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Lavesson, Niklas, ProfessorORCID iD iconorcid.org/0000-0002-0535-1761
Publications (10 of 75) Show all publications
Flyckt, J., Andersson, F., Westphal, F., Mansson, A. & Lavesson, N. (2023). Explaining rifle shooting factors through multi-sensor body tracking. Intelligent Data Analysis, 27(2), 535-554
Open this publication in new window or tab >>Explaining rifle shooting factors through multi-sensor body tracking
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2023 (English)In: Intelligent Data Analysis, ISSN 1088-467X, E-ISSN 1571-4128, Vol. 27, no 2, p. 535-554Article in journal (Refereed) Published
Abstract [en]

There is a lack of data-driven training instructions for sports shooters, as instruction has commonly been based on subjective assessments. Many studies have correlated body posture and balance to shooting performance in rifle shooting tasks, but have mostly focused on single aspects of postural control. This study has focused on finding relevant rifle shooting factors by examining the entire body over sequences of time. A data collection was performed with 13 human participants carrying out live rifle shooting scenarios while being recorded with multiple body tracking sensors. A pre-processing pipeline produced a novel skeleton sequence representation, which was used to train a transformer model. The predictions from this model could be explained on a per sample basis using the attention mechanism, and visualised in an interactive format for humans to interpret. It was possible to separate the different phases of a shooting scenario from body posture with a high classification accuracy (80%). Shooting performance could be detected to an extent by separating participants using their strong and weak shooting hand. The dataset and pre-processing pipeline, as well as the techniques for generating explainable predictions presented in this study have laid the groundwork for future research in the sports shooting domain.

Place, publisher, year, edition, pages
IOS Press, 2023
Keywords
Machine learning, explainable AI, transformers, skeleton graphs, rifle shooting
National Category
Sport and Fitness Sciences Computer Sciences
Identifiers
urn:nbn:se:bth-24522 (URN)10.3233/IDA-216457 (DOI)000970251100014 ()2-s2.0-85161187936 (Scopus ID)
Projects
Mining Actionable Patterns from complex Physical Environments (MAPPE)
Funder
Knowledge Foundation, 20180191
Note

A correction to this paper has been published:

DOI: 10.3233/IDA-230950

Available from: 2023-05-12 Created: 2023-05-12 Last updated: 2023-06-27Bibliographically approved
Lidberg, W., Paul, S. S., Westphal, F., Richter, K. F., Lavesson, N., Melniks, R., . . . Agren, A. M. (2023). Mapping Drainage Ditches in Forested Landscapes Using Deep Learning and Aerial Laser Scanning. Journal of irrigation and drainage engineering, 149(3), Article ID 04022051.
Open this publication in new window or tab >>Mapping Drainage Ditches in Forested Landscapes Using Deep Learning and Aerial Laser Scanning
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2023 (English)In: Journal of irrigation and drainage engineering, ISSN 0733-9437, E-ISSN 1943-4774, Vol. 149, no 3, article id 04022051Article in journal (Refereed) Published
Abstract [en]

Extensive use of drainage ditches in European boreal forests and in some parts of North America has resulted in a major change in wetland and soil hydrology and impacted the overall ecosystem functions of these regions. An increasing understanding of the environmental risks associated with forest ditches makes mapping these ditches a priority for sustainable forest and land use management. Here, we present the first rigorous deep learning-based methodology to map forest ditches at regional scale. A deep neural network was trained on airborne laser scanning data (ALS) and 1,607 km of manually digitized ditch channels from 10 regions spread across Sweden. The model correctly mapped 86% of all ditch channels in the test data, with a Matthews correlation coefficient of 0.78. Further, the model proved to be accurate when evaluated on ALS data from other heavily ditched countries in the Baltic Sea Region. This study leads the way in using deep learning and airborne laser scanning for mapping fine-resolution drainage ditches over large areas. This technique requires only one topographical index, which makes it possible to implement on national scales with limited computational resources. It thus provides a significant contribution to the assessment of regional hydrology and ecosystem dynamics in forested landscapes.

