Por favor, use este identificador para citar o enlazar este ítem:
http://repositoriodspace.unipamplona.edu.co/jspui/handle/20.500.12744/5465
Registro completo de metadatos
Campo DC | Valor | Lengua/Idioma |
---|---|---|
dc.contributor.author | Sierra Álvarez, Andrés Felipe. | - |
dc.date.accessioned | 2022-12-15T19:31:24Z | - |
dc.date.available | 2022-03-20 | - |
dc.date.available | 2022-12-15T19:31:24Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | Sierra Álvarez, A. F. (2021). Caracterización teórica de las técnicas de la ciencia de datos e inteligencia artificial implementadas en el campo de la ciencia e ingeniería de los materiales: Oportunidades, descubrimientos e innovación [Trabajo de Grado Pregrado, Universidad de Pamplona] Repositorio Hulago Universidad de Pamplona. http://repositoriodspace.unipamplona.edu.co/jspui/handle/20.500.12744/5465 | es_CO |
dc.identifier.uri | http://repositoriodspace.unipamplona.edu.co/jspui/handle/20.500.12744/5465 | - |
dc.description | El autor no proporciona la información sobre este ítem. | es_CO |
dc.description.abstract | El autor no proporciona la información sobre este ítem. | es_CO |
dc.format.extent | 49 | es_CO |
dc.format.mimetype | application/pdf | es_CO |
dc.language.iso | es | es_CO |
dc.publisher | Universidad de Pamplona – Facultad de Ingenieras y Arquitectura. | es_CO |
dc.subject | El autor no proporciona la información sobre este ítem. | es_CO |
dc.title | Caracterización teórica de las técnicas de la ciencia de datos e inteligencia artificial implementadas en el campo de la ciencia e ingeniería de los materiales: Oportunidades, descubrimientos e innovación. | es_CO |
dc.type | http://purl.org/coar/resource_type/c_7a1f | es_CO |
dc.date.accepted | 2021-12-20 | - |
dc.relation.references | Addin, O., Sapuan, S. M., Mahdi, E., & Othman, M. (2007). A Naïve-Bayes classifier for damage detection in engineering materials. Materials and Design, 28(8), 2379–2386. https://doi.org/10.1016/j.matdes.2006.07.018 | es_CO |
dc.relation.references | Agrawal, A., & Choudhary, A. (2016). Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science. APL Materials, 4(5). https://doi.org/10.1063/1.494689 | es_CO |
dc.relation.references | Agrawal, A., Deshpande, P. D., Cecen, A., Basavarsu, G. P., Choudhary, A. N., & Kalidindi, S. R. (2014). Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters. 1–19. | es_CO |
dc.relation.references | Aha, D. W., Kibler, D., & Albert, M. K. (1991). Instance-based learning algorithms. Machine Learning, 6(1), 37–66. https://doi.org/10.1007/BF00153759 | es_CO |
dc.relation.references | Asaduzzaman, M., Hossain, N., Ahmed, B., Fotouhi, M., Islam, S., Ali, R., & Abul, M. (2021). Recent machine learning guided material research - A review. Computational Condensed Matter, 29(June), e00597. https://doi.org/10.1016/j.cocom.2021.e00597 | es_CO |
dc.relation.references | Austin, T. (2016). Towards a digital infrastructure for engineering materials data. Materials Discovery, 3, 1–12. https://doi.org/10.1016/J.MD.2015.12.003 | es_CO |
dc.relation.references | Bastidas-Rodriguez, M. X., Prieto-Ortiz, F. A., & Espejo, E. (2016). Fractographic classification in metallic materials by using computer vision. Engineering Failure Analysis, 59, 237–252. https://doi.org/https://doi.org/10.1016/j.engfailanal.2015.10.008 | es_CO |
dc.relation.references | Beck, D. A. C., Carothers, J. M., Subramanian, V. R., & Pfaendtner, J. (2016). Data Science: Accelerating Innovation and Discovery in Chemical Engineering. AIChE Journal, 62. https://doi.org/10.1002/aic | es_CO |
