Predictive model for the identification of activities of daily living (ADL) in indoor environments using classification techniques based on Machine Learning

AI-based techniques have included countless applications within the engineering field. These range from the automation of important procedures in Industry and companies, to the field of Process Control. Smart Home (SH) technology is designed to help house residents improve their daily activities and...

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Autores:
García-Restrepo, Johanna
Ariza Colpas, Paola Patricia
Oñate-Bowen, Alvaro Agustín
Suarez Brieva, Eydy
Urina-Triana, Miguel
De-La-Hoz-Franco, Emiro
Díaz-Martínez, Jorge Luis
Butt Shariq, Aziz
Molina Estren, Diego
Tipo de recurso:
Article of journal
Fecha de publicación:
2021
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/8691
Acceso en línea:
https://hdl.handle.net/11323/8691
https://doi.org/10.1016/j.procs.2021.07.069
https://repositorio.cuc.edu.co/
Palabra clave:
HAR
Human activity recognition
Machine learning
ADL
Activity daily living
Rights
openAccess
License
CC0 1.0 Universal
id RCUC2_283713785965d699d5028aeea019c0d9
oai_identifier_str oai:repositorio.cuc.edu.co:11323/8691
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Predictive model for the identification of activities of daily living (ADL) in indoor environments using classification techniques based on Machine Learning
title Predictive model for the identification of activities of daily living (ADL) in indoor environments using classification techniques based on Machine Learning
spellingShingle Predictive model for the identification of activities of daily living (ADL) in indoor environments using classification techniques based on Machine Learning
HAR
Human activity recognition
Machine learning
ADL
Activity daily living
title_short Predictive model for the identification of activities of daily living (ADL) in indoor environments using classification techniques based on Machine Learning
title_full Predictive model for the identification of activities of daily living (ADL) in indoor environments using classification techniques based on Machine Learning
title_fullStr Predictive model for the identification of activities of daily living (ADL) in indoor environments using classification techniques based on Machine Learning
title_full_unstemmed Predictive model for the identification of activities of daily living (ADL) in indoor environments using classification techniques based on Machine Learning
title_sort Predictive model for the identification of activities of daily living (ADL) in indoor environments using classification techniques based on Machine Learning
dc.creator.fl_str_mv García-Restrepo, Johanna
Ariza Colpas, Paola Patricia
Oñate-Bowen, Alvaro Agustín
Suarez Brieva, Eydy
Urina-Triana, Miguel
De-La-Hoz-Franco, Emiro
Díaz-Martínez, Jorge Luis
Butt Shariq, Aziz
Molina Estren, Diego
dc.contributor.author.spa.fl_str_mv García-Restrepo, Johanna
Ariza Colpas, Paola Patricia
Oñate-Bowen, Alvaro Agustín
Suarez Brieva, Eydy
Urina-Triana, Miguel
De-La-Hoz-Franco, Emiro
Díaz-Martínez, Jorge Luis
Butt Shariq, Aziz
Molina Estren, Diego
dc.subject.spa.fl_str_mv HAR
Human activity recognition
Machine learning
ADL
Activity daily living
topic HAR
Human activity recognition
Machine learning
ADL
Activity daily living
description AI-based techniques have included countless applications within the engineering field. These range from the automation of important procedures in Industry and companies, to the field of Process Control. Smart Home (SH) technology is designed to help house residents improve their daily activities and therefore enrich the quality of life while preserving their privacy. An SH system is usually equipped with a collection of software interrelated with hardware components to monitor the living space by capturing the behavior of the resident and their occupations. By doing so, the system can report risks, situations, and act on behalf of the resident to their satisfaction. This research article shows the experimentation carried out with the human activity recognition dataset, CASAS Kyoto, through preprocessing and cleaning processes of the data, showing the Vía Regression classifier as an excellent option to process this type of data with an accuracy 99.7% effective
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-09-14T16:21:56Z
dc.date.available.none.fl_str_mv 2021-09-14T16:21:56Z
dc.date.issued.none.fl_str_mv 2021
dc.type.spa.fl_str_mv Artículo de revista
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dc.type.content.spa.fl_str_mv Text
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dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
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dc.identifier.issn.spa.fl_str_mv 1877-0509
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/8691
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1016/j.procs.2021.07.069
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 1877-0509
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/8691
https://doi.org/10.1016/j.procs.2021.07.069
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv [1][Nagi, S. Z., Burk, R. D., & Potter, H. R. (1965). Back disorders and rehabilitation achievement. Journal of Chronic Diseases, 18(2), 181–197. https://doi.org/10.1016/0021-9681(65)90101-3
[2] Lladó, M. R., Código, H., Lennin, A., Quiroz, P., & Lima -Perú, V. (2020). ENTORNO DOMÓTICO ADAPTADO A PERSONAS CON DISCAPACIDAD FÍSICA UTILIZANDO MODELOS OCULTOS DE MARKOV Tesis para optar el Título Profesional de Ingeniero de Sistemas. In Repositorio Institucional - Ulima. Universidad de Lima. http://repositorio.ulima.edu.pe/handle/20.500.12724/11664
[3] Carlos, A., D’negri, E., De Vito, E. L., & Zadeh, L. A. (2006). Introducción al razonamiento aproximado: lógica difusa. In Revista Argentina de Medicina Respiratoria Año (Vol. 6).
