نتائج البحث - "Mera-Banguero, Carlos"

1

Academic Journal

Postcontrast Medical Image Synthesis in Breast DCE- MRI Using Deep Learning ; Síntesis de imagen médica postcontraste en estudios de DCE-MRI de mama usando aprendizaje profundo

المؤلفون: Cañaveral, Sara, Mera-Banguero, Carlos, Fonnegra, Rubén D.

المصدر: TecnoLógicas; Vol. 27 No. 60 (2024); e3052 ; TecnoLógicas; Vol. 27 Núm. 60 (2024); e3052 ; 2256-5337 ; 0123-7799

مصطلحات موضوعية: Cáncer de mama, imagen médica, resonancia magnética, generación de imagen postcontraste, aprendizaje profundo, Breast cancer, diagnostic imaging, magnetic resonance imaging, postcontrast image generation, deep learning

وصف الملف: application/pdf

Relation: https://revistas.itm.edu.co/index.php/tecnologicas/article/view/3052/3306; https://revistas.itm.edu.co/index.php/tecnologicas/article/view/3052/3318; M. P. Jimenez Herrera, “Informe de Evento Cáncer de Mama y Cuello Uterino en Colombia 2018,” Instituto Nacional de Salud, Colombia, Versión 04, May 2018. [Online]. Available: https://bit.ly/3J1FcnV; M. Martín, A. Herrero, and I. Echavarría, “El cáncer de mama,” Arbor, vol. 191, no. 773, p. a234, Jun. 2015. https://doi.org/10.3989/arbor.2015.773n3004; IARC. “Data visualization tools for exploring the global cancer burden in 2022.” iarc.who. Accessed: Feb. 20, 2024. [Online.] Available: https://gco.iarc.who.int/today/en; X. Zhou et al., “A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks,” IEEE Access, vol. 8, pp. 90931-90956, May. 2020. https://doi.org/10.1109/ACCESS.2020.2993788; H. V. Guleria et al., “Enhancing the breast histopathology image analysis for cancer detection using Variational Autoencoder,” Int. J. Environ. Res. Public Health., vol. 20, no. 5, p. 4244, Feb. 2023. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10002012/; Instituto Nacional del Cáncer. “Tratamiento del cáncer de seno.” cancer.gov. Accessed: Feb. 20, 2024. [Online.] Available: https://www.cancer.gov/espanol/tipos/seno/paciente/tratamiento-seno-pdq; S. G. Macias, “Métodos de imagen en el estudio de la mama - Ecografía mamaria,” Editorial Medica Panamericana, Bogotá, Colombia, Módulo 1, 2019. https://bit.ly/4aFIg4y; P. E. Freer, “Mammographic breast density: Impact on breast cancer risk and implications for screening,” Radiographics, vol. 35, no. 2, pp. 302–315, Mar. 2015. https://doi.org/10.1148/rg.352140106; P. Campáz-Usuga, R. D. Fonnegra, and C. Mera, “Quality Enhancement of Breast DCE-MRI Images Via Convolutional Autoencoders,” in 2021 IEEE 2nd International Congress of Biomedical Engineering and Bioengineering (CI-IB&BI), Bogotá D.C., Colombia, 2021, pp. 1-4. https://doi.org/10.1109/CI-IBBI54220.2021.9626097; Y. M. Rodríguez Marcano, I. González, H. Palencia, M. Sandoval, and L. León, “Mamografía espectral con realce de contraste. Nuestra experiencia,” Revista Venezolana de Oncologia, vol. 26, no. 4, pp. 743–751, Dec. 2014. https://www.redalyc.org/articulo.oa?id=375633971003; I. Pérez-Zúñiga, Y. Villaseñor-Navarro, M. P. Pérez-Badillo, R. Cruz-Morales, C. Pavón-Hernández, and L. Aguilar-Cortázar, “Resonancia magnética de mama y sus aplicaciones,” Gaceta Mexicana de Oncologia, vol. 11, no. 4, pp. 268–280, 2012. https://www.elsevier.es/es-revista-gaceta-mexicana-oncologia-305-articulo-resonancia-magnetica-mama-sus-aplicaciones-X1665920112544919; C. Balleyguier et al., “New potential and applications of contrast-enhanced ultrasound of the breast: Own investigations and review of the literature,” Eur. J. Radiol., vol. 69, no. 1, pp. 14–23, Jan. 2009. https://doi.org/10.1016/J.EJRAD.2008.07.037; R. Valenzuela, O. Arevalo, A. Tavera, R. Riascos, E. Bonfante, and R. Patel, “Imágenes del depósito de gadolinio en el sistema nervioso central,” Revista Chilena de Radiologia, vol. 23, no. 2, pp. 59–65, Jul.2017. https://doi.org/10.4067/S0717-93082017000200005; F. Gao, T. Wu, X. Chu, H. Yoon, Y. Xu, and B. Patel, “Deep Residual Inception Encoder-Decoder Network for Medical Imaging Synthesis,” IEEE J. Biomed. Health Inform., vol. 24, no. 1, pp. 39–49, Jan. 2020. https://doi.org/10.1109/JBHI.2019.2912659; F. Gao et al., “SD-CNN: A shallow-deep CNN for improved breast cancer diagnosis,” Computerized Medical Imaging and Graphics, vol. 70, pp. 53–62, Dec. 2018. https://doi.org/10.1016/j.compmedimag.2018.09.004; K. Wu et al., “Image synthesis in contrast MRI based on super resolution reconstruction with multi-refinement cycle-consistent generative adversarial networks,” J. Intell. Manuf., vol. 31, no. 5, pp. 1215–1228, Jun. 2020. https://doi.org/10.1007/s10845-019-01507-7; E. Kim, C. Hwan-Ho, J. Kwon, O, Young-Tack, E. S. Ko, and H. Park, “Tumor-Attentive Segmentation-Guided GAN for Synthesizing Breast Contrast-Enhanced MRI Without Contrast Agents,” IEEE Journal of Translational Engineering in Health and Medicine, vol. 11, pp. 32-43, Nov. 2023. https://doi.org/10.1109/JTEHM.2022.3221918; Y. Jiang, Y. Zheng, W. Jia, S. Song, and Y. Ding, “Synthesis of contrast-enhanced spectral mammograms from low-energy mammograms using cGAN-based synthesis network,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, M. de Bruijne, et al., Eds. Cham: Springer International Publishing, 2021, pp. 68–77. https://doi.org/10.1007/978-3-030-87234-2_7; D. Huangz, and M. Feng, “Understanding Deep Convolutional Networks for Biomedical Imaging: A Practical Tutorial,” in 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, 2019, pp. 857-863. https://doi.org/10.1109/EMBC.2019.8857529; C. Shorten, and T. M. Khoshgoftaar, “A survey on Image Data Augmentation for Deep Learning,” J. Big Data, vol. 6, no. 1, Jul. 2019. https://doi.org/10.1186/s40537-019-0197-0; A. Beers et al., “High-resolution medical image synthesis using progressively grown generative adversarial networks,” 2018, ArXiv: 1805.03144. https://arxiv.org/abs/1805.03144; T. Shen, C. Gou, J. Wang, and F. -Y. Wang, “Collaborative Adversarial Networks for Joint Synthesis and Segmentation of X-ray Breast Mass Images,” in 2020 Chinese Automation Congress (CAC), Shanghai, China, 2020, pp. 1743-1747. https://doi.org/10.1109/CAC51589.2020.9326848; Y. Pang, J. Lin, T. Qin, and Z. Chen, “Image-to-Image Translation: Methods and Applications,” IEEE Trans. Multimedia, vol. 24, pp. 3859–3881, Sep. 2021. https://doi.org/10.1109/TMM.2021.3109419; M. Carmen, J. Lizandra, C. Monserrat, A. José, and H. Orallo, “Síntesis de Imágenes en Imagen Médica,” Universidad Politécnica de Valencia, 2003. https://josephorallo.webs.upv.es/escrits/ACTA3.pdf; A. Anwar “Difference between AutoEncoder (AE) and Variational AutoEncoder (VAE),” towardsdatascience.com Accessed: Feb. 20, 2024. [Online]. Available: https://towardsdatascience.com/difference-between-autoencoder-ae-and-variational-autoencoder-vae-ed7be1c038f2; W. Weng, and X. Zhu, “INet: Convolutional Networks for Biomedical Image Segmentation,” IEEE Access, vol. 9, pp. 16591-16603, 2021. https://doi.org/10.1109/ACCESS.2021.3053408; I. J. Goodfellow et al., “Generative Adversarial Networks,” Advances in Neural Information Processing Systems, vol. 14, Jun. 2014. https://doi.org/https://doi.org/10.48550/arXiv.1406.2661; I. C. Moreira, I. Amaral, I. Domingues, A. Cardoso, M. J. Cardoso, and J. S. Cardoso “INbreast: toward a full-field digital mammographic database,” Acad. Radiol., vol. 19, no. 2, pp. 236-248, Feb. 2012. https://doi.org/10.1016/j.acra.2011.09.014; F. Gao, T. Wu, X. Chu, H. Yoon, Y. Xu, and B. Patel, “Deep Residual Inception Encoder-Decoder Network for Medical Imaging Synthesis,” IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 1, pp. 39–49, Apr. 2020. https://doi.org/10.1109/JBHI.2019.2912659; M. Mori et al., “Feasibility of new fat suppression for breast MRI using pix2pix,” Jpn. J. Radiol., vol. 38, no. 11, pp. 1075–1081, Nov. 2020. https://doi.org/10.1007/s11604-020-01012-5; P. Isola, Z. Jun-Yan, T. Zhou, and A. A. Efros, “Image-to-Image Translation with Conditional Adversarial Networks,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, pp. 5967-5976. https://doi.org/10.1109/CVPR.2017.632; P. Wang et al., “Synthesizing the First Phase of Dynamic Sequences of Breast MRI for Enhanced Lesion Identification,” Front. Oncol., vol. 11, Dec. 2021. https://doi.org/10.3389/fonc.2021.792516; Z. Sani, R. Prasad, and E. K. M. Hashim, “Breast Cancer Detection in Mammography using Faster Region Convolutional Neural Networks and Group Convolution,” ETE J. Res., pp. 1–17, May 2024. https://doi.org/10.1080/03772063.2024.2352643; M. Fan et al., “Generative adversarial network-based synthesis of contrast-enhanced MR images from precontrast images for predicting histological characteristics in breast cancer,” Phys. Med. Biol., vol. 69, no. 9, p. 095002, Apr. 2024. https://doi.org/10.1088/1361-6560/ad3889; O. Young-Tack, E. Ko, and H. Park, “TDM-Stargan: Stargan Using Time Difference Map to Generate Dynamic Contrast-Enhanced Mri from Ultrafast Dynamic Contrast-Enhanced Mri,” in 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), Kolkata, India, 2022, pp. 1-5. https://doi.org/10.1109/ISBI52829.2022.9761463; T. Fujioka et al., “Proposal to improve the image quality of short-acquisition time-dedicated breast positron emission tomography using the Pix2pix generative adversarial network,” Diagnostics, vol. 12, no. 12, p. 3114, Dec. 2022. https://doi.org/10.3390/diagnostics12123114; G. Jiang, Y. Lu, J. Wei, and Y. Xu, “Synthesize Mammogram from Digital Breast Tomosynthesis with Gradient Guided cGANs,” Springer International Publishing, D. Shen et al., Eds. vol. 11769, Oct. 2019. https://doi.org/10.1007/978-3-030-32226-7_89; B. Yu, L. Zhou, L. Wang, Y. Shi, J. Fripp, and P. Bourgeat, “Ea-GANs: Edge-Aware Generative Adversarial Networks for Cross-Modality MR Image Synthesis,” IEEE Transactions on Medical Imaging, vol. 38, no. 7, pp. 1750–1762, Jan. 2019. https://doi.org/10.1109/TMI.2019.2895894; B. H. Menze et al., “The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS),” IEEE Transactions on Medical Imaging, vol. 34, no. 10, pp. 1993-2024, Dec. 2015. https://doi.org/10.1109/TMI.2014.2377694; D. Duque-Arias et al., “On power jaccard losses for semantic segmentation,” in Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Setúbal, Portugal, 2021, pp. 561–568. https://doi.org/10.5220/0010304005610568; M. Berman, A. R. Triki, and M. B. Blaschko, “The Lovasz-Softmax Loss: A Tractable Surrogate for the Optimization of the Intersection-Over-Union Measure in Neural Networks,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, pp. 4413-4421. https://doi.org/10.1109/CVPR.2018.00464; B. Xu, N. Wang, T. Chen, and M. Li, “Empirical Evaluation of Rectified Activations in Convolutional Network,” 2015, arXiv:1505.00853. http://arxiv.org/abs/1505.00853; A. Horé, and D. Ziou, “Image Quality Metrics: PSNR vs. SSIM,” in 2010 20th International Conference on Pattern Recognition, Istanbul, Turkey, 2010, pp. 2366-2369. https://doi.org/10.1109/ICPR.2010.579; https://revistas.itm.edu.co/index.php/tecnologicas/article/view/3052