dc.relation.references | Bishop, C. M., Bishop, P. N. C. C. M., Hinton, G., & Press, O. U. (1995). Neural Networks for Pattern Recognition. Clarendon Press. https://books.google.com.co/books?id=- aAwQO%5C_-rXwC | es_CO |
dc.relation.references | Bolstad, W. (2004). Introduction to Bayesian Statistics. In University of Waikato. John Wiley. | es_CO |
dc.relation.references | Bouckaert, R. (2004). Naive Bayes Classifiers That Perform Well with Continuous Variables (Vol. 3339). https://doi.org/10.1007/978-3-540-30549-1_106 | es_CO |
dc.relation.references | Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140. https://doi.org/10.1007/BF00058655 | es_CO |
dc.relation.references | Butler, K. T., & Daniel, W. (2018). Machine learning for molecular and materials science. Nature. https://doi.org/10.1038/s41586-018-0337-2 | es_CO |
dc.relation.references | Cai, J., Chu, X., Xu, K., Li, H., & Wei, J. (2020). Machine learning-driven new material discovery. 3115–3130. https://doi.org/10.1039/d0na00388c | es_CO |
dc.relation.references | Chen, L.-Q., Chen, L.-D., Kalinin, S. V, Klimeck, G., Kumar, S. K., Neugebauer, J., & Terasaki, I. (2015). Design and discovery of materials guided by theory and computation. Npj Computational Materials, 1(1), 15007. https://doi.org/10.1038/npjcompumats.2015 | es_CO |
dc.relation.references | Chibani, S., & Coudert, F. (2021). Machine learning approaches for the prediction of materials properties. 080701(August 2020). https://doi.org/10.1063/5.0018384 | es_CO |
dc.relation.references | Curtarolo, S., Hart, G. L. W., Nardelli, M. B., Mingo, N., Sanvito, S., & Levy, O. (2013). The high-throughput highway to computational materials design. Nature Materials, 12(3), 191–201. https://doi.org/10.1038/nmat3568 | es_CO |
dc.relation.references | DeCost, B. L., & Holm, E. A. (2017). Characterizing powder materials using keypoint-based computer vision methods. Computational Materials Science, 126, 438–445. 46 https://doi.org/https://doi.org/10.1016/j.commatsci.2016.08.038 | es_CO |
dc.relation.references | Fausett, L., & Fausett, L. V. (1994). Fundamentals of Neural Networks: Architectures, Algorithms, and Applications. Prentice-Hall. https://books.google.com.co/books?id=ONylQgAACAAJ | es_CO |
dc.relation.references | Fernandez, R., Okariz, A., Ibarretxe, J., Iturrondobeitia, M., & Guraya, T. (2014). Use of decision tree models based on evolutionary algorithms for the morphological classification of reinforcing nano-particle aggregates. COMPUTATIONAL MATERIALS SCIENCE, 92, 102–113. https://doi.org/10.1016/j.commatsci.2014.05.038 | es_CO |
dc.relation.references | Feynman, R. P. (1939). Forces in Molecules. Phys. Rev., 56(4), 340–343. https://doi.org/10.1103/PhysRev.56.340 | es_CO |
dc.relation.references | François-lavet, S. C. V., Henderson, P., Islam, R., François-lavet, V., Pineau, J., & Bellemare, M. G. (2018). An Introduction to Deep Reinforcement Learning. https://doi.org/10.1561/2200000071.Vincent | es_CO |
dc.relation.references | Freund, Y., & Hill, M. (1996). Experiments with a New Boosting Algorithm. | es_CO |
dc.relation.references | Gibert, X., Patel, V. M., & Chellappa, R. (2017). Deep Multitask Learning for Railway Track Inspection. Trans. Intell. Transport. Sys., 18(1), 153–164. https://doi.org/10.1109/TITS.2016.2568758 | es_CO |