[4] Marcondes, C. H., & Almeida Campos, M. L. de. (2008). ONTOLOGIA E WEB SEMÂNTICA: O ESPAÇO DA PESQUISA EM CIÊNCIA DA INFORMAÇÃO. PontodeAcesso, 2(1), 107. https://doi.org/10.9771/1981-6766rpa.v2i1.2669
[5] DANE. Archivo Nacional de Datos ANDA. 2014. [Citado Marzo 20,2016]. Available in: http://formularios.dane.gov.co/Anda_4_1/index.php/home
[6] Ronao, C. A., & Cho, S. B. (2016). Human activity recognition with smartphone sensors using deep learning neural networks. Expert Systems with Applications, 59, 235–244. https://doi.org/10.1016/j.eswa.2016.04.032
[7] Capela, N. A., Lemaire, E. D., & Baddour, N. (2015). Feature selection for wearable smartphone-based human activity recognition with able bodied, elderly, and stroke patients. PLoS ONE, 10(4), e0124414. https://doi.org/10.1371/journal.pone.0124414
[8] Gudivada, V. N., Ding, J., & Apon, A. (2017). Data Quality Considerations for Big Data and Machine Learning: Going Beyond Data Cleaning and Transformations Flow Cytometry of 3-D structure View project Data Quality Considerations for Big Data and Machine Learning: Going Beyond Data Cleaning and Transf. October, 1–20. https://www.researchgate.net/publication/318432363
[9] Ren, X., & Malik, J. (2003). Learning a classification model for segmentation. Proceedings of the IEEE International Conference on Computer Vision, 1, 10– 17. https://doi.org/10.1109/iccv.2003.1238308
[10] Galván-Tejada, C. E., Galván-Tejada, J. I., Celaya-Padilla, J. M., elgadoContreras, J. R., Magallanes-Quintanar, R., Martinez-Fierro, M. L., GarzaVeloz, I., López-Hernández, Y., & Gamboa-Rosales, H. (2016). An Analysis of Audio Features to Develop a Human Activity Recognition Model Using Genetic Algorithms, Random Forests, and Neural Networks. Mobile Information Systems, 2016, 1–10. https://doi.org/10.1155/2016/1784101
[11] Eddy, S. R. (1998). Profile hidden Markov models. Academic.Oup.Com, 144(9), 755–763. https://academic.oup.com/bioinformatics/articleabstract/14/9/755/259550 Envejecimiento y salud. (2018, February 5).
[12] Shah,C. (2020). Supervised Learning. In A Hands-On Introduction to Data Science (pp.235–289).
