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Project type/Form of grant
Grant to research environment
Title [sv]
HINTS – Intelligenta verkligheter med människan i centrum
Title [en]
HINTS - Human-Centered Intelligent Realities
Abstract [sv]
HINTS syftar till att vara den främsta svenska noden med hög inverkan internationellt inom intelligenta verkligheter med människan i centrum för nästa generations digitala samhällen. HINTS-projektet är mitt i BTH:s strategi mot digitalisering och det ligger i linje med BTH:s strategi att bygga fokuserade och kompletta miljöer baserade på starka akademiska program, forskningsexpertis och samproduktion med externa partners.
Abstract [en]
The overall objective of the HINTS project is to develop concepts, principles, methods, algorithms, and tools for human-centered intelligent realities, in co-production with industrial partners and society, in order to lead the way for future immersive, user-aware, and smart interactive digital environments.
Publications (10 of 15) Show all publications
Hu, Y., Goswami, P. & Sundstedt, V. (2023). A Review on XR in Home-based Nursing Education. In: HEALTHINFO 2023: The Eighth International Conference on Informatics and Assistive Technologies for Health-Care, Medical Support and Wellbeing. Paper presented at HEALTHINFO 2023 : The Eighth International Conference on Informatics and Assistive Technologies for Health-Care, Medical Support and Wellbeing, Valencia, 13/11 - 17/11 2023 (pp. 39-43). International Academy, Research, and Industry Association (IARIA)
Open this publication in new window or tab >>A Review on XR in Home-based Nursing Education
2023 (English)In: HEALTHINFO 2023: The Eighth International Conference on Informatics and Assistive Technologies for Health-Care, Medical Support and Wellbeing, International Academy, Research, and Industry Association (IARIA) , 2023, p. 39-43Conference paper, Published paper (Refereed)
Abstract [en]

Recent developments using extended reality (XR) technologies have allowed for increased use in healthcare in the last few years. This review paper explores how XR applications are utilized in home-based nursing education, in particular, to identify future challenges and opportunities. The systematic literature review evaluates relevant extracted papers based on publication information, XR technology used for education purposes, target users, and study design and evaluation, including sample size. The results show potential for using XR technologies in home-based nursing education. In particular, Virtual Reality (VR) has become quite popular and the most used to date. However, Augmented Reality (AR) has also emerged as an alternative for the future.

Place, publisher, year, edition, pages
International Academy, Research, and Industry Association (IARIA), 2023
National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:bth-25602 (URN)9781685581053 (ISBN)
Conference
HEALTHINFO 2023 : The Eighth International Conference on Informatics and Assistive Technologies for Health-Care, Medical Support and Wellbeing, Valencia, 13/11 - 17/11 2023
Funder
Knowledge Foundation, 20220068
Available from: 2023-11-13 Created: 2023-11-13 Last updated: 2023-12-28Bibliographically approved
Fu, Y., Hu, Y., Sundstedt, V. & Forsell, Y. (2023). A Systematic Literature Review of Extended Reality Exercise Games for the Elderly. In: Ana Cecília A. Roque, Denis Gracanin, Ronny Lorenz, Athanasios Tsanas, Nathalie Bier, Ana Fred, Hugo Gamboa (Ed.), Biomedical Engineering Systems and Technologies: 15th International Joint Conference, BIOSTEC 2022, Virtual Event, February 9–11, 2022, Revised Selected Papers. Paper presented at 15th International Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2022. Online, February 9-11, 2022. (pp. 333-352). Springer Science+Business Media B.V.
Open this publication in new window or tab >>A Systematic Literature Review of Extended Reality Exercise Games for the Elderly
2023 (English)In: Biomedical Engineering Systems and Technologies: 15th International Joint Conference, BIOSTEC 2022, Virtual Event, February 9–11, 2022, Revised Selected Papers / [ed] Ana Cecília A. Roque, Denis Gracanin, Ronny Lorenz, Athanasios Tsanas, Nathalie Bier, Ana Fred, Hugo Gamboa, Springer Science+Business Media B.V., 2023, p. 333-352Conference paper, Published paper (Refereed)
Abstract [en]

