المؤلفون: Bello, Hugo J.

مصطلحات موضوعية: Statistics - Methodology, Mathematics - Statistics Theory

URL الوصول: http://arxiv.org/abs/2301.03085

المؤلفون: Bello, Hugo J., Palomar-Ciria, Nora, Baca-García, Enrique, Lozano, Celia

مصطلحات موضوعية: Computer Science - Social and Information Networks

URL الوصول: http://arxiv.org/abs/2103.03727

المؤلفون: Bello, Hugo J., Palomar, Nora, Gallego, Elisa, Navascués, Lourdes Jiménez, Lozano, Celia

مصطلحات موضوعية: Computer Science - Computation and Language, Computer Science - Social and Information Networks

URL الوصول: http://arxiv.org/abs/2012.07490

المؤلفون: Bello, Hugo J., Palomar Ciria, Nora, Lozano, Celia, Gutiérrez-Alonso, Carlos, Baca García, Enrique

المساهمون: UAM. Departamento de Psiquiatría

مصطلحات موضوعية: Big data, machine learning, public health, suicide, topic classification, Medicina

وصف الملف: application/pdf

Relation: European Journal of Psychiatry; https://doi.org/10.1016/j.ejpsy.2023.100227; The European Journal of Psychiatry 38.1 (2024): 100227; 0213-6163 (print); 2340-4469 (online); http://hdl.handle.net/10486/714272; 100227-1; 100227-10; 38

الاتاحة: http://hdl.handle.net/10486/714272
https://doi.org/10.1016/j.ejpsy.2023.100227

المؤلفون: Bello, Hugo J.

مصطلحات موضوعية: Mathematics - Group Theory

URL الوصول: http://arxiv.org/abs/1603.02091

المؤلفون: Mariano-Hernández, Deyslen, Hernández Callejo, Luis, Solís, Martín, Zorita Lamadrid, Angel Luis, Duque-Perez, Oscar, Gonzalez Morales, Luis Gerardo, Santos Garcia, Felix, Jaramillo Duque, Álvaro, Ospino C., Adalberto, Alonso Gómez, Víctor, Bello, Hugo J.

المصدر: https://www.mdpi.com/2071-1050/14/10/5857.

مصطلحات موضوعية: Drift detection, Electrical consumption forecasting, Energy forecasting, Machine learning, Smart buildings

