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Publications (10 of 16) Show all publications
Kalidindi, S. S., Banaee, H., Karlsson, H. & Loutfi, A. (2023). Indoor temperature prediction with context-aware models in residential buildings. Building and Environment, 244, Article ID 110772.
Open this publication in new window or tab >>Indoor temperature prediction with context-aware models in residential buildings
2023 (English)In: Building and Environment, ISSN 0360-1323, E-ISSN 1873-684X, Vol. 244, article id 110772Article in journal (Refereed) Published
Abstract [en]

This paper presents a novel approach for predicting average indoor temperature in residential buildings, utilizing contextual factors of the rise of the building and geographical location. The proposed approach employs advanced deep learning architectures, such as Long Short-Term Memory (LSTM) and Transformers, to create generalized predictive models applicable to a range of residential buildings. The models are trained using historical data from 18 residential buildings over a period of 6 to 10 years, where the buildings are located in different climate zones. Testing is done on nine different data sets representing three different locations and three different types of buildings. The study demonstrates that incorporating the context of building rise significantly improves the models' predictive performance. Specifically, the transformer-based models show improvements in R2 of 4%-27% in a 6 h prediction horizon. The proposed approach explicitly using context information significantly improves the accuracy of predicting, making learnt models a good starting point for optimizing district heating distribution.

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Residential buildings, Indoor temperature prediction, Context-aware models, Long Short-Term Memory (LSTM), Transformer
National Category
Computer Sciences
Identifiers
urn:nbn:se:oru:diva-109061 (URN)10.1016/j.buildenv.2023.110772 (DOI)001075152300001 ()2-s2.0-85171620775 (Scopus ID)
Funder
Knowledge Foundation, 20190128
Note

This work has been supported by the Industrial Graduate School Collaborative AI & Robotics funded by the Swedish Knowledge Foundation Dnr:20190128 and in collaboration with industrial partner Eco-Guard AB, Sweden.

Available from: 2023-10-24 Created: 2023-10-24 Last updated: 2023-10-24Bibliographically approved
Gutiérrez Maestro, E., Banaee, H. & Loutfi, A. (2023). Stress Lingers: Recognizing the Impact of Task Order on Design of Stress and Emotion Detection Systems. In: : . Paper presented at IEEE EMBS International Conference on Data Science and Engineering in Healthcare, Medicine & Biology, Portomaso, St. Julians, Malta, December 7-9, 2023.
Open this publication in new window or tab >>Stress Lingers: Recognizing the Impact of Task Order on Design of Stress and Emotion Detection Systems
2023 (English)Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

This paper examines the significance of the priming effect in designing and developing models for recognizing of affective states. Using a public dataset, often considered a benchmark in automatic stress recognition, the significance of the priming effect is explicated. Two experimental setups confirm the importance of task ordering in this problem. The results demonstrate the statistical significance of the model's confusion when the subject has previously experienced stress and illustrate the importance for the Affective Computing community to develop methods to mitigate the priming effect where the order of tasks impacts how data should be modelled. 

Keywords
Artificial Intelligence, Deep Learning, Digital Health
National Category
Computer Sciences
Identifiers
urn:nbn:se:oru:diva-111192 (URN)
Conference
IEEE EMBS International Conference on Data Science and Engineering in Healthcare, Medicine & Biology, Portomaso, St. Julians, Malta, December 7-9, 2023
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)
Available from: 2024-01-29 Created: 2024-01-29 Last updated: 2024-01-29Bibliographically approved
Kalidindi, S. S., Banaee, H. & Loutfi, A. (2022). Transformers and Contextual Information in Temperature Prediction of Residential Buildings for Improved Energy Consumption. In: : . Paper presented at 1st Annual AAAI Workshop on AI to Accelerate Science and Engineering (AI2ASE), February 28, 2022.
Open this publication in new window or tab >>Transformers and Contextual Information in Temperature Prediction of Residential Buildings for Improved Energy Consumption
2022 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Energy optimization plays a vital role in decreasing the carbon footprint of residential buildings. In this paper, we present a prediction model of indoor temperature in residential buildings in three different case studies in different towns in Sweden. To predict the indoor temperature accurately, a dataset based on several years of data collection (up to 7 years) has been used. This paper applies both the traditional LSTM model as well as the more recent transformer model. The latter has been used because of its ability to perform a mechanism of self-attention that shows particular promise in multivariate sensor data. In addition to these algorithms, the data set is also modified based on contextual information and compared against an approach where no contextual information is used. Contextual information in this case takes into account the physical location of specific apartment units within the full residence and builds individual models based on the location of the unit. The results demonstrate that transformers are better suited for task of prediction, and that transformers combined with contextual information, provide a suitable approach for energy consumption prediction. 

