Publicaciones

Publications Revistas Indexadas WOS, ESCI, SCOPUS, ISI

2024

1. Godoy, E., de Ferrari, J., Lazo, S., Pancheco, C., Mellado, D., Saez, A., Chabert, S. & Salas, R. (2024). A Framework for Extraction of Clinical Information from Radiological Mammography Reports Using Large Language Models and Retrieval Augmented Generation. Research Square. Preprint.

2. Ponce, S., Chabert, S., Mayeta, L., Franco, P., Plaza-Vega, F., Querales, M., & Salas, R. (2024). Explainable Machine-Learning for identifying the genetic biomarker MGMT in brain tumors using magnetic resonance imaging radiomics. In 2024 14th International Conference on Pattern Recognition Systems (ICPRS) (pp. 1-6). IEEE.

3. Godoy, E., de Ferrari, J., Mellado, D., Chabert, S., & Salas, R. (2024). Automatic detection of contextual laterality in Mammography Reports using Large Language Models. In 2024 14th International Conference on Pattern Recognition Systems (ICPRS) (pp. 1-6). IEEE.

4. Cantor, E., Guauque-Olarte, S., León, R., Chabert, S., & Salas, R. (2024). Knowledge-slanted random forest method for high-dimensional data and small sample size with a feature selection application for gene expression data. BioData Mining, 17(1), 1-17.

5. Bertini, A., Varela, M. J., Holz, A., Gonzalez, P., Bastias, D., Giovanetti, M., Salas, R. & Pardo, F. (2024). Impact of pregestational obesity on perinatal complications: update in a Latin American cohort. Public Health, 233, 170-176.

6. Chabert, S., Salas, R., Cantor, E., Veloz, A., Cancino, A., González, M., ... & Bennett, C. (2024). Hemodynamic response function description in patients with glioma. Journal of Neuroradiology, 51(4), 101156.

7. Cotacallapa, H., Saboya, N., Rodrigues, P. C., Salas, R., & López-Gonzales, J. L. (2024). A flat-hierarchical approach based on machine learning model for e-commerce product classification. IEEE Access.

8. Calderón-Díaz, M., Silvestre Aguirre, R., Vásconez, J. P., Yáñez, R., Roby, M., Querales, M., & Salas, R. (2024). Explainable Machine Learning Techniques to Predict Muscle Injuries in Professional Soccer Players through Biomechanical Analysis. Sensors, 24(1), 119.

2023

9. D. Mellado, M. Querales, M., J. Sotelo,  E. Godoy, F. Pardo, S. Lever, S. Chabert, R. Salas (2023). A Deep Learning Classifier Using Sliding Patches For Detection of Mammographical Findings, 2023 19th International Symposium on Medical Information Processing and Analysis (SIPAIM), Mexico City, Mexico, 2023, pp. 1-5.

10. López-Gonzales, J. L., Gómez Lamus, A. M., Torres, R., Canas Rodrigues, P., & Salas, R. (2023). Self-Organizing Topological Multilayer Perceptron: A Hybrid Method to Improve the Forecasting of Extreme Pollution Values. Stats, 6(4), 1241-1259.

11. Torres, R., Solis, M. A., Martínez, V., & Salas, R. (2023, December). A Nested-Cascade Machine Learning Based Model for Intrusion Detection Systems. In 2023 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON) (pp. 1-6). IEEE

12. Ubal, C., Di-Giorgi, G., Contreras-Reyes, J. E., and Salas, R. (2023). Predicting the Long-Term Dependencies in Time Series Using Recurrent Artificial Neural Networks. Machine Learning and Knowledge Extraction, 5(4), 1340-1358.

13. Oscar Valencia, Alejandro Weinstein, Rodrigo Salas, Rodrigo Guzmán-Venegas, Michail Arvanitidis, Eduardo Martinez-Valdes (2023). Temporal differences in the myoelectric activity of lower limb muscles during rearfoot and forefoot running: A statistical parametric mapping approach. European Journal of Sport Science, vol. 23, no 6, p. 983-991. DOI: 10.1080/17461391.2022.2081094

14. Di-Giorgi, G., Salas, R., Avaria, R., Ubal, C., Rosas, H., & Torres, R. (2023). Volatility forecasting using deep recurrent neural networks as GARCH models. Computational Statistics pp. 1-27. 