dc.relation.references | Greeley, J., Jaramillo, T. F., Bonde, J., Chorkendorff, I. B., & Nørskov, J. K. (2006). Computational high-throughput screening of electrocatalytic materials for hydrogen evolution. Nature Materials, 5(11), 909–913. https://doi.org/10.1038/nmat1752 | es_CO |
dc.relation.references | Hansen, K., Biegler, F., Fazli, S., Rupp, M., Sche, M., Lilienfeld, O. A. Von, Tkatchenko, A., & Mu, K. (2013). Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies. | es_CO |
dc.relation.references | Hautier, G., Jain, A., & Ping, S. (2012). From the computer to the laboratory : materials discovery and design using first-principles calculations. https://doi.org/10.1007/s10853- 012-6424-0 | es_CO |
dc.relation.references | Himanen, L., Geurts, A., Foster, A. S., & Rinke, P. (2019). Data-Driven Materials Science: Status, Challenges, and Perspectives. Advanced Science, 6(21). https://doi.org/10.1002/advs.201900808 | es_CO |
dc.relation.references | 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 | es_CO |
dc.relation.references | Hosmer, D., & Lemeshow, S. (1989). Applied Logistic Regression. John Wiley and Sons. | es_CO |
dc.relation.references | Jablonka, K. M., Ongari, D., Moosavi, S. M., & Smit, B. (2020). Big-Data Science in Porous Materials: Materials Genomics and Machine Learning. C | es_CO |
dc.relation.references | John, G., & Langley, P. (2013). Estimating Continuous Distributions in Bayesian Classifiers. Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence, 1. | es_CO |
dc.relation.references | Kessler, S., Spearing, S., Atalla, M., & Cesnik, C. (2001). Structural health monitoring in composite materials using frequency response methods. Proceedings of SPIE - The International Society for Optical Engineering, 4336. https://doi.org/10.1117/12.435552 | es_CO |
dc.relation.references | Kohavi, R. (1997). The Power of Decision Tables. Proceedings of European Conference on Machine Learning. https://doi.org/10.1007/3-540-59286-5_57 | es_CO |
dc.relation.references | Landwehr, N., Hall, M., & Frank, E. (2005). Logistic Model Trees. Machine Learning, 59(1), 161–205. https://doi.org/10.1007/s10994-005-0466-3 | es_CO |
dc.relation.references | Langley, P., Carbonell, J. G., & IBM. (2018). Machine Learning For Dummies. In Journal of the American Society for Information Science (Vol. 35, Issue 5). https://doi.org/10.1002/asi.4630350509 | es_CO |
dc.relation.references | LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436–444. | es_CO |
dc.relation.references | Li, M.-X., Zhao, S.-F., Lu, Z., Hirata, A., Wen, P., Bai, H.-Y., Chen, M., Schroers, J., Liu, Y., & Wang, W.-H. (2019). High-temperature bulk metallic glasses developed by combinatorial methods. Nature, 569(7754), 99–103. https://doi.org/10.1038/s41586-019- 1145-z | es_CO |
dc.relation.references | Liu, H., Bimbo, J., Seneviratne, L., Althoefer, K., & Mary, Q. (2014). Surface material recognition through haptic exploration using an intelligent contact sensing finger. October 2012. https://doi.org/10.1109/IROS.2012.6385815 | es_CO |
dc.relation.references | Liu, Y., Zhao, T., Ju, W., Shi, S., Shi, S., & Shi, S. (2017). Materials discovery and design using machine learning. Journal of Materiomics, 3(3), 159–177. https://doi.org/10.1016/j.jmat.2017.08.002 | es_CO |
dc.relation.references | Maimon, O., & Rokach, L. (2005). Data Mining And Knowledge Discovery Handbook. | es_CO |
dc.relation.references | Mason, L. (2002). The Alternating Decision Tree Learning Algorithm. Proc. 16th International Conference on Machine Learning, 99. | es_CO |