Keywords
Transformers, Contextual Information, Residential Buildings
National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-112197 (URN)
Conference
1st Annual AAAI Workshop on AI to Accelerate Science and Engineering (AI2ASE), February 28, 2022
Funder
Knowledge Foundation, 20190128
Note

This work has been supported by the Industrial Graduate School Collaborative AI & Robotics funded by the Swedish Knowledge Foundation Dnr:20190128.

Available from: 2024-03-07 Created: 2024-03-07 Last updated: 2024-03-08Bibliographically approved
Banaee, H., Chimamiwa, G., Alirezaie, M. & Loutfi, A. (2020). Explaining Habits and Changes of Activities in Smart Homes. In: : . Paper presented at Artificial Intelligence for Health, Personalised Medicine and Wellbeing (HELPLINE), in conjunction with ECAI 2020, Santiago de Compostela, Spain (Digital Conference), August 29 - September 8, 2020.
Open this publication in new window or tab >>Explaining Habits and Changes of Activities in Smart Homes
2020 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Early cognitive deterioration can emerge in the form of changes in daily habits and there is a need to go beyond activity recognition for recognising habits and detecting changes in smart homes. In this paper, we propose a system composed of 1) data-driven habit recognition, 2) change detection in the trends of habits, and 3) linguistic descriptions of both habits and changes. Our habit recognition approach relies on both attribute-based analysis and association-based analysis. The generated outputs of the habit recognition and change detection are finally interpreted in linguistic descriptions for the end-users of the system.

National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-88860 (URN)
Conference
Artificial Intelligence for Health, Personalised Medicine and Wellbeing (HELPLINE), in conjunction with ECAI 2020, Santiago de Compostela, Spain (Digital Conference), August 29 - September 8, 2020
Available from: 2021-01-24 Created: 2021-01-24 Last updated: 2021-01-26Bibliographically approved
Chimamiwa, G., Alirezaie, M., Banaee, H., Köckemann, U. & Loutfi, A. (2019). Towards Habit Recognition in Smart Homes for People with Dementia. In: Ioannis Chatzigiannakis, Boris De Ruyter, Irene Mavrommati (Ed.), Ambient Intelligence: 15th European Conference, AmI 2019, Rome, Italy, November 13–15, 2019, Proceedings. Paper presented at 15th European Conference on Ambient Intelligence (AmI 2019), Rome, Italy, November 13-15, 2019 (pp. 363-369). Springer Nature, 11912
Open this publication in new window or tab >>Towards Habit Recognition in Smart Homes for People with Dementia
Show others...
2019 (English)In: Ambient Intelligence: 15th European Conference, AmI 2019, Rome, Italy, November 13–15, 2019, Proceedings / [ed] Ioannis Chatzigiannakis, Boris De Ruyter, Irene Mavrommati, Springer Nature, 2019, Vol. 11912, p. 363-369Conference paper, Published paper (Refereed)
Abstract [en]

The demand for smart home technologies that enable ageingin place is rising. Through activity recognition, users’ activities can be monitored. However, for dementia patients, activity recognition alone cannot address the challenges associated with changes in the user’s habits along the disease’s stage transitions. Extending activity recognition to habit recognition enables the capturing of patients’ habits and change sin habits in order to detect anomalies. This paper aims to introduce relevant features for habit recognition solutions, extracted from data, in order to enrich the representation of the user’s habits. This solution is personalisable to meet the specific needs of the patients and generalizable for use in different scenarios. In this way caregivers are better informed on the expected changes of the patient’s habits, which can help to mitigate further deterioration through early treatment and intervention.