Place, publisher, year, edition, pages
American Society of Civil Engineers (ASCE), 2023
Keywords
Ditches, Channel, airborne laser scanning, Deep learning, Semantic segmentation
National Category
Forest Science Remote Sensing
Identifiers
urn:nbn:se:bth-24303 (URN)10.1061/JIDEDH.IRENG-9796 (DOI)000922209100003 ()
Funder
Vinnova, 2014-03319Swedish Research Council Formas, 2019-00173Swedish Research Council Formas, 2021-00115
Available from: 2023-02-24 Created: 2023-02-24 Last updated: 2023-02-24Bibliographically approved
Flyckt, J., Andersson, F., Lavesson, N., Nilsson, L. & Ågren, A. M. (2022). Detecting ditches using supervised learning on high-resolution digital elevation models. Expert systems with applications, 201, Article ID 116961.
Open this publication in new window or tab >>Detecting ditches using supervised learning on high-resolution digital elevation models
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2022 (English)In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 201, article id 116961Article in journal (Refereed) Published
Abstract [en]

Drained wetlands can constitute a large source of greenhouse gas emissions, but the drainage networks in these wetlands are largely unmapped, and better maps are needed to aid in forest production and to better understand the climate consequences. We develop a method for detecting ditches in high resolution digital elevation models derived from LiDAR scans. Thresholding methods using digital terrain indices can be used to detect ditches. However, a single threshold generally does not capture the variability in the landscape, and generates many false positives and negatives. We hypothesise that, by combining the digital terrain indices using supervised learning, we can improve ditch detection at a landscape-scale. In addition to digital terrain indices, additional features are generated by transforming the data to include neighbouring cells for better ditch predictions. A Random Forests classifier is used to locate the ditches, and its probability output is processed to remove noise, and binarised to produce the final ditch prediction. The confidence interval for the Cohen's Kappa index ranges [0.655, 0.781] between the evaluation plots with a confidence level of 95%. The study demonstrates that combining information from a suite of digital terrain indices using machine learning provides an effective technique for automatic ditch detection at a landscape-scale, aiding in both practical forest management and in combatting climate change. © 2022 The Authors

Place, publisher, year, edition, pages
Elsevier Ltd, 2022
Keywords
Classification and regression trees, Geographic information systems, Machine learning, Supervised learning by classification, Classification (of information), Climate change, Decision trees, Digital instruments, E-learning, Forestry, Gas emissions, Geomorphology, Greenhouse gases, Information use, Metadata, Supervised learning, Surveying, Wetlands, Classification trees, Digital elevation model, Digital terrain, Drainage networks, Forest production, Greenhouse gas emissions, High resolution, Landscape scale, Regression trees
National Category
Physical Geography Remote Sensing
Identifiers
urn:nbn:se:bth-22881 (URN)10.1016/j.eswa.2022.116961 (DOI)000830107400002 ()2-s2.0-85128240716 (Scopus ID)
Funder
VinnovaSwedish Research Council Formas
Note

open access

Available from: 2022-04-29 Created: 2022-04-29 Last updated: 2022-08-12Bibliographically approved
García Martín, E., Bifet, A. & Lavesson, N. (2021). Energy Modeling of Hoeffding Tree Ensembles. Intelligent Data Analysis, 25(1), 81-104
Open this publication in new window or tab >>Energy Modeling of Hoeffding Tree Ensembles
2021 (English)In: Intelligent Data Analysis, ISSN 1088-467X, E-ISSN 1571-4128, Vol. 25, no 1, p. 81-104Article in journal (Refereed) Published
Abstract [en]

Energy consumption reduction has been an increasing trend in machine learning over the past few years due to its socio-ecological importance. In new challenging areas such as edge computing, energy consumption and predictive accuracy are key variables during algorithm design and implementation. State-of-the-art ensemble stream mining algorithms are able to create highly accurate predictions at a substantial energy cost. This paper introduces the nmin adaptation method to ensembles of Hoeffding tree algorithms, to further reduce their energy consumption without sacrificing accuracy. We also present extensive theoretical energy models of such algorithms, detailing their energy patterns and how nmin adaptation affects their energy consumption. We have evaluated the energy efficiency and accuracy of the nmin adaptation method on five different ensembles of Hoeffding trees under 11 publicly available datasets. The results show that we are able to reduce the energy consumption significantly, by 21% on average, affecting accuracy by less than one percent on average. © 2021 - IOS Press. All rights reserved.