In recent years, with the rise of the ageing population worldwide, the health of the elderly has attracted increasing attention. This study explored existing extended reality (XR) game applications aiming at physical exercise for the elderly. Through the review of 1847 papers from the Scopus database, 17 articles were included. Based on these papers, we explored the existing contributions of exercise XR games for the elderly, the development opportunities and challenges of such games, and their special considerations in adapting to the characteristics and requirements of the target user. The results were organized into several perspectives: publication information and keywords, immersive technologies and game concepts, teamwork and social games, evaluation, opportunities and challenges, and adapting designs. We found the elderly interested in and accepted using XR games. The reported research results proved positive effects on such games’ physical and mental health. XR exercise games for the elderly should considerately adapt to the elder’s cognition, behaviour, and demand. Although problems existed, such as simulator sickness, safety risks, device problems, and cost, there were opportunities and space for research and future developments. Researchers and developers could refer to this paper for XR exercise games for the elderly and create or enhance future XR applications by learning from existing work. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2023
Series
Communications in Computer and Information Science, ISSN 18650929 ; 1814
Keywords
Augmented reality, Elderly, Exercise, Extended reality, Game, Health, Mixed reality, Old people, Physical training, Virtual reality, E-learning, Aging population, Older People, Physical exercise, Systematic literature review
National Category
Human Computer Interaction
Identifiers
urn:nbn:se:bth-25447 (URN)10.1007/978-3-031-38854-5_17 (DOI)2-s2.0-85172189765 (Scopus ID)9783031388538 (ISBN)
Conference
15th International Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2022. Online, February 9-11, 2022.
Funder
Knowledge Foundation, 20220068
Available from: 2023-10-10 Created: 2023-10-10 Last updated: 2023-10-30Bibliographically approved
Elwardy, M., Zepernick, H.-J., Hu, Y. & Chu, T. M. (2023). ACR360: A Dataset on Subjective 360° Video Quality Assessment Using ACR Methods. In: Wysocki B.J., Wysocki T.A. (Ed.), 2023 16th International Conference on Signal Processing and Communication System, ICSPCS 2023 - Proceedings: . Paper presented at 16th International Conference on Signal Processing and Communication System, ICSPCS 2023, Bydgoszcz, 6 Sept - 8 Sept 2023. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>ACR360: A Dataset on Subjective 360° Video Quality Assessment Using ACR Methods
2023 (English)In: 2023 16th International Conference on Signal Processing and Communication System, ICSPCS 2023 - Proceedings / [ed] Wysocki B.J., Wysocki T.A., Institute of Electrical and Electronics Engineers (IEEE), 2023Conference paper, Published paper (Refereed)
Abstract [en]

The recent advances in immersive technologies have been essential in the development of a wide range of novel standalone and networked immersive media applications. The concepts of virtual reality, augmented reality, and mixed reality relate to different compositions of real and computer-generated virtual objects. In this context, 360° video streaming has become increasingly popular offering improved immersive experiences when viewed on a head-mounted display (HMD). An important component in the development of novel immersive media systems are subjective tests in which participants assess the quality of experience of representative test stimuli. In this paper, the annotated ACR360 dataset is presented which is publicly available on GitHub. The ACR360 dataset contains a wide range of psychophysical and psychophysiological data that was collected in Subjective tests on 360° video quality. The test stimuli were shown on an HMD and rated according to the absolute category rating (ACR) and modified ACR (MACR) methods. To support an easy exploration and utilization of the ACR360 dataset by the research community, its structure on GitHub is described and a comprehensive illustration of analysis options are provided for each data category. The ACR360 dataset may be used for conducting meta-analysis in combination with other datasets to improve precision and to pursue research questions that cannot be answered by an individual study. © 2023 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
360° video, absolute category rating, annotated dataset, Immersive media, quality assessment, Subjective test, Helmet mounted displays, Image quality, Mixed reality, Quality of service, Statistical tests, Subjective testing, Video streaming, Absolute category ratings, Annotated datasets, Head-mounted-displays, Immersive technologies, Media application, Video quality, Augmented reality
National Category
Telecommunications
Identifiers
urn:nbn:se:bth-25548 (URN)10.1109/ICSPCS58109.2023.10261151 (DOI)2-s2.0-85174510305 (Scopus ID)9798350333510 (ISBN)
Conference
16th International Conference on Signal Processing and Communication System, ICSPCS 2023, Bydgoszcz, 6 Sept - 8 Sept 2023
Funder
Knowledge Foundation, 20220068Knowledge Foundation, 20170056
Note

