-
Notifications
You must be signed in to change notification settings - Fork 0
/
biomed_1.csv
We can make this file beautiful and searchable if this error is corrected: It looks like row 2 should actually have 14 columns, instead of 15. in line 1.
149 lines (149 loc) · 73.8 KB
/
biomed_1.csv
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
Institutos,Título,Año,Autores,¿Mujeres?,DOI,reales/ficticios,tamaño,de donde,se puede usar,pacientes mexicanos,URL datos,LINK,cantidad de mujeres
0,CIMAT;CIMAT;CIMAT;IMSS;UNITEC,Automatic Segmentation of Coronary Arteries in X-ray Angiograms using Multiscale Analysis and Artificial Neural Networks,2019,Fernando Cervantes Sanchez;Ivan Cruz Aceves;Arturo Hernandez Aguirre;Martha Alicia Hernandez Gonzalez;Sergio Eduardo Solorio Meza,4,https://doi.org/10.3390/app9245507,reales,130,creado,si,si,http://personal.cimat.mx:8181/~ivan.cruz/DB_Angiograms.html,,1
1,IMSS;CIMAT;CIMAT;IMSS;IMSS,Determinación de la parábola de la vasculatura de la retina mediante un algoritmo computacional de segmentación,2019,David Jaime Giacinti;Fernando Cervantes Sánchez;Iván Cruz Aceves;Martha Alicia Hernández González;Luis Miguel López Montero,4,doi.org/10.21640/ns.v11i23.1902,reales,40,DRIVE: Digital Retinal Images for Vessel Extraction,si,no,https://drive.grand-challenge.org/,,1
2,University of Firenze;INAOE;University of Bologna;University of Firenze;University of Firenze,Application of Pattern Recognition Techniques to the Classification of Full-Term and Preterm Infant Cry,2015,Silvia Orlandi;Carlos Alberto Reyes Garcia;Andrea Bandini;Gianpaolo Donzelli;Claudia Manfredi,1;3;5,https://doi.org/10.1016/j.jvoice.2015.08.007,reales,28,creado,no,no,None,,3
3,INAOE;INAOE;INAOE;University of Alberta;INAOE;University of Firenze;University of Bologna,Classifying infant cry patterns by the Genetic Selection of a Fuzzy Model,2014,Alejandro Rosales Pérez; Carlos A. Reyes Gracía; Jesus A. Gonzales; Orion F. Reyes Galaviz; Hugo Jair Escalante; Silvia Orlandi,6,https://doi.org/10.1016/j.bspc.2014.10.002,reales,1.918,Baby Chillanto,si,si,None,,1
4,"INAOE;UANL;INAOE;University of Alabama at Birmingham;INAOE;INAOE;INAOE;INAOE;INAOE;INAOE",Acute leukemia classification by ensemble particle swarm model selection,2012,Hugo Jair Escalante;Manuel Montes y Gómez;Jesús A. González;Pilar Gómez Gil;Leopoldo Altamirano;Carlos A. Reyes;Carolina Reta;Alejandro Rosales,4;7,https://doi.org/10.1016/j.artmed.2012.03.005,reales,633,Coleccion de Imagenes del IMSS,si,si,None,sci-hub.se/10.1016/j.artmed.2012.03.005,2
5,INAOE;INAOE;INAOE,Acoustic feature selection and classification of emotions in speech using a 3D continuous emotion model,2011,Humberto Pérez Espinoza;Carlos A Reyes García;Luis Villaseñor Pineda,0,https://doi.org/10.1016/j.bspc.2011.02.008,reales,,FAU Aibo;Berlin Database of Emotional Speech;Spanish Emotional Speech;Emotion Primitives VAM Corpus;EMOCAP Database,si;si;si;si;si,no;no;no;no;no,http://emodb.bilderbar.info/start.html;,https://scihubtw.tw/https://doi.org/10.1016/j.bspc.2011.02.008,0
6,BUAP;INAOE;BUAP;INAOE;Instituto Nacional de Rehabilitación,Automatic infant cry analysis for the identification of qualitative features to help opportune diagnosis,2012,María A. Ruíz Díaz;Carlos A Reyes García;Luis C. Altamirano Robles;Jorge E. Xalteno Altamirano;Antonio Verduzco Mendoza,1,https://www.sciencedirect.com/science/article/abs/pii/S1746809411001030,reales,195;26,Baby Chillanto;Cuban Infant Cries,si;si,si;no,Se necesita permiso,,1