15. Vivas, E., de Guenni, L. B., Allende-Cid, H., & Salas, R. (2023) Deep Lagged-Wavelet for monthly rainfall forecasting in a tropical region. Stochastic Environmental Research and Risk Assessment, 37(3), 831-848. 

16. Torres, R., Zurita, C., Mellado, D., Nicolis, O., Saavedra, C., Tuesta, M., Salinas, M., Bertini, A., Pedemonte, O., Querales, M. & Salas, R.(2023). Predicting Cardiovascular Rehabilitation of Patients with Coronary Artery Disease Using Transfer Feature Learning. Diagnostics, 13(3), 508.

17. Querales, M., Salas, R., Torres, R., Aguilera, A., & Morales, Y. (2023). Explainability of Machine Learning Models for Hydrological Time Series Forecasting: The Case of Neuro-Fuzzy Approaches. In 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) (pp. 1-6). IEEE. DOI: 10.1109/ICECCME57830.2023.10252284

2022

18. David Orrego Granados, Jonathan Ugalde, J., Rodrigo Salas, Romina Torres, and Javier Linkolk López-Gonzales  (2022). Visual-Predictive Data Analysis Approach for the Academic Performance of Students from a Peruvian University. Applied Sciences Basel, 12(21), 11251.

19. Yaneet Reyes, Mario G. Santamarina, Diego Villagrán, Francisco Torres, Iván Vial, Cristobal Villarroel, Rodrigo Salas and Steren Chabert (2022). Dosis de radiación en tomografía computarizada: observación en tres hospitales de la Región de Valparaíso, Chile. Revista chilena de radiología, 28(3), 83-91. DOI: 10.24875/rchrad.21000027 

20. Erika Cantor, Romina Torres, Rodrigo Salas (2022). Femicide and Attempted Femicide before and during the COVID-19 Pandemic in Chile. International journal of environmental research and public health, vol. 19, no 13, pp. 8012. DOI: 10.3390/ijerph19138012

21. Antonio Glaría, Rodrigo Salas, Stéren Chabert, Pablo Roncagliolo, Alexis Arriola, Gonzalo Tapia, Matías Salinas, Herman Zepeda, Carla Taramasco, Kayode Oshinubi and Jacques Demongeot (2022). A step forward to formalize Tailored to Problem Specificity Mathematical Transforms. Frontiers in Applied Mathematics and Statistics, vol. 8, article 855862. DOI: 10.3389/fams.2022.855862

22. Paola Valero, Rodrigo Salas, Fabián Pardo, Marcelo Cornejo, Gonzalo Fuentes, Sofía Vega, Adriana Grismaldo, Jan-Luuk Hillebrands, Eline M.van der Beek, Harryvan Goor, Luis Sobrevia (2022). Glycaemia dynamics in gestational diabetes mellitus. Biochimica et Biophysica Acta (BBA) - General Subjects, Volume 1866, Issue 7, 130134. ISSN 0304-4165. DOI: 10.1016/j.bbagen.2022.130134.

23. Ayleen Bertini, Bárbara Gárate, Fabián Pardo, Julie Pelicand, Luis Sobrevia, Romina Torres, Steren Chabert, Rodrigo Salas (2022). Impact of Remote Monitoring Technologies for Assisting Patients With Gestational Diabetes Mellitus: A Systematic Review. Frontiers in Bioengineering and Biotechnology, vol. 10. ISSN=2296-4185. DOI: 10.3389/fbioe.2022.819697.

24. Marvin Querales, Rodrigo Salas, Yerel Moreales, Héctor Allende-Cid, Harvey Rosas (2022). A stacking neuro-fuzzy framework to forecast runoff from distributed meteorological stations. Applied Soft Computing, 118, 108535.

25. FLC da Silva, K da Costa, PC Rodrigues, R Salas, JL López-Gonzales (2022). Statistical and Artificial Neural Networks Models for Electricity Consumption Forecasting in the Brazilian Industrial Sector. Energies 15 (2), 1-12. DOI: 10.3390/en15020588.

26. César Cheuque, Marvin Querales, Roberto León, Rodrigo Salas, Romina Torres (2022). An Efficient Multi-Level Convolutional Neural Network Approach for White Blood Cells Classification. Diagnostics, 12(2), 248. DOI: 10.3390/diagnostics12020248.