  

Available from: 2023-11-07 Created: 2023-11-07 Last updated: 2023-11-08Bibliographically approved
Jagtap, S. & Goswami, P. (2023). Design of Artificial Intelligence-Based Products: Barriers and Enablers. In: Amaresh Chakrabarti, Vishal Singh (Ed.), Amaresh Chakrabarti, Vishal Singh (Ed.), Design in the Era of Industry 4.0, Volume 3: Proceedings of ICoRD 2023. Paper presented at 15th ICORD Conference 6th – 7th February 2023 (pp. 647-658). Springer
Open this publication in new window or tab >>Design of Artificial Intelligence-Based Products: Barriers and Enablers
2023 (English)In: Design in the Era of Industry 4.0, Volume 3: Proceedings of ICoRD 2023 / [ed] Amaresh Chakrabarti, Vishal Singh, Springer, 2023, p. 647-658Conference paper, Published paper (Refereed)
Abstract [en]

Artificial Intelligence (AI) embodied products are becoming ubiquitous in the modern world. Organizations are hence updating themselves to design and develop such products. In this paper, we aim at identifying enablers and barriers in designing such products across several sectors. Our analysis of a broad range of literature in this field allowed us to identify these enablers and barriers. We have developed SOTCUT and SEECUT models representing these enablers and barriers. We have discussed implication of the findings for the practice of designing AI-embodied products.

Place, publisher, year, edition, pages
Springer, 2023
Series
Smart Innovation, Systems and Technologies book series (SIST), ISSN 2190-3026
Keywords
Artificial intelligence (AI), Design practice, Barriers, Enablers
National Category
Computer Sciences Design
Research subject
Computer Science
Identifiers
urn:nbn:se:bth-25201 (URN)10.1007/978-981-99-0428-0_53 (DOI)
Conference
15th ICORD Conference 6th – 7th February 2023
Available from: 2023-07-28 Created: 2023-07-28 Last updated: 2023-12-28Bibliographically approved
Casalicchio, E., Esposito, S. & Al-Saedi, A. A. (2023). FLWB: a Workbench Platform for Performance Evaluation of Federated Learning Algorithms. In: 2023 IEEE International Workshop on Technologies for Defense and Security, TechDefense 2023 - Proceedings: . Paper presented at 2023 IEEE International Workshop on Technologies for Defense and Security, TechDefense 2023, Rome, 20-22 Nov 2023 (pp. 401-405). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>FLWB: a Workbench Platform for Performance Evaluation of Federated Learning Algorithms
2023 (English)In: 2023 IEEE International Workshop on Technologies for Defense and Security, TechDefense 2023 - Proceedings, Institute of Electrical and Electronics Engineers (IEEE), 2023, p. 401-405Conference paper, Published paper (Refereed)
Abstract [en]