الاتاحة: https://revistas.itm.edu.co/index.php/tecnologicas/article/view/3052

View record in BASE

2

Academic Journal

Síntesis de imagen médica postcontraste en estudios de DCE-MRI de mama usando aprendizaje profundo. (Spanish)

Alternate Title: Postcontrast Medical Image Synthesis in Breast DCE-MRI Using Deep Learning. (English)

المؤلفون: Cañaveral, Sara, Mera-Banguero, Carlos, Fonnegra, Rubén D.

المصدر: Revista Tecno Lógicas; may-ago2024, Vol. 27 Issue 60, p1-18, 38p

3

Academic Journal

Vire-youtube: visualizando los resultados de búsquedas en youtube

المؤلفون: Mera Banguero, Carlos Andrés, Therón Sánchez, Roberto

مصطلحات موضوعية: Visualización de información, Previsualización de vídeos, Visualización de resultados de búsqueda de vídeos

وصف الملف: application/pdf

Relation: http://revistas.unal.edu.co/index.php/avances/article/view/14473; Universidad Nacional de Colombia Revistas electrónicas UN Avances en Sistemas e Informática; Avances en Sistemas e Informática; Avances en Sistemas e Informática; Vol. 6, núm. 1 (2009); 135-144 Avances en Sistemas e Informática; Vol. 6, núm. 1 (2009); 135-144 1909-0056 1657-7663; Mera Banguero, Carlos Andrés and Therón Sánchez, Roberto (2009) Vire-youtube: visualizando los resultados de búsquedas en youtube. Avances en Sistemas e Informática; Vol. 6, núm. 1 (2009); 135-144 Avances en Sistemas e Informática; Vol. 6, núm. 1 (2009); 135-144 1909-0056 1657-7663 .; https://repositorio.unal.edu.co/handle/unal/28559; http://bdigital.unal.edu.co/18607/

الاتاحة: https://repositorio.unal.edu.co/handle/unal/28559
http://bdigital.unal.edu.co/18607/

View record in BASE

4

Academic Journal

Usando diferencias en la estimación de movimiento

المؤلفون: Mera Banguero, Carlos Andres, Trujillo, María Patricia

المصدر: Revista Colombiana de Computación; Vol. 8 Núm. 2 (2007): Revista Colombiana de Computación; 62-78