Place, publisher, year, edition, pages
IOS Press, 2021
Keywords
Energy efficiency, Energy utilization, Forestry, Adaptation methods, Algorithm design, Energy patterns, Predictive accuracy, Socio-ecological, State of the art, Substantial energy, Tree algorithms, Green computing
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-19151 (URN)10.3233/IDA-194890 (DOI)000618065600006 ()
Funder
Knowledge Foundation, 20140032
Note

open access

Available from: 2020-01-23 Created: 2020-01-23 Last updated: 2021-04-01Bibliographically approved
García Martín, E., Lavesson, N., Grahn, H., Casalicchio, E. & Boeva, V. (2021). Energy-Aware Very Fast Decision Tree. International Journal of Data Science and Analytics, 11(2), 105-126
Open this publication in new window or tab >>Energy-Aware Very Fast Decision Tree
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2021 (English)In: International Journal of Data Science and Analytics, ISSN 2364-415X, Vol. 11, no 2, p. 105-126Article in journal (Refereed) Published
Abstract [en]

Recently machine learning researchers are designing algorithms that can run in embedded and mobile devices, which introduces additional constraints compared to traditional algorithm design approaches. One of these constraints is energy consumption, which directly translates to battery capacity for these devices. Streaming algorithms, such as the Very Fast Decision Tree (VFDT), are designed to run in such devices due to their high velocity and low memory requirements. However, they have not been designed with an energy efficiency focus. This paper addresses this challenge by presenting the nmin adaptation method, which reduces the energy consumption of the VFDT algorithm with only minor effects on accuracy. nmin adaptation allows the algorithm to grow faster in those branches where there is more confidence to create a split, and delays the split on the less confident branches. This removes unnecessary computations related to checking for splits but maintains similar levels of accuracy. We have conducted extensive experiments on 29 public datasets, showing that the VFDT with nmin adaptation consumes up to 31% less energy than the original VFDT, and up to 96% less energy than the CVFDT (VFDT adapted for concept drift scenarios), trading off up to 1.7 percent of accuracy.

Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2021
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-19150 (URN)10.1007/s41060-021-00246-4 (DOI)000631559600001 ()2-s2.0-85102938796 (Scopus ID)
Funder
Knowledge Foundation, 20140032
Note

open access

Available from: 2020-01-23 Created: 2020-01-23 Last updated: 2021-07-30Bibliographically approved
Devagiri, V. M., Boeva, V., Abghari, S., Basiri, F. & Lavesson, N. (2021). Multi-view data analysis techniques for monitoring smart building systems. Sensors, 21(20), Article ID 6775.
Open this publication in new window or tab >>Multi-view data analysis techniques for monitoring smart building systems
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2021 (English)In: Sensors, E-ISSN 1424-8220, Vol. 21, no 20, article id 6775Article in journal (Refereed) Published
Abstract [en]

In smart buildings, many different systems work in coordination to accomplish their tasks. In this process, the sensors associated with these systems collect large amounts of data generated in a streaming fashion, which is prone to concept drift. Such data are heterogeneous due to the wide range of sensors collecting information about different characteristics of the monitored systems. All these make the monitoring task very challenging. Traditional clustering algorithms are not well equipped to address the mentioned challenges. In this work, we study the use of MV Multi-Instance Clustering algorithm for multi-view analysis and mining of smart building systems’ sensor data. It is demonstrated how this algorithm can be used to perform contextual as well as integrated analysis of the systems. Various scenarios in which the algorithm can be used to analyze the data generated by the systems of a smart building are examined and discussed in this study. In addition, it is also shown how the extracted knowledge can be visualized to detect trends in the systems’ behavior and how it can aid domain experts in the systems’ maintenance. In the experiments conducted, the proposed approach was able to successfully detect the deviating behaviors known to have previously occurred and was also able to identify some new deviations during the monitored period. Based on the results obtained from the experiments, it can be concluded that the proposed algorithm has the ability to be used for monitoring, analysis, and detecting deviating behaviors of the systems in a smart building domain. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Place, publisher, year, edition, pages
MDPI, 2021
Keywords
Closed patterns, Evolutionary clustering, Formal concept analysis, Multi-instance learning, Multi-view clustering, Smart buildings, Streaming data, Buildings, Clustering algorithms, Building systems, Closed pattern, Concept drifts, Data analysis techniques, Large amounts of data, Multi-views
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-22225 (URN)10.3390/s21206775 (DOI)000716120000001 ()2-s2.0-85116801515 (Scopus ID)
Note