Federated learning is a technique that allows to collaboratively train a shared machine learning model across distributed devices, where the data are stored locally on devices. Most innovations the research community proposes in federated learning are tested through custom simulators. An analysis of the literature shows the lack of workbench platforms for the performance evaluation of FL projects. This paper aims to fill the gap by presenting FLWB, a general-purpose, configurable, and scalable workbench platform for easy deployment and performance evaluation of Federated Learning projects. Through experiments, we demonstrated the ease with which a FL system can be implemented and deployed with FLWB. © 2023 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
Federated Learning, microservice, performance evaluation, security, Learning algorithms, Custom simulators, Deployment evaluations, Distributed devices, Learning projects, Machine learning models, Performances evaluation, Research communities, Learning systems
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-25969 (URN)10.1109/TechDefense59795.2023.10380832 (DOI)2-s2.0-85183927234 (Scopus ID)9798350319392 (ISBN)
Conference
2023 IEEE International Workshop on Technologies for Defense and Security, TechDefense 2023, Rome, 20-22 Nov 2023
Funder
Knowledge Foundation, 20220068
Available from: 2024-02-16 Created: 2024-02-16 Last updated: 2024-02-26Bibliographically approved
Al-Saedi, A. A. & Boeva, V. (2023). Group-Personalized Federated Learning for Human Activity Recognition Through Cluster Eccentricity Analysis. In: Iliadis L., Maglogiannis I., Alonso S., Jayne C., Pimenidis E. (Ed.), Engineering Applications of Neural Networks: 24th International Conference, EAAAI/EANN 2023, León, Spain, June 14–17, 2023, Proceedings. Paper presented at 24th International Conference on Engineering Applications of Neural Networks, EANN 2023, León, 14 June through 17 June 2023 (pp. 505-519). Springer Science+Business Media B.V.
Open this publication in new window or tab >>Group-Personalized Federated Learning for Human Activity Recognition Through Cluster Eccentricity Analysis
2023 (English)In: Engineering Applications of Neural Networks: 24th International Conference, EAAAI/EANN 2023, León, Spain, June 14–17, 2023, Proceedings / [ed] Iliadis L., Maglogiannis I., Alonso S., Jayne C., Pimenidis E., Springer Science+Business Media B.V., 2023, p. 505-519Conference paper, Published paper (Refereed)
Abstract [en]

Human Activity Recognition (HAR) plays a significant role in recent years due to its applications in various fields including health care and well-being. Traditional centralized methods reach very high recognition rates, but they incur privacy and scalability issues. Federated learning (FL) is a leading distributed machine learning (ML) paradigm, to train a global model collaboratively on distributed data in a privacy-preserving manner. However, for HAR scenarios, the existing action recognition system mainly focuses on a unified model, i.e. it does not provide users with personalized recognition of activities. Furthermore, the heterogeneity of data across user devices can lead to degraded performance of traditional FL models in the smart applications such as personalized health care. To this end, we propose a novel federated learning model that tries to cope with a statistically heterogeneous federated learning environment by introducing a group-personalized FL (GP-FL) solution. The proposed GP-FL algorithm builds several global ML models, each one trained iteratively on a dynamic group of clients with homogeneous class probability estimations. The performance of the proposed FL scheme is studied and evaluated on real-world HAR data. The evaluation results demonstrate that our approach has advantages in terms of model performance and convergence speed with respect to two baseline FL algorithms used for comparison. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2023
Series
Communications in Computer and Information Science (CCIS), ISSN 1865-0929, E-ISSN 1865-0937 ; 1826
Keywords
Clustering, Eccentricity Analysis, Federated Learning, HAR, Non-IID data, Computer aided instruction, Iterative methods, Learning systems, Pattern recognition, Privacy-preserving techniques, Centralised, Clusterings, Eccentricity analyse, Human activity recognition, IID data, ITS applications, Learning models, Well being, Health care
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-25227 (URN)10.1007/978-3-031-34204-2_41 (DOI)2-s2.0-85164039066 (Scopus ID)9783031342035 (ISBN)
Conference
24th International Conference on Engineering Applications of Neural Networks, EANN 2023, León, 14 June through 17 June 2023
Funder
Knowledge Foundation, 20220068
Available from: 2023-08-07 Created: 2023-08-07 Last updated: 2023-08-07Bibliographically approved
Kelkkanen, V., Lindero, D., Fiedler, M. & Zepernick, H.-J. (2023). Hand-Controller Latency and Aiming Accuracy in 6-DOF VR. Advances in Human-Computer Interaction, Article ID 1563506.
Open this publication in new window or tab >>Hand-Controller Latency and Aiming Accuracy in 6-DOF VR
2023 (English)In: Advances in Human-Computer Interaction, ISSN 1687-5893, E-ISSN 1687-5907, article id 1563506Article in journal (Refereed) Published
Abstract [en]

