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Rakus-Andersson, ElisabethORCID iD iconorcid.org/0000-0002-9920-7946
Publications (10 of 91) Show all publications
Rutkowska, D., Kurach, D. & Rakus-Andersson, E. (2021). Fuzzy Granulation Approach to Face Recognition. In: Rutkowski L., Scherer R., Korytkowski M., Pedrycz W., Tadeusiewicz R., Zurada J.M. (Ed.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): . Paper presented at 20th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2021,Virtual, Online, 21 June through 23 June (pp. 495-510). Springer Science and Business Media Deutschland GmbH, 12855
Open this publication in new window or tab >>Fuzzy Granulation Approach to Face Recognition
2021 (English)In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) / [ed] Rutkowski L., Scherer R., Korytkowski M., Pedrycz W., Tadeusiewicz R., Zurada J.M., Springer Science and Business Media Deutschland GmbH , 2021, Vol. 12855, p. 495-510Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, a new approach to face description is proposed. The linguistic description of human faces in digital pictures is generated within a framework of fuzzy granulation. Fuzzy relations and fuzzy relational rules are applied in order to create the image description. By use of type-2 fuzzy sets, fuzzy relations, and fuzzy IF-THEN rules, an image recognition system can infer and explain its decision. Such a system can retrieve an image, recognize, and classify – especially a human face – based on the linguistic description. © 2021, Springer Nature Switzerland AG.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2021
Series
Lecture Notes in Computer Science, ISSN 03029743, E-ISSN 16113349
Keywords
Explainable AI, Face recognition, Fuzzy granulation, Fuzzy relations, Fuzzy rules, Linguistic description, Type-2 fuzzy sets, Fuzzy inference, Granulation, Intelligent systems, Linguistics, Digital picture, Face descriptions, Human faces, Image descriptions, Linguistic descriptions, New approaches, Type-2 fuzzy set
National Category
Computer Sciences Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:bth-22305 (URN)10.1007/978-3-030-87897-9_44 (DOI)000811814800043 ()2-s2.0-85117706489 (Scopus ID)9783030878962 (ISBN)
Conference
20th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2021,Virtual, Online, 21 June through 23 June
Available from: 2021-11-08 Created: 2021-11-08 Last updated: 2022-08-08Bibliographically approved
Rutkowska, D., Kurach, D. & Rakus-Andersson, E. (2020). Face Recognition with Explanation by Fuzzy Rules and Linguistic Description. In: Rutkowski L.,Scherer R.,Korytkowski M.,Pedrycz W.,Tadeusiewicz R.,Zurada J.M. (Ed.), Lecture Notes in Computer Science: . Paper presented at 19th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2020, Zakopane, Poland, 12 October 2020 through 14 October 2020 (pp. 338-350). Springer Science and Business Media Deutschland GmbH, 12415
Open this publication in new window or tab >>Face Recognition with Explanation by Fuzzy Rules and Linguistic Description
2020 (English)In: Lecture Notes in Computer Science / [ed] Rutkowski L.,Scherer R.,Korytkowski M.,Pedrycz W.,Tadeusiewicz R.,Zurada J.M., Springer Science and Business Media Deutschland GmbH , 2020, Vol. 12415, p. 338-350Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, a new approach to face recognition is proposed. The knowledge represented by fuzzy IF-THEN rules, with type-1 and type-2 fuzzy sets, are employed in order to generate the linguistic description of human faces in digital pictures. Then, an image recognition system can recognize and retrieve a picture (image of a face) or classify face images based on the linguistic description. Such a system is explainable – it can explain its decision based on the fuzzy rules. © 2020, Springer Nature Switzerland AG.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2020
Series
Lecture Notes in Computer Science (LNCS), ISSN 0302-9743, E-ISSN 1611-3349
Keywords
Explainable AI, Face recognition and classification, Fuzzy IF-THEN rules, Linguistic description, Type-2 fuzzy sets, Artificial intelligence, Fuzzy inference, Fuzzy rules, Image recognition, Linguistics, Soft computing, Decision-based, Digital picture, Face images, Image recognition system, Linguistic descriptions, New approaches, Type-2 fuzzy set, Face recognition
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:bth-20822 (URN)10.1007/978-3-030-61401-0_32 (DOI)2-s2.0-85096516465 (Scopus ID)9783030614003 (ISBN)
Conference
19th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2020, Zakopane, Poland, 12 October 2020 through 14 October 2020
Available from: 2020-12-08 Created: 2020-12-08 Last updated: 2023-03-24Bibliographically approved
Rakus-Andersson, E. (2018). Fuzzy decision-making model with qualitative states and fuzzified outcomes. In: Robert J. Howlett, Carlos Toro, Yulia Hicks, Lakhmi C. Jain (Ed.), Proceedia Computer Science: Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 22nd International Conference, KES-2018. Paper presented at KES 2018, Belgrade, Serbia (pp. 2030-2039). Elsevier, 126, Article ID k18is-162.
Open this publication in new window or tab >>Fuzzy decision-making model with qualitative states and fuzzified outcomes
2018 (English)In: Proceedia Computer Science: Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 22nd International Conference, KES-2018 / [ed] Robert J. Howlett, Carlos Toro, Yulia Hicks, Lakhmi C. Jain, Elsevier, 2018, Vol. 126, p. 2030-2039, article id k18is-162Conference paper, Published paper (Refereed)
Abstract [en]

