveness of our method by evaluating key hyperparameters, that is, population size, mutation rate, and the number of generations.

Ort, förlag, år, upplaga, sidor
Elsevier, 2023
Nyckelord
genetic algorithm, mobile stroke unit (MSU), optimization, healthcare, time to treatment
Nationell ämneskategori
Kommunikationssystem Neurologi
Identifikatorer
urn:nbn:se:mau:diva-64632 (URN)10.1016/j.procs.2023.10.349 (DOI)
Konferens
27th International Conference on Knowledge Based and Intelligent Information and Engineering Systems (KES 2023), Athens, Greece, 6-8 September 2023
Forskningsfinansiär
Familjen Kamprads stiftelse
Tillgänglig från: 2023-12-20 Skapad: 2023-12-20 Senast uppdaterad: 2023-12-20Bibliografiskt granskad
Amouzad Mahdiraji, S., Abid, M. A., Holmgren, J., Mihailescu, R.-C., Lorig, F. & Petersson, J. (2023). An Optimization Model for the Placement of Mobile Stroke Units. In: Teresa Guarda; Filipe Portela; Jose Maria Diaz-Nafria (Ed.), Advanced Research in Technologies, Information, Innovation and Sustainability: Third International Conference, ARTIIS 2023, Madrid, Spain, October 18–20, 2023, Proceedings, Part I. Paper presented at Advanced Research in Technologies, Information, Innovation and Sustainability, Third International Conference, ARTIIS 2023, Madrid, Spain, October 18–20, 2023 (pp. 297-310). Springer
Öppna denna publikation i ny flik eller fönster >>An Optimization Model for the Placement of Mobile Stroke Units
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2023 (Engelska)Ingår i: Advanced Research in Technologies, Information, Innovation and Sustainability: Third International Conference, ARTIIS 2023, Madrid, Spain, October 18–20, 2023, Proceedings, Part I / [ed] Teresa Guarda; Filipe Portela; Jose Maria Diaz-Nafria, Springer, 2023, s. 297-310Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Mobile Stroke Units (MSUs) are specialized ambulances that can diagnose and treat stroke patients; hence, reducing the time to treatment for stroke patients. Optimal placement of MSUs in a geographic region enables to maximize access to treatment for stroke patients. We contribute a mathematical model to optimally place MSUs in a geographic region. The objective function of the model takes the tradeoff perspective, balancing between the efficiency and equity perspectives for the MSU placement. Solving the optimization problem enables to optimize the placement of MSUs for the chosen tradeoff between the efficiency and equity perspectives. We applied the model to the Blekinge and Kronoberg counties of Sweden to illustrate the applicability of our model. The experimental findings show both the correctness of the suggested model and the benefits of placing MSUs in the considered regions.

Ort, förlag, år, upplaga, sidor
Springer, 2023
Serie
Communications in Computer and Information Science, ISSN 1865-0929, E-ISSN 1865-0937 ; 1935
Nyckelord
Optimization, MILP, Time to Treatment, Mobile Stroke Unit (MSU), MSU Placement
Nationell ämneskategori
Neurologi Beräkningsmatematik
Identifikatorer
urn:nbn:se:mau:diva-64865 (URN)10.1007/978-3-031-48858-0_24 (DOI)2-s2.0-85180781530 (Scopus ID)978-3-031-48857-3 (ISBN)978-3-031-48858-0 (ISBN)
Konferens
Advanced Research in Technologies, Information, Innovation and Sustainability, Third International Conference, ARTIIS 2023, Madrid, Spain, October 18–20, 2023
Tillgänglig från: 2024-01-08 Skapad: 2024-01-08 Senast uppdaterad: 2024-01-08Bibliografiskt granskad
Jamali, M., Davidsson, P., Khoshkangini, R., Ljungqvist, M. G. & Mihailescu, R.-C. (2023). Specialized Indoor and Outdoor Scene-specific Object Detection Models. In: SPIE Digital Library: . Paper presented at International Conference on Machine Vision (ICMV 2023), Nov. 15-18, 2023, Yerevan, Armenia.
Öppna denna publikation i ny flik eller fönster >>Specialized Indoor and Outdoor Scene-specific Object Detection Models
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2023 (Engelska)Ingår i: SPIE Digital Library, 2023Konferensbidrag, Publicerat paper (Refereegranskat)
Serie
Studies in Computer Science
Nyckelord
object detection, YOLOv5, indoor object detection, outdoor object detection, scene classification
Nationell ämneskategori
Annan elektroteknik och elektronik
Identifikatorer
urn:nbn:se:mau:diva-66441 (URN)
Konferens
International Conference on Machine Vision (ICMV 2023), Nov. 15-18, 2023, Yerevan, Armenia
Anmärkning