open access

Available from: 2021-10-22 Created: 2021-10-22 Last updated: 2022-02-11Bibliographically approved
Westphal, F., Grahn, H. & Lavesson, N. (2020). Representative Image Selection for Data Efficient Word Spotting. In: Bai X.,Karatzas D.,Lopresti D. (Ed.), Lecture Notes in Computer Science: . Paper presented at 14th IAPR International Workshop on Document Analysis Systems, DAS 2020, Wuhan, China, 26 July 2020 through 29 July 2020 (pp. 383-397). Springer, 12116
Open this publication in new window or tab >>Representative Image Selection for Data Efficient Word Spotting
2020 (English)In: Lecture Notes in Computer Science / [ed] Bai X.,Karatzas D.,Lopresti D., Springer, 2020, Vol. 12116, p. 383-397Conference paper, Published paper (Refereed)
Abstract [en]

This paper compares three different word image representations as base for label free sample selection for word spotting in historical handwritten documents. These representations are a temporal pyramid representation based on pixel counts, a graph based representation, and a pyramidal histogram of characters (PHOC) representation predicted by a PHOCNet trained on synthetic data. We show that the PHOC representation can help to reduce the amount of required training samples by up to 69% depending on the dataset, if it is learned iteratively in an active learning like fashion. While this works for larger datasets containing about 1 700 images, for smaller datasets with 100 images, we find that the temporal pyramid and the graph representation perform better.

Place, publisher, year, edition, pages
Springer, 2020
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349
Keywords
word spotting, sample selection, graph representation, PHOCNet, active learning
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:bth-19528 (URN)10.1007/978-3-030-57058-3_27 (DOI)000885905800027 ()9783030570576 (ISBN)
Conference
14th IAPR International Workshop on Document Analysis Systems, DAS 2020, Wuhan, China, 26 July 2020 through 29 July 2020
Funder
Knowledge Foundation, 20140032
Note

open access

Available from: 2020-05-29 Created: 2020-05-29 Last updated: 2023-03-24Bibliographically approved
Westphal, F., Lavesson, N. & Grahn, H. (2019). A Case for Guided Machine Learning. In: Andreas Hozinger, Peter Kieseberg, A Min Tjoa and Edgar Weippl (Ed.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): . Paper presented at 3rd IFIP Cross Domain Conference for Machine Learning and Knowledge Extraction, CD-MAKE 2019; Canterbury; United Kingdom; 26 August 2019 through 29 August (pp. 353-361). Springer, 11713
Open this publication in new window or tab >>A Case for Guided Machine Learning
2019 (English)In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) / [ed] Andreas Hozinger, Peter Kieseberg, A Min Tjoa and Edgar Weippl, Springer, 2019, Vol. 11713, p. 353-361Conference paper, Published paper (Refereed)
Abstract [en]

Involving humans in the learning process of a machine learning algorithm can have many advantages ranging from establishing trust into a particular model to added personalization capabilities to reducing labeling efforts. While these approaches are commonly summarized under the term interactive machine learning (iML), no unambiguous definition of iML exists to clearly define this area of research. In this position paper, we discuss the shortcomings of current definitions of iML and propose and define the term guided machine learning (gML) as an alternative.

Place, publisher, year, edition, pages
Springer, 2019
Series
Lecture Notes in Computer Science, ISSN 03029743, E-ISSN 16113349
Keywords
guided machine learning, interactive machine learning, human-in-the-loop, definition
National Category
Human Computer Interaction Computer Sciences
Identifiers
urn:nbn:se:bth-18708 (URN)10.1007/978-3-030-29726-8_22 (DOI)000558148400022 ()978-3-030-29726-8 (ISBN)
Conference
3rd IFIP Cross Domain Conference for Machine Learning and Knowledge Extraction, CD-MAKE 2019; Canterbury; United Kingdom; 26 August 2019 through 29 August
Funder
Knowledge Foundation, 20140032
Note