مصطلحات موضوعية: Innovaciones tecnológicas, Ciencia de los computadores, Desarrollo de tecnología, Ingeniería de sistemas, Investigaciones, Tecnologías de la información y las comunicaciones, TIC´s, Technological innovations, Computer science, Technology development, Systems engineering, Investigations, Information and communication technologies, ICT's

وصف الملف: application/pdf

Relation: https://revistas.unab.edu.co/index.php/rcc/article/view/1036/1009; https://revistas.unab.edu.co/index.php/rcc/article/view/1036; http://hdl.handle.net/20.500.12749/8994; instname:Universidad Autónoma de Bucaramanga UNAB; repourl:https://repository.unab.edu.co

الاتاحة: https://hdl.handle.net/20.500.12749/8994

View record in BASE

Full Text Finder

5

Electronic Resource

Esquema metodológico apoyado en una herramienta (software) para la detección y prevención de Crypto Ransomware en una estación de trabajo

المؤلفون: Mera Banguero, Carlos Andres, Mateus Hernández, Milton Javier, Osorio Sierra, Andrés Felipe

مصطلحات الفهرس: Seguridad de tecnología, Seguridad en la red, Malware (Programa para computador), Protección de datos, Criptografía (Informática), Python (Lenguaje de programación)

URL: http://hdl.handle.net/20.500.12622/1391
Revista CEA

View record in OAIster

6

Detección de defectos en sistemas de inspección visual automática a través del aprendizaje de múltiples instancias

المؤلفون: Mera Banguero, Carlos Andres

المساهمون: Branch Bedoya, John Willian, Orozco Alzate, Mauricio

المصدر: Repositorio UN
Universidad Nacional de Colombia
instacron:Universidad Nacional de Colombia

مصطلحات موضوعية: Aprendizaje con clases debalanceadas, Class imbalance learning, 51 Matemáticas / Mathematics, 62 Ingeniería y operaciones afines / Engineering, 02 Bibliotecología y ciencias de la información / Library and information sciences, Aprendizaje de múltiples instancias, Inspección visual automática, Aprendizaje incremental, Automatic visual inspection, Multi-instance learning, Incremental learning

وصف الملف: application/pdf

URL الوصول: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::f026e4ecba5488341da899d75581305c
http://bdigital.unal.edu.co/56773/

View record in OpenAIRE

7

Deep Learning Model for automatic generation of synthetic antimicrobials peptides with specific functionalities

المؤلفون: Orrego Pérez, Andrés

المساهمون: Mera Banguero, Carlos Andres, Branch Bedoya, John Willian, Orduz Peralta, Sergio, Biología Funcional, Gidia: Grupo de Investigación YyDesarrollo en Inteligencia Artificial

مصطلحات موضوعية: Sequence generation, Péptidos antimicrobianos, Aprendizaje profundo, 004 - Procesamiento de datos Ciencia de los computadores [000 - Ciencias de la computación, información y obras generales], Péptidos, Antimicrobial peptides, Deep learning, Redes neuronales, GAN, Generación de secuencias

وصف الملف: 65 páginas; application/pdf

URL الوصول: https://explore.openaire.eu/search/publication?articleId=od______1326::594932a1200d8c95c3621998e6500dc2
https://repositorio.unal.edu.co/handle/unal/83835

View record in OpenAIRE

8

Dissertation/ Thesis

Modelo de aprendizaje profundo para la generación automática de péptidos antimicrobianos sintéticos con funcionalidades específicas ; Deep Learning Model for automatic generation of synthetic antimicrobials peptides with specific functionalities

المؤلفون: Orrego Pérez, Andrés

المساهمون: Mera Banguero, Carlos Andres, Branch Bedoya, John Willian, Orduz Peralta, Sergio, Biología Funcional, Gidia: Grupo de Investigación YyDesarrollo en Inteligencia Artificial, orcid:0000-0002-5143-0276, https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001737101, https://scholar.google.com/citations?user=K6Tz_4QAAAAJ&hl=es

مصطلحات موضوعية: 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores, Redes neuronales, Péptidos, GAN, Antimicrobial peptides, Deep learning, Sequence generation, Péptidos antimicrobianos, Aprendizaje profundo, Generación de secuencias