[13] Nettleton, D. F., Orriols-Puig, A., & Fornells, A. (2010). A study of the effect of different types of noise on the precision of supervised learning techniques. Artificial Intelligence Review, 33(4), 275–306.https://doi.org/10.1007/s10462-010-9156-z
[14] Caruana, R., & Niculescu-Mizil, A. (2006). An empirical comparison of supervised learning algorithms. ACM International Conference Proceeding Series, 148, 161–168. https://doi.org/10.1145/1143844.1143865
[15] Mejia-Ricart, L. F., Helling, P., & Olmsted, A. (2018). Evaluate action primitives for human activity recognition using unsupervised learning approach. 2017 12th International Conference for Internet Technology and Secured Transactions, ICITST 2017, 186–188. https://doi.org/10.23919/ICITST.2017.8356374
[16] Crandall, A. S. (2011). BEHAVIOMETRICS FOR MULTIPLE RESIDENTS IN A SMART ENVIRONMENT. https://scihub.si/http://research.wsulibs.wsu.edu/xmlui/handle/2376/2855
[17] Hoey, J., Pltz, T., Jackson, D., Monk, A., Pham, C., & Olivier, P. (2011). Rapid specification and automated generation of prompting systems to assist people with dementia. Pervasive and Mobile Computing, 7(3), 299–318. https://doi.org/10.1016/j.pmcj.2010.11.007
[18] Fahad, L. G., Tahir, S. F., & Rajarajan, M. (2015). Feature selection and data balancing for activity recognition in smart homes. IEEE International Conference on Communications, 2015-Septe, 512–517. https://doi.org/10.1109/ICC.2015.724837310
[19] Chawla, N. V, Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. In Journal of Artificial Intelligence Research (Vol. 16). https://scihub.si/http://www.jair.org/index.php/jair/article/view/10302
[20] López Saca, F., Ferreyra Ramírez, A., Avilés Cruz, C., Villegas Cortez, J., Zúñiga López, A., & Rodrigez Martinez, E. (2018). Preprocesamiento de bases de datos de imágenes para mejorar el rendimiento de redes neuronales convolucionales. Research in Computing Science, 147(7), 35–45. https://doi.org/10.13053/rcs147-7-3
[21] Ruan, Y. X., Lin, H. T., & Tsai, M. F. (2014). Improving ranking performance with cost-sensitive ordinal classification via regression. Information Retrieval, 17(1), 1–20. https://doi.org/10.1007/s10791-013-9219-2
[22] Ho, T. K. (1998). The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8), 832– 844. https://doi.org/10.1109/34.709601
[23] Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140. https://doi.org/10.1007/bf00058655
[24] Nagalla, R., Pothuganti, P., & Pawar, D. S. (2017). Analyzing Gap Acceptance Behavior at Unsignalized Intersections Using Support Vector Machines, Decision Tree and Random Forests. Procedia Computer Science, 109, 474–481. https://doi.org/10.1016/j.procs.2017.05.312
[25] Salzberg, S. L. (1994). C4.5: Programs for Machine Learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993. Machine Learning, 16(3), 235–240. https://doi.org/10.1007/bf00993309
[26] Kumar, K., Kumar, G., & Kumar, Y. (2013). Feature Selection Approach for Intrusion Detection System. International Journal of Advanced Trends in Computer Science and Engineering (IJATCSE), 2(5), 47–53.
[27] Ariza Colpas, P., Vicario, E., De-La-Hoz-Franco, E., Pineres-Melo, M., Oviedo-Carrascal, A., & Patara, F. (2020). Unsupervised human activity recognition using the clustering approach: A review. Sensors, 20(9), 2702.
[28] Chandra, S., & Maheshkar, S. (2017). Verification of static signature pattern based on random subspace, REP tree and bagging. Multimedia Tools and Applications, 76(18), 19139–19171. https://doi.org/10.1007/s11042-017-4531-2
[29] Asaju, L. B., Shola, P. B., Franklin, N., & Abiola, H. M. (2017). Intrusion Detection System on a Computer Network Using an Ensemble of Randomizable Filtered Classifier, K-Nearest …. Ftstjournal.Com, 2(1), 550– 553. www.ftstjournal.com
[30] Kalmegh, S. (2015). Analysis of WEKA Data Mining Algorithm REPTree , Simple Cart and RandomTree for Classification of Indian News. International Journal of Innovative Science, Engineering & Technology, 2(2), 438–446. www.ijiset.com
[31] Rajput, A., Aharwal, R. P., Dubey, M., Saxena, S. P., & Raghuvanshi, M. (2011). J48 and JRIP rules for e-governance data. International Journal of Computer Science and Security, 5(2), 201–207.11
[32] Cai, Y. D., Feng, K. Y., Lu, W. C., & Chou, K. C. (2006). Using LogitBoost classifier to predict protein structural classes. Journal of Theoretical Biology, 238(1), 172–176. https://doi.org/10.1016/j.jtbi.2005.05.034
[33] Qian, H., Mao, Y., Xiang, W., & Wang, Z. (2010). Recognition of human activities using SVM multi-class classifier. Pattern Recognition Letters, 31(2), 100–111. https://doi.org/10.1016/j.patrec.2009.09.019