27. Ayleen Bertini, Rodrigo Salas, Steren Chabert, Luis Sobrevia, Fabian Pardo (2022). Using machine learning to predict complications in pregnancy: a systematic review. Frontiers in Bioengineering and Biotechnology, section Preclinical Cell and Gene Therapy. DOI: 10.3389/fbioe.2021.780389.

28. Pamela Franco; Julio Sotelo; Andrea Guala; Lydia Dux-Santoy; Arturo Evangelista; José Rodríguez-Palomares; Domingo Mery; Rodrigo Salas; Sergio Uribe (2022). Identification of Hemodynamic Biomarkers for Bicuspid Aortic Valve induced Aortic Dilation using Machine Learning. Computers in Biology and Medicine. doi: 10.1016/j.compbiomed.2021.105147.

2021

29. Chardin Hoyos Cordova, Manuel Niño Lopez Portocarrero, Rodrigo Salas, Romina Torres, Paulo Canas Rodrigues, and Javier Linkolk López-Gonzales (2021). Air Quality Assessment and Pollution Forecasting using Artificial Neural Networks in Metropolitan Lima-Peru. Scientific Reports, Nature. doi: 10.1038/s41598-021-03650-9.

30. Oscar Valencia, Carlos de la Fuente, Rodrigo Guzmán-Venegas, Rodrigo Salas, Alejandro Weinstein (2021). Propuesta de Flujo de Procesamiento utilizando Python para ajustar la Señal Electromiográfica Funcional a la Contracción Voluntaria Máxima. Kinesiología, vol. 40, número 3, pp. 171-175. 

31. Erika Cantor, Rodrigo Salas, Sandra Guauque-Olarte (2021). Biological knowledge-slanted random forest approach for the classification of calcified aortic valve stenosis. BioData Mining 14, 35 (2021). BMC Springer Nature. doi: 10.1186/s13040-021-00269-4 (WOS)

32. Steren Chabert, Juan Castro, Leonardo Muñoz, Pablo Cox, Rodrigo Riveros, Juan Vielma, Gamaliel Huerta, Marvin Querales, Carolina Saavedra, Alejandro Veloz, Rodrigo Salas (2021). Image quality assessment to emulate experts’ perception in lumbar MRI using Machine Learning. Applied Sciences-Basel 2021; 11(14):6616, doi: 10.3390/app11146616 MDPI. ISSN 2076-3417 (WOS)

33. Alexandra Abigail Encalada-Malca, Javier David Cochachi-Bustamante, Paulo Canas Rodrigues, Rodrigo Salas, Javier Linkolk López-Gonzales (2021). A Spatio-Temporal Visualization Approach to the Exploration of PM10 Concentration Data in Metropolitan Lima. Atmosphere 2021; 12(5):609, doi: 10.3390/atmos12050609, MDPI. ISSN 2073-4433. IF 2.397 (WOS)

34. Mailyn Calderón-Díaz, Ricardo Ulloa-Jiménez, Rodrigo Salas and Carolina Saavedra (2021). Wavelet-based semblance analysis to determine muscle synergy for different handstand postures of Chilean circus athletes. Computer Methods in Biomechanics and Biomedical Engineering, ISSN: 1476-8259. doi: 10.1080/10255842.2020.1867113. IF 1.502, Q3, (WOS)

35. Yerel Morales, Marvin Querales, Harvey Rosas, Héctor Allende-Cid and Rodrigo Salas (2021). A Self-Identification Neuro-Fuzzy Inference framework for modeling Rainfall-Runoff in a Chilean watershed. Journal of Hydrology, Volume 594, pages 125910. doi: 10.1016/j.jhydrol.2020.125910, ISSN: 0022-1694. IF 5.08, Q1, (WOS)

2020

36. Eliana Vivas, Héctor Allende-Cid and Rodrigo Salas (2020). A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score. Entropy 2020, 22(12), 1412 doi: 10.3390/e22121412, ISSN: eISSN: 1099-4300. IF 2.53, Q2, (WOS)

37. [2020] Romina Torres, Miguel A. Solis, Rodrigo Salas and Aurelio F. Bariviera. A dynamic linguistic decision making approach for a cryptocurrency investment scenario. IEEE ACCESS doi: 10.1109/ACCESS.2020.3045923, ISSN: 2169-3536, (WOS)

