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Aoga, J. O. R., Bae, J., Veljanoska, S., Nijssen, S. & Schaus, P. (2024). Impact of Weather Factors on Migration Intention Using Machine Learning Algorithms. Operations Research Forum, 5(1), Article ID 8.
Open this publication in new window or tab >>Impact of Weather Factors on Migration Intention Using Machine Learning Algorithms
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2024 (English)In: Operations Research Forum, E-ISSN 2662-2556, Vol. 5, no 1, article id 8Article in journal (Refereed) Published
Abstract [en]

A growing attention in the empirical literature has been paid on the incidence of climate shocks and change on migration decisions. Previous literature leads to different results and uses a multitude of traditional empirical approaches. This paper proposes a tree-based Machine Learning (ML) approach to analyze the role of the weather shocks toward an individual’s intention to migrate in the six agriculture-dependent-economy countries such as Burkina Faso, Ivory Coast, Mali, Mauritania, Niger, and Senegal. We performed several tree-based algorithms (e.g., XGB, Random Forest) using the train-validation-test workflow to build robust and noise-resistant approaches. Then we determine the important features showing in which direction they influence the migration intention. This ML-based estimation accounts for features such as weather shocks captured by the Standardized Precipitation-Evapotranspiration Index (SPEI) for different timescales and various socioeconomic features/covariates. We find that (i) the weather features improve the prediction performance, although socioeconomic characteristics have more influence on migration intentions, (ii) a country-specific model is necessary, and (iii) the international move is influenced more by the longer timescales of SPEIs while general move (which includes internal move) by that of shorter timescales.

Place, publisher, year, edition, pages
Springer Nature, 2024
Keywords
Migration, Weather shocks, Machine learning, Tree-based algorithms
National Category
Probability Theory and Statistics
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-23539 (URN)10.1007/s43069-023-00271-y (DOI)2-s2.0-85181458324 (Scopus ID)
Note

John O.R. Aoga: johnaoga@gmail.com

This research is supported by the ARC Convention on “New approaches to understanding andmodeling global migration trends” (convention 18/23-091). This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curiegrant agreement No. 754412.

Available from: 2024-01-15 Created: 2024-01-15 Last updated: 2024-01-24Bibliographically approved
Ståhl, N., Mathiason, G. & Bae, J. (2022). Utilising Data from Multiple Production Lines for Predictive Deep Learning Models. In: Kenji Matsui; Sigeru Omatu; Tan Yigitcanlar; Sara Rodríguez González (Ed.), Distributed Computing and Artificial Intelligence, Volume 1: 18th International Conference. Paper presented at 18th International Symposium on Distributed Computing and Artificial Intelligence, DCAI 2021, Salamanca, 6 October 2021 - 8 October 2021, 264809 (pp. 67-76). Cham: Springer
Open this publication in new window or tab >>Utilising Data from Multiple Production Lines for Predictive Deep Learning Models
2022 (English)In: Distributed Computing and Artificial Intelligence, Volume 1: 18th International Conference / [ed] Kenji Matsui; Sigeru Omatu; Tan Yigitcanlar; Sara Rodríguez González, Cham: Springer, 2022, p. 67-76Conference paper, Published paper (Refereed)
Abstract [en]

A Basic Oxygen Furnace (BOF) for steel making is a complex industrial process that is difficult to monitor due to the harsh environment, so the collected production data is very limited given the process complexity. Also, such production data has a low degree of variability. An accurate machine learning (ML) model for predicting production outcome requires both large and varied data, so utilising data from multiple BOFs will allow for more capable ML models, since both the amount and variability of data increases. Data collection setups for different BOFs are different, such that data sets are not compatible to directly join for ML training. Our approach is to let a neural network benefit from these collection differences in a joint training model. We present a neural network-based approach that simultaneously and jointly co-trains on several data sets. Our novelty is that the first network layer finds an internal representation of each individual BOF, while the other layers use this representation to concurrently learn a common BOF model. Our evaluation shows that the prediction accuracy of the common model increases compared to separate models trained on individual furnaces’ data sets. It is clear that multiple data sets can be utilised this way to increase model accuracy for better production prediction performance. For the industry, this means that the amount of available data for model training increases and thereby more capable ML models can be trained when having access to multiple data sets describing the same or similar manufacturing processes. 