The classical fuzzy decision-making model is now tested for qualitative compound states-symptoms to select the most efficacious medicine, acting on all symptoms. Instead of terminating the decision procedure in the way comparing values of total utilities of decisions-treatments, we test the aggregated utility values in utility levels. This activity lets us assign a verbally verified utility to each medicine.

Place, publisher, year, edition, pages
Elsevier, 2018
Series
Proceedia Computer Science, ISSN 1877-0509 ; 126
Keywords
fuzzy decision-making, utility matrix, qualitative states, total utilities, parametric s-functions, fuzzy utility levels
National Category
Other Mathematics
Identifiers
urn:nbn:se:bth-17007 (URN)10.1016/j.procs.2018.07.249 (DOI)000525954400214 ()
Conference
KES 2018, Belgrade, Serbia
Note

open access

Available from: 2018-09-13 Created: 2018-09-13 Last updated: 2021-01-13Bibliographically approved
Wiaderek, K., Rutkowska, D. & Rakus-Andersson, E. (2018). Image Retrieval by Use of Linguistic Description in Databases. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (Ed.), Artificial Intelligence and Soft Computing, Part II: . Paper presented at 17th International Conference, ICAISC 2018, Zakopane, Poland (pp. 92-103). Springer, 10842
Open this publication in new window or tab >>Image Retrieval by Use of Linguistic Description in Databases
2018 (English)In: Artificial Intelligence and Soft Computing, Part II / [ed] Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M., Springer, 2018, Vol. 10842, p. 92-103Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, a new method of image retrieval is proposed. This concerns retrieving color digital images from a database that contains a specific linguistic description considered within the theory of fuzzy granulation and computing with words. The linguistic description is generated by use of the CIE chromaticity color model. The image retrieval is performed in different way depending on users' knowledge about the color image. Specific database queries can be formulated for the image retrieval.

Place, publisher, year, edition, pages
Springer, 2018
Series
Lecture Notes in Artificial Intelligence
Keywords
image retrieval, image recognition, information granulation, linguistic description, fuzzy sets, computing with words, image databases, CIE chromaticity color model, knowledge-based system
National Category
Media Engineering
Identifiers
urn:nbn:se:bth-16634 (URN)10.1007/978-3-319-91262-2_9 (DOI)000552709100009 ()978-3-319-91261-5 (ISBN)978-3-319-91262-2 (ISBN)
Conference
17th International Conference, ICAISC 2018, Zakopane, Poland
Available from: 2018-06-27 Created: 2018-06-27 Last updated: 2021-01-13Bibliographically approved
Wiaderek, K., Rutkowska, D. & Rakus-Andersson, E. (2018). Parallel Processing of Color Digital Images for Linguistic Description of Their Content. In: Roman Wyrzykowski, Jack J. Dongarra, Ewa Deelman, Konrad Karczewski (Ed.), PARALLEL PROCESSING AND APPLIED MATHEMATICS (PPAM 2017), PT I: Proceedings of the 12th International Conference, PPAM 2017. Paper presented at Parallel Processing and Applied Mathematics, PPAM, Lublin,SEP 10-13, 2017 (pp. 544-554). Springer, I
Open this publication in new window or tab >>Parallel Processing of Color Digital Images for Linguistic Description of Their Content
2018 (English)In: PARALLEL PROCESSING AND APPLIED MATHEMATICS (PPAM 2017), PT I: Proceedings of the 12th International Conference, PPAM 2017 / [ed] Roman Wyrzykowski, Jack J. Dongarra, Ewa Deelman, Konrad Karczewski, Springer, 2018, Vol. I, p. 544-554Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents different aspects of parallelization of a problem of processing color digital images in order to generate linguistic description of their content. A parallel architecture of an intelligent image recognition system is proposed. Fuzzy classifcation and inference is performed in parallel, based on the CIE chromaticity color model and granulation approach. In addition, the parallelization concerns, e.g., processing a large collection of images or parts of a single image.