8,INAOE;INAOE;INAOE;INAOE,Aplicación de la Sonificación de Señales Cerebrales en Clasificación Automática,2015,"Erick Fernando Gonzales Castañeda;Alejandro Torres Garcia;Carlos A. Reyes Gracia;Luis Villaseñor Pineda",0,dx.doi.org/10.17488/RMIB.36.3.8,reales,4455,creado,no,si,None,http://www.scielo.org.mx/scielo.php?script=sci_abstract&pid=S0188-95322015000300008&lng=en,0
9,UAM;UPAEP;UAM;UAM;INAOE;INAOE,The role of n-grams in firstborns identification,2015,Gabriela Ramírez de la Rosa;Verónica Reyes Meza;Esaú Villatoro Tello;Héctor Jiménez Salazar;Manuel Montes y Gómez;Luis Villaseñor Pineda,1;2,10.1007/978-3-319-27060-9_8,reales,129,creado,no,si,None,https://www.scopus.com/record/display.uri?eid=2-s2.0-84952022363&origin=inward&txGid=91672f548b23ebc9cef8281f241fb259,2
10,INAOE;INAOE;INAOE,Análisis de señales electroencefalográficas para la clasificación de habla imaginada,2013,A. A. Torres-García;C. A. Reyes-García;L. Villaseñor-Pineda,0,"23-39, ISSN 0188-9532.",reales,4455,creado,no,si,None,https://www.medigraphic.com/pdfs/inge/ib-2013/ib131b.pdf,0
11,ITESA;Instituto Nacional de Rehabilitación; INAOE; Instituto Nacional de Rehabilitación;INAOE;Instituto Nacional de Rehabilitación,Comparison of Machine Learning Models to Predict Risk of Falling in Osteoporosis Elderly,2020,German Cuaya Simbro;Alberto Isaac Perez-Sanpablo;Angélica Muñoz Meléndez;Ivett Quiñones Uriostegui;Eduardo F. Morales-Manzanares;Lidia Nuñez Carrera,3;4;6,10.2478/fcds-2020-0005,reales,253,creado,no,si,None,https://www.researchgate.net/publication/342840344_Comparison_of_Machine_Learning_Models_to_Predict_Risk_of_Falling_in_Osteoporosis_Elderly,3
12,ITESA;ITESA;Instituto Nacional de Rehabilitación; INAOE;Instituto Nacional de Rehabilitación; INAOE,Análisis de readmisión hospitalaria de pacientes diabéticos mediante aprendizaje computacional,2017,German Cuaya Simbro;Elias Ruiz;Angélica Muñoz Meléndez;Eduardo F. Morales,3,10.13053/rcs-138-1-15,reales,101766,Health Facts database,si,no,https://www.hindawi.com/journals/bmri/2014/781670/#supplementary-materials,https://www.researchgate.net/publication/339207151_Analisis_de_readmision_hospitalaria_de_pacientes_diabeticos_mediante_aprendizaje_computacional,1
13,University of Trento;Instituto Nacional de Rehabilitación;INAOE;INAOE;INAOE;CREATE-NET;Instituto Nacional de Rehabilitación;INAOE;CREATE-NET,Using Intermediate Models and Knowledge Learning to Improve Stress Prediction,2017,Alban Maxhuni;Pablo Hernandez Leal;Eduardo F. Morales;L. Enrique Sucar;Venet Osmani;Angelica Muñoz Meléndez;Oscar Mayora,6,10.1007/978-3-319-49622-1_16,reales,5767,creado ,no,no,None,https://www.researchgate.net/publication/311460961_Using_Intermediate_Models_and_Knowledge_Learning_to_Improve_Stress_Prediction,1
14,CREATE-NET;INAOE;CREATE-NET;INAOE;CREATE-NET;INAOE,Classification of bipolar disorder episodes based on analysis of voice and motor activity of patients,2016,Alban Maxhuni;Angelica Muñoz Meléndez;Venet Osmani;Humberto Perez;Oscar Mayora;Eduardo F. Morales,2,10.1016/j.pmcj.2016.01.008,reales,2143,creado,no,no,None,https://www.researchgate.net/publication/292344172_Classification_of_bipolar_disorder_episodes_based_on_analysis_of_voice_and_motor_activity_of_patients,1