Place, publisher, year, edition, pages
Cham: Springer, 2022
Series
Lecture Notes in Networks and Systems, ISSN 2367-3370, E-ISSN 2367-3389 ; 327
Keywords
Data fusion, Deep learning, Joint training, Steel making
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-20613 (URN)10.1007/978-3-030-86261-9_7 (DOI)2-s2.0-85115207112 (Scopus ID)978-3-030-86260-2 (ISBN)978-3-030-86261-9 (ISBN)
Conference
18th International Symposium on Distributed Computing and Artificial Intelligence, DCAI 2021, Salamanca, 6 October 2021 - 8 October 2021, 264809
Note

© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Available from: 2021-09-30 Created: 2021-09-30 Last updated: 2021-10-29Bibliographically approved
Bae, J., Mathiason, G., Li, Y., Kojola, N. & Ståhl, N. (2021). Understanding Robust Target Prediction in Basic Oxygen Furnace. In: IEIM 2021: The 2nd International Conference on Industrial Engineering and Industrial Management. Paper presented at 2nd International Conference on Industrial Engineering and Industrial Management, IEIM 2021, Virtual, Online, Spain, 8 January 2021 through 11 January 2021, Code 168526 (pp. 56-62). New York, NY: Association for Computing Machinery (ACM)
Open this publication in new window or tab >>Understanding Robust Target Prediction in Basic Oxygen Furnace
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2021 (English)In: IEIM 2021: The 2nd International Conference on Industrial Engineering and Industrial Management, New York, NY: Association for Computing Machinery (ACM), 2021, p. 56-62Conference paper, Published paper (Refereed)
Abstract [en]

The problem of using machine learning (ML) to predict the process endpoint for a Basic Oxygen Furnace (BOF) process used for steelmaking has been largely studied. However, current research often lacks both the usage of a rich dataset and does not address revealing influential factors that explain the process. The process is complex and difficult to control and has a multi-objective target endpoint with a proper range of heat temperature combined with sufficiently low levels of carbon and phosphorus. Reaching this endpoint requires skilled process operators, who are manually controlling the heat throughout the process by using both implicit and explicit control variables in their decisions. Trained ML models can reach good BOF target prediction results, but it is still a challenge to extract the influential factors that are significant to the ML prediction accuracy. Thus, it becomes a challenge to explain and validate an ML prediction model that claims to capture the process well. This paper makes use of a complex and full production dataset to evaluate and compare different approaches for understanding how the data can determine the process target prediction. One approach is based on the collected process data and the other on the ML approach trained on that data to find the influential factors. These complementary approaches aim to explain the BOF process to reveal actionable information on how to improve process control.

Place, publisher, year, edition, pages
New York, NY: Association for Computing Machinery (ACM), 2021
Series
ACM International Conference Proceeding Series
Keywords
Basic Oxygen Furnace, Explainable AI, Machine learning, Production Data, Basic oxygen converters, Forecasting, Industrial management, Oxygen, Predictive analytics, Steelmaking furnaces, Implicit and explicit controls, Influential factors, Multi objective, Prediction accuracy, Prediction model, Process data, Process operators, Target prediction, Process control
National Category
Computer Sciences Computer and Information Sciences Metallurgy and Metallic Materials
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-19701 (URN)10.1145/3447432.3447435 (DOI)2-s2.0-85104954252 (Scopus ID)978-1-4503-8914-3 (ISBN)
Conference
2nd International Conference on Industrial Engineering and Industrial Management, IEIM 2021, Virtual, Online, Spain, 8 January 2021 through 11 January 2021, Code 168526
Funder
Knowledge Foundation, 20170297
Note

© 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM.

We would like to thank Carl Ellström, Patrik Wikström, and Lennart Gustavsson at SSAB for their close collaboration in this project. This project is funded by the Knowledge Foundation in Sweden, under grant number 20170297.

Available from: 2021-05-17 Created: 2021-05-17 Last updated: 2021-09-13Bibliographically approved
Bae, J. & Aoga, J. (2020). Forecasting migration intention using multivariate time series. In: ICVISP 2020: Proceedings of the 2020 4th International Conference on Vision, Image and Signal Processing. Paper presented at ICVISP 2020: 4th International Conference on Vision, Image and Signal Processing, Bangkok, Thailand, 9 December 2020 through 11 December 2020 (pp. 1-6). New York: Association for Computing Machinery (ACM), Article ID 3448883.
Open this publication in new window or tab >>Forecasting migration intention using multivariate time series
2020 (English)In: ICVISP 2020: Proceedings of the 2020 4th International Conference on Vision, Image and Signal Processing, New York: Association for Computing Machinery (ACM), 2020, p. 1-6, article id 3448883Conference paper, Published paper (Refereed)
Abstract [en]

This paper aims to analyze international migrations in western African countries using irregular multivariate monthly time series containing a few values. Existing methods of filling in missing values have limitations because there are not enough values to infer them. In this study, we explore two approaches to solve this problem. One approach is to aggregate the values annually to eliminate missing values. The other is to use the Random Forest (RF) based approach to fill in the missing values. Then, we predict the international migration intentions using deep learning approaches and time series dataset. We demonstrate that a RF-based imputation outperforms a zero filling approach (used as the baseline) with Long Short-Term Memory (LSTM) method. Moreover, we show that analyzing the monthly subregion-based time series provides better insights than the yearly country-based time series. 

Place, publisher, year, edition, pages
New York: Association for Computing Machinery (ACM), 2020
Series
ACM International Conference Proceeding Series
Keywords
Forecasting, Imputation, Long Short-term Memory, Migration Intention, Recurrent Neural Network, Decision trees, Deep learning, Image processing, Regional planning, Time series, Filling in, Learning approach, Missing values, Multivariate time series, Zero filling
National Category
Computer and Information Sciences Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-19583 (URN)10.1145/3448823.3448883 (DOI)2-s2.0-85102951113 (Scopus ID)978-1-4503-8953-2 (ISBN)
Conference
ICVISP 2020: 4th International Conference on Vision, Image and Signal Processing, Bangkok, Thailand, 9 December 2020 through 11 December 2020
Note

© 2020 ACM.

Article No.: 37

Available from: 2021-04-01 Created: 2021-04-01 Last updated: 2021-04-26Bibliographically approved
Bae, J., Helldin, T., Riveiro, M., Nowaczyk, S., Bouguelia, M.-R. & Falkman, G. (2020). Interactive clustering: A comprehensive review. ACM Computing Surveys, 53(1), Article ID 1.
Open this publication in new window or tab >>Interactive clustering: A comprehensive review
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2020 (English)In: ACM Computing Surveys, ISSN 0360-0300, E-ISSN 1557-7341, Vol. 53, no 1, article id 1Article in journal (Refereed) Published
Abstract [en]

In this survey, 105 papers related to interactive clustering were reviewed according to seven perspectives: (1) on what level is the interaction happening, (2) which interactive operations are involved, (3) how user feedback is incorporated, (4) how interactive clustering is evaluated, (5) which data and (6) which clustering methods have been used, and (7) what outlined challenges there are. This article serves as a comprehensive overview of the field and outlines the state of the art within the area as well as identifies challenges and future research needs.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2020
Keywords
Clustering, Evaluation, Feedback, Interaction, Interactive, User, Surveys, Computer science
National Category
Computer Sciences Human Computer Interaction
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-18266 (URN)10.1145/3340960 (DOI)000582585800001 ()2-s2.0-85079573488 (Scopus ID)
Note

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). © 2020 Copyright held by the owner/author(s).

Available from: 2020-02-28 Created: 2020-02-28 Last updated: 2020-11-17Bibliographically approved
Bae, J., Li, Y., Ståhl, N., Mathiason, G. & Kojola, N. (2020). Using Machine Learning for Robust Target Prediction in a Basic Oxygen Furnace System. Metallurgical and materials transactions. B, process metallurgy and materials processing science, 51(4), 1632-1645
Open this publication in new window or tab >>Using Machine Learning for Robust Target Prediction in a Basic Oxygen Furnace System
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2020 (English)In: Metallurgical and materials transactions. B, process metallurgy and materials processing science, ISSN 1073-5615, E-ISSN 1543-1916, Vol. 51, no 4, p. 1632-1645Article in journal (Refereed) Published
Abstract [en]