Place, publisher, year, edition, pages
Springer, 2018
Series
Lecture Notes in Computer Science ; 10777
Keywords
Parallel processing, image recognition, information granulation, linguistic description, fuzzy sets, knowledge-based system, CIE chromaticity color model
National Category
Media Engineering
Identifiers
urn:nbn:se:bth-17102 (URN)10.1007/978-3-319-78024-5_47 (DOI)000458563300047 ()978-3-319-78023-8 (ISBN)
Conference
Parallel Processing and Applied Mathematics, PPAM, Lublin,SEP 10-13, 2017
Available from: 2018-10-10 Created: 2018-10-10 Last updated: 2019-03-07Bibliographically approved
Wiaderek, K., Rutkowska, D. & Rakus-Andersson, E. (2017). Linguistic description of color images generated by a granular recognition system. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (Ed.), ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, ICAISC 2017, PT I: . Paper presented at 16th International Conference on Artificial Intelligence and Soft Computing, ICAISC, Zakopane (pp. 603-615). Springer Verlag, 10245
Open this publication in new window or tab >>Linguistic description of color images generated by a granular recognition system
2017 (English)In: ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, ICAISC 2017, PT I / [ed] Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M., Springer Verlag , 2017, Vol. 10245, p. 603-615Conference paper, Published paper (Refereed)
Abstract [en]

The paper proposes a new method employed in an intelligent pattern recognition system that generates linguistic description of color digital images. The linguistic description is produced based on fuzzy rules and information granules concerning colors as most important among image attributes. With regard to the color, the CIE chromaticity color model is applied, with the concept of fuzzy color areas. The linguistic description uses information about location of color granules in input images. © Springer International Publishing AG 2017.

Place, publisher, year, edition, pages
Springer Verlag, 2017
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 10245
Keywords
CIE chromaticity color model, Fuzzy sets, Image recognition, Information granulation, Knowledge-based system, Linguistic description, Artificial intelligence, Color, Color codes, Granulation, Information granules, Knowledge based systems, Linguistics, Pattern recognition, Soft computing, CIE chromaticity, Color digital images, Color granules, Image attributes, Intelligent pattern recognition, Linguistic descriptions, Recognition systems, Pattern recognition systems
National Category
Other Mathematics Media Engineering
Identifiers
urn:nbn:se:bth-14900 (URN)10.1007/978-3-319-59063-9_54 (DOI)000426204500054 ()2-s2.0-85020906742 (Scopus ID)9783319590622 (ISBN)
Conference
16th International Conference on Artificial Intelligence and Soft Computing, ICAISC, Zakopane
Available from: 2017-07-06 Created: 2017-07-06 Last updated: 2018-03-23Bibliographically approved
Zalasinski, M., Cpalka, K. & Rakus-Andersson, E. (2016). An Idea of the Dynamic Signature Verification Based on a Hybrid Approach. In: Leszek Rutkowski et al. (Ed.), Artificial Intelligence and Soft Computing LNAI 9693: Proceedings of the 15th International Conference, ICAISC 2016. Paper presented at 15th International Conference, ICAISC 2016 (pp. 232-246). Berlin Heidelberg: Springer, II
Open this publication in new window or tab >>An Idea of the Dynamic Signature Verification Based on a Hybrid Approach
2016 (English)In: Artificial Intelligence and Soft Computing LNAI 9693: Proceedings of the 15th International Conference, ICAISC 2016 / [ed] Leszek Rutkowski et al., Berlin Heidelberg: Springer, 2016, Vol. II, p. 232-246Conference paper, Published paper (Refereed)
Abstract [en]

Dynamic signature verification is a very interesting biometric issue. It is difficult to realize because signatures of the user are characterized by relatively high intra-class and low inter-class variability. However, this method of an identity verification is commonly socially acceptable.

