his.sePublications
Change search
Link to record
Permanent link

Direct link
BETA
Publications (10 of 21) Show all publications
Ståhl, N., Falkman, G., Karlsson, A., Mathiason, G. & Boström, J. (2019). Improving the use of deep convolutional neural networks for the prediction of molecular properties. In: Florentino Fdez-Riverola, Mohd Saberi Mohamad, Miguel Rocha, Juan F. De Paz, Pascual González (Ed.), Practical Applications of Computational Biology and Bioinformatics, 12th International Conference: . Paper presented at PACBB2018: International Conference on Practical Applications of Computational Biology & Bioinformatics, Toledo, June 20-22, 2018 (pp. 71-79). Cham: Springer
Open this publication in new window or tab >>Improving the use of deep convolutional neural networks for the prediction of molecular properties
Show others...
2019 (English)In: Practical Applications of Computational Biology and Bioinformatics, 12th International Conference / [ed] Florentino Fdez-Riverola, Mohd Saberi Mohamad, Miguel Rocha, Juan F. De Paz, Pascual González, Cham: Springer, 2019, p. 71-79Conference paper, Published paper (Refereed)
Abstract [en]

We present a flexible deep convolutional neural network method for the analyse of arbitrary sized graph structures representing molecules. The method makes use of RDKit, an open-source cheminformatics software, allowing the incorporation of any global molecular (such as molecular charge) and local (such as atom type) information. We evaluate the method on the Side Effect Resource (SIDER) v4.1 dataset and show that it significantly outperforms another recently proposed method based on deep convolutional neural networks. We also reflect on how different types of information and input data affect the predictive power of our model. This reflection highlights several open problems that should be solved to further improve the use of deep learning within cheminformatics.

Place, publisher, year, edition, pages
Cham: Springer, 2019
Series
Advances in Intelligent Systems and Computing, ISSN 2194-5357, E-ISSN 2194-5365 ; 803
Keywords
drug discovery, graph convolutional neural network, molecular property prediction, bioinformatics, convolution, neural networks, open source software, open systems, cheminformatics, convolutional neural network, deep convolutional neural networks, graph structures, molecular charge, molecular properties, predictive power, deep neural networks
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-16230 (URN)10.1007/978-3-319-98702-6_9 (DOI)2-s2.0-85052956812 (Scopus ID)978-3-319-98701-9 (ISBN)978-3-319-98702-6 (ISBN)
Conference
PACBB2018: International Conference on Practical Applications of Computational Biology & Bioinformatics, Toledo, June 20-22, 2018
Available from: 2018-09-25 Created: 2018-09-25 Last updated: 2018-12-19Bibliographically approved
Ståhl, N., Falkman, G., Mathiason, G. & Karlsson, A. (2018). A self-organizing ensemble of deep neural networks for the classification of data from complex processes. In: : . Paper presented at 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Jun 11 - Jun 15, 2018, Cadiz, Spain (pp. 248-259). , 855
Open this publication in new window or tab >>A self-organizing ensemble of deep neural networks for the classification of data from complex processes
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

We present a new self-organizing algorithm for classification of a data that combines and extends the strengths of several common machine learning algorithms, such as algorithms in self-organizing neural networks, ensemble methods and deep neural networks. The increased expression power is combined with the explanation power of self-organizing networks. Our algorithm outperforms both deep neural networks and ensembles of deep neural networks. For our evaluation case, we use production monitoring data from a complex steel manufacturing process, where data is both high-dimensional and has many nonlinear interdependencies. In addition to the improved prediction score, the algorithm offers a new deep-learning based approach for how computational resources can be focused in data exploration, since the algorithm points out areas of the input space that are more challenging to learn.