The paper has not been published yet

Tillgänglig från: 2024-03-22 Skapad: 2024-03-22 Senast uppdaterad: 2024-03-27Bibliografiskt granskad
Persson, J. A., Bugeja, J., Davidsson, P., Holmberg, J., Kebande, V. R., Mihailescu, R.-C., . . . Tegen, A. (2023). The Concept of Interactive Dynamic Intelligent Virtual Sensors (IDIVS): Bridging the Gap between Sensors, Services, and Users through Machine Learning. Applied Sciences, 13(11), Article ID 6516.
Öppna denna publikation i ny flik eller fönster >>The Concept of Interactive Dynamic Intelligent Virtual Sensors (IDIVS): Bridging the Gap between Sensors, Services, and Users through Machine Learning
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2023 (Engelska)Ingår i: Applied Sciences, E-ISSN 2076-3417, Vol. 13, nr 11, artikel-id 6516Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

This paper concerns the novel concept of an Interactive Dynamic Intelligent Virtual Sensor (IDIVS), which extends virtual/soft sensors towards making use of user input through interactive learning (IML) and transfer learning. In research, many studies can be found on using machine learning in this domain, but not much on using IML. This paper contributes by highlighting how this can be done and the associated positive potential effects and challenges. An IDIVS provides a sensor-like output and achieves the output through the data fusion of sensor values or from the output values of other IDIVSs. We focus on settings where people are present in different roles: from basic service users in the environment being sensed to interactive service users supporting the learning of the IDIVS, as well as configurators of the IDIVS and explicit IDIVS teachers. The IDIVS aims at managing situations where sensors may disappear and reappear and be of heterogeneous types. We refer to and recap the major findings from related experiments and validation in complementing work. Further, we point at several application areas: smart building, smart mobility, smart learning, and smart health. The information properties and capabilities needed in the IDIVS, with extensions towards information security, are introduced and discussed.

Ort, förlag, år, upplaga, sidor
MDPI, 2023
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:mau:diva-60144 (URN)10.3390/app13116516 (DOI)001004726600001 ()2-s2.0-85163091186 (Scopus ID)
Tillgänglig från: 2023-06-07 Skapad: 2023-06-07 Senast uppdaterad: 2023-09-05Bibliografiskt granskad
Amouzad Mahdiraji, S., Holmgren, J., Alshaban, A., Mihailescu, R.-C., Petersson, J. & Al Fatah, J. (2022). A Framework for Constructing Discrete Event Simulation Models for Emergency Medical Service Policy Analysis. Paper presented at 12th International Conference on Current and Future Trends of Information and Communication Technologies in Health care (ICTH 2022) October 26-28, 2022, Leuven, Belgium. Procedia Computer Science, 210, 133-140
Öppna denna publikation i ny flik eller fönster >>A Framework for Constructing Discrete Event Simulation Models for Emergency Medical Service Policy Analysis
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2022 (Engelska)Ingår i: Procedia Computer Science, E-ISSN 1877-0509, Vol. 210, s. 133-140Artikel i tidskrift (Refereegranskat) Published
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.

Ort, förlag, år, upplaga, sidor
Elsevier, 2022
Nationell ämneskategori
Sannolikhetsteori och statistik
Identifikatorer
urn:nbn:se:mau:diva-56003 (URN)10.1016/j.procs.2022.10.129 (DOI)2-s2.0-85144819456 (Scopus ID)
Konferens
12th International Conference on Current and Future Trends of Information and Communication Technologies in Health care (ICTH 2022) October 26-28, 2022, Leuven, Belgium
Tillgänglig från: 2022-11-14 Skapad: 2022-11-14 Senast uppdaterad: 2024-02-05Bibliografiskt granskad
Amouzad Mahdiraji, S., Holmgren, J., Mihailescu, R.-C. & Petersson, J. (2022). A Micro-Level Simulation Model for Analyzing the Use of MSUs in Southern Sweden. Paper presented at 11th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH 2021) November 1-4, 2021, Leuven, Belgium. Procedia Computer Science, 198, 132-139
Öppna denna publikation i ny flik eller fönster >>A Micro-Level Simulation Model for Analyzing the Use of MSUs in Southern Sweden
2022 (Engelska)Ingår i: Procedia Computer Science, E-ISSN 1877-0509, Vol. 198, s. 132-139Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