It is a big advantage of the dynamic signature biometric attribute. In this paper, we propose a new hybrid algorithm for the dynamic signature verification based on global and regional approach. We present the simulation results of the proposed method for BioSecure DS2 database, distributed by the BioSecure Association.

Place, publisher, year, edition, pages
Berlin Heidelberg: Springer, 2016
Series
Lecture Notes in Artificial Intelligence, ISSN 0302-9743 ; 9693
Keywords
behavioral biometrics, dynamic signature, hybrid approach, flexible neuro-fuzzy system, one-class classifier
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-11962 (URN)10.1007/978-3-319-39384-1_21 (DOI)000400688200021 ()978-3-319-39383-4 (ISBN)978-3-319-39384-1 (ISBN)
Conference
15th International Conference, ICAISC 2016
Available from: 2016-06-03 Created: 2016-06-03 Last updated: 2018-05-22Bibliographically approved
Rakus-Andersson, E. & Frey, J. (2016). Fuzzy One-Decision Making Model with Fuzzified Outcomes in the Treatment of Necrotizing Fasciitis. In: Marike Hettinga et al. (Ed.), Proceedings of eTELEMED 2016 : The Eighth International Conference on eHealth, Telemedicine, and Social Medicine: . Paper presented at eTELEMED 2016, Venice, Italy (pp. 145-152). International Academy, Research and Industry Association (IARIA)
Open this publication in new window or tab >>Fuzzy One-Decision Making Model with Fuzzified Outcomes in the Treatment of Necrotizing Fasciitis
2016 (English)In: Proceedings of eTELEMED 2016 : The Eighth International Conference on eHealth, Telemedicine, and Social Medicine / [ed] Marike Hettinga et al., International Academy, Research and Industry Association (IARIA), 2016, p. 145-152Conference paper, Published paper (Refereed)
Abstract [en]

By proposing a new approach to fuzzy decision making, we try to support the medical decision, concerning recommendations for the treatment with hyperbaric oxygen (HBO). This treatment can be used for patients, suffering from necrotizing fasciitis. Due to the disease rarity, it sometimes is difficult for a physician to determine, if a single patient needs the treatment with HBO. We thus identify the decision with a linguistic variable, equipped with treatment recommendation levels. The choice of the appropriate level is based on values of clinical symptoms, found in the patient. To extract the optimal recommendation level for the treatment with HBO, we involve fuzzy set techniques in the decision model. In the paper, we mainly concentrate on designs of fuzzy sets, standing for clinical symptoms and recommendation levels. The levels act as the outcomes, dependent on the cumulative input of the patient’s clinical markers. Since the focus is laid on a parametric structure of the outcomes, then we can categorize the model as robust approach to algorithmic modeling of outcomes, being part of eHealth data records.

Place, publisher, year, edition, pages
International Academy, Research and Industry Association (IARIA), 2016
Series
International journal on advances in life sciences, ISSN 2308-4359
Keywords
fuzzy one-decision making, fuzzy sets, families of membership functions, s-functions, necrotizing fasciitis, treatment with hyperbaric oxygen
National Category
Other Mathematics Other Clinical Medicine
Identifiers
urn:nbn:se:bth-11844 (URN)978-1-61208-470-1 (ISBN)
Conference
eTELEMED 2016, Venice, Italy
Available from: 2016-05-01 Created: 2016-05-01 Last updated: 2016-09-20Bibliographically approved
Wiaderek, K., Rutkowska, D. & Rakus-Andersson, E. (2016). New Algorithms for a Granular Image Recognition System. In: Leszek Rutkowski et al. (Ed.), Artificial Intelligence and Soft Computing LNAI 9693: Proceedings of the 15th International Conference, ICAISC 2016. Paper presented at 15th International Conference, ICAISC 2016, Zakopane, Poland, June 12-16, 2016 (pp. 755-766). Berlin Heidelberg: Springer, II
Open this publication in new window or tab >>New Algorithms for a Granular Image Recognition System
2016 (English)In: Artificial Intelligence and Soft Computing LNAI 9693: Proceedings of the 15th International Conference, ICAISC 2016 / [ed] Leszek Rutkowski et al., Berlin Heidelberg: Springer, 2016, Vol. II, p. 755-766Conference paper, Published paper (Refereed)
Abstract [en]