38. [2020] O. Nicolis, F. Plaza and R. Salas. Prediction of intensity and location of seismic events using deep learning. Spatial Statistics doi: 10.1016/j.spasta.2020.100442, ISSN: 2211-6753 (WOS)

39. Fabiola Herrara, Romina Torres, Orietta Nicolis and Rodrigo Salas (2020). Characterization of the Chilean public procurement ecosystem using social network analysis. IEEE ACCESS. doi: 10.1109/ACCESS.2020.3011947  (WoS) 

40. Steren Chabert, J. Verdu, Gamaliel Huerta, C. Montalba, P. Cox, R. Riveros, S. Uribe, Rodrigo Salas, A. Veloz (2020). Impact of b-value sampling scheme on Brain IVIM estimation in Healthy subjects. Journal Magnetic Resonance in Medical Sciences. Article ID mp.2019-0061, ISSN 1880-2206, doi: 10.2463/mrms.mp.2019-0061. (WoS)

41. Alejandro Veloz, Claudio Moraga, Alejandro Weinstein, Luis Hernández-García, Steren Chabert, Rodrigo Salas, Rodrigo Riveros, Carlos Bennett, Héctor Allende (2020). Fuzzy General Linear Modeling for Functional Magnetic Resonance Imaging Analysis. IEEE Trans. Fuzzy Systems. vol 28, issue 1, pp.100-111. doi: 10.1109/TFUZZ.2019.2936807 (WoS)

42. Oscar Valencia, Iver Cristi, Darío Ahumada, Keiny Meza, Rodrigo Salas, Alejandro Weinstein, Rodrigo Guzmán-Venegas (2020) El impacto inicial con antepié incrementa la actividad muscular del gastrocnemios durante la carrera. Un estudio cuantitativo de actividad electromiográfica. The initial impact with forefoot increases the muscular activity of gastrocnemius during running. A quantitative study of electromyographic activity. Retos, vol 38, pp. 271-275. (ESCI)

43. Juan Castro, Steren Chabert, Carolina Savedra and Rodrigo Salas (2020) Convolutional neural network for detection intracraneal hemorrhage in CT images. Fifth Congress on Robotics and Neuroscience CRoNe2019, UTFSM. Valparíso, Chile, 2019, vol. 2564, pp. 37-43 (SCOPUS)

44. Daniela Montilla Trochez, Rodrigo Salas, Alejandro Bertin, Inga Griskova-Bulanova, Carolina Saavedra and Paulo Lisboa (2020). Convolutional Neural Network for Cognitive Task Prediction from EEG's Auditory Steady State Responses. Fifth Congress on Robotics and Neuroscience CRoNe2019, UTFSM. Valparíso, Chile, 2019, , vol. 2564, pp. 44-50 (SCOPUS)

45. Astrid Cancino, Matías Salinas, Alejandra Zazueta and Rodrigo Salas (2020) Computational vision and machine learning to evaluate Metacarpophalangeal and Interphalangeal deviation in fingers for clinical purpose. Fifth Congress on Robotics and Neuroscience CRoNe2019, UTFSM. Valparíso, Chile, 2019, vol. 2564, pp. 31-36(SCOPUS)

2019

46. Francisco Plaza, Rodrigo Salas, Orietta Nicolis (2019). Assessing Seismic Hazard in Chile Using Deep Neural Networks. Natural Hazards. Intech Open. DOI: 10.5772/intechopen.83403.

47. Diego Mellado, Rodrigo Salas, Carolina Saavedra, Romina Torres, Steren Chabert (2019). Self-Improving Generative Artificial Neural Network with novelty detection for Incremental Class Learning. Algorithms 2019, 12(10), 206. DOI: 10.3390/a12100206 (ESCI)

48. C. Saavedra, Rodrigo Salas, L. Bougrain. Wavelet-based semblance methods to enhance single-trial ERP detection. Computational Intelligence and Neuroscience. Article ID 8432953, 10 pages https://doi.org/10.1155/2019/8432953. (WoS)

49. E. Vivas, H. Allende-Cid, Rodrigo Salas and L. Bravo (2019). Polynomial and wavelet-type transfer function models to improve the  fisheries landing forecasting with exogenous variable. Entropy 2019, 21(11), 1-17 MDPI. DOI:10.3390/e21111082 (WoS)