[34] Suykens, J. A. K., & Vandewalle, J. (1999). Training multilayer perceptron classifiers based on a modified support vector method. IEEE Transactions on Neural Networks, 10(4), 907–911. https://doi.org/10.1109/72.774254
[35] Khalajzadeh, H., Mansouri, M., & Teshnehlab, M. (2014). Face recognition using convolutional neural network and simple logistic classifier. Advances in Intelligent Systems and Computing, 223, 197–207. https://doi.org/10.1007/978-3-319-00930-8_18
[36] Choudhury, S., & Bhowal, A. (2015). Comparative analysis of machine learning algorithms along with classifiers for network intrusion detection. 2015 International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials, ICSTM 2015 - Proceedings, 89–95. https://doi.org/10.1109/ICSTM.2015.7225395
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spelling García-Restrepo, JohannaAriza Colpas, Paola PatriciaOñate-Bowen, Alvaro AgustínSuarez Brieva, EydyUrina-Triana, MiguelDe-La-Hoz-Franco, EmiroDíaz-Martínez, Jorge LuisButt Shariq, AzizMolina Estren, Diego2021-09-14T16:21:56Z2021-09-14T16:21:56Z20211877-0509https://hdl.handle.net/11323/8691https://doi.org/10.1016/j.procs.2021.07.069Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/AI-based techniques have included countless applications within the engineering field. These range from the automation of important procedures in Industry and companies, to the field of Process Control. Smart Home (SH) technology is designed to help house residents improve their daily activities and therefore enrich the quality of life while preserving their privacy. An SH system is usually equipped with a collection of software interrelated with hardware components to monitor the living space by capturing the behavior of the resident and their occupations. By doing so, the system can report risks, situations, and act on behalf of the resident to their satisfaction. This research article shows the experimentation carried out with the human activity recognition dataset, CASAS Kyoto, through preprocessing and cleaning processes of the data, showing the Vía Regression classifier as an excellent option to process this type of data with an accuracy 99.7% effectiveGarcía-Restrepo, JohannaAriza Colpas, Paola Patricia-will be generated-orcid-0000-0003-4503-5461-600Oñate-Bowen, Alvaro AgustínSuarez Brieva, Eydy-will be generated-orcid-0000-0002-6296-0117-600Urina-Triana, MiguelDe-La-Hoz-Franco, Emiro-will be generated-orcid-0000-0002-4926-7414-600Díaz-Martínez, Jorge LuisButt Shariq, AzizMolina Estren, Diegoapplication/pdfengCorporación Universidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Procedia Computer Sciencehttps://www.sciencedirect.com/science/article/pii/S1877050921014721HARHuman activity recognitionMachine learningADLActivity daily livingPredictive model for the identification of activities of daily living (ADL) in indoor environments using classification techniques based on Machine LearningArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion[1][Nagi, S. Z., Burk, R. D., & Potter, H. R. (1965). Back disorders and rehabilitation achievement. Journal of Chronic Diseases, 18(2), 181–197. https://doi.org/10.1016/0021-9681(65)90101-3[2] Lladó, M. R., Código, H., Lennin, A., Quiroz, P., & Lima -Perú, V. (2020). ENTORNO DOMÓTICO ADAPTADO A PERSONAS CON DISCAPACIDAD FÍSICA UTILIZANDO MODELOS OCULTOS DE MARKOV Tesis para optar el Título Profesional de Ingeniero de Sistemas. In Repositorio Institucional - Ulima. Universidad de Lima. http://repositorio.ulima.edu.pe/handle/20.500.12724/11664[3] Carlos, A., D’negri, E., De Vito, E. L., & Zadeh, L. A. (2006). Introducción al razonamiento aproximado: lógica difusa. In Revista Argentina de Medicina Respiratoria Año (Vol. 6).[4] Marcondes, C. H., & Almeida Campos, M. L. de. (2008). ONTOLOGIA E WEB SEMÂNTICA: O ESPAÇO DA PESQUISA EM CIÊNCIA DA INFORMAÇÃO. PontodeAcesso, 2(1), 107. https://doi.org/10.9771/1981-6766rpa.v2i1.2669[5] DANE. Archivo Nacional de Datos ANDA. 2014. [Citado Marzo 20,2016]. Available in: http://formularios.dane.gov.co/Anda_4_1/index.php/home[6] Ronao, C. A., & Cho, S. B. (2016). Human activity recognition with smartphone sensors using deep learning neural networks. Expert Systems with Applications, 59, 235–244. https://doi.org/10.1016/j.eswa.2016.04.032[7] Capela, N. A., Lemaire, E. D., & Baddour, N. (2015). Feature selection for wearable smartphone-based human activity recognition with able bodied, elderly, and stroke patients. PLoS ONE, 10(4), e0124414. https://doi.org/10.1371/journal.pone.0124414[8] Gudivada, V. N., Ding, J., & Apon, A. (2017). Data Quality Considerations for Big Data and Machine Learning: Going Beyond Data Cleaning and Transformations Flow Cytometry of 3-D structure View project Data Quality Considerations for Big Data