15,ITESA;INAOE;Instituto Nacional de Rehabilitación;INAOE;Instituto Nacional de Rehabilitación;Instituto Nacional de Rehabilitación;Instituto Nacional de Rehabilitación,A dynamic Bayesian network for estimating the risk of falls from real gait data,2012,German Cuaya Simbro;Angélica Muñoz Meléndez;Lidia Nuñez Carrera;Eduardo F. Morales;Ivett Quiñones;Alberto Isaac Pérez; Aldo Alessi,2;3;5,10.1007/s11517-012-0960-2,reales,66,creado,no,si,None,https://www.researchgate.net/publication/232248056_A_dynamic_Bayesian_network_for_estimating_the_risk_of_falls_from_real_gait_data,3
16,INAOE;INAOE;INAOE,Recognition of Affective States in Virtual Rehabilitation using Late Fusion with Semi-Naive Bayesian Classifier,2019,Jesús Joel Rivas;Felipe Orihuela Espina;Luis Enrique Sucar,0,https://doi.org/10.1145/3329189.3329222,reales,75,creado para otro articulo previo mismos autores,no,si,None,https://dl.acm.org/doi/10.1145/3329189.3329222,0
17,IPN;IPN;IPN;IPN;Hospital General de Mexico “Dr. Eduardo Liceaga”;IPN;IPN;IPN;UNAM,Combined methods of optical spectroscopy and artificial intelligence in the assessment of experimentally induced non-alcoholic fatty liver,2020,Eduardo Javier Arista Romeu;Josue D. Rivera Fernandez;Karen Roa Tort;Alma Valor;Galileo Escobedo;Diego A. Fabila Bustos;Suren Stolik Isakina;José Manuel de la Rosa;Carolina Guzmán,3;4;9,https://doi.org/10.1016/j.cmpb.2020.105777,reales,2298,"ratones de las Instalaciones de Cuidado Animal, Unidad de Medicina Experimental del Hospital General de México",no,no,None,https://www.sciencedirect.com/science/article/pii/S0169260720316102#bib0021,3
18,UAEM;UAEM;UAEM;UAEM,Towards an anxiety and stress recognition system for academic environments based on physiological features,2020,Jorge Rodríguez Arce;Liliana Lara Flores;Otniel Portillo Rodríguez;Rigoberto Martínez Méndez,2,https://doi.org/10.1016/j.cmpb.2020.105408,reales,21,creado,no,si,None,https://www.sciencedirect.com/science/article/abs/pii/S0169260719304687#!,1
19,"BITS-Pilani;UNAM;Sanatorio Güemes, Buenos Aires 01188;UMSNH;Western Sydney University, Kingswood",Automated detection of congestive heart failure from electrocardiogram signal using Stockwell transform and hybrid classification scheme,2019,R.K. Tripathy;Mario R.A. Paternina;Juan G. Arrieta;Alejandro Zamora Méndez;Ganesh R. Naik,0,https://doi.org/10.1016/j.cmpb.2019.03.008,reales,15;17,Beth Israel Deaconess Medical Center (BIDMC) CHF database;Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database,si;si,no;no,https://physionet.org/content/chfdb/1.0.0/;https://www.physionet.org/content/mitdb/1.0.0/,https://www.sciencedirect.com/science/article/abs/pii/S0169260718302980#!,0
20,IPN;IPN;IPN;IPN,An associative memory approach to medical decision support systems,2011,Mario Aldape Pérez;Cornelio Yánez Márquez;Oscar Camacho Nieto;Amadeo J.Argüelles Cruz,0,https://doi.org/10.1016/j.cmpb.2011.05.002,reales,306;345;120;768;699;270;155,Haberman survival dataset; Liver disorders dataset;Acute inflammations dataset;Pima Indians diabetes dataset;Breast cancer dataset;Heart disease dataset;Hepatitis disease dataset,si;si;si;si;si;si;si,no;no;no;no;no;no;no;no,https://archive.ics.uci.edu/ml/datasets/Haberman%27s+Survival;https://archive.ics.uci.edu/ml/datasets/Liver+Disorders;https://archive.ics.uci.edu/ml/datasets/Acute+Inflammations;https://www.kaggle.com/uciml/pima-indians-diabetes-database;https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29;http://archive.ics.uci.edu/ml/datasets/Heart+Disease;https://archive.ics.uci.edu/ml/datasets/Hepatitis,https://www.sciencedirect.com/science/article/abs/pii/S0169260711001179#!,0