2018

50. M. Salinas, Rodrigo Salas, D. Mellado, A. Glaría, C. Saavedra. A Computational Fractional Signal Derivative Method (2018). Modelling and Simulation in Engineering. Research Article (10 pages), Article ID 7280306, Volume 2018. ISSN: 1687-5591 https://doi.org/10.1155/2018/7280306. (ESCI)

51. F. Plaza, Rodrigo Salas, E. Yáñez (2018). Identifying ecosystem patterns from time series of anchovy (Engraulis ringens) and sardine (Sardinops Sagax) landings in northern Chile. Published in the Journal of Statistical Computation and Simulation. doi: 0.1080/00949655.2017.1410150 (ISI)

52. R. Torres, Rodrigo Salas, N. Bencomo, H. Astudillo (2018). An architecture based on computing with words to support runtime reconfiguration decisions of service-based systems. Published in the International Journal of Computational Intelligence Systems. Vol 11, issue 1, pp. 272-281.  doi:10.2991/ijcis.11.1.21 (IJCIS) (ISI)

2017

53. Romina Torres, Mauricio Poblete, Rodrigo Salas (2017). Classifying human actions in daily life using computational intelligence techniques. 2017 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON), Pucon, Chile, 2017, pp. 1-5. IEEE Press. doi: 10.1109/CHILECON.2017.8229514 (SCOPUS)

54. Diego Mellado, Carolina Saavedra, Steren Chabert and Rodrigo Salas (2017). Pseudorehearsal Approach for Incremental Learning of Deep Convolutional Neural Networks. Computational Neuroscience, Communications in Computer and Information Science, vol. 720, Chapter 10. Springer. doi: 10.1007/978-3-319-71011-2_10 (SCOPUS)

55. G. Tapia, M. Salinas, J. Plaza, D. Mellado, Rodrigo Salas, C. Saavedra,  A. Veloz, A. Arriola, J. Idiaquez, A. Glaría (2017). Photoplethysmogram fits Finger Blood Pressure waveform for non-Invasive and minimally-Intrusive Technologies. Evaluation of derivative approaches. BIOSIGNALS 2017. Volume 4: Biosignals, pp. 155-162. SCITEPRESS ISBN: 978-989-758-212-7. DOI:  20.5220/0006143901550162 (SCOPUS)

56. Steren Chabert, Tomás Mardones, Rodrigo Riveros, Maximiliano Godoy, Alejandro Veloz, Rodrigo Salas, Pablo Cox (2017). Applying machine learning and image feature extraction techniques to the problem of cerebral aneurysm rupture. Research Ideas and Outcomes 3: e11731. doi: 10.3897/rio.3.e11731

2016

57. H. Allende-Cid, Rodrigo Salas, A. Veloz, H. Allende and C. Moraga (2016). Structure Identification and Modeling with a Self-Organizing Neural Network. Published in International Journal of Computational Intelligence Systems, vol 9, issue 4, pages 416-432. Atlantis Press. ISSN 1875-6883. [doi: 10.1080/18756891.2016.1175809] Impact Factor (2014): 0.574 (ISI)

58. A. Veloz, Rodrigo Salas, H. Allende-Cid, H. Allende and C. Moraga (2016) Identification of lags in nonlinear autoregressive time series using a flexible fuzzy model, Published in the journal Neural Processing Letters. Volume 43 Issue 3, Pages 641-666  Impact Factor: 1.448 [doi: 10.1007/s11063-015-9438-1] (ISI)

59. D. Biscay, R. Torres, M. Aliquintuy, H. Astudillo, Rodrigo Salas (2016). Decentralized Strategy for Supporting Multi-agent Negotiation of Several Aspects of Different Products. Proceedings - International Conference of the Chilean Computer Science Society, SCCC Volume 2016 Article number 7559669, Pages 34-38. DOI: 10.1109/SCCC.2014.29 (SCOPUS)

60. Romina Torres, Diana Biscay, Rodrigo Salas, Oscar Cornejo, Marcelo Aliquintuy, H. Astudillo (2016). VirtualMarket: Extending Chilecompra with Agent Capabilities for Identifying Providers Associativity Opportunities and Negotiate Alliance Participation. Proceedings - International Conference of the Chilean Computer Science Society, SCCC Volume 2016 Article number 7559669, Pages 39-43. DOI: 10.1109/SCCC.2014.30 (SCOPUS)