All virtual reality (VR) systems have some inherent hand-controller latency even when operated locally. In remotely rendered VR, additional latency may be added due to the remote transmission of data, commonly conducted through shared low-capacity channels. Increased latency will negatively affect the performance of the human VR operator, but the level of detriment depends on the given task. This work quantifies the relations between aiming accuracy and hand-controller latency, virtual target speed, and the predictability of the target motion. The tested context involves a target that changes direction multiple times while moving in straight lines. The main conclusions are, given the tested context, first, that the predictability of target motion becomes significantly more important as latency and target speed increase. A significant difference in accuracy is generally observed at latencies beyond approximately 130 ms and at target speeds beyond approximately 3.5 degrees/s. Second, latency starts to significantly impact accuracy at roughly 90 ms and approximately 3.5 degrees/s if the target motion cannot be predicted. If it can, the numbers are approximately 130 ms and 12.7 degrees/s. Finally, reaction times are on average 190-200 ms when the target motion changes to a new and unpredictable direction.

Place, publisher, year, edition, pages
Hindawi Publishing Corporation, 2023
Keywords
Tracking, Robot, Artificial intelligence
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:bth-24387 (URN)10.1155/2023/1563506 (DOI)001077344100001 ()2-s2.0-85174505323 (Scopus ID)
Funder
Knowledge Foundation, 20170056Knowledge Foundation, 20220068
Available from: 2023-03-21 Created: 2023-03-21 Last updated: 2023-11-09Bibliographically approved
Sundstedt, V., Boeva, V., Zepernick, H.-J., Goswami, P., Cheddad, A., Tutschku, K., . . . Arlos, P. (2023). HINTS: Human-Centered Intelligent Realities. In: Håkan Grahn, Anton Borg and Martin Boldt (Ed.), 35th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2023: . Paper presented at 35th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2023, Karlskrona, June 12-13, 2023 (pp. 9-17). Linköping University Electronic Press
Open this publication in new window or tab >>HINTS: Human-Centered Intelligent Realities
Show others...
2023 (English)In: 35th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2023 / [ed] Håkan Grahn, Anton Borg and Martin Boldt, Linköping University Electronic Press, 2023, p. 9-17Conference paper, Published paper (Refereed)
Abstract [en]

During the last decade, we have witnessed a rapiddevelopment of extended reality (XR) technologies such asaugmented reality (AR) and virtual reality (VR). Further, therehave been tremendous advancements in artificial intelligence(AI) and machine learning (ML). These two trends will havea significant impact on future digital societies. The vision ofan immersive, ubiquitous, and intelligent virtual space opensup new opportunities for creating an enhanced digital world inwhich the users are at the center of the development process,so-calledintelligent realities(IRs).The “Human-Centered Intelligent Realities” (HINTS) profileproject will develop concepts, principles, methods, algorithms,and tools for human-centered IRs, thus leading the wayfor future immersive, user-aware, and intelligent interactivedigital environments. The HINTS project is centered aroundan ecosystem combining XR and communication paradigms toform novel intelligent digital systems.HINTS will provide users with new ways to understand,collaborate with, and control digital systems. These novelways will be based on visual and data-driven platforms whichenable tangible, immersive cognitive interactions within realand virtual realities. Thus, exploiting digital systems in a moreefficient, effective, engaging, and resource-aware condition.Moreover, the systems will be equipped with cognitive featuresbased on AI and ML, which allow users to engage with digitalrealities and data in novel forms. This paper describes theHINTS profile project and its initial results. ©2023, Copyright held by the authors   