21,INAOE;INAOE;Instituto Nacional de Neurologia y Neurocirugia;University College of London;BUAP;Instituto Nacional de Neurologia y Neurocirugia;INAOE,Unobtrusive Inference of Affective States in Virtual Rehabilitation from Upper Limb Motions: A Feasibility Study,2018,Jesus Joel Rivas;Felipe Orihuela-Espina;Lorena Palafox;Nadia Bianchi Berthouze;Maria del Carmen Lara;Jorge Hernandez Franco;Luis Enrique Sucar,4;5,10.1109/taffc.2018.2808295,reales,79,creado,no,si,None,,2
22,INAOE;INAOE;INAOE,Toward a silent speech interface based on unspoken speech,2012,Alejandro Antonio Torres García; Carlos Alberto Reyes García;Luis Villaseñor Pineda,0,978-989-8425-89-8;conference paper,reales,256,creado,no,si,None,https://www.scopus.com/record/display.uri?eid=2-s2.0-84861990915&origin=inward&txGid=734f90a4d3babc226c17be2e68920672,0
23,UNAM;UNAM,AUTOMATIC DETECTION OF HARD EXUDATES AND OPTIC DISC IN DIGITAL FUNDUS IMAGES,2012,Elizabeth Chavez Hernandez;M. Elena Martinez Perez,1;2,978-989-8425-89-8;Conference paper,reales,25, IMAGERET,si,no,https://www.it.lut.fi/project/imageret/,https://www.scopus.com/record/display.uri?eid=2-s2.0-84861986789&origin=inward&txGid=d780ccaff33ee22f1fa9d9036387d992#,2
24,INAOE;INAOE;INAOE;INAOE,Sonificación de EEG para la clasificación de palabras no pronunciadas,2014,Erick Fernando Gonzales Castañeda;Alenjandro Antonio Torries Garcia;Carlos Alberto Reyes Garcia;Luis Villaseñor Pineda,0,10.13053/rcs-74-1-5,reales,27,creado,no,si,None,https://www.rcs.cic.ipn.mx/2014_74/Sonificacion%20de%20EEG%20para%20la%20clasificacion%20de%20palabras%20no%20pronunciadas.pdf,0
25,BUAP;BUAP;BUAP;BUAP,Feature extraction from EEG spectrograms for epileptic seizure detection,2020,Ricardo Ramos Aguilar;J. Arturo Olvera Lopez; Ivan Olmos Pineda;Susana Sanchez Urrieta,4,https://doi.org/10.1016/j.patrec.2020.03.006,reales,100,open acces Epilepsy EGG,si,no,http://epileptologie-bonn.de/cms/front_content.php?idcat=193&lang=3&changelang=3,,1
26,BUAP;BUAP;BUAP,Automatic region of interest segmentation for breast thermogram image classification,2020,Daniel Sanchez Ruiz;Ivan Olmos Pineda;J. Arturo Olvera Lopez,0,https://doi.org/10.1016/j.patrec.2020.03.025,reales,175,Mastology Research Database,si,no,http://visual.ic.uff.br/dmi,https://sci-hub.se/https://doi.org/10.1016/j.patrec.2020.03.025,0
27,UAEM;UAEM;UAEM;UAEM,Towards an anxiety and stress recognition system for academic environments based on physiological features,2020,Jorge Rodriguez Arce;Liliana Lara Flores;Otniel Portillo Rodriguez;Rigoberto Martinez Mendez,2,https://doi.org/10.1016/j.cmpb.2020.105408,reales,21,creado,no,si,None,,1
28,BUAP;BUAP;INAOE;INAOE,Hacia la clasificacion de actividad e inactividad lingüística a partir de señales de electroencefalogramas (EEG),2017,Luis Alfredo Moctezuma;Maya Carrillo;Luis Villaseñor Pineda;Alejandro A. Torres García,2,10.13053/rcs-140-1-11,reales,165;160,creado;creado,no,si,None,https://www.rcs.cic.ipn.mx/2017_140/Hacia%20la%20clasificacion%20de%20actividad%20e%20inactividad%20linguistica%20a%20partir%20de%20senales.pdf,1
29,IPN;IPN;IPN,Extracting medical events from clinical records using conditional random fields and parameter tuning for hidden Markov models,2018,Carolina Fócil Arias;Grigori Sidorov;Alexander Gelbukh;Fernando Arce,1,10.3233/JIFS-169479,reales,750,THYME de la clinica Mayo,si,no,https://healthnlp.hms.harvard.edu/center/pages/data-sets.html,,1