The steel-making process in a Basic Oxygen Furnace (BOF) must meet a combination of target values such as the final melt temperature and upper limits of the carbon and phosphorus content of the final melt with minimum material loss. An optimal blow end time (cut-off point), where these targets are met, often relies on the experience and skill of the operators who control the process, using both collected sensor readings and an implicit understanding of how the process develops. If the precision of hitting the optimal cut-off point can be improved, this immediately increases productivity as well as material and energy efficiency, thus decreasing environmental impact and cost. We examine the usage of standard machine learning models to predict the end-point targets using a full production dataset. Various causes of prediction uncertainty are explored and isolated using a combination of raw data and engineered features. In this study, we reach robust temperature, carbon, and phosphorus prediction hit rates of 88, 92, and 89 pct, respectively, using a large production dataset. © 2020, The Author(s).

Place, publisher, year, edition, pages
Springer, 2020
Keywords
Steelmaking, Basic oxygen converters, BOF steelmaking
National Category
Metallurgy and Metallic Materials Computer Sciences Computer and Information Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-18500 (URN)10.1007/s11663-020-01853-5 (DOI)000550894300031 ()2-s2.0-85085877036 (Scopus ID)
Available from: 2020-06-12 Created: 2020-06-12 Last updated: 2021-05-18Bibliographically approved
Olson, N. & Bae, J. (2019). Biosensors-Publication Trends and Knowledge Domain Visualization. Sensors, 19(11), Article ID 2615.
Open this publication in new window or tab >>Biosensors-Publication Trends and Knowledge Domain Visualization
2019 (English)In: Sensors, E-ISSN 1424-8220, Vol. 19, no 11, article id 2615Article in journal (Refereed) Published
Abstract [en]

The number of scholarly publications on the topic of biosensors has increased rapidly; as a result, it is no longer easy to build an informed overview of the developments solely by manual means. Furthermore, with many new research results being continually published, it is useful to form an up-to-date understanding of the recent trends or emergent directions in the field. This paper utilizes bibliometric methods to provide an overview of the developments in the topic based on scholarly publications. The results indicate an increasing interest in the topic of biosensor(s) with newly emerging sub-topics. The US is identified as the country with highest total contribution to this area, but as a collective, EU countries top the list of total contributions. An examination of trends over the years indicates that in recent years, China-based authors have been more productive in this area. If research contribution per capita is considered, Singapore takes the top position, followed by Sweden, Switzerland and Denmark. While the number of publications on biosensors seems to have declined in recent years in the PubMed database, this is not the case in the Web of Science database. However, there remains an indication that the rate of growth in the more recent years is slowing. This paper also presents a comparison of the developments in publications on biosensors with the full set of publications in two of the main journals in the field. In more recent publications, synthetic biology, smartphone, fluorescent biosensor, and point-of-care testing are among the terms that have received more attention. The study also identifies the top authors and journals in the field, and concludes with a summary and suggestions for follow up research.

Place, publisher, year, edition, pages
MDPI, 2019
Keywords
bibliometrics, biosensors, emerging trends, scholarly publications
National Category
Information Studies Biomedical Laboratory Science/Technology
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-17409 (URN)10.3390/s19112615 (DOI)000472133300193 ()31181820 (PubMedID)2-s2.0-85067796636 (Scopus ID)
Available from: 2019-07-08 Created: 2019-07-08 Last updated: 2022-02-10Bibliographically approved
Bae, J., Karlsson, A., Mellin, J., Ståhl, N. & Torra, V. (2019). Complex Data Analysis. In: Alan Said, Vicenç Torra (Ed.), Data science in Practice: (pp. 157-169). Springer
Open this publication in new window or tab >>Complex Data Analysis
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2019 (English)In: Data science in Practice / [ed] Alan Said, Vicenç Torra, Springer, 2019, p. 157-169Chapter in book (Refereed)
Abstract [en]

Data science applications often need to deal with data that does not fit into the standard entity-attribute-value model. In this chapter we discuss three of these other types of data. We discuss texts, images and graphs. The importance of social media is one of the reason for the interest on graphs as they are a way to represent social networks and, in general, any type of interaction between people. In this chapter we present examples of tools that can be used to extract information and, thus, analyze these three types of data. In particular, we discuss topic modeling using a hierarchical statistical model as a way to extract relevant topics from texts, image analysis using convolutional neural networks, and measures and visual methods to summarize information from graphs.