open access

Available from: 2019-09-27 Created: 2019-09-27 Last updated: 2022-05-06Bibliographically approved
Angelova, M., Vishnu Manasa, D., Boeva, V., Linde, P. & Lavesson, N. (2019). An Expertise Recommender System based on Data from an Institutional Repository (DiVA). In: Leslie Chan, Pierre Mounier (Ed.), Connecting the Knowledge Common from Projects to sustainable Infrastructure: The 22nd International conference on Electronic Publishing - Revised Selected Papers (pp. 135-149). OpenEdition Press
Open this publication in new window or tab >>An Expertise Recommender System based on Data from an Institutional Repository (DiVA)
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2019 (English)In: Connecting the Knowledge Common from Projects to sustainable Infrastructure: The 22nd International conference on Electronic Publishing - Revised Selected Papers / [ed] Leslie Chan, Pierre Mounier, OpenEdition Press , 2019, p. 135-149Chapter in book (Refereed)
Abstract [en]

Finding experts in academics is an important practical problem, e.g. recruiting reviewersfor reviewing conference, journal or project submissions, partner matching for researchproposals, finding relevant M. Sc. or Ph. D. supervisors etc. In this work, we discuss anexpertise recommender system that is built on data extracted from the Blekinge Instituteof Technology (BTH) instance of the institutional repository system DiVA (DigitalScientific Archive). DiVA is a publication and archiving platform for research publicationsand student essays used by 46 publicly funded universities and authorities in Sweden andthe rest of the Nordic countries (www.diva-portal.org). The DiVA classification system isbased on the Swedish Higher Education Authority (UKÄ) and the Statistic Sweden's (SCB)three levels classification system. Using the classification terms associated with studentM. Sc. and B. Sc. theses published in the DiVA platform, we have developed a prototypesystem which can be used to identify and recommend subject thesis supervisors in academy.

Place, publisher, year, edition, pages
OpenEdition Press, 2019
Keywords
Text mining, Recommender system, Institutional repository, Ontology
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:bth-18095 (URN)979-1-0365-3801-8 (ISBN)979-1-0365-3802-5 (ISBN)
Note

open access

Available from: 2019-06-18 Created: 2019-06-18 Last updated: 2021-10-25Bibliographically approved
García Martín, E., Lavesson, N., Grahn, H., Casalicchio, E. & Boeva, V. (2019). How to Measure Energy Consumption in Machine Learning Algorithms. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): ECMLPKDD 2018: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases Workshops. Lecture Notes in Computer Science. Springer, Cham. Paper presented at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2018; Dublin; Ireland; 10 September 2018 through 14 September 2018 (pp. 243-255). Springer, 11329
Open this publication in new window or tab >>How to Measure Energy Consumption in Machine Learning Algorithms
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2019 (English)In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): ECMLPKDD 2018: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases Workshops. Lecture Notes in Computer Science. Springer, Cham, Springer, 2019, Vol. 11329, p. 243-255Conference paper, Published paper (Refereed)
Abstract [en]

Machine learning algorithms are responsible for a significant amount of computations. These computations are increasing with the advancements in different machine learning fields. For example, fields such as deep learning require algorithms to run during weeks consuming vast amounts of energy. While there is a trend in optimizing machine learning algorithms for performance and energy consumption, still there is little knowledge on how to estimate an algorithm’s energy consumption. Currently, a straightforward cross-platform approach to estimate energy consumption for different types of algorithms does not exist. For that reason, well-known researchers in computer architecture have published extensive works on approaches to estimate the energy consumption. This study presents a survey of methods to estimate energy consumption, and maps them to specific machine learning scenarios. Finally, we illustrate our mapping suggestions with a case study, where we measure energy consumption in a big data stream mining scenario. Our ultimate goal is to bridge the current gap that exists to estimate energy consumption in machine learning scenarios.

Place, publisher, year, edition, pages
Springer, 2019
Series
Lecture Notes in Computer Science, ISSN 03029743, E-ISSN 16113349 ; 11329
Keywords
Computer architecture, Energy efficiency, Green computing, Machine learning
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-17209 (URN)10.1007/978-3-030-13453-2_20 (DOI)9783030134525 (ISBN)
Conference
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2018; Dublin; Ireland; 10 September 2018 through 14 September 2018
Funder
Knowledge Foundation, 20140032
Note

open access

Available from: 2018-11-01 Created: 2018-11-01 Last updated: 2023-02-16Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0002-0535-1761

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