وصف الملف: 65 páginas; application/pdf

Relation: LaReferencia; DR. Daza, “Resistencia bacteriana a antimicrobianos: su importancia en la toma de decisiones en la práctica diaria,” Inf ormaciónTerapeutica del Sistema Nacional de Salud, vol. 22, no. 3, 1998.; F. Del Castillo Martin, “Neumococo resistente a la penicilina. Un grave problema de salud publica,” Anales Espanoles de Pediatria, vol. 45, no. 3. 1996.; World Health Organization, “Resistencia a los antimicrobianos,” 2020. https://www.who.int/es/news-room/fact-sheets/detail/antimicrobial-resistance (accessed Dec. 20, 2021).; J. Oromí Durich, “Resistencia bacteriana a los antibióticos. Medicina Integral,” Medicina Integral, vol. 36, no. 10, 2000.; Interagency Coordination Group on Antimicrobial Resistance, “No podemos esperar: asegurar el futuro contra las infecciones farmacorresistentes,” 2019.; World Health Organization, “2019 Antibacterial agents in clinical development: an analysis of the antibacterial clinical development pipeline. Geneva: World Health Organization; 2019. Licence: CC BY-NC-SA 3.0 IGO.,” 2019.; J. O’Neill, “Antimicrobial Resistance : Tackling a crisis for the health and wealth of nations, Review on Antimicrobial Resistance, Chaired by Jim O’Neill, December 2014,” Review on Antimicrobial Resistance, no. December, 2016.; WHO, “Proyecto de plan de acción mundial sobre la resistencia a los antimicrobianos. Informe de la Secretaría.,” Resistencia a los antimicrobianos, 2015.; A. K. Marr, W. J. Gooderham, and R. E. Hancock, “Antibacterial peptides for therapeutic use: obstacles and realistic outlook,” Current Opinion in Pharmacology, vol. 6, no. 5. 2006. doi:10.1016/j.coph.2006.04.006.; C. D. Fjell, J. A. Hiss, R. E. W. Hancock, and G. Schneider, “Designing antimicrobial peptides: Form follows function,” Nature Reviews Drug Discovery, vol. 11, no. 1. 2012. doi:10.1038/nrd3591.; A. Talevi and L. E. Bruno-Blanch, “Screening virtual: Una herramienta eficaz para el desarrollo de nuevos fármacos en Latinoamérica,” Latin American Journal of Pharmacy, vol. 28, no. 1. 2009.; J. Yan et al., “Deep-AmPEP30: Improve Short Antimicrobial Peptides Prediction with Deep Learning,” Mol Ther Nucleic Acids, vol. 20, 2020, doi:10.1016/j.omtn.2020.05.006.; D. Veltri, U. Kamath, and A. Shehu, “Deep learning improves antimicrobial peptide recognition,” Bioinformatics, vol. 34, no. 16, 2018, doi:10.1093/bioinformatics/bty179.; M. Kozić et al., “Predicting the Minimal Inhibitory Concentration for Antimicrobial Peptides with Rana-Box Domain,” J Chem Inf Model, vol. 55, no. 10, 2015, doi:10.1021/acs.jcim.5b00161.; E. G. Sevillano, “¿Cuánto cuesta fabricar un medicamento?,” EL PAÍS, 2015.; A. Orrego Pérez, J. W. Branch Bedoya, C. A. Mera Banguero, and S. Orduz Peralta, “Sistema de Inteligencia Artificial para la Predicción o Generación Automática de péptidos Bioactivos,” Universidad Nacional de Colombia, 2020.; A. Orrego Pérez, C. A. Mera Banguero, S. Orduz Peralta, and J. W. Branch Bedoya, “Red Generativa Antagónica para la Generación de Péptidos Antimicrobianos Sintéticos,” 2021.; A. T. Müller, J. A. Hiss, and G. Schneider, “Recurrent Neural Network Model for Constructive Peptide Design,” J Chem Inf Model, vol. 58, no. 2, pp. 472–479, 2018, doi:10.1021/acs.jcim.7b00414.; J. R. Mxkee. Trudy Mckee, Bioquimica (las bases moleculares de la vida), vol. 53, no. 9. 2013.; P. Gutiérrez and S. Orduz, “PÉPTIDOS ANTIMICROBIANOS: ESTRUCTURA, FUNCIÓN Y APLICACIONES,” Actual Biol, vol. 25, no. 78, 2003.; A. A. Bahar and D. Ren, “Antimicrobial Peptides,” Pharmaceuticals , vol. 6, no. 12. 2013. doi:10.3390/ph6121543.; I. J. Goodfellow et al., “Generative Adversarial Networks,” Jun. 2014, Accessed: Dec. 14, 2021. [Online]. Available: https://arxiv.org/abs/1406.2661; T. Karras, S. Laine, and T. Aila, “A Style-Based Generator Architecture for Generative Adversarial Networks,” IEEE Trans Pattern Anal Mach Intell, vol. 43, no. 12, pp. 4217–4228, 2021, doi:10.1109/TPAMI.2020.2970919.; C. Donahue, J. McAuley, and M. Puckette, “Adversarial Audio Synthesis,” Feb. 2018.; A. Clark, J. Donahue, and K. Simonyan, “Adversarial Video Generation on Complex Datasets,” Jul. 2019.; P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-Image Translation with Conditional Adversarial Networks,” Nov. 2016.; Z. Xu, M. Wilber, C. Fang, A. Hertzmann, and H. Jin, “Learning from Multi-domain Artistic Images for Arbitrary Style Transfer,” May 2018.; H. Zhang et al., “StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks,” Dec. 2016.; I. Goodfellow, “NIPS 2016 Tutorial: Generative Adversarial Networks,” Dec. 2016.; M. Arjovsky and L. Bottou, “Towards principled methods for training generative adversarial networks,” in 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings, 2017.; M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein GAN,” Jan. 2017.; L. Metz, J. Sohl-Dickstein, B. Poole, and D. Pfau, “Unrolled generative adversarial networks,” in 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings, 2017.; K. Roth, A. Lucchi, S. Nowozin, and T. Hofmann, “Stabilizing training of generative adversarial networks through regularization,” in Advances in Neural Information Processing Systems, 2017, vol. 2017-Decem, pp. 2019–2029.; T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, “Improved Techniques for Training GANs,” Jun. 2016.; M. Mirza and S. Osindero, “Conditional Generative Adversarial Nets,” Nov. 2014, Accessed: Dec. 16, 2021. [Online]. Available: https://arxiv.org/abs/1411.1784; Y. Shen, J. Gu, X. Tang, and B. Zhou, “Interpreting the Latent Space of GANs for Semantic Face Editing,” Jul. 2019.; J. Langr and V. Bok, GANs in action, vol. 53, no. 9. 2019.; S. Hochreiter and J. Schmidhuber, “Long Short-term Memory,” Neural Comput, vol. 9, pp. 1735–1780, Dec. 1997, doi:10.1162/neco.1997.9.8.1735.; F. Chollet, Deep Learning with Python, 1st ed. USA: Manning Publications Co., 2017.; Aurélien Géron, Hands-on machine learning with Scikit-Learn, Keras and TensorFlow: concepts, tools, and techniques to build intelligent systems. 2019.; K. Cho et al., “Learning phrase representations using RNN encoder-decoder for statistical machine translation,” in EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, 2014. doi:10.3115/v1/d14-1179.; M. Schuster and K. K. Paliwal, “Bidirectional recurrent neural networks,” IEEE Transactions on Signal Processing, vol. 45, no. 11, 1997, doi:10.1109/78.650093.; F. Grisoni, C. S. Neuhaus, G. Gabernet, A. T. Müller, J. A. Hiss, and G. Schneider, “Designing Anticancer Peptides by Constructive Machine Learning,” ChemMedChem, vol. 13, no. 13, pp. 1300–1302, 2018, doi:10.1002/cmdc.201800204.; Renaud Samuel and Mansbach Rachael, “Latent Spaces for Antimicrobial Peptide Design,” ChemRxiv, Sep. 2022.; C. Wang, S. Garlick, and M. Zloh, “Deep learning for novel antimicrobial peptide design,” Biomolecules, vol. 11, no. 3, pp. 1–17, 2021, doi:10.3390/biom11030471.; P. Das et al., “Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations,” Nat Biomed Eng, vol. 5, no. 6, 2021, doi:10.1038/s41551-021-00689-x.; A. Rossetto and W. Zhou, “GANDALF: Peptide Generation for Drug Design using Sequential and Structural Generative Adversarial Networks,” in Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2020, 2020. doi:10.1145/3388440.3412487.; P. Das et al., “PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide Sequences,” Oct. 2018, Accessed: Dec. 14, 2021. [Online]. Available: https://arxiv.org/abs/1810.07743; K. Hasegawa, Y. Moriwaki, T. Terada, C. Wei, and K. Shimizu, “Feedback-AVPGAN: Feedback-guided generative adversarial network for generating antiviral peptides,” J Bioinform Comput Biol, vol. 20, no. 6, 2022, doi:10.1142/S0219720022500263.; S. N. Dean, J. A. E. Alvarez, D. Zabetakis, S. A. Walper, and A. P. Malanoski, “PepVAE: Variational Autoencoder Framework for Antimicrobial Peptide Generation and Activity Prediction,” Front Microbiol, vol. 12, 2021, doi:10.3389/fmicb.2021.725727.; A. Vélez, C. Mera, S. Orduz, and J. W. Branch, “Synthetic antimicrobial peptides generation using recurrent neural networks %7C Generación de péptidos antimicrobianos mediante redes neuronales recurrentes,” DYNA (Colombia), vol. 88, no. 216, pp. 210–219, 2021, doi:10.15446/dyna.v88n221.88799.; C. M. van Oort, J. B. Ferrell, J. M. Remington, S. Wshah, and J. Li, “AMPGAN v2: Machine Learning-Guided Design of Antimicrobial Peptides,” J Chem Inf Model, vol. 61, no. 5, pp. 2198–2207, 2021, doi:10.1021/acs.jcim.0c01441.; J. B. Ferrell et al., “A Generative Approach Toward Precision Antimicrobial Peptide Design,” bioRxiv, p. 2020.10.02.324087, Jan. 2020, doi:10.1101/2020.10.02.324087.; P. Szymczak et al., “HydrAMP: a deep generative model for antimicrobial peptide discovery,” bioRxiv, p. 2022.01.27.478054, Jan. 2022, doi:10.1101/2022.01.27.478054.; A. Capecchi, X. Cai, H. Personne, T. Köhler, C. van Delden, and J. L. Reymond, “Machine learning designs non-hemolytic antimicrobial peptides,” Chem Sci, vol. 12, no. 26, 2021, doi:10.1039/d1sc01713f.; D. Nagarajan et al., “Computational antimicrobial peptide design and evaluation against multidrug-resistant clinical isolates of bacteria,” J. Biol. Chem., vol. 293, p. 3492, 2018.; M. Ghorbani, S. Prasad, B. R. Brooks, and J. B. Klauda, “Deep attention based variational autoencoder for antimicrobial peptide discovery,” bioRxiv, p. 2022.07.08.499340, Jan. 2022, doi:10.1101/2022.07.08.499340.; M. Pirtskhalava et al., “DBAASP v3: Database of antimicrobial/cytotoxic activity and structure of peptides as a resource for development of new therapeutics,” Nucleic Acids Res, vol. 49, no. D1, 2021, doi:10.1093/nar/gkaa991.; A. Bateman, “UniProt: A worldwide hub of protein knowledge,” Nucleic Acids Res, vol. 47, no. D1, 2019, doi:10.1093/nar/gky1049.; H. T. Lee, C. C. Lee, J. R. Yang, J. Z. C. Lai, K. Y. Chang, and O. Ray, “A large-scale structural classification of Antimicrobial peptides,” BioMed Research International, vol. 2015. 2015. doi:10.1155/2015/475062.; G. Wang, X. Li, and Z. Wang, “APD3: The antimicrobial peptide database as a tool for research and education,” Nucleic Acids Res, vol. 44, no. D1, 2016, doi:10.1093/nar/gkv1278.; X. Zhao, H. Wu, H. Lu, G. Li, and Q. Huang, “LAMP: A Database Linking Antimicrobial Peptides,” PLoS One, vol. 8, no. 6, 2013, doi:10.1371/journal.pone.0066557.; S. N. Dean and S. A. Walper, “Variational autoencoder for generation of antimicrobial peptides,” ACS Omega, vol. 5, no. 33, pp. 20746–20754, 2020, doi:10.1021/acsomega.0c00442.; F. Chollet and others, “Keras.” 2015.; A. Paszke et al., “Automatic differentiation in PyTorch,” 2017.; Martín~Abadi et al., “ TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems.” 2015. [Online]. Available: https://www.tensorflow.org/; R. Collobert, K. Kavukcuoglu, and C. Farabet, “Torch7: A Matlab-like Environment for Machine Learning,” in BigLearn, NIPS Workshop, 2011.; S. Chen and H. U. Kim, “Designing Novel Functional Peptides by Manipulating a Temperature in the Softmax Function Coupled with Variational Autoencoder,” in Proceedings -2019 IEEE International Conference on Big Data, Big Data 2019, 2019. doi:10.1109/BigData47090.2019.9006253.; A. Tucs, D. P. Tran, A. Yumoto, Y. Ito, T. Uzawa, and K. Tsuda, “Generating ampicillin-level antimicrobial peptides with activity-aware generative adversarial networks,” ACS Omega, vol. 5, no. 36, pp. 22847–22851, 2020, doi:10.1021/acsomega.0c02088.; T.-T. Lin et al., “Discovering Novel Antimicrobial Peptides in Generative Adversarial Network,” bioRxiv, 2021.; A. Orrego Pérez et al., “PepMultiTools,” 2019. https://ciencias.medellin.unal.edu.co/gruposdeinvestigacion/prospeccionydisenobiomoleculas/herramientas/pepmultitools.html; A. Gupta and J. Zou, “Feedback GAN for DNA optimizes protein functions,” Nat Mach Intell, vol. 1, no. 2, pp. 105–111, 2019, doi:10.1038/s42256-019-0017-4.; M. Lu and T. Gibson, “Development of Predictive Tools for Anti-Cancer Peptide Candidates using Generative Machine Learning Models,” The Journal of Young Investigators, May 2021.; D. Wang, Z. Wen, L. Li, and H. Zhou, “Generating Antimicrobial Peptides from Latent Secondary Structure Space.” 2022. [Online]. Available: https://openreview.net/forum?id=ajOSNLwqssu; J. Mao et al., “Application of a deep generative model produces novel and diverse functional peptides against microbial resistance,” Comput Struct Biotechnol J, vol. 21, pp. 463–471, 2023, doi: https://doi.org/10.1016/j.csbj.2022.12.029.; J. W. Branch Bedoya, C. A. Mera Banguero, and S. Orduz Peralta, “Sistema de inteligencia artificial para la predicción y generación automática de péptidos bioactivos”.; J. W. Branch Bedoya, C. A. Mera Banguero, and S. Orduz Peralta, “Prototipo de una Máquina de Inteligencia Artificial para la Predicción de la Actividad Antimicrobiana a Partir del Análisis de Proteomas,” 2020.; E. Asgari and M. R. K. Mofrad, “ProtVec: A Continuous Distributed Representation of Biological Sequences,” Mar. 2015, doi:10.1371/journal.pone.0141287.; P. J. A. Cock et al., “Biopython: Freely available Python tools for computational molecular biology and bioinformatics,” Bioinformatics, vol. 25, no. 11, 2009, doi:10.1093/bioinformatics/btp163.; D. S. Cao, Y. Z. Liang, J. Yan, G. S. Tan, Q. S. Xu, and S. Liu, “PyDPI: Freely available python package for chemoinformatics, bioinformatics, and chemogenomics studies,” J Chem Inf Model, vol. 53, no. 11, 2013, doi:10.1021/ci400127q.; A. T. Müller, G. Gabernet, J. A. Hiss, and G. Schneider, “modlAMP: Python for antimicrobial peptides,” Bioinformatics, vol. 33, no. 17, 2017, doi:10.1093/bioinformatics/btx285.; S. Ramírez Montaño, “FastAPI,” 2023.; J. Amat Rodrigo, “Comparación de distribuciones con python: test Kolmogorov–Smirnov.” https://www.cienciadedatos.net/documentos/pystats08-comparacion-distribuciones-test-kolmogorov-smirnov-python.html (accessed May 24, 2022).; M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “GANs trained by a two time-scale update rule converge to a local Nash equilibrium,” in Advances in Neural Information Processing Systems, 2017, vol. 2017-December.; D. C. Dowson and B. v. Landau, “The Fréchet distance between multivariate normal distributions,” J Multivar Anal, vol. 12, no. 3, 1982, doi:10.1016/0047-259X(82)90077-X.; F. H. Waghu, R. S. Barai, P. Gurung, and S. Idicula-Thomas, “CAMPR3: A database on sequences, structures and signatures of antimicrobial peptides,” Nucleic Acids Res, vol. 44, no. D1, pp. D1094–D1097, 2016, doi:10.1093/nar/gkv1051.; C. R. Chung, T. R. Kuo, L. C. Wu, T. Y. Lee, and J. T. Horng, “Characterization and identification of antimicrobial peptides with different functional activities,” Brief Bioinform, vol. 21, no. 3, 2020, doi:10.1093/bib/bbz043.; https://repositorio.unal.edu.co/handle/unal/83835; Universidad Nacional de Colombia; Repositorio Institucional Universidad Nacional de Colombia; https://repositorio.unal.edu.co/