A mobile stroke unit (MSU) is a special type of ambulance, where stroke patients can be diagnosed and provided intravenous treatment, hence allowing to cut down the time to treatment for stroke patients. We present a discrete event simulation (DES) model to study the potential benefits of using MSUs in the southern health care region of Sweden (SHR). We included the activities and actions used in the SHR for stroke patient transportation as events in the DES model, and we generated a synthetic set of stroke patients as input for the simulation model. In a scenario study, we compared two scenarios, including three MSUs each, with the current situation, having only regular ambulances. We also performed a sensitivity analysis to further evaluate the presented DES model. For both MSU scenarios, our simulation results indicate that the average time to treatment is expected to decrease for the whole region and for each municipality of SHR. For example, the average time to treatment in the SHR is reduced from 1.31h in the baseline scenario to 1.20h and 1.23h for the two MSU scenarios. In addition, the share of stroke patients who are expected to receive treatment within one hour is increased by a factor of about 3 for both MSU scenarios.

Ort, förlag, år, upplaga, sidor
Elsevier, 2022
Nyckelord
Ischemic stroke; stroke transport; MSU; DES; time to treatment; stroke logistics
Nationell ämneskategori
Beräkningsmatematik
Identifikatorer
urn:nbn:se:mau:diva-54479 (URN)10.1016/j.procs.2021.12.220 (DOI)2-s2.0-85124617439 (Scopus ID)
Konferens
11th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH 2021) November 1-4, 2021, Leuven, Belgium
Tillgänglig från: 2022-08-22 Skapad: 2022-08-22 Senast uppdaterad: 2024-02-05Bibliografiskt granskad
Skiöld, D., Arora, S., Mihailescu, R.-C. & Balaghi, R. (2022). Forecasting key performance indicators for smart connected vehicles. In: A C B Garcia, M Ferro, J C R Ribon (Ed.), Advances in artificial intelligence: IBERAMIA 2022. Paper presented at 17th Ibero-American Conference on Artificial Intelligence (IBERAMIA), NOV 23-25 2022, Cartagena de Indias, COLOMBIA (pp. 414-415). Springer, 13788
Öppna denna publikation i ny flik eller fönster >>Forecasting key performance indicators for smart connected vehicles
2022 (Engelska)Ingår i: Advances in artificial intelligence: IBERAMIA 2022 / [ed] A C B Garcia, M Ferro, J C R Ribon, Springer, 2022, Vol. 13788, s. 414-415Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

As connectivity has been introduced to the car industry, automotive companies have in-use cars which are connected to the internet. A key concern in this context represents the difficulty of knowing how the connection quality changes over time and if there are associated issues. In this work we describe the use of CDR data from connected cars supplied by Volvo to build and study forecasting models that predict how relevant KPIs change over time. Our experiments show promising results for this predictive task, which can lead to improving user experience of connectivity in smart vehicles.

Ort, förlag, år, upplaga, sidor
Springer, 2022
Serie
Lecture Notes in Artificial Intelligence, ISSN 0302-9743, E-ISSN 1611-3349 ; 13788
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:mau:diva-61083 (URN)000972628400037 ()978-3-031-22418-8 (ISBN)978-3-031-22419-5 (ISBN)
Konferens
17th Ibero-American Conference on Artificial Intelligence (IBERAMIA), NOV 23-25 2022, Cartagena de Indias, COLOMBIA
Tillgänglig från: 2023-06-20 Skapad: 2023-06-20 Senast uppdaterad: 2023-12-13Bibliografiskt granskad
Tell, A., Hägred, C. & Mihailescu, R.-C. (2022). Perceptions of Time: Determine the Time of an Analogue Watch using Computer Vision. In: 2022 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT): . Paper presented at 2022 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT), 18-21 December 2022, Alamein New City, Egypt. Institute of Electrical and Electronics Engineers (IEEE)
Öppna denna publikation i ny flik eller fönster >>Perceptions of Time: Determine the Time of an Analogue Watch using Computer Vision
2022 (Engelska)Ingår i: 2022 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT), Institute of Electrical and Electronics Engineers (IEEE), 2022Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