وصف الملف: 14 páginas; application/pdf

Relation: Sustainability; 1. IEA Tracking Buildings. 2021. Available online: https://www.iea.org/reports/tracking-buildings-2021 (accessed on 16 March 2022).; 2. Cholewa, T.; Siuta-Olcha, A.; Smolarz, A.; Muryjas, P.; Wolszczak, P.; Guz, Ł.; Bocian, M.; Balaras, C.A. An easy and widelapplicable forecast control for heating systems in existing and new buildings: First field experiences. J. Clean. Prod. 2022, 352, 131605. [CrossRef]; 3. Devagiri, V.M.; Boeva, V.; Abghari, S.; Basiri, F.; Lavesson, N. Multi-view data analysis techniques for monitoring smart building systems. Sensors 2021, 21, 6775. [CrossRef] [PubMed]; 4. Izidio, D.M.; de Mattos Neto, P.S.; Barbosa, L.; de Oliveira, J.F.; Marinho, M.H.D.N.; Rissi, G.F. Evolutionary hybrid system for energy consumption forecasting for smart meters. Energies 2021, 14, 1794. [CrossRef]; 5. Hong, T.; Wang, Z.; Luo, X.; Zhang, W. State-of-the-art on research and applications of machine learning in the building life cycle. Energy Build. 2020, 212, 109831. [CrossRef]; 6. Kim, J.Y.; Cho, S.B. Electric energy consumption prediction by deep learning with state explainable autoencoder. Energies 2019, 12, 739. [CrossRef]; 7. Zeng, A.; Ho, H.; Yu, Y. Prediction of building electricity usage using Gaussian Process Regression. J. Build. Eng. 2020, 28, 101054. [CrossRef]; 8. Xu, W.; Peng, H.; Zeng, X.; Zhou, F.; Tian, X.; Peng, X. A hybrid modelling method for time series forecasting based on a linear regression model and deep learning. Appl. Intell. 2019, 49, 3002–3015. [CrossRef]; 9. Cholewa, T.; Siuta-Olcha, A.; Smolarz, A.; Muryjas, P.; Wolszczak, P.; Anasiewicz, R.; Balaras, C.A. A simple building energy model in form of an equivalent outdoor temperature. Energy Build. 2021, 236, 110766. [CrossRef]; 11. Iwashita, A.S.; Papa, J.P. An Overview on Concept Drift Learning. IEEE Access 2019, 7, 1532–1547. [CrossRef]; 12. Baier, L.; Kühl, N.; Satzger, G.; Hofmann, M.; Mohr, M. Handling concept drifts in regression problems—the error intersection approach. In WI2020 Zentrale Tracks; GITO Verlag: Berlin, Germany, 2020; pp. 210–224.; 13. Kahraman, A.; Kantardzic, M.; Kahraman, M.; Kotan, M. A data-driven multi-regime approach for predicting energy consumption. Energies 2021, 14, 6763. [CrossRef]; 14. Webb, G.I.; Lee, L.K.; Goethals, B.; Petitjean, F. Analyzing concept drift and shift from sample data. Data Min. Knowl. Discov. 2018, 32, 1179–1199. [CrossRef]; 15. Lu, J.; Liu, A.; Dong, F.; Gu, F.; Gama, J.; Zhang, G. Learning under Concept Drift: A Review. IEEE Trans. Knowl. Data Eng. 2019, 31, 2346–2363. [CrossRef]; 16. Brzezinski, D.; Stefanowski, J. Reacting to different types of concept drift: The accuracy updated ensemble algorithm. IEEE Trans. Neural Netw. Learn. Syst. 2014, 25, 81–94. [CrossRef]; 17. Wadewale, K.; Desai, S.; Tennant, M.; Stahl, F.; Rana, O.; Gomes, J.B.; Thakre, A.A.; Redes, E.M.; Padmalatha, E.; Rani, P.; et al. Survey on Method of Drift Detection and Classification for time varying data set. Comput. Biol. Med. 2016, 32, 1–7.; 18. Khezri, S.; Tanha, J.; Ahmadi, A.; Sharifi, A. A novel semi-supervised ensemble algorithm using a performance-based selection metric to non-stationary data streams. Neurocomputing 2021, 442, 125–145. [CrossRef]; 19. Fekri, M.N.; Patel, H.; Grolinger, K.; Sharma, V. Deep learning for load forecasting with smart meter data: Online Adaptive Recurrent Neural Network. Appl. Energy 2021, 282, 116177. [CrossRef]; 20. Jagait, R.K.; Fekri, M.N.; Grolinger, K.; Mir, S. Load Forecasting Under Concept Drift: Online Ensemble Learning With Recurrent Neural Network and ARIMA. IEEE Access 2021, 9, 98992–99008. [CrossRef]; 21. Fenza, G.; Gallo, M.; Loia, V. Drift-aware methodology for anomaly detection in smart grid. IEEE Access 2019, 7, 9645–9657. [CrossRef]; 22. Mehmood, H.; Kostakos, P.; Cortes, M.; Anagnostopoulos, T.; Pirttikangas, S.; Gilman, E. Concept drift adaptation techniques in distributed environment for real-world data streams. Smart Cities 2021, 4, 349–371. [CrossRef]; 23. Ceci, M.; Corizzo, R.; Japkowicz, N.; Mignone, P.; Pio, G. ECHAD: Embedding-Based Change Detection from Multivariate Time Series in Smart Grids. IEEE Access 2020, 8, 156053–156066. [CrossRef]; 24. Yang, Z.; Al-Dahidi, S.; Baraldi, P.; Zio, E.; Montelatici, L. A Novel Concept Drift Detection Method for Incremental Learning in Nonstationary Environments. IEEE Trans. Neural Netw. Learn. Syst. 2020, 31, 309–320. [CrossRef] [PubMed]; 25. Silva, R.P.; Zarpelão, B.B.; Cano, A.; Barbon Junior, S. Time series segmentation based on stationarity analysis to improve new samples prediction. Sensors 2021, 21, 7333. [CrossRef] [PubMed]; 26. Heusinger, M.; Raab, C.; Schleif, F.M. Passive concept drift handling via variations of learning vector quantization. Neural Comput. Appl. 2022, 34, 89–100. [CrossRef]; 27. Raab, C.; Heusinger, M.; Schleif, F.M. Reactive