الاتاحة: https://repositorio.unal.edu.co/handle/unal/83835
https://repositorio.unal.edu.co/

View record in BASE

9

Dissertation/ Thesis

Visualización de conjunto de datos de múltiples instancias

المؤلفون: Valencia Duque, Jorge Eliecer

المساهمون: Mera Banguero, Carlos Andrés, Sepúlveda Cano, Lina María

مصطلحات موضوعية: Aprendizaje de múltiples instancias, Visualización, Representación, Análisis Visual, MIL, Bases de datos multidimensionales, Estructura de datos, Procesamiento electrónico de datos, Visualización de la información, Multi-instances learnning, Visualization, Representation, Visual Analysis

جغرافية الموضوع: Lat: 06 15 00 N degrees minutes Lat: 6.2500 decimal degrees Long: 075 36 00 W degrees minutes Long: -75.6000 decimal degrees, Lat: 06 15 00 N degrees minutes Lat: 6.2500 decimal degreesLong: 075 36 00 W degrees minutes Long: -75.6000 decimal degrees

وصف الملف: p. 1-96; Electrónico; application/pdf

Relation: 96; "J. Amores, ""Multiple instance classification: Review, taxonomy and comparative study,"" aug 2013.; C. Mera, M. Orozco-Alzate, J. Branch, and D. Mery, ""Automatic visual inspection: An approach with multi-instance learning,"" Computers in Industry, vol. 83, pp. 46-54, dec 2016.; F. Herrera, S. Ventura, R. Bello, C. Cornelis, A. Zafra, D. S´anchez-Tarrag´o, and S. Vluymans, Multiple instance learning: Foundations and algorithms. Cham: Springer International Publishing, 2016.; W. W.-y. Chan, ""A Survey on Multivariate Data Visualization,"" Science And Techno- logy, no. June, pp. 1-29, 2006.; S. Liu, W. Cui, Y. Wu, and M. Liu, ""A survey on information visualization: recent advances and challenges,"" The Visual Computer, vol. 30, pp. 1373-1393, dec 2014.; D. J. Janvrin, R. L. Raschke, and W. N. Dilla, ""Making sense of complex data using interactive data visualization,"" Journal of Accounting Education, vol. 32, pp. 31-48, dec 2014.; A. P. H. Kiyadeh, A. Zamiri, H. S. Yazdi, and H. Ghaemi, ""Discernible visualization of high dimensional data using label information,"" Applied Soft Computing Journal, vol. 27, pp. 474-486, feb 2015.; W. Yang, Y. Gao, and L. Cao, ""TRASMIL: A local anomaly detection framework based on trajectory segmentation and multi-instance learning,"" Computer Vision and Image Understanding, vol. 117, pp. 1273-1286, oct 2013.; Y. Yi and M. Lin, ""Human action recognition with graph-based multiple-instance learning,"" Pattern Recognition, vol. 53, pp. 148-162, may 2016.; V. Cheplygina and D. M. J. Tax, ""Characterizing Multiple Instance Datasets,"" pp. 15-27, 2015.; N. Elmqvist, P. Dragicevic, and J.-D. Fekete, ""Rolling the Dice: Multidimensional Visual Exploration using Scatterplot Matrix Navigation,"" IEEE Transactions on Vi- sualization and Computer Graphics, vol. 14, pp. 1539-1148, nov 2008.; N. C. Hkust, ""A Survey on Multidimensional Visual Analysis Techniques Introduction - Motivation - Real world data contain multiple dimensions,"" 2011.; S. Barlowe, T. Zhang, Y. Liu, J. Yang, and D. Jacobs, ""Multivariate visual explanation for high dimensional datasets,"" in VAST'08 - IEEE Symposium on Visual Analytics Science and Technology, Proceedings, pp. 147-154, IEEE, oct 2008.; T. Muhammad and Z. Halim, ""Employing artificial neural networks for constructing metadata-based model to automatically select an appropriate data visualization tech- nique,"" Applied Soft Computing, vol. 49, pp. 365-384, dec 2016.; S. M. Kocherlakota and C. G. Healey, ""Interactive visual summarization of multidimen- sional data,"" in Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, pp. 362-369, IEEE, oct 2009.; Q. Li, L. Chen, H. Liao, and J. Yong, ""PatternTrack: A Visual Pattern Detection Technique for Multidimensional Data,"" 2012 International Conference on Computer Science and Service System, pp. 1360-1365, aug 2012.; J. Kehrer and H. Hauser, ""Visualization and visual analysis of multifaceted scientific data: A survey,"" mar 2013.; T. Jirka, Multidimensional Data Visualization, vol. 34. Springer, 2003.; J. Foulds and E. Frank, ""A review of multi-instance learning assumptions,"" 2010.; T. G. Dietterich, R. H. Lathrop, and T. Lozano-P´erez, ""Solving the multiple instance problem with axis-parallel rectangles,"" Artificial Intelligence, vol. 89, no. 1-2, pp. 31-71, 1997.; N. Weidmann, E. Frank, and B. Pfahringer, ""A two-level learning method for genera- lized multi-instance problems,"" in Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), vol. 2837, pp. 468-479, Springer, Berlin, Heidel- berg, 2003.; L. Dong, ""A Comparison of Multi-instance Learning Algorithms,"" tech. rep., 2006. [23] J.-D. Zucker and Y. Chevaleyre, ""Solving multiple-instance and multiple-part learning problems with decision trees and decision rules. Application to the mutagenesis problem,""; Y. Chen, J. Bi, and J. Z. Wang, ""MILES: Multiple-Instance Learning via Embedded Instance Selection,""; S. Vluymans, D. S. Tarrago, Y. Saeys, C. Cornelis, and F. Herrera, ""Fuzzy multi- instance classifiers,"" IEEE Transactions on Fuzzy Systems, vol. 24, pp. 1395-1409, dec 2016.; Y. Ma, J. Xu, X. Wu, F. Wang, and W. Chen, ""A visual analytical approach for transfer learning in classification,"" Information Sciences, vol. 390, pp. 54-69, jun 2017.; B. Kitchenham, O. Pearl Brereton, D. Budgen, M. Turner, J. Bailey, and S. Link- man, ""Systematic literature reviews in software engineering - A systematic literature review,"" jan 2009.; D. Quin?ones and C. Rusu, ""How to develop usability heuristics: A systematic literature review,"" Computer Standards and Interfaces, vol. 53, pp. 89-122, aug 2017.; M. A. Carbonneau, V. Cheplygina, E. Granger, and G. Gagnon, ""Multiple instance learning: A survey of problem characteristics and applications,"" Pattern Recognition, vol. 77, pp. 329-353, may 2018.; R. Langone and J. A. Suykens, ""Supervised aggregated feature learning for multiple instance classification,"" Information Sciences, vol. 375, pp. 234-245, 2017.; X. S. Wei, J. Wu, and Z. H. Zhou, ""Scalable algorithms for multi-instance learning,"" IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 4, pp. 975-987, 2017.; Z. H. Zhou, Y. Y. Sun, and Y. F. Li, ""Multi-instance learning by treating instances as non-I.I.D. samples,"" Proceedings of the 26th International Conference On Machine Learning, ICML 2009, pp. 1249-1256, 2009.; T. ZHANG, W. ZHANG, W. XU, and H. HAO, ""Multiple instance learning for credit risk assessment with transaction data,"" Knowledge-Based Systems, vol. 161, no. No- vember, pp. 65-77, 2018.; C. Liu, T. Chen, X. Ding, H. Zou, and Y. Tong, ""A multi-instance multi-label learning algorithm based on instance correlations,"" Multimedia Tools and Applications, vol. 75, no. 19, pp. 12263-12284, 2016.; F. Sun, J. Tang, H. Li, G. J. Qi, and T. S. Huang, ""Multi-label image categorization with sparse factor representation,"" IEEE Transactions on Image Processing, vol. 23, no. 3, pp. 1028-1037, 2014.; Y. Shen, J. Peng, X. Feng, and J. Fan, ""Multi-label multi-instance learning with mis- sing object tags,"" Multimedia Systems, vol. 19, no. 1, pp. 17-36, 2013.; Y.-Y. Sun, M. K. Ng, and Z.