Place, publisher, year, edition, pages
Springer Nature, 2019
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11912
Keywords
Habit recognition, Dementia, Smart homes
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-88468 (URN)10.1007/978-3-030-34255-5_29 (DOI)000582723500029 ()2-s2.0-85076292763 (Scopus ID)978-3-030-34254-8 (ISBN)978-3-030-34255-5 (ISBN)
Conference
15th European Conference on Ambient Intelligence (AmI 2019), Rome, Italy, November 13-15, 2019
Funder
EU, Horizon 2020, 754285
Available from: 2021-01-12 Created: 2021-01-12 Last updated: 2024-04-05Bibliographically approved
Banaee, H., Schaffernicht, E. & Loutfi, A. (2018). Data-Driven Conceptual Spaces: Creating Semantic Representations for Linguistic Descriptions of Numerical Data. The journal of artificial intelligence research, 63, 691-742
Open this publication in new window or tab >>Data-Driven Conceptual Spaces: Creating Semantic Representations for Linguistic Descriptions of Numerical Data
2018 (English)In: The journal of artificial intelligence research, ISSN 1076-9757, E-ISSN 1943-5037, Vol. 63, p. 691-742Article in journal (Refereed) Published
Abstract [en]

There is an increasing need to derive semantics from real-world observations to facilitate natural information sharing between machine and human. Conceptual spaces theory is a possible approach and has been proposed as mid-level representation between symbolic and sub-symbolic representations, whereby concepts are represented in a geometrical space that is characterised by a number of quality dimensions. Currently, much of the work has demonstrated how conceptual spaces are created in a knowledge-driven manner, relying on prior knowledge to form concepts and identify quality dimensions. This paper presents a method to create semantic representations using data-driven conceptual spaces which are then used to derive linguistic descriptions of numerical data. Our contribution is a principled approach to automatically construct a conceptual space from a set of known observations wherein the quality dimensions and domains are not known a priori. This novelty of the approach is the ability to select and group semantic features to discriminate between concepts in a data-driven manner while preserving the semantic interpretation that is needed to infer linguistic descriptions for interaction with humans. Two data sets representing leaf images and time series signals are used to evaluate the method. An empirical evaluation for each case study assesses how well linguistic descriptions generated from the conceptual spaces identify unknown observations. Furthermore,  comparisons are made with descriptions derived on alternative approaches for generating semantic models.