Soft Prototype Computing for Concept Drift Streams. Neurocomputing 2020, 416, 340–351. [CrossRef]; 28. Togbe, M.U.; Chabchoub, Y.; Boly, A.; Barry, M.; Chiky, R.; Bahri, M. Anomalies detection using isolation in concept-drifting data streams. Computers 2021, 10, 13. [CrossRef]; 29. Moon, J.; Park, S.; Rho, S.; Hwang, E. A comparative analysis of artificial neural network architectures for building energy consumption forecasting. Int. J. Distrib. Sens. Netw. 2019, 15, 155014771987761. [CrossRef]; 30. Kiprijanovska, I.; Stankoski, S.; Ilievski, I.; Jovanovski, S.; Gams, M.; Gjoreski, H. HousEEC: Day-Ahead Household Electrical Energy Consumption Forecasting Using Deep Learning. Energies 2020, 13, 2672. [CrossRef]; 31. Zor, K.; Çelik, Ö.; Timur, O.; Teke, A. Short-term building electrical energy consumption forecasting by employing gene expression programming and GMDH networks. Energies 2020, 13, 1102. [CrossRef]; 32. Li, Z.; Friedrich, D.; Harrison, G.P. Demand Forecasting for a Mixed-Use Building Using Agent-Schedule Information with a Data-Driven Model. Energies 2020, 13, 780. [CrossRef]; 33. Culaba, A.B.; Del Rosario, A.J.R.; Ubando, A.T.; Chang, J.-S. Machine learning-based energy consumption clustering and forecasting for mixed-use buildings. Int. J. Energy Res. 2020, 44, 9659–9673. [CrossRef]; 34. Wang, Z.; Wang, Y.; Zeng, R.; Srinivasan, R.S.; Ahrentzen, S. Random Forest based hourly building energy prediction. Energy Build. 2018, 171, 11–25. [CrossRef]; 35. Sauer, J.; Mariani, V.C.; dos Santos Coelho, L.; Ribeiro, M.H.D.M.; Rampazzo, M. Extreme gradient boosting model based on improved Jaya optimizer applied to forecasting energy consumption in residential buildings. Evol. Syst. 2021, 1–12. [CrossRef]; 36. Bassi, A.; Shenoy, A.; Sharma, A.; Sigurdson, H.; Glossop, C.; Chan, J.H. Building energy consumption forecasting: A comparison of gradient boosting models. In Proceedings of the 12th International Conference on Advances in Information Technology, Bangkok, Thailand, 29 June–1 July 2021. [CrossRef]; 37. Mariano-Hernández, D.; Hernández-Callejo, L.; Solís, M.; Zorita-Lamadrid, A.; Duque-Perez, O.; Gonzalez-Morales, L.; SantosGarcía, F. A Data-Driven Forecasting Strategy to Predict Continuous Hourly Energy Demand in Smart Buildings. Appl. Sci. 2021, 11, 7886. [CrossRef]; 38. Olu-Ajayi, R.; Alaka, H.; Sulaimon, I.; Sunmola, F.; Ajayi, S. Building energy consumption prediction for residential buildings using deep learning and other machine learning techniques. J. Build. Eng. 2022, 45, 103406. [CrossRef]; 39. Lemos, V.H.B.; Almeida, J.D.S.; Paiva, A.C.; Junior, G.B.; Silva, A.C.; Neto, S.M.B.; Lima, A.C.M.; Cipriano, C.L.S.; Fernandes, E.C.; Silva, M.I.A. Temporal convolutional network applied for forecasting individual monthly electric energy consumption. In Proceedings of the 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Toronto, ON, Canada, 11–14 October 2020; pp. 2002–2007.; 40. Bendaoud, N.M.M.; Farah, N. Using deep learning for short-term load forecasting. Neural Comput. Appl. 2020, 32, 15029–15041. [CrossRef]; 41. Gao, Y.; Ruan, Y.; Fang, C.; Yin, S. Deep learning and transfer learning models of energy consumption forecasting for a building with poor information data. Energy Build. 2020, 223, 110156. [CrossRef]; 42. Bifet, A.; Gavaldà, R. Learning from time-changing data with adaptive windowing. In Proceedings of the 7th SIAM International Conference on Data Mining, Minneapolis, MN, USA, 26–28 April 2007; pp. 443–448.; 43. Moon, J.; Kim, Y.; Son, M.; Hwang, E. Hybrid Short-Term Load Forecasting Scheme Using Random Forest and Multilayer Perceptron. Energies 2018, 11, 3283. [CrossRef]; 44. Khosravani, H.; Castilla, M.; Berenguel, M.; Ruano, A.; Ferreira, P. A Comparison of Energy Consumption Prediction Models Based on Neural Networks of a Bioclimatic Building. Energies 2016, 9, 57. [CrossRef]; 45. Ali, U.; Shamsi, M.H.; Bohacek, M.; Hoare, C.; Purcell, K.; Mangina, E.; O’Donnell, J. A data-driven approach to optimize urban scale energy retrofit decisions for residential buildings. Appl. Energy 2020, 267, 114861. [CrossRef]; 46. Andelkovi´c, A.S.; Bajatovi´c, D. Integration of weather forecast and artificial intelligence for a short-term city-scale natural gas ¯ consumption prediction. J. Clean. Prod. 2020, 266, 122096. [CrossRef]; 14; 10; Mariano-Hernández, D.; Hernández-Callejo, L.; Solís, M.; Zorita-Lamadrid, A.; Duque-Pérez, O.; Gonzalez-Morales, L.; García, F.S.; Jaramillo-Duque, A.; Ospino-Castro, A.; Alonso-Gómez, V.; et al. Analysis of the Integration of Drift Detection Methods in Learning Algorithms for Electrical Consumption Forecasting in Smart Buildings. Sustainability 2022, 14, 5857. https://doi.org/10.3390/su14105857; https://hdl.handle.net/11323/9472; https://doi.org/10.3390/su14105857; Corporación Universidad de la Costa; REDICUC - Repositorio CUC; https://repositorio.cuc.edu.co/