-H. Zhou, ""Multi-Instance Dimensionality Reduction,"" pp. 587-592.; C. Mera, M. Orozco-Alzate, and J. Branch, ""Incremental learning of concept drift in Multiple Instance Learning for industrial visual inspection,"" Computers in Industry, vol. 109, pp. 153-164, 2019.; S. Andrews, I. Tsochantaridis, and T. Hofmann, ""Support Vector Machines for Multi ple-Instance Learning,"" tech. rep., 2003.; Y. Chen, ""Multiple-Instance Learning via Embedded Instance Selection,"" tech. rep.; W. S. Cleveland, R. Mcgill, and S. Cleveland, ""The Many Faces of a Scafferplot,"" Faces, vol. 79, no. 388, pp. 807- 822, 2011.; A. Inselberg, ""The plane with parallel coordinates,"" The Visual Computer, vol. 1, pp. 69-91, dec 1985.; P. Hoffman, ""Table Visualization: A formal model and its applications,"" 1999.; E. Kandogan, ""Star coordinates: A multi-dimensional visualization technique with uni- form treatment of dimensions,"" In Proceedings of the IEEE Information Visualization Symposium, Late Breaking Hot Topics, vol. 650, pp. 9--12, 2000.; R. Rao and S. K. Card, ""The table lens,"" pp. 318-322, Association for Computing Machinery (ACM), 1994.; D. A. Keim and H. P. Kriegel, ""Visualization techniques for mining large databases: A comparison,"" IEEE Transactions on Knowledge and Data Engineering, vol. 8, no. 6, pp. 923-938, 1996.; D. A. Keim, H. P. Kriegel, and M. Ankerst, ""Recursive pattern: a technique for visuali- zing very large amounts of data,"" in Proceedings of the IEEE Visualization Conference, pp. 279-286, 1995.; D. A. Keim and H.-P. Kriegel, ""VisDB: Database Exploration Using Multidimensional Visualization,"" tech. rep., 1994.; M. Ankerst, D. Keim, and H. Kriegel, ""'Circle Segments': A Technique for Visually Ex- ploring Large Multidimensional Data Sets,"" Proc. IEEE Visualization '96, Hot Topic Session, pp. 5-8, 1996.; D. Keim, M. C. Hao, J. Ladisch, M. Hsu, and U. Dayal, ""Pixel Bar Charts : A New Technique for Visualizing Large Multi-Attribute Data Sets without Aggregation,"" tech. rep.; T. Mihalisin, J. Timlin, and J. Schwegler, ""Visualization and analysis of multi-variate data: A technique for all fields,"" in Proceedings of the 2nd Conference on Visualization 1991, VIS 1991, pp. 171-178, 1991.; J. LeBlanc, M. Ward, N. W. o. t. F. I. C. on . . . , and undefined 1990, ""Exploring n-dimensional databases,"" ieeexplore.ieee.org.; S. Feiner and C. Beshers, ""Visualizing n-dimensional virtual worlds with n-vision,"" in Proceedings of the 1990 Symposium on Interactive 3D Graphics, I3D 1990, pp. 37-38, Association for Computing Machinery, Inc, feb 1990.; W. Wang, H. Wang, G. Dai, and H. Wang, ""Visualization of large hierarchical data by circle packing,"" in Conference on Human Factors in Computing Systems - Proceedings, vol. 1, pp. 517-520, 2006.; H. Chernoff, ""The use of faces to represent points in k-dimensional space graphically,"" Journal of the American Statistical Association, vol. 68, no. 342, pp. 361-368, 1973. [56] W. S. Cleveland and R. McGill, ""Graphical perception: Theory, experimentation, and application to the development of graphical methods,"" Journal of the American Statistical Association, vol. 79, no. 387, pp. 531-554, 1984.; R. M. Pickett, ""Iconographic Displays For Visualizing Multidimensional Data Compu- tational geometry View project Information Visualization View project,"" researchga- te.net.; J. B. P. o. t. F. I. C. On and undefined 1990, ""Shape coding of multidimensional data on a microcomputer display,"" ieeexplore.ieee.org.; H. L. P. o. t. N. C. on Visualization' and undefined 1991, ""Color icons: Merging color and texture perception for integrated visualization of multiple parameters,"" dl.acm.org.; A. A. Efros and W. T. Freeman, ""Image quilting for texture synthesis and transfer,"" in Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 2001, pp. 341-346, Association for Computing Machinery, 2001.; Y. Xiao, N. Rodriguez, and O. Strauss, ""Proceedings of the IADIS International Confe- rence Computer Graphics, Visualization, Computer Vision and Image Processing 2013, CGVCVIP 2013,"" 2013.; S. Liu, D. Maljovec, B. Wang, P. T. Bremer, and V. Pascucci, ""Visualizing High- Dimensional Data: Advances in the Past Decade,"" IEEE Transactions on Visualization and Computer Graphics, vol. 23, no. 3, pp. 1249-1268, 2017.; E. Diday, ""An Introduction to Symbolic data Analysis and its Application to the Sodas Project,"" Revista de Matem´atica: Teor´?a y Aplicaciones, vol. 7, no. 1-2, p. 1, 2012.; A. Maalej, N. Rodriguez, A. Maalej, N. Rodriguez, and R. Nancy, ""Survey of multidimensional visualization techniques To cite this version :,"" CGVCVIP'12: Computer Graphics, Visualization, Computer Vision and Image Processing Conference, p. 11, 2012.; S. Ribecca, ""The Data Visualisation Catalogue,"" pp. 1-4, 2015.; G. M. Draper, Y. Livnat, and R. F. Riesenfeld, ""A survey of radial methods for infor- mation visualization,"" IEEE Transactions on Visualization and Computer Graphics, vol. 15, no. 5, pp. 759-776, 2009.; B. Filipi?c and T. Tu?sar, ""A taxonomy of methods for visualizing pareto front ap- proximations,"" in GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference, pp. 649-656, Association for Computing Machinery, Inc, jul 2018.; A. Srinivasan, S. Muggleton, and R. D. King, ""Comparing the use of background know- ledge by inductive logic programming systems,"" in Proceedings of the 5th International Workshop on Inductive Logic Programming, pp. 199-230, 1995.; Z. H. Zhou, K. Jiang, and M. Li, ""Multi-instance learning based web mining,"" Applied Intelligence, vol. 22, no. 2, pp. 135-147, 2005.; M. A. Carreira-Perpin?´an, ""A Review of Dimension Reduction Techniques; "" tech. rep., 1997.; X. Huang, L. Wu, and Y. Ye, ""A Review on Dimensionality Reduction Techniques,"" International Journal of Pattern Recognition and Artificial Intelligence, vol. 33, no. 10, pp. 975-8887, 2019.; B. Scholkopf, A. Smola, and K. R. Mu¨ller, ""Kernel principal component analysis,"" Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1327, no. 3, pp. 583-588, 1997.; A. Hyv¨arinen and E. Oja, ""Independent component analysis: algorithms and appli- cations.,"" Neural networks : the official journal of the International Neural Network Society, vol. 13, pp. 411-30, jun 2000.; J. B. Tenenbaum, V. De Silva, and J. C. Langford, ""A global geometric framework for nonlinear dimensionality reduction,"" Science, vol. 290, no. 5500, pp. 2319-2323, 2000.; S. T. Roweis and L. K. Saul, ""Nonlinear dimensionality reduction by locally linear embedding,"" Science, vol. 290, pp. 2323-2326, dec 2000.; J. B. Kruskal, ""Nonmetric multidimensional scaling: A numerical method,"" Psycho- metrika, vol. 29, no. 2, pp. 115-129, 1964.; L. Van Der Maaten and G. Hinton, ""Visualizing Data using t-SNE,"" tech. rep., 2008.; M. E. Tipping ME and C. M. Bishop CMBishop, ""Probabilistic Principal Component Analysis,"" tech. rep., 1997.; P. O. Box, L. Van Der Maaten, E. Postma, and J. Van Den Herik, ""Tilburg centre for Creative Computing Dimensionality Reduction: A Comparative Review Dimensiona- lity Reduction: A Comparative Review,"" tech. rep., 2009.; F. S. Tsai and K. L. Chan, ""Dimensionality reduction techniques for data exploration,"" in 2007 6th International Conference on Information, Communications and Signal Processing, ICICS, 2007.; S. Surendran Associate, ""A Review of Various Linear and Non Linear Dimensionality Reduction Techniques,"" tech. rep.; C. Mera, M. Orozco-Alzate, and J. Branch, ""Improving representation of the positive class in imbalanced multiple-instance learning,"" in Lecture Notes in Computer Scien- ce (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8814, pp. 266-273, Springer Verlag, 2014.; on statistics, B. S. M. applied Probability, and undefined 1986, ""Kernel density esti- mation technique for statistics and data analysis,""; J. Kim and C. D. Scott, ""Robust Kernel Density Estimation,"" tech. rep., 2012.; S. J. Sheather, ""Density Estimation,"" Statistical Science, vol. 19, no. 4, pp. 588-597, 2004.; S. G. Kobourov, ""Spring Embedders and Force Directed Graph Drawing Algorithms,"" 2012.; P. Gajdo?s, T. Je?zowicz, V. Uher, and P. Dohn´alek, ""A parallel Fruchterman-Reingold algorithm optimized for fast visualization of large graphs and swarms of data,"" Swarm and Evolutionary Computation, vol. 26, pp. 56-63, feb 2016.; J. Goldberger, S. Roweis, G. Hinton, and R. Salakhutdinov, ""Neighbourhood Compo- nents Analysis,"" tech. rep.; N. Turner, ""A guide to carrying out usability reviews -,"" 2011.; C. He, J. Shao, J. Zhang, and X. Zhou, ""Clustering-based multiple instance learning with multi-view feature,"" Expert Systems with Applications, no. xxxx, 2019.; G. Melki, A. Cano, and S. Ventura, ""MIRSVM: Multi-instance support vector machine with bag representatives,"" Pattern Recognition, vol. 79, pp. 228-241, 2018.; D. Xu, J. Wu, D. Li, Y. Tian, X. Zhu, and X. Wu, ""SALE: Self-adaptive LSH encoding for multi-instance learning,"" Pattern Recognition, vol. 71, pp. 460-482, apr 2017.; S. Sastrawaha and P. Horata, ""Ensemble extreme learning machine for multi-instance learning,"" ACM International Conference Proceeding Series, vol. Part F1283, pp. 56-60, 2017.; T. Luo, W. Zhang, S. Qiu, Y. Yang, D. Yi, G. Wang, J. Ye, and J. Wang, ""Functional annotation of human protein coding isoforms via non-convex multi-instance learning,"" Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. Part F1296, pp. 345-354, 2017.; M. Qiao, L. Liu, J. Yu, C. Xu, and D. Tao, ""Diversified dictionaries for multi-instance learning,"" Pattern Recognition, vol. 64, pp. 407-416, 2017.; M. Kahng, D. Fang, and D. H. P. Chau, ""Visual exploration of machine learning results using data cube analysis,"" in Proceedings of the Workshop on Human-In-the-Loop Data Analytics - HILDA '16, (New York, New York, USA), pp. 1-6, ACM Press, 2016.; Q. Liu, S. Zhou, C. Zhu, X. Liu, and J. Yin, ""MI-ELM: Highly efficient multi-instance learning based on hierarchical extreme learning machine,"" Neurocomputing, vol. 173, pp. 1044-1053, 2016.; J. Hu, J. Lu, and Y. P. Tan, ""Deep Metric Learning for Visual Tracking,"" IEEE Transactions on Circuits and Systems for Video Technology, vol. 26, no. 11, pp. 2056-2068, 2016.; G. Vanwinckelen, V. Tragante do O, D. Fierens, and H. Blockeel, ""Instance-level accuracy versus bag-level accuracy in multi-instance learning,"" Data Mining and Knowledge Discovery, vol. 30, no. 2, pp. 313-341, 2016.; F. Gu, M. Sridhar, A. Cohn, D. Hogg, F. Fl´orez-Revuelta, D. Monekosso, and P. Re- magnino, ""Weakly supervised activity analysis with spatio-temporal localisation,"" Neurocomputing, vol. 216, pp. 778-789, 2016.; V. Cheplygina and D. M. J. Tax, ""Characterizing Multiple Instance Datasets,""; J. Dou and J. Li, ""Robust visual tracking based on interactive multiple model particle filter by integrating multiple cues,"" Neurocomputing, vol. 135, pp. 118-129, 2014.; G. Chen, M. Giuliani, D. Clarke, A. Gaschler, and A. Knoll, ""Action recognition using ensemble weighted multi-instance learning,"" in 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 4520-4525, IEEE, may 2014.; J. Wu, Z. Hong, S. Pan, X. Zhu, Z. Cai, and C. Zhang, ""Exploring features for compli- cated objects: Cross-view feature selection for multi-instance learning,"" CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management, pp. 1699-1708, 2014.; H. J. Song, J. W. Son, and S. B. Park, ""Identifying user attributes through non-i.i.d. multi-instance learning,"" Proceedings of the 2013 IEEE/ACM International Conferen- ce on Advances in Social Networks Analysis and Mining, ASONAM 2013, no. Mil, pp. 1467-1468, 2013.; D. Zhang, J. He, and R. Lawrence, ""MI2LS: multi-instance learning from multiple informationsources,"" in KDD '13: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, (New York, New York, USA), pp. 149-157, ACM Press, 2013.; R. R. Vatsavai, ""Gaussian multiple instance learning approach for mapping the slums of the world using very high resolution imagery,"" Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. Part F1288, pp. 1419-1426, 2013.; R. Du, Q. Wu, X. He, and J. Yang, ""MIL-SKDE: Multiple-instance learning with supervised kernel density estimation,"" Signal Processing, vol. 93, no. 6, pp. 1471-1484, 2013.; J. Wu, X. Zhu, C. Zhang, and Z. Cai, ""Multi-instance multi-graph dual embedding lear- ning,"" Proceedings - IEEE International Conference on Data Mining, ICDM, pp. 827-836, 2013.; S. Sabato and N. Tishby, ""Multi-instance learning with any hypothesis class,"" Journal of Machine Learning Research, vol. 13, pp. 2999-3039, 2012.; Y. Shen and J. Fan, ""Multiple instance learning with missing object tags,"" ACM International Conference Proceeding Series, pp. 9-12, 2011.; S. Feng, C. Lang, and D. Xu, ""Beyond tag relevance: Integrating visual attention model and multi-instance learning for tag saliency ranking,"" CIVR 2010 - 2010 ACM International Conference on Image and Video Retrieval, pp. 288-295, 2010.; H. Cheng, K. A. Hua, and N. Yu, ""An automatic feature generation approach to multiple instance learning and its applications to image databases,"" Multimedia Tools and Applications, vol. 47, no. 3, pp. 507-524, 2010.; A. Zafra and S. Ventura, ""G3P-MI: A genetic programming algorithm for multiple instance learning,"" Information Sciences, vol. 180, no. 23, pp. 4496-4513, 2010.; M. L. Zhang, ""Generalized multi-instance learning: Problems, algorithms and data sets,"" Proceedings of the 2009 WRI Global Congress on Intelligent Systems, GCIS 2009, vol. 3, pp. 539-543, 2009.; E. Coronado, C. Gim´enez-Saiz, C. J. G´omez-Garc´?a, and F. M. Romero, ""Multi- instance Multi-label Learning for Relation Extraction,"" Solid State Sciences, vol. 10, no. 12, pp. 1794-1799, 2008.; W. Liu, W. Xu, H. Li, and G. Li, ""Two new bag generators with multi-instance lear- ning for image retrieval,"" 2008 3rd IEEE Conference on Industrial Electronics and Applications, ICIEA 2008, pp. 255-259, 2008."; T 0095 2019; http://hdl.handle.net/11407/6394; reponame:Repositorio Institucional Universidad de Medellín; instname:Universidad de Medellín

الاتاحة: http://hdl.handle.net/11407/6394

View record in BASE