Place, publisher, year, edition, pages
Springer, 2019
Series
Studies in Big Data, ISSN 2197-6503, E-ISSN 2197-6511 ; 46
National Category
Computer and Information Sciences Computer Sciences Other Computer and Information Science
Research subject
Skövde Artificial Intelligence Lab (SAIL); Distributed Real-Time Systems
Identifiers
urn:nbn:se:his:diva-16811 (URN)10.1007/978-3-319-97556-6_9 (DOI)000464719500010 ()978-3-319-97556-6 (ISBN)978-3-319-97555-9 (ISBN)
Available from: 2019-04-24 Created: 2019-04-24 Last updated: 2020-06-18Bibliographically approved
Holst, A., Bouguelia, M.-R., Görnerup, O., Pashami, S., Al-Shishtawy, A., Falkman, G., . . . Soliman, A. (2019). Eliciting structure in data. In: Christoph Trattner, Denis Parra, Nathalie Riche (Ed.), CEUR Workshop Proceedings: . Paper presented at 2019 Joint ACM IUI Workshops, ACMIUI-WS 2019, Los Angeles, United States, 20 March 2019. CEUR-WS, 2327
Open this publication in new window or tab >>Eliciting structure in data
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2019 (English)In: CEUR Workshop Proceedings / [ed] Christoph Trattner, Denis Parra, Nathalie Riche, CEUR-WS , 2019, Vol. 2327Conference paper, Published paper (Refereed)
Abstract [en]

This paper demonstrates how to explore and visualize different types of structure in data, including clusters, anomalies, causal relations, and higher order relations. The methods are developed with the goal of being as automatic as possible and applicable to massive, streaming, and distributed data. Finally, a decentralized learning scheme is discussed, enabling finding structure in the data without collecting the data centrally. 

Place, publisher, year, edition, pages
CEUR-WS, 2019
Series
CEUR Workshop Proceedings, ISSN 1613-0073 ; 2327
Keywords
Anomaly detection, Causal inference, Clustering, Distributed analytics, Higher-order structure, Information visualization, Information systems, User interfaces, Causal inferences, Data acquisition
National Category
Computer Sciences Human Computer Interaction
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-16748 (URN)2-s2.0-85063227224 (Scopus ID)
Conference
2019 Joint ACM IUI Workshops, ACMIUI-WS 2019, Los Angeles, United States, 20 March 2019
Available from: 2019-04-05 Created: 2019-04-05 Last updated: 2020-06-18Bibliographically approved
Said, A., Bae, J., Parra, D. & Pashami, S. (2019). IDM-WSDM 2019: Workshop on interactive data mining. In: WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining: . Paper presented at 12th ACM International Conference on Web Search and Data Mining, WSDM 2019, 11 February 2019 through 15 February 2019 (pp. 846-847). Association for Computing Machinery (ACM)
Open this publication in new window or tab >>IDM-WSDM 2019: Workshop on interactive data mining
2019 (English)In: WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining, Association for Computing Machinery (ACM), 2019, p. 846-847Conference paper, Published paper (Refereed)
Abstract [en]

The first Workshop on Interactive Data Mining is held in Melbourne, Australia, on February 15, 2019 and is co-located with 12th ACM International Conference on Web Search and Data Mining (WSDM 2019). The goal of this workshop is to share and discuss research and projects that focus on interaction with and interactivity of data mining systems. The program includes invited speaker, presentation of research papers, and a discussion session. © 2019 held by the owner/author(s).

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2019
Keywords
Data mining, Human-in-the-loop, Interactive classification and clustering, Interactive dashboards, Visual modeling, Information retrieval, Websites, Data mining system, Interactive classification, Interactive data mining, Melbourne, Australia, Research papers, Visual model
National Category
Other Computer and Information Science Information Systems Interaction Technologies
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-16671 (URN)10.1145/3289600.3291376 (DOI)000482120400120 ()2-s2.0-85061736320 (Scopus ID)978-1-4503-5940-5 (ISBN)
Conference
12th ACM International Conference on Web Search and Data Mining, WSDM 2019, 11 February 2019 through 15 February 2019
Note

Conference code: 144821; Export Date: 1 March 2019; Conference Paper

Available from: 2019-03-01 Created: 2019-03-01 Last updated: 2020-06-18Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-2415-7243

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