Series
Communications in Computer and Information Science, ISSN 1865-0929
Keywords
artificial neural networks, complex processes, ensemble methods, self organisation
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-15008 (URN)10.1007/978-3-319-91479-4_21 (DOI)2-s2.0-85048061876 (Scopus ID)
Conference
17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Jun 11 - Jun 15, 2018, Cadiz, Spain
Available from: 2018-04-04 Created: 2018-04-04 Last updated: 2018-11-26Bibliographically approved
Steinhauer, H. J., Helldin, T., Mathiason, G. & Karlsson, A. (2018). Anomaly Detection in Telecommunication Networks using Topic Models. In: : . Paper presented at Modeling Decisions for Artificial Intelligence, 15th International Conference, MDAI 2018, Mallorca, Spain, October 15–18, 2018.
Open this publication in new window or tab >>Anomaly Detection in Telecommunication Networks using Topic Models
2018 (English)Conference paper, Published paper (Refereed)
National Category
Computer and Information Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-16491 (URN)
Conference
Modeling Decisions for Artificial Intelligence, 15th International Conference, MDAI 2018, Mallorca, Spain, October 15–18, 2018
Available from: 2018-12-12 Created: 2018-12-12 Last updated: 2018-12-19
Ståhl, N., Falkman, G., Karlsson, A., Mathiason, G. & Boström, J. (2018). Deep Convolutional Neural Networks for the Prediction of Molecular Properties: Challenges and Opportunities Connected to the Data. Journal of Integrative Bioinformatics
Open this publication in new window or tab >>Deep Convolutional Neural Networks for the Prediction of Molecular Properties: Challenges and Opportunities Connected to the Data
Show others...
2018 (English)In: Journal of Integrative Bioinformatics, E-ISSN 1613-4516Article in journal (Refereed) Epub ahead of print
Abstract [en]

We present a flexible deep convolutional neural network method for the analysis of arbitrary sized graph structures representing molecules. This method, which makes use of the Lipinski RDKit module, an open-source cheminformatics software, enables the incorporation of any global molecular (such as molecular charge and molecular weight) and local (such as atom hybridization and bond orders) information. In this paper, we show that this method significantly outperforms another recently proposed method based on deep convolutional neural networks on several datasets that are studied. Several best practices for training deep convolutional neural networks on chemical datasets are also highlighted within the article, such as how to select the information to be included in the model, how to prevent overfitting and how unbalanced classes in the data can be handled.

Place, publisher, year, edition, pages
De Gruyter Open, 2018
Keywords
Molecular property prediction, Deep learning, Unbalanced data, Side effects prediction
National Category
Computer Sciences Medicinal Chemistry
Research subject
Skövde Artificial Intelligence Lab (SAIL); Bioinformatics; INF301 Data Science
Identifiers
urn:nbn:se:his:diva-16480 (URN)10.1515/jib-2018-0065 (DOI)30517077 (PubMedID)
Available from: 2018-12-09 Created: 2018-12-09 Last updated: 2019-01-02
Helldin, T., Steinhauer, H. J., Karlsson, A. & Mathiason, G. (2018). Situation Awareness in Telecommunication Networks Using Topic Modeling. In: 2018 21st International Conference on Information Fusion, FUSION 2018: . Paper presented at FUSION 2018 21st International Conference on Information Fusion, 10-13 July 2018, Cambridge, United Kingdom (pp. 549-556). IEEE
Open this publication in new window or tab >>Situation Awareness in Telecommunication Networks Using Topic Modeling
2018 (English)In: 2018 21st International Conference on Information Fusion, FUSION 2018, IEEE, 2018, p. 549-556Conference paper, Published paper (Refereed)
Abstract [en]