This paper explores the problem of determining the time of an analogue wristwatch by developing two systems and conducting a comparative study. The first system uses OpenCV to find the watch hands and applies geometrical techniques to calculate the time. The second system uses Machine Learning by building a neural network to classify images in Tensorflow using a multi-labelling approach. The results show that in a set environment the geometric-based approach performs better than the Machine Learning model. The geometric system predicted time correctly with an accuracy of 80% whereas the best Machine Learning model only achieves 74%. Experiments show that the accuracy of the neural network model did increase when using data augmentation, however there was no significant improvement when adding synthetic data to our training set.

Ort, förlag, år, upplaga, sidor
Institute of Electrical and Electronics Engineers (IEEE), 2022
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:mau:diva-59126 (URN)10.1109/gcaiot57150.2022.10019054 (DOI)000972037000008 ()2-s2.0-85147650610 (Scopus ID)979-8-3503-0984-3 (ISBN)979-8-3503-0985-0 (ISBN)
Konferens
2022 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT), 18-21 December 2022, Alamein New City, Egypt
Tillgänglig från: 2023-04-05 Skapad: 2023-04-05 Senast uppdaterad: 2023-12-13Bibliografiskt granskad
Mihailescu, R.-C. (2021). A weakly-supervised deep domain adaptation method for multi-modal sensor data. In: 2021 IEEE GLOBAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND INTERNET OF THINGS (GCAIOT): . Paper presented at IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT), DEC 12-16, 2021, Dubai, U ARAB EMIRATES (pp. 45-50). IEEE
Öppna denna publikation i ny flik eller fönster >>A weakly-supervised deep domain adaptation method for multi-modal sensor data
2021 (Engelska)Ingår i: 2021 IEEE GLOBAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND INTERNET OF THINGS (GCAIOT), IEEE , 2021, s. 45-50Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Nearly every real-world deployment of machine learning models suffers from some form of shift in data distributions in relation to the data encountered in production. This aspect is particularly pronounced when dealing with streaming data or in dynamic settings (e.g. changes in data sources, behaviour and the environment). As a result, the performance of the models degrades during deployment. In order to account for these contextual changes, domain adaptation techniques have been designed for scenarios where the aim is to learn a model from a source data distribution, which can perform well on a different, but related target data distribution. In this paper we introduce a variational autoencoder-based multi-modal approach for the task of domain adaptation, that can be trained on a large amount of labelled data from the source domain, coupled with a comparably small amount of labelled data from the target domain. We demonstrate our approach in the context of human activity recognition using various IoT sensing modalities and report superior results when benchmarking against the effective mSDA method for domain adaptation.

Ort, förlag, år, upplaga, sidor
IEEE, 2021
Nyckelord
Domain adaptation, Neural Networks, Internet of Things, Human Activity Recognition
Nationell ämneskategori
Data- och informationsvetenskap
Identifikatorer
urn:nbn:se:mau:diva-51709 (URN)10.1109/GCAIoT53516.2021.9693050 (DOI)000790983800008 ()2-s2.0-85126734760 (Scopus ID)978-1-6654-3841-4 (ISBN)
Konferens
IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT), DEC 12-16, 2021, Dubai, U ARAB EMIRATES
Tillgänglig från: 2022-05-30 Skapad: 2022-05-30 Senast uppdaterad: 2024-02-05Bibliografiskt granskad
Projekt
Dynamic Intelligent Sensor Intensive Systems; Malmö universitet; Publikationer
Persson, J. A., Bugeja, J., Davidsson, P., Holmberg, J., Kebande, V. R., Mihailescu, R.-C., . . . Tegen, A. (2023). The Concept of Interactive Dynamic Intelligent Virtual Sensors (IDIVS): Bridging the Gap between Sensors, Services, and Users through Machine Learning. Applied Sciences, 13(11), Article ID 6516.
AVANS projekt: "Internet of Things Master's Program"; Malmö universitet
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