الاتاحة: https://hdl.handle.net/11323/9472
https://doi.org/10.3390/su14105857
https://repositorio.cuc.edu.co/

المؤلفون: Bello, Hugo J., Chasco, María Jesús, Domínguez, Xabier

مصطلحات موضوعية: Mathematics - General Topology

URL الوصول: http://arxiv.org/abs/1305.4515

المؤلفون: Palomar Ciria, Nora, Bello, Hugo J.

Relation: Palomar-Ciria N, Bello HJ. Superheroes, resilience and covid-19. Actual Med. 2021; 106(813): 222-223. [DOI:10.15568/am.2021.813.cd01]; http://hdl.handle.net/10481/72963

الاتاحة: http://hdl.handle.net/10481/72963
https://doi.org/10.15568/am.2021.813.cd01

المؤلفون: Girela-Serrano, Braulio, Miguélez-Fernández, Carolina, Abascal-Peiró, Sofía, Peñuelas-Calvo, Inmaculada, Jiménez-Muñoz, Laura, Moreno, Manon, Delgado-Gómez, David, Bello, Hugo J., Nicholls, Dasha, Baca-García, Enrique, Carballo, Juan José, Porras-Segovia, Alejandro

المصدر: European Child & Adolescent Psychiatry; May2024, Vol. 33 Issue 5, p1481-1494, 14p

مصطلحات موضوعية: PSYCHIATRIC diagnosis, CHILD psychopathology, RESEARCH funding, INTERVIEWING, LOGISTIC regression analysis, DESCRIPTIVE statistics, LONGITUDINAL method, STATISTICS, ADOLESCENCE, CHILDREN

المؤلفون: Bello, Hugo J., Palomar-Ciria, Nora, Gallego, Elisa, Jiménez Navascués, Lourdes, Lozano, Celia

المصدر: Central European Journal of Communication; 2023, 16, 1(33); 101-116 ; 1899-5101

مصطلحات موضوعية: lang, droit

Relation: https://bibliotekanauki.pl/articles/16648183.pdf; https://bibliotekanauki.pl/articles/16648183

الاتاحة: https://doi.org/10.51480/1899-5101.16.1(33).6
https://bibliotekanauki.pl/articles/16648183.pdf
https://bibliotekanauki.pl/articles/16648183

المؤلفون: Girela-Serrano, Braulio, Miguélez-Fernández, Carolina, Abascal-Peiró, Sofía, Peñuelas-Calvo, Inmaculada, Jiménez-Muñoz, Laura, Moreno, Manon, Delgado-Gómez, David, Bello, Hugo J., Nicholls, Dasha, Baca-García, Enrique, Carballo, Juan José, Porras-Segovia, Alejandro

المساهمون: Alicia Koplowitz Foundation, and the Spanish National Project, Ministerio de Ciencia e Innovación, Proyectos de Transición Ecológica y Transición Digital, UC3M, Instituto de Salud Carlos III

المصدر: European Child & Adolescent Psychiatry ; volume 33, issue 5, page 1481-1494 ; ISSN 1018-8827 1435-165X

الاتاحة: http://dx.doi.org/10.1007/s00787-023-02254-0
https://link.springer.com/content/pdf/10.1007/s00787-023-02254-0.pdf
https://link.springer.com/article/10.1007/s00787-023-02254-0/fulltext.html