For an operator of wireless telecommunication networks to make timely interventions in the network before minor faults escalate into issues that can lead to substandard system performance, good situation awareness is of high importance. Due to the increasing complexity of such networks, as well as the explosion of traffic load, it has become necessary to aid human operators to reach a good level of situation awareness through the use of exploratory data analysis and information fusion techniques. However, to understand the results of such techniques is often cognitively challenging and time consuming. In this paper, we present how telecommunication operators can be aided in their data analysis and sense-making processes through the usage and visualization of topic modeling results. We present how topic modeling can be used to extract knowledge from base station counter readings and make design suggestions for how to visualize the analysis results to a telecommunication operator.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
Data handling, Data visualization, Information fusion, Design suggestions, Exploratory data analysis, Human operator, Information fusion techniques, Situation awareness, Telecommunication operators, Topic Modeling, Wireless telecommunications, Data mining
National Category
Computer and Information Sciences Computer Sciences Human Computer Interaction
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-16297 (URN)10.23919/ICIF.2018.8455529 (DOI)2-s2.0-85054066646 (Scopus ID)978-0-9964527-6-2 (ISBN)978-0-9964527-7-9 (ISBN)978-1-5386-4330-3 (ISBN)
Conference
FUSION 2018 21st International Conference on Information Fusion, 10-13 July 2018, Cambridge, United Kingdom
Available from: 2018-10-15 Created: 2018-10-15 Last updated: 2018-12-06Bibliographically approved
Steinhauer, H. J., Helldin, T. & Mathiason, G. (2018). Spatio-Temporal Awareness for Wireless Telecommunication Networks. In: Working Papers and Documents of the IJCAI-ECAI-2018 Workshop on Learning and Reasoning: Principles & Applications to Everyday Spatial and Temporal Knowledge. Paper presented at International Journal Conference for Artificial Intelligence (IJCAI), IJCAI-ECAI-2018 Workshop on Learning and Reasoning, July 13 - 14, 2018 Stockholm (Sweden) (pp. 49-50).
Open this publication in new window or tab >>Spatio-Temporal Awareness for Wireless Telecommunication Networks
2018 (English)In: Working Papers and Documents of the IJCAI-ECAI-2018 Workshop on Learning and Reasoning: Principles & Applications to Everyday Spatial and Temporal Knowledge, 2018, p. 49-50Conference paper, Published paper (Refereed)
National Category
Computer and Information Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-16490 (URN)
Conference
International Journal Conference for Artificial Intelligence (IJCAI), IJCAI-ECAI-2018 Workshop on Learning and Reasoning, July 13 - 14, 2018 Stockholm (Sweden)
Note

http://www.iiia.csic.es/LR2018/talks_proceedings

Available from: 2018-12-12 Created: 2018-12-12 Last updated: 2018-12-19Bibliographically approved
Atif, Y., Stylianos, S., Demetrios, S. & Mathiason, G. (2017). A Cyberphysical Learning Approach for Digital Smart Citizenship Competence Development. In: WWW '17: Proceedings of the 26th International Conference on World Wide Web Companion. Paper presented at International World Wide Web Conference, Perth, Australia, April 3–7, 2017 (pp. 397-405). ACM Digital Library
Open this publication in new window or tab >>A Cyberphysical Learning Approach for Digital Smart Citizenship Competence Development
2017 (English)In: WWW '17: Proceedings of the 26th International Conference on World Wide Web Companion, ACM Digital Library, 2017, p. 397-405Conference paper, Published paper (Refereed)
Abstract [en]

Smart Cities have emerged as a global concept that argues for the effective exploitation of digital technologies to drive sustainable innovation and well-being for citizens. Despite the large investments being placed on Smart City infrastructure, however, there is still very scarce attention on the new learning approaches that will be needed for cultivating Digital Smart Citizenship competences, namely the competences which will be needed by the citizens and workforce of such cities for exploiting the digital technologies in creative and innovative ways for driving financial and societal sustainability. In this context, this paper introduces cyberphysical learning as an overarching model of cultivating Digital Smart Citizenship competences by exploiting the potential of Internet of Things technologies and social media, in order to create authentic blended and augmented learning experiences.