61. H. Allende-Cid, C. Valle, C. Moraga, H. Allende, Rodrigo Salas (2016) Improving the weighted distribution estimation for AdaBoost using a Novel Concurrent approach. Intelligent Distributed Computing IX, volume 616, pp. 223-232,  Studies in Computational Intelligence. Springer International Publishing. ISSN: 1860-949X. http://dx.doi.org/10.1007/978-3-319-25017-5_21 (SCOPUS)

2014

62. R. Torres, Rodrigo Salas, H. Astudillo (2014). “Time-based hesitant fuzzy information aggregation approach for decision making problems”. Journal: International Journal of Intelligent Systems, vol 29, issue 6, pp. 579-595. Wiley Interscience. [doi:10.1002/int.21658] (ISI)

2013

63. J. Zamora, M. Guevara, G. Dominguez, H. Allende, A. Veloz, Rodrigo Salas (2013). On the Understanding of the Stream Volume Behavior On Twitter. Pattern Recognition – Application and Methods. Advances in Intelligent Systems and Computing. Volume 204. pp. 171-180. DOI: 10.1007/978-3-642-36530-0_14. ISSN: 15224902 (SCOPUS)

2012

64. J. Reyes-Lopez, S. Campos, H. Allende, Rodrigo Salas  (2012). Zernike's feature descriptors for Iris Recognition with SVM. IEEE Proceedings of the 30th International Conference of the Chilean Computer Science Society, pp. 283-288. DOI 10.1109/SCCC.2011.36. (SCOPUS)

65. J. Sotelo, Rodrigo Salas, C. Tejos, S. Chabert, S. Uribe (2012) Análisis Cuantitativo de Variables Hemodinámicas de la Aorta Obtenidas de 4D Flow. Revista Chilena de Radiología, vol.18 no.2 Santiago. doi: 10.4067/S0717-93082012000200005 (SCIELO)

66. A. Veloz, Rodrigo Salas, H. Allende-Cid, H. Allende (2012) SIFAR: Self-Identification of Lags of an Autoregressive TSK-based Model. IEEE Press. pp. 226-231. ISBN 978-1-4673-0908-0. doi: 10.1109/ISMVL.2012.42. ISSN: 0195623X (SCOPUS)

67. B. Cessac, Rodrigo Salas, T. Vieville (2012) Using event-based metric for event-based neural network weight adjustment. In the Proceeding of the 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN’2012). Bruges, Belgium. 25-27th of April, 2012. ISBN 978-2-87419-047-6 (SCOPUS)

68. G. Dominguez , J. Zamora , M. Guevara , H. Allende, Rodrigo Salas (2012) Stream volume prediction in Twitter with Artificial Neural Networks. Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods, Volume 2, pp. 488-493. SciTePress 2012. ISBN 978-989-8425-99-7 (SCOPUS)

69. S. Chabert, M. Villalobos, P. Ulloa, Rodrigo Salas, C. Tejos, S. San Martín and J. Pereda (2012). “Quantitative description of the morphology and ossification center in axial skeleton of 20 week-gestation formalin-fixed human fetuses using magnetic resonance images”. Accepted to the Journal of Prenatal Diagnosis. Interscience Wiley (ISI)

2011

70. Rodrigo Salas, C. Saavedra, H. Allende, C. Moraga (2011). “Machine Fusion to Enhance the Topology Preservation of Vector Quantization Artificial Neural Networks”. Pattern Recognition Letters, vol 32, issue 7, pp. 962-972, May 2011. Elsevier. (ISI)

71. R. Torres, H. Astudillo, Rodrigo Salas (2011). Self-Adaptive Fuzzy QoS-Driven Web Service Discovery. IEEE Press. pp. 64-71. DOI 10.1109/SCC.2011.87. (SCOPUS)

72. A. Veloz, A. Orellana, J. Vielma, Rodrigo Salas, S. Chabert (2011). Brain Tumors: How Can Images and Segmentation Techniques Help?. Chapter 4 in Diagnostic Techniques and Surgical Management of Brain Tumors. Ed. A. Lucía Abujamra. pp. 67-92. ISBN  978-953-307-589-1. In Tech.

73. Rodrigo Salas (2011). Inteligencia Artificial. 18 Tesis Doctorales Destacadas. Período 2009-2010. Editorial  Red Universitaria Cruz del Sur. Ed. C. Reyes y C. Kappes. pp. 88-94.