The paper describes new algorithms proposed for the granular pattern recognition system that retrieves an image from a collection of color digital pictures based on the knowledge contained in the object information granule (OIG). The algorithms use the granulation approach that employs fuzzy and rough granules. The information granules present knowledge concerning attributes of the object to be recognized. Different problems are considered depending on the full or partial knowledge where attributes are "color", "location", "size", and  "shape".

Place, publisher, year, edition, pages
Berlin Heidelberg: Springer, 2016
Series
Lecture Notes in Artificial Intelligence, ISSN 0302-9743 ; 9693
Keywords
image recognition, information granulation, fuzzy sets, rough sets, knowledge-based system
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Other Mathematics
Identifiers
urn:nbn:se:bth-11964 (URN)10.1007/978-3-319-39384-1_67 (DOI)000400688200067 ()978-3-319-39383-4 (ISBN)978-3-319-39384-1 (ISBN)
Conference
15th International Conference, ICAISC 2016, Zakopane, Poland, June 12-16, 2016
Available from: 2016-06-03 Created: 2016-06-03 Last updated: 2018-05-23Bibliographically approved
Rakus-Andersson, E. & Frey, J. (2016). Similarity coefficients of normal distributions in selecting the optimal treatments. In: Mario Macedo (Ed.), Proccedings of the International e-HEALTH Conference 2016. Part of Proceedings of the Multi-Conference of Computer Science and Information Systems 2016: . Paper presented at eHEALTH 2016, 21-24 July, Madeira, Portugal (pp. 115-122). IADIS Press, Article ID 13.
Open this publication in new window or tab >>Similarity coefficients of normal distributions in selecting the optimal treatments
2016 (English)In: Proccedings of the International e-HEALTH Conference 2016. Part of Proceedings of the Multi-Conference of Computer Science and Information Systems 2016 / [ed] Mario Macedo, IADIS Press, 2016, p. 115-122, article id 13Conference paper, Published paper (Refereed)
Abstract [en]

In the current research, we aim to define a new form of the similarity coefficient to compare the resemblance grade of two Gaussian density functions. We aim to assess the method utility on a theoretical model. The density functions are stated for a biological marker “survival length”, observed in three groups of patients, suffering from a hypothetical disease. The first group consists of patients who are not treated, whereas we recommend 2 possible treatment methods for the second and the third group, respectively. All the “survival length” assumptions of the model (mean values and standard deviations) are made to exclude the equivocal conclusion, regarding a selection of the better treatment. At the first stage, we apply the measure of similarity to populations: survival among untreated patients contra survival among patients after Treatment 1. Another similarity coefficient estimates a relation between populations: survival among untreated patients versus survival among patients after Treatment 2. The lower value of the coefficient points out the more effective treatment. In order to simplify calculations, proposed in the definition of a similarity coefficient, we approximate the Gaussian curve by a specially designed polynomial, known as the p-function.

Place, publisher, year, edition, pages
IADIS Press, 2016
Keywords
Gaussian density function, pi-function, similarity coefficient, survival length, optimal treatment.
National Category
Natural Sciences Probability Theory and Statistics Medical and Health Sciences
Identifiers
urn:nbn:se:bth-12889 (URN)978-989-8533-53-1 (ISBN)
Conference
eHEALTH 2016, 21-24 July, Madeira, Portugal
Available from: 2016-07-10 Created: 2016-07-10 Last updated: 2016-09-20Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-9920-7946

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