Place, publisher, year, edition, pages
AAAI Press, 2018
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-70433 (URN)10.1613/jair.1.11258 (DOI)000455091500015 ()2-s2.0-85057746407 (Scopus ID)
Available from: 2018-12-04 Created: 2018-12-04 Last updated: 2024-01-03Bibliographically approved
Banaee, H. (2018). From Numerical Sensor Data to Semantic Representations: A Data-driven Approach for Generating Linguistic Descriptions. (Doctoral dissertation). Örebro: Örebro University
Open this publication in new window or tab >>From Numerical Sensor Data to Semantic Representations: A Data-driven Approach for Generating Linguistic Descriptions
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In our daily lives, sensors recordings are becoming more and more ubiquitous. With the increased availability of data comes the increased need of systems that can represent the data in human interpretable concepts. In order to describe unknown observations in natural language, an artificial intelligence system must deal with several issues involving perception, concept formation, and linguistic description. These issues cover various subfields within artificial intelligence, such as machine learning, cognitive science, and natural language generation.The aim of this thesis is to address the problem of semantically modelling and describing numerical observations from sensor data. This thesis introduces data-driven approaches to perform the tasks of mining numerical data and creating semantic representations of the derived information in order to describe unseen but interesting observations in natural language.The research considers creating a semantic representation using the theory of conceptual spaces. In particular, the central contribution of this thesis is to present a data-driven approach that automatically constructs conceptual spaces from labelled numerical data sets. This constructed conceptual space then utilises semantic inference techniques to derive linguistic interpretations for novel unknown observations. Another contribution of this thesis is to explore an instantiation of the proposed approach in a real-world application. Specifically, this research investigates a case study where the proposed approach is used to describe unknown time series patterns that emerge from physiological sensor data. This instantiation first presents automatic data analysis methods to extract time series patterns and temporal rules from multiple channels of physiological sensor data, and then applies various linguistic description approaches (including the proposed semantic representation based on conceptual spaces) to generate human-readable natural language descriptions for such time series patterns and temporal rules.The main outcome of this thesis is the use of data-driven strategies that enable the system to reveal and explain aspects of sensor data which may otherwise be difficult to capture by knowledge-driven techniques alone. Briefly put, the thesis aims to automate the process whereby unknown observations of data can be 1) numerically analysed, 2) semantically represented, and eventually 3) linguistically described.

Place, publisher, year, edition, pages
Örebro: Örebro University, 2018. p. 188
Series
Örebro Studies in Technology, ISSN 1650-8580 ; 78
Keywords
Semantic representations, Conceptual spaces, Natural language generation, Temporal rule mining, Physiological sensors, Health monitoring system
National Category
Computer Sciences
Identifiers
urn:nbn:se:oru:diva-65318 (URN)978-91-7529-240-3 (ISBN)
Public defence
2018-04-20, Örebro universitet, Teknikhuset, Hörsal T, Fakultetsgatan 1, Örebro, 13:15 (English)
Opponent
Supervisors
Available from: 2018-02-28 Created: 2018-02-28 Last updated: 2024-01-03Bibliographically approved
Vajdi, A., Haspel, N. & Banaee, H. (2015). A New DP Algorithm for Comparing Gene Expression Data Using Geometric Similarity. In: Proceedings 2015 IEEE International Conference on Bioinformatics and Biomedicine: . Paper presented at IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2015), Washington, DC, USA, November 9-12, 2015 (pp. 1157-1161). New York: IEEE conference proceedings
Open this publication in new window or tab >>A New DP Algorithm for Comparing Gene Expression Data Using Geometric Similarity
2015 (English)In: Proceedings 2015 IEEE International Conference on Bioinformatics and Biomedicine, New York: IEEE conference proceedings , 2015, p. 1157-1161Conference paper, Published paper (Refereed)
Abstract [en]

Microarray gene expression data comes as a time series, where the expression level of a gene is recorded at specific time points. Comparing the time series produced by two genes can give us information about the regulatory or inhibitory relationship between the genes. We present a Dynamic Programming (DP) method to compare gene expression data using geometric similarity. We aim to detect similarities and relationships between genes, based on their expression time series. By representing the time series as polygons and compare them, we can find relationships that are not available when the two time series are compared point-by-point. We applied our algorithm on a dataset of 343 regulatory pairs from the alpha dataset and compared them to randomly generated pairs. Using an SVM classifier, we find the optimal similarity score that separates the regulatory dataset from the random pairs. Our results show that we can detect similar pairs better than simple Pearson correlation and we outperform many of the existing methods. This method is an ongoing approach, that can be applied to finding the similarity of any data that can convert to 2D polygon. In the future, we plan to introduce this method as a new classifier.