2010

74. H. Allende, C. Moraga, R. Ñanculef, Rodrigo  Salas (2010). Ensembles Methods for Machine Learning. Pattern Recognition and Machine Vision – In honor and memory of prof. King-Sun Fu. P. Shen-Pei Wang (Ed.), pp. 247-261. The River Publishers Series in Information Science and Technology.

2009

75. S. Campos, Rodrigo Salas, H. Allende, Carlos Castro (2009). Multimodal Algorithm for Iris Recognition with Local Topological Descriptors. Lecture Notes in Computer Science (Bayro-Corrochano et al.), vol. 5856, pp. 766-773. Springer Verlag. (SCOPUS)

76. A. Veloz, H. Allende-Cid, H. Allende, C. Moraga, Rodrigo Salas (2009). A Flexible Neuro-Fuzzy Autoregressive Technique for Non-linear Time Series Forecasting. Lecture Notes in Computer Science (J. Velásquez et al Eds.), vol. 5711, pp. 22-29. Springer Verlag. (SCOPUS)

77. C. Saavedra, Rodrigo Salas, H. Allende, C. Moraga (2009) Fusion of Topology preserving Neural Networks. Lecture Notes in Artificial Intelligence (E. Corchado et al Eds.), Vol. 5572, pp. 517-524, Springer Verlag. (SCOPUS)

2008

78. H. Allende-Cid, A. Veloz, Rodrigo Salas, S. Chabert, H. Allende (2008) Self-Organizing Neuro-Fuzzy Inference System. Lecture Notes in Computer Science, Vol. 5197, pp. 422-429. Springer Verlag. (SCOPUS)

2007

79. Rodrigo Salas, S. Moreno, H. Allende y C. Moraga (2007). “A Robust and Flexible model of Hierarchical Self Organizing Maps for Nonstationary Environments“. Neurocomputing, vol. 70, issues 16-18, pp. 2744-2757, october 2007. Elsevier. (ISI)

80. A. Veloz, S. Chabert, Rodrigo Salas, A. Orellana, J. Vielma (2007) Fuzzy Spatial Growing for Glioblastoma Multiforme segmentation on Brain Magnetic Resonance Imaging. Lecture Notes in Computer Science. Vol. 4756, pp. 861-870. Springer Verlag. (SCOPUS)

81. H. Allende-Cid, Rodrigo Salas, H. Allende, R. Ñanculef (2007). Robust Alternating AdaBoost. Lecture Notes in Computer Science. Vol. 4756, pp. 427-436. Springer Verlag. (SCOPUS)

82. E. Malo, Rodrigo Salas, M. Catalán, P. López (2007). A mixed data clustering algorithm to identify population patterns of cancer mortality in Hijuelas-Chile. Lecture Notes in Artificial Intelligence. Vol. 4595. pp. 190-194. Springer Verlag. (SCOPUS)

83. C. Saavedra, Rodrigo Salas, S. Moreno, H. Allende (2007). Fusion of Self Organizing Maps. Lecture Notes in Computer Science. Vol. 4507. pp. 227-234. Springer Verlag. (SCOPUS)

84. S. Moreno, H. Allende, Rodrigo Salas, C. Saavedra (2007). Fusion of Neural Gas.Lecture Notes in Computer Science, vol. 4527, pp. 558-567. Springer Verlag. (SCOPUS)

2006

85. C. Saavedra, S. Moreno, Rodrigo Salas, H. Allende (2006) Robustness Analysis of the Neural Gas Learning Algorithm. Lecture Notes in Computer Science, Vol. 4225, pp. 559-568. Springer Verlag ISI-ISSN- 0302-9743. doi: 10.1007/11892755-58. (ISI)

2005

86. Rodrigo Salas, H. Allende, S. Moreno, C. Saavedra (2005). Flexible Architecture of Self Organizing Maps for Changing Environments. Lecture Notes in Computer Science Volume 3773, p. 642-653. Springer Verlag ISI-ISSN- 0302-9743. (ISI)

87. C. Saavedra, H. Allende, S. Moreno, H. Allende, Rodrigo Salas (2005). K-Dynamical Self Organizing Maps. Lecture Notes in Artificial Intelligence. Volume 3789, p. 702-711. Springer Verlag ISI-ISSN- 0302-9743. (ISI)

88. S. Moreno, H. Allende, C. Rogel, Rodrigo Salas (2005). Robust Growing Hierarchical Self Organizing Map. Lecture Notes in Computer Science Volume 3512, pp. 341-348. Springer Verlag ISI-ISSN- 0302-9743. (ISI)

89. H. Allende, Rodrigo Salas, I. Suazo (2005). Selección de arquitectura de una red neuronal feedforward basado en el análisis de sensibilidad y el criterio de metrópolis. ICHIO: Revista del Instituto Chileno de investigación Operativa. vol. 7, no 1, pp. 1-13.