and Machine Learning: Going Beyond Data Cleaning and Transf. October, 1–20. https://www.researchgate.net/publication/318432363[9] Ren, X., & Malik, J. (2003). Learning a classification model for segmentation. Proceedings of the IEEE International Conference on Computer Vision, 1, 10– 17. https://doi.org/10.1109/iccv.2003.1238308[10] Galván-Tejada, C. E., Galván-Tejada, J. I., Celaya-Padilla, J. M., elgadoContreras, J. R., Magallanes-Quintanar, R., Martinez-Fierro, M. L., GarzaVeloz, I., López-Hernández, Y., & Gamboa-Rosales, H. (2016). An Analysis of Audio Features to Develop a Human Activity Recognition Model Using Genetic Algorithms, Random Forests, and Neural Networks. Mobile Information Systems, 2016, 1–10. https://doi.org/10.1155/2016/1784101[11] Eddy, S. R. (1998). Profile hidden Markov models. Academic.Oup.Com, 144(9), 755–763. https://academic.oup.com/bioinformatics/articleabstract/14/9/755/259550 Envejecimiento y salud. (2018, February 5).[12] Shah,C. (2020). Supervised Learning. In A Hands-On Introduction to Data Science (pp.235–289).[13] Nettleton, D. F., Orriols-Puig, A., & Fornells, A. (2010). A study of the effect of different types of noise on the precision of supervised learning techniques. Artificial Intelligence Review, 33(4), 275–306.https://doi.org/10.1007/s10462-010-9156-z[14] Caruana, R., & Niculescu-Mizil, A. (2006). An empirical comparison of supervised learning algorithms. ACM International Conference Proceeding Series, 148, 161–168. https://doi.org/10.1145/1143844.1143865[15] Mejia-Ricart, L. F., Helling, P., & Olmsted, A. (2018). Evaluate action primitives for human activity recognition using unsupervised learning approach. 2017 12th International Conference for Internet Technology and Secured Transactions, ICITST 2017, 186–188. https://doi.org/10.23919/ICITST.2017.8356374[16] Crandall, A. S. (2011). BEHAVIOMETRICS FOR MULTIPLE RESIDENTS IN A SMART ENVIRONMENT. https://scihub.si/http://research.wsulibs.wsu.edu/xmlui/handle/2376/2855[17] Hoey, J., Pltz, T., Jackson, D., Monk, A., Pham, C., & Olivier, P. (2011). Rapid specification and automated generation of prompting systems to assist people with dementia. Pervasive and Mobile Computing, 7(3), 299–318. https://doi.org/10.1016/j.pmcj.2010.11.007[18] Fahad, L. G., Tahir, S. F., & Rajarajan, M. (2015). Feature selection and data balancing for activity recognition in smart homes. IEEE International Conference on Communications, 2015-Septe, 512–517. https://doi.org/10.1109/ICC.2015.724837310[19] Chawla, N. V, Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. In Journal of Artificial Intelligence Research (Vol. 16). https://scihub.si/http://www.jair.org/index.php/jair/article/view/10302[20] López Saca, F., Ferreyra Ramírez, A., Avilés Cruz, C., Villegas Cortez, J., Zúñiga López, A., & Rodrigez Martinez, E. (2018). Preprocesamiento de bases de datos de imágenes para mejorar el rendimiento de redes neuronales convolucionales. Research in Computing Science, 147(7), 35–45. https://doi.org/10.13053/rcs147-7-3[21] Ruan, Y. X., Lin, H. T., & Tsai, M. F. (2014). Improving ranking performance with cost-sensitive ordinal classification via regression. Information Retrieval, 17(1), 1–20. https://doi.org/10.1007/s10791-013-9219-2[22] Ho, T. K. (1998). The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8), 832– 844. https://doi.org/10.1109/34.709601[23] Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140. https://doi.org/10.1007/bf00058655[24] Nagalla, R., Pothuganti, P., & Pawar, D. S. (2017). Analyzing Gap Acceptance Behavior at Unsignalized Intersections Using Support Vector Machines, Decision Tree and Random Forests. Procedia Computer Science, 109, 474–481. https://doi.org/10.1016/j.procs.2017.05.312[25] Salzberg, S. L. (1994). C4.5: Programs for Machine Learning by J. Ross Quinlan. 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Comparative analysis of machine learning algorithms along with classifiers for network intrusion detection. 2015 International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials, ICSTM 2015 - Proceedings, 89–95. https://doi.org/10.1109/ICSTM.2015.7225395PublicationORIGINALPredictive model for the identification of activies of daily living (ADL) in indoor environments using classification techniques basedon machine learning.pdfPredictive model for the identification of activies of daily living (ADL) in indoor environments using classification techniques basedon machine learning.pdfapplication/pdf612557https://repositorio.cuc.edu.co/bitstreams/483fe9b6-d7cb-4a28-ac93-973a061423d6/download20aa6eceff6a13142b147bab29234c5bMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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