Place, publisher, year, edition, pages
Linköping University Electronic Press, 2023
Series
Linköping Electronic Conference Proceedings, ISSN 1650-3686, E-ISSN 1650-3740 ; 199
National Category
Human Computer Interaction
Identifiers
urn:nbn:se:bth-25413 (URN)10.3384/ecp199001 (DOI)9789180752749 (ISBN)
Conference
35th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2023, Karlskrona, June 12-13, 2023
Funder
Knowledge Foundation, 20220068
Available from: 2023-09-22 Created: 2023-09-22 Last updated: 2023-12-28Bibliographically approved
van Dreven, J., Boeva, V., Abghari, S., Grahn, H., Al Koussa, J. & Motoasca, E. (2023). Intelligent Approaches to Fault Detection and Diagnosis in District Heating: Current Trends, Challenges, and Opportunities. Electronics, 12(6), Article ID 1448.
Open this publication in new window or tab >>Intelligent Approaches to Fault Detection and Diagnosis in District Heating: Current Trends, Challenges, and Opportunities
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2023 (English)In: Electronics, E-ISSN 2079-9292, Vol. 12, no 6, article id 1448Article in journal (Refereed) Published
Abstract [en]

This paper presents a comprehensive survey of state-of-the-art intelligent fault detection and diagnosis in district heating systems. Maintaining an efficient district heating system is crucial, as faults can lead to increased heat loss, customer discomfort, and operational cost. Intelligent fault detection and diagnosis can help to identify and diagnose faulty behavior automatically by utilizing artificial intelligence or machine learning. In our survey, we review and discuss 57 papers published in the last 12 years, highlight the recent trends, identify current research gaps, discuss the limitations of current techniques, and provide recommendations for future studies in this area. While there is an increasing interest in the topic, and the past five years have shown much advancement, the absence of open-source high-quality labeled data severely hinders progress. Future research should aim to explore transfer learning, domain adaptation, and semi-supervised learning to improve current performance. Additionally, a researcher should increase knowledge of district heating data using data-centric approaches to establish a solid foundation for future fault detection and diagnosis in district heating.

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
artificial intelligence, data mining, machine learning, review
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-24457 (URN)10.3390/electronics12061448 (DOI)000958374200001 ()2-s2.0-85152400101 (Scopus ID)
Funder
Knowledge Foundation, 20220068
Available from: 2023-04-20 Created: 2023-04-20 Last updated: 2023-04-28Bibliographically approved
Tsiporkova, E., De Vis, M., Klein, S., Hristoskova, A. & Boeva, V. (2023). Mitigating Concept Drift in Distributed Contexts with Dynamic Repository of Federated Models. In: Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023: . Paper presented at IEEE International Conference on Big Data, BigData 2023, Sorrento, 15 December through 18 December 2023 (pp. 2690-2699). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Mitigating Concept Drift in Distributed Contexts with Dynamic Repository of Federated Models
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2023 (English)In: Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023, Institute of Electrical and Electronics Engineers (IEEE), 2023, p. 2690-2699Conference paper, Published paper (Refereed)
Abstract [en]

This paper proposes a novel federated learning methodology, called FedRepo, that copes with concept drift issues in a statistically heterogeneous distributed learning environment. The proposed horizontal federated learning methodology, based on random forest (RF), can be used for collaborative training and maintenance of a dynamic repository of federated RF models, each one customized to a group of clients/devices. The clients are grouped together if their performance patterns with respect to the global RF model are similar. The performance of the customized RF global models is continuously monitored during the inference phase and the repository is accordingly adapted to mitigate the detected concept drift. The proposed methodology is studied and evaluated against an electricity consumption forecasting use case. The evaluation results demonstrate clearly that the proposed methodology is able to deal with concept drift issues in an efficient and adequate fashion without compromising the overall performance of the distributed environment. © 2023 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
clustering, concept drift, distributed learning, federated learning, particle swarm optimization, random forest
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-25990 (URN)10.1109/BigData59044.2023.10386236 (DOI)2-s2.0-85184984122 (Scopus ID)9798350324457 (ISBN)
Conference
IEEE International Conference on Big Data, BigData 2023, Sorrento, 15 December through 18 December 2023
Funder
Knowledge Foundation, 20220068
Available from: 2024-02-28 Created: 2024-02-28 Last updated: 2024-02-29Bibliographically approved
Principal InvestigatorSundstedt, Veronica
Coordinating organisation
Blekinge Institute of Technology
Period
2022-09-01 - 2028-08-31
National Category
Computer Sciences
Identifiers
DiVA, id: project:3003Project, id: 20220068

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HINTS