30,CIMAT;CIMAT;CONACyT-CentroGeo,A community-based topological distance for brain-connectome classification,2020,Juan Luis Villareal–Haro;Alonso Ramirez–Manzanares;Juan Antonio Pichardo-Corpus,0,10.1093/comnet/cnaa034,sinteticos,1,Tractography Challenge ISMRM 2015 Data,si,no,https://zenodo.org/record/572345,https://sci-hub.se/10.1093/comnet/cnaa034,0
31,UNAM;CIMAT;CIMAT;UNAM;Universidad Autonoma de San Luis Potosi;CIMAT,Electrophysiological auditory responses and language development in infants with periventricular leukomalacia,2011,"Gloria Nélida Avecilla-Ramirez;Ruiz Correa Salvador;Marroquín Zaleta José Luis;Thalía Harmony;Alfonso Alba;Omar Mendoza-Montoya",1,https://doi.org/10.1016/j.bandl.2011.06.002,reales,25,creados,no,si,None,https://sci-hub.se/https://doi.org/10.1016/j.bandl.2011.06.002,1
32,CIMAT;CIMAT;Universidad de Guanajuato,A Novel Multiscale Gaussian-Matched Filter Using Neural Networks for the Segmentation of X-Ray Coronary Angiograms,2018,Ivan Cruz-Aceves;Fernando Cervantes-Sanchez;Maria Susana Avila-Garcia,3,https://doi.org/10.1155/2018/5812059,reales,100,radiografias de angiogramaas coronarios del UMAE IMSS Leon,con permiso,si,None,https://sci-hub.se/https://doi.org/10.1155/2018/5812059,1
33,CIMAT;CIMAT;CIMAT;IMSS;Universidad de Guanajuato;Universidad de Guanajuato,Coronary artery segmentation in X-ray angiograms using gabor filters and differential evolution,2018,Fernando Cervantes-Sanchez;Ivan Cruz-Aceves;Arturo Hernandez-Aguirre;Sergio Solorio-Meza;Teodoro Cordova-Fraga;Juan Gabriel Aviña-Cervantes,0,https://doi.org/10.1016/j.apradiso.2017.08.007,reales,80,radiografias de angiogramaas coronarios del UMAE IMSS Leon,con permiso,si,None,https://sci-hub.se/https://doi.org/10.1016/j.apradiso.2017.08.007,0
34,Universidad de Guanajuato;CIMAT;Universidad de Guanajuato;Universidad de Guanajuato;Universidad de Guanajuato,Processing of MRI Images Weighted in TOF for Blood Vessels Analysis: 3-D Reconstruction,2019,José Hernández-Delgado;Ivan Cruz-Aceves;Teodoro Cordova-Fraga;Modesto Sosa-Aquino;Rafael Guzman Cabrera,0, 10.13053/CyS-23-1-3147,reales,90,creados,no,si,None,https://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/view/3147/2598,0
35,Universidad de Guanajuato;Hospital Universitario de Leipzig;Universidad de Guanajuato;Hospital Universitario de Leipzig;Universidad de Guanajuato;CIMAT;Universidad de Leipzig,Fusion of Intraoperative 3D B-mode and Contrast-Enhanced Ultrasound Data for Automatic Identification of Residual Brain Tumors,2017,Elisee Ilunga-Mbuyamba;Dirk Lindner;Juan Gabriel Avina-Cervantes;Felix Arlt;Horacio Rostro-Gonzalez;Ivan Cruz-Aceves;Claire Chalopin,1;7,https://doi.org/10.3390/app7040415,reales,23,Escaneos de Department of Neurosurgery at the University Hospital of Leipzig,Con permiso,no,None,https://www.mdpi.com/2076-3417/7/4/415/htm,2
36,Universidad de Guanajuato;CIMAT;Universidad de Guanajuato;Universidad de Guanajuato;CIMAT;Universidad de Guanajuato;Universidad de Guanajuato;Universidad de Guanajuato;Universidad de Guanajuato,Fast Parabola Detection Using Estimation of Distribution Algorithms,2017,Jose de Jesus Guerrero-Turrubiates;Ivan Cruz-Aceves;Sergio Ledesma;Juan Manuel Sierra-Hernandez;Jonas Velasco;Juan Gabriel Avina-Cervantes;Maria Susana Avila-Garcia;Horacio Rostro-Gonzalez;Roberto Rojas-Laguna,7,https://doi.org/10.1155/2017/6494390,sinteticos;reales:reales,1;20;8,creado;DRIVE;human plantar arch,no;si;si,no;no;no,None:https://drive.grand-challenge.org/;permiso,,1