dc.relation.references | Mohri, M. (2018). Foundations of Machine Learning (F. Bach (ed.); 2nd ed.). The MIT Press. | es_CO |
dc.relation.references | Morgan, D., & Jacobs, R. (2020). Opportunities and Challenges for Machine Learning in Materials Science. Annual Review of Materials Research, 50, 71–103. https://doi.org/10.1146/annurev-matsci-070218-010015 | es_CO |
dc.relation.references | Nigsch, F., Bender, A., Buuren, B. Van, Tissen, J., Nigsch, E., & Mitchell, J. B. O. (2006). Melting Point Prediction Employing k -Nearest Neighbor Algorithms and Genetic Parameter Optimization. 2412–2422. | es_CO |
dc.relation.references | Ong, S. P., Richards, W. D., Jain, A., Hautier, G., Kocher, M., Cholia, S., Gunter, D., Chevrier, V. L., Persson, K. A., & Ceder, G. (2013). Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis. Computational Materials Science, 68, 314–319. https://doi.org/https://doi.org/10.1016/j.commatsci.2012.10.028 | es_CO |
dc.relation.references | Paul, D., Sanap, G., Shenoy, S., Kalyane, D., Kalia, K., & Tekade, R. K. (2021). Artificial intelligence in drug discovery and development. Drug Discovery Today, 26(1), 80–93. https://doi.org/10.1016/j.drudis.2020.10.010 | es_CO |
dc.relation.references | Philip Chen, C. L., & Zhang, C.-Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275, 314–347. https://doi.org/https://doi.org/10.1016/j.ins.2014.01.015 | es_CO |
dc.relation.references | Pizzi, G., Cepellotti, A., Sabatini, R., Marzari, N., & Kozinsky, B. (2016). AiiDA: automated interactive infrastructure and database for computational science. Computational Materials Science, 111, 218–230. https://doi.org/https://doi.org/10.1016/j.commatsci.2015.09.013 | es_CO |
dc.relation.references | Quinlan, J. R. (1992). Learning With Continuous Classes. | es_CO |
dc.relation.references | Riveret, R., Gao, Y., Governatori, G., Rotolo, A., Pitt, J., & Sartor, G. (2019). A probabilistic argumentation framework for reinforcement learning agents: Towards a mentalistic approach to agent profiles. In Autonomous Agents and Multi-Agent Systems (Vol. 33, Issues 1–2). Springer US. https://doi.org/10.1007/s10458-019-09404-2 | es_CO |
dc.relation.references | Rodriguez, J. J., Kuncheva, L. I., & Alonso, C. J. (2006). Rotation Forest: A New Classifier Ensemble Method. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(10), 1619–1630. https://doi.org/10.1109/TPAMI.2006.211 | es_CO |
dc.relation.references | Rouhiainen, L. (2018). Inteligencia artificial 101 cosas que debes saber. Alienta Editorial, 352. https://planetadelibrosar0.cdnstatics.com/libros_contenido_extra/40/39307_Inteligencia_ artificial.pdf | es_CO |
dc.relation.references | Russell, S. J., & Norvig, P. (2019). Artificial Intelligence A Modern Approach (4th ed., Vol. 1). | es_CO |
dc.relation.references | Sajid, S., Haleem, A., Bahl, S., Javaid, M., Goyal, T., & Mittal, M. (2021). Data science applications for predictive maintenance and materials science in context to Industry 4.0. Materials Today: Proceedings, 45(xxxx), 4898–4905. https://doi.org/10.1016/j.matpr.2021.01.357 | es_CO |
dc.relation.references | 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 | es_CO |
dc.relation.references | Sánchez-Lengeling, B., & Aspuru-Guzik, A. (2018). Inverse molecular design using machine learning: Generative models for matter engineering. Science, 361, 360–365. | es_CO |