Place, publisher, year, edition, pages
New York: IEEE conference proceedings, 2015
Series
IEEE International Conference on Bioinformatics and Biomedicine-BIBM, ISSN 2156-1125
Keywords
gene expression time series, clustering, Polygons, dynamic programming, polygonal chain alignment
National Category
Computer Sciences Biological Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-51688 (URN)10.1109/BIBM.2015.7359846 (DOI)000377335600307 ()2-s2.0-84962385706 (Scopus ID)978-1-4673-6798-1 (ISBN)
Conference
IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2015), Washington, DC, USA, November 9-12, 2015
Available from: 2016-08-17 Created: 2016-08-17 Last updated: 2018-09-12Bibliographically approved
Banaee, H. & Loutfi, A. (2015). Data-driven rule mining and representation of temporal patterns in physiological sensor data. IEEE journal of biomedical and health informatics, 19(5), 1557-1566
Open this publication in new window or tab >>Data-driven rule mining and representation of temporal patterns in physiological sensor data
2015 (English)In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, Vol. 19, no 5, p. 1557-1566Article in journal (Refereed) Published
Abstract [en]

Mining and representation of qualitative patterns is a growing field in sensor data analytics. This paper leverages from rule mining techniques to extract and represent temporal relation of prototypical patterns in clinical data streams. The approach is fully data-driven, where the temporal rules are mined from physiological time series such as heart rate, respiration rate, and blood pressure. To validate the rules, a novel similarity method is introduced, that compares the similarity between rule sets. An additional aspect of the proposed approach has been to utilize natural language generation techniques to represent the temporal relations between patterns. In this study, the sensor data in the MIMIC online database was used for evaluation, in which the mined temporal rules as they relate to various clinical conditions (respiratory failure, angina, sepsis, ...) were made explicit as a textual representation. Furthermore, it was shown that the extracted rule set for any particular clinical condition was distinct from other clinical conditions.

Keywords
Data-driven modeling, health informatics, linguistic representation, pattern abstraction, physiological sensor data, sensor data analysis, temporal rule mining
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-46037 (URN)10.1109/JBHI.2015.2438645 (DOI)000360791200004 ()26340684 (PubMedID)2-s2.0-84940989008 (Scopus ID)
Available from: 2015-10-07 Created: 2015-10-07 Last updated: 2018-01-11Bibliographically approved
Banaee, H., Ahmed, M. U. & Loutfi, A. (2015). Descriptive Modelling of Clinical Conditions with Data-driven Rule Mining in Physiological Data. In: Proceedings of the 8th International conference of Health Informatics (HEALTHINF 2015): . Paper presented at HEALTHINF 2015 : HEALTHINF 8th International Conference on Health Informatics, 12-15 january, Lisabon, Portugal. SciTePress
Open this publication in new window or tab >>Descriptive Modelling of Clinical Conditions with Data-driven Rule Mining in Physiological Data
2015 (English)In: Proceedings of the 8th International conference of Health Informatics (HEALTHINF 2015), SciTePress, 2015Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents an approach to automatically mine rules in time series data representing physiologicalparameters in clinical conditions. The approach is fully data driven, where prototypical patterns are mined foreach physiological time series data. The generated rules based on the prototypical patterns are then describedin a textual representation which captures trends in each physiological parameter and their relation to the otherphysiological data. In this paper, a method for measuring similarity of rule sets is introduced in order tovalidate the uniqueness of rule sets. This method is evaluated on physiological records from clinical classesin the MIMIC online database such as angina, sepsis, respiratory failure, etc.. The results show that the rulemining technique is able to acquire a distinctive model for each clinical condition, and represent the generatedrules in a human understandable textual representation

Place, publisher, year, edition, pages
SciTePress, 2015
Keywords
rule mining, pattern abstraction, health parameters, physiological time series, clinical condition.
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-39650 (URN)978-989-758-068-0 (ISBN)
Conference
HEALTHINF 2015 : HEALTHINF 8th International Conference on Health Informatics, 12-15 january, Lisabon, Portugal
Available from: 2014-12-14 Created: 2014-12-14 Last updated: 2018-01-11Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-9607-9504

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