المؤلفون: Palomar-Ciria, Nora, Bello, Hugo J

المصدر: Journal of Equine Veterinary Science ; volume 128, page 104871 ; ISSN 0737-0806

الاتاحة: http://dx.doi.org/10.1016/j.jevs.2023.104871
https://api.elsevier.com/content/article/PII:S0737080623006834?httpAccept=text/xml
https://api.elsevier.com/content/article/PII:S0737080623006834?httpAccept=text/plain

المؤلفون: Bello, Hugo J., Chasco, MJ, Domínguez, Xabier, Stevens, T. Christine

مصطلحات موضوعية: Local cross section, Extension of topological abelian groups, Local pseudo-homomorphism, Quasi-homomorphism, Weakened, Lie group

Relation: info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MTM2016-79422-P/ES/GRUPOS TOPOLOGICOS: DUALIDAD, GRUPOS DE LIE, APLICACIONES; https://doi.org/10.1007/s13398-018-0599-4; Bello, H. J., Chasco, M. J., Domínguez, X., & Stevens, T. C. (2019). On local cross sections in topological abelian groups. Revista de la Real Academia de Ciencias Exactas, Físicas y Naturales. Serie A. Matemáticas, 113, 2145-2154. https://doi.org/10.1007/s13398-018-0599-4; http://hdl.handle.net/2183/35331

الاتاحة: http://hdl.handle.net/2183/35331
https://doi.org/10.1007/s13398-018-0599-4

المؤلفون: Bello, Hugo J.¹ hugojose.bello@uva.es, Palomar-Ciria, Nora², Baca-García, Enrique^{3,4,5,6,7,8,9,10,11,12}, Lozano, Celia¹³

المصدر: Health Communication. Oct2023, Vol. 38 Issue 10, p2178-2187. 10p. 1 Chart, 8 Graphs.

مصطلحات موضوعية: *MASS media, *SUICIDE prevention, *ARTIFICIAL intelligence, *MACHINE learning, *COMPARATIVE studies, *PREVENTIVE health services, *DESCRIPTIVE statistics, *ARTIFICIAL neural networks, *DATA analytics, *THEMATIC analysis, *HEALTH promotion, *ALGORITHMS

Reviews & Products: TWITTER (Web resource)

المؤلفون: Pérez-Valdecantos, Daniel, Caballero-García, Alberto, Bello, Hugo J., Noriega-González, David, Palomar-Ciria, Nora, Roche, Alba, Roche, Enrique, Córdova-Martínez, Alfredo

المصدر: Behav Sci (Basel) ; ftpubmed

مصطلحات موضوعية: Article, psy, socio

Relation: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9220185/

الاتاحة: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9220185/

المؤلفون: Córdova-Martínez, Alfredo, Pérez-Valdecantos, Daniel, Caballero-García, Alberto, Bello, Hugo J., Roche, Enrique, Noriega-González, David

المصدر: International Journal of Environmental Research & Public Health; Feb2023, Vol. 20 Issue 3, p2052, 10p

المؤلفون: Palomar Ciria, Nora, Bello, Hugo J.

URL الوصول: https://explore.openaire.eu/search/publication?articleId=od______1648::f0ee22985a5b59de19f9b465e6b0337d
http://hdl.handle.net/10481/72963

المؤلفون: Sedano-Capdevila, Alba, Porras-Segovia, Alejandro, Bello, Hugo J., Baca-García, Enrique, Barrigon, Maria Luisa

المصدر: Current Psychiatry Reports ; volume 23, issue 7 ; ISSN 1523-3812 1535-1645

الاتاحة: http://dx.doi.org/10.1007/s11920-021-01255-7
https://link.springer.com/content/pdf/10.1007/s11920-021-01255-7.pdf
https://link.springer.com/article/10.1007/s11920-021-01255-7/fulltext.html

المؤلفون: Palomar-Ciria, Nora, Migoya-Borja, Marta, Cegla-Schvartzman, Fanny, Ovejero, Santiago, Alvarez-Garcia, Raquel, Bello, Hugo J., Baca-García, Enrique

المصدر: International Clinical Psychopharmacology ; volume 36, issue 2, page 97-100 ; ISSN 0268-1315

الاتاحة: http://dx.doi.org/10.1097/yic.0000000000000345
https://journals.lww.com/10.1097/YIC.0000000000000345

المؤلفون: Córdova, Alfredo, Caballero-García, Alberto, Noriega-González, David, Bello, Hugo J., Pons, Antoni, Roche, Enrique

المصدر: International Journal of Environmental Research & Public Health; May2022, Vol. 19 Issue 10, p5938-5938, 12p