dc.relation.references | Schleder, G. R., Padilha, A. C. M., Acosta, C. M., Costa, M., & Fazzio, A. (2019). From DFT to machine learning: Recent approaches to materials science - A review. JPhys Materials, 2(3). https://doi.org/10.1088/2515-7639/ab084b | es_CO |
dc.relation.references | Shakhnarovich, G., & Darrell, T. (2005). Nearest-Neighbor Methods in Learning and Vision: Theory and Practice. In The MIT Press | es_CO |
dc.relation.references | Shiraiwa, T., Miyazawa, Y., & Enoki, M. (2018). Prediction of Fatigue Strength in Steels by Linear Regression and Neural Network. 60(2), 189–198. | es_CO |
dc.relation.references | Solomatine, D. P., & Xue, Y. (2004). M5 Model Trees and Neural Networks : Application to Flood Forecasting in the Upper Reach of the Huai River in China. 9(6). https://doi.org/10.1061/(ASCE)1084-0699(2004)9 | es_CO |
dc.relation.references | Song, I. Y., & Zhu, Y. (2016). Big data and data science: what should we teach? Expert Systems, 33(4), 364–373. https://doi.org/10.1111/exsy.12130 | es_CO |
dc.relation.references | Sumner, M., Frank, E., & Hall, M. (2005). Speeding Up Logistic Model Tree Induction. In 9th European Conference on Principles and Practice of Knowledge Discovery in Databases. https://doi.org/10.1007/11564126_72 | es_CO |
dc.relation.references | Thomas, C. R., George, S., Horst, A. M., Ji, Z., Miller, R. J., Peralta-Videa, J. R., Xia, T., Pokhrel, S., Mädler, L., Gardea-Torresdey, J. L., Holden, P. A., Keller, A. A., Lenihan, H. S., Nel, A. E., & Zink, J. I. (2011). Nanomaterials in the environment: from materials to high-throughput screening to organisms. ACS Nano, 5(1), 13–20. https://doi.org/10.1021/nn103485 | es_CO |
dc.relation.references | Vapnik, V. (2000). The Nature of Statistical Learning Theory. In Statistics for Engineering and Information Science (Vol. 8, pp. 1–15). https://doi.org/10.1007/978-1-4757-3264-1_1 | es_CO |
dc.relation.references | Virkus, S., & Garoufallou, E. (2019). Data science from a library and information science perspective. Data Technologies and Applications, 53, 442–441. https://doi.org/10.1108/DTA-05-2019-0076 | es_CO |
dc.relation.references | Wang, Y., & Witten, I. (1997). Induction of model trees for predicting continuous classes. Induction of Model Trees for Predicting Continuous Classes. | es_CO |
dc.relation.references | Ward, C. (2012). Materials Genome Initiative for Global Competitiveness (Issue June). | es_CO |
dc.relation.references | Weher, E. (1977). An introduction to linear regression and correlation. (A series of books in psychology.). Biometrical Journal, 19(1), 83–84. https://doi.org/https://doi.org/10.1002/bimj.4710190121 | es_CO |
dc.relation.references | Wei, J., Chu, X., Sun, X., Xu, K., Deng, H., Chen, J., Wei, Z., & Lei, M. (2019). Machine learning in materials science. Wiley Interdisciplinary Reviews, 1(3), 338–358. https://doi.org/10.1002/inf2.12028 | es_CO |
dc.relation.references | Witten, I. H., Frank, E., Hall, M. A., & Pal, C. (2016). Data Mining: Practical Machine Learning Tools and Techniques. Elsevier Science. https://books.google.co.uk/books?id=1SylCgAAQBAJ | es_CO |
dc.rights.accessrights | http://purl.org/coar/access_right/c_abf2 | es_CO |
dc.type.coarversion | http://purl.org/coar/resource_type/c_2df8fbb1 | es_CO |
Aparece en las colecciones: | Ingeniería Química |
Ficheros en este ítem:
Fichero | Descripción | Tamaño | Formato | |
---|---|---|---|---|
Sierra_2020_TG.pdf | Sierra_2020_TG | 1,34 MB | Adobe PDF | Visualizar/Abrir |
Los ítems de DSpace están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.