90. H. Allende, Rodrigo Salas, R. Torres, C. Moraga (2005). Modular Neural Network applied to non-stationary Time Series. Computational Intelligence, Theory and applications. Series: Advance in Soft Computing. pp. 585-598. Ed. B. Reusch. Springer Verlag ISSN 1615-3871. https://doi.org/10.1007/3-540-31182-3_54. (SCOPUS)

2004

91. Claudio Moraga, Rodrigo Salas (2005). A new aspect for the optimization of fuzzy if-then rules. IEEE-CS Press. pp. 160-165. (SCOPUS)

92. H. Allende, C. Rogel , S. Moreno, R. Salas (2004). Robust Neural Gas for the Analysis of Data with Outliers. IEEE-CS Press. pp. 149-155. (SCOPUS)

93. H. Allende, S. Moreno, C. Rogel, R. Salas (2004). Robust Self Organizing Maps. Lecture Notes in Computer Science Volume 3287, p. 179-186. Springer Verlag ISI-ISSN- 0302-9743. (ISI)

94. H. Allende, R. Ñanculef, R. Salas (2004). Robust Bootstrapping Neural Networks. Lecture Notes in Artificial Intelligence Volume 2972, pp. 813-822. Springer Verlag ISI – ISSN- 0302-9743. (ISI)

95. H. Allende, C. Moraga, R. Salas and R. Torres (2004). Modular Neural Network applied to non-stationary Time Series. 8th Fuzzy Days. International Conference on Computational Intelligence. Dortmund, Germany / Sept. 29 – Oct 01. 2004 (SCOPUS)

2003

96. R. Salas, R. Torres, H. Allende, C. Moraga (2003). Robust Estimation of Confidence Interval in Neural Networks applied to Time Series. Lecture Notes in Computer Science Volume 2687, p. 441-448. Springer Verlag ISI – ISSN- 0302-9743 (ISI)

97. R. Torres, R. Salas, H. Allende, C. Moraga (2003). Robust Expectation Maximization Learning Algorithm for Mixture of Experts. Lecture Notes in Computer Science Volume 2686, pp. 238-245. Springer Verlag ISI – ISSN- 0302-9743 (ISI)

98. H. Allende, R. Torres, R. Salas, C. Moraga (2003). Robust Learning Algorithm for the Mixture of Experts. Lecture Notes in Computer Science Volume 2652, p. 19-27. Springer Verlag ISI – ISSN- 0302-9743 (ISI)

99. H. Allende, R. Salas, C. Moraga (2003). A Robust and Effective Learning Algorithm for Feedforward Neural Networks based on the Influence Function. Lecture Notes in Computer Science Volume 2652, p. 28-36. Springer Verlag ISI – ISSN- 0302-9743 (ISI)

2002

100. Héctor Allende, Claudio Moraga, Rodrigo Salas (2002). Robust Estimator for the Learning Process in Neural Networks applied in Time Series. Lecture Notes in Computer Science Volume 2415, pp. 1080-1086. Springer Verlag ISI – ISSN- 0302-9743 (ISI)

2001

101. Héctor Allende, Claudio Moraga, Rodrigo Salas. Artificial Neural Networks in Time Series Forecasting: A Comparative Analysis (2001). Kybernetika, Volume 38, number 6, pages 685-707. ISI- ISSN: 0023-5954 (ISI)

102. Héctor Allende, Claudio Moraga, Rodrigo Salas (2001) Neural Model Identification Using Local Robustness Analysis. Lecture Notes in Computer Science Volume 2206, pp. 162-173. Springer Verlag ISI-ISSN-0302-9743. (ISI)

103. Sergio Ahumada, Luis Pizarro, Romina Torres, Rodrigo Salas (2001). Detección y Seguimiento de Movimiento mediante Optical Flow. Technical Report, Universidad Técnica Federico Santa María. DOI: 10.13140/RG.2.2.23830.75846