Place, publisher, year, edition, pages
ACM Digital Library, 2017
Keywords
Digital Smart citizenship, smart city, smart citizenship competences, cyberphysical systems, learning design, social networks, learning technology, smart grid, collaborative learning, Internet of Things
National Category
Learning Computer and Information Sciences
Research subject
Distributed Real-Time Systems; INF302 Autonomous Intelligent Systems
Identifiers
urn:nbn:se:his:diva-13400 (URN)10.1145/3041021.3054167 (DOI)978-1-4503-4913-0 (ISBN)
Conference
International World Wide Web Conference, Perth, Australia, April 3–7, 2017
Projects
Kraftsamling Smarta Nät
Funder
Region Västra Götaland, dnr MN 39-2015
Available from: 2017-02-18 Created: 2017-02-18 Last updated: 2018-06-01Bibliographically approved
Rose, J., Berndtsson, M., Mathiason, G. & Larsson, P. (2017). The advanced analytics Jumpstart: definition, process model, best practices. Journal of Information Systems and Technology Management, 14(3), 339-360
Open this publication in new window or tab >>The advanced analytics Jumpstart: definition, process model, best practices
2017 (English)In: Journal of Information Systems and Technology Management, ISSN 1809-2640, E-ISSN 1807-1775, Vol. 14, no 3, p. 339-360Article in journal (Refereed) Published
National Category
Information Systems, Social aspects
Research subject
Information Systems; Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-14768 (URN)10.4301/S1807-17752017000300003 (DOI)
Projects
Big Data Fusion (BISON)
Available from: 2018-02-22 Created: 2018-02-22 Last updated: 2018-04-25Bibliographically approved
Steinhauer, H. J., Helldin, T., Karlsson, A. & Mathiason, G. (2017). Topic Modeling for Situation Understanding in Telecommunication Networks. In: 2017 27th International Telecommunication Networks and Applications Conference (ITNAC): . Paper presented at 27th International Telecommunication Networks and Applications Conference (ITNAC), 22-24 November 2017, Melbourne, Australia (pp. 73-78). IEEE
Open this publication in new window or tab >>Topic Modeling for Situation Understanding in Telecommunication Networks
2017 (English)In: 2017 27th International Telecommunication Networks and Applications Conference (ITNAC), IEEE, 2017, p. 73-78Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2017
Series
International Telecommunication Networks and Applications Conference (ITNAC), E-ISSN 2474-154X
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-14591 (URN)10.1109/ATNAC.2017.8215362 (DOI)000427574400013 ()2-s2.0-85046642703& (Scopus ID)978-1-5090-6796-1 (ISBN)978-1-5090-6795-4 (ISBN)978-1-5090-6797-8 (ISBN)
Conference
27th International Telecommunication Networks and Applications Conference (ITNAC), 22-24 November 2017, Melbourne, Australia
Available from: 2017-12-18 Created: 2017-12-18 Last updated: 2018-09-03Bibliographically approved
Steinhauer, H. J., Karlsson, A., Mathiason, G. & Helldin, T. (2016). Root-Cause Localization using Restricted Boltzmann Machines. In: 2016 19th International Conference on Information Fusion Proceedings: . Paper presented at 19th International Conference on Information Fusion, Heidelberg, Germany - July 5-8, 2016 (pp. 248-255). IEEE Computer Society
Open this publication in new window or tab >>Root-Cause Localization using Restricted Boltzmann Machines
2016 (English)In: 2016 19th International Conference on Information Fusion Proceedings, IEEE Computer Society, 2016, p. 248-255Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE Computer Society, 2016
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-12884 (URN)000391273400034 ()2-s2.0-84992092150 (Scopus ID)9780996452748 (ISBN)978-1-5090-2012-6 (ISBN)
Conference
19th International Conference on Information Fusion, Heidelberg, Germany - July 5-8, 2016
Available from: 2016-09-07 Created: 2016-09-07 Last updated: 2018-04-12Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-7106-0025

Search in DiVA

Show all publications