37,Universidad de Guanajuato;Universidad de Guanajuato;Universidad de Guanajuato;Universidad de Guanajuato;Universidad de Guanajuato;CIMAT;Universidad de Leipzig,Localized Active Contour Model with Background Intensity Compensation Applied on Automatic MR Brain Tumor Segmentation,2017,Elisee Ilunga–Mbuyambaa;Juan Gabriel Avina–Cervantes;Arturo Garcia–Perez;Rene de Jesus Romero–Troncoso;Hugo Aguirre–Ramos;Ivan Cruz–Aceves;Claire Chalopin,7,https://doi.org/10.1016/j.neucom.2016.07.057,reales;sinteticos,312;2,MICCAI 2012,si,no,http://www.imm.dtu.dk/projects/BRATS2012/,https://sci-hub.se/https://doi.org/10.1016/j.neucom.2016.07.057#,1
38,CIMAT;CIMAT;CIMAT;Universidad de Guanajuato;IMSS;Instituto Tecnologico de León;Universidad de Guanajuato,Segmentation of Coronary Angiograms Using Gabor Filters and Boltzmann Univariate Marginal Distribution Algorithm,2016,Fernando Cervantes-Sanchez;Ivan Cruz-Aceves;Arturo Hernandez-Aguirre;Juan Gabriel Aviña-Cervantes;Sergio Solorio-Meza;Manuel Ornelas-Rodriguez;Miguel Torres-Cisneros,0,https://doi.org/10.1155/2016/2420962,reales,80,radiografias de angiogramaas coronarios del UMAE IMSS Leon,Con permiso,si,None,https://www.hindawi.com/journals/cin/2016/2420962/,0
39,CIMAT;CIMAT;CIMAT;CIMAT;Universidad de Juarez,A novel Gaussian matched filter based on entropy minimization for automatic segmentation of coronary angiograms,2016,Ivan Cruz-Aceves;Fernando Cervantes-Sanchez;Arturo Hernandez-Aguirre;Ricardo Perez-Rodriguez;Alberto Ochoa-Zezzatti,0,https://doi.org/10.1016/j.compeleceng.2016.05.002,reales,90,radiografias de angiogramaas coronarios del UMAE IMSS Leon,con permiso,si,None,https://sci-hub.se/https://doi.org/10.1016/j.compeleceng.2016.05.002#,0
40,Universidad de Guanajuato;Universidad de Guanajuato;Hospital Universitario de Leipzig;CIMAT;Hospital Universitario de Leipzig;Universidad de Leipzig,Vascular Structure Identification in Intraoperative 3D Contrast-Enhanced Ultrasound Data,2016,"Elisee Ilunga-Mbuyamba;Juan Gabriel Avina-Cervantes;Dirk Lindner;Ivan Cruz-Aceves;Felix Arlt;Claire Chalopin",6,https://doi.org/10.3390/s16040497,reales,10,Escaneos de Department of Neurosurgery at the University Hospital of Leipzig,con permiso,no,None,https://www.mdpi.com/1424-8220/16/4/497/htm,1
41,CIMAT;CIMAT;CIMAT,On the performance of nature inspired algorithms for the automatic segmentation of coronary arteries using Gaussian matched filters,2016,Ivan Cruz-Aceves;Arturo Hernandez-Aguirre;S. Ivvan Valdez,0,https://doi.org/10.1016/j.asoc.2016.01.030,reales,80,radiografias de angiogramaas coronarios del UMAE IMSS Leon,con permiso,si,None,https://www.sciencedirect.com/science/article/abs/pii/S1568494616300175#!,0
42,CIMAT;Universidad de Calgary;Universidad de Calgary;Universidad de Guanajuato;CIMAT,Automatic segmentation of coronary arteries using Gabor filters and thresholding based on multiobjective optimization,2016,Ivan Cruz-Aceves;Faraz Oloumi;Rangaraj M. Rangayyan;Juan G. Avina-Cervantes;Arturo Hernandez-Aguirre,0,https://doi.org/10.1016/j.bspc.2015.11.001,reales,80,radiografias de angiogramaas coronarios del UMAE IMSS Leon,con permiso,si,None,https://www.sciencedirect.com/science/article/abs/pii/S1746809415001792,0
43,Universidad de Guanajuato;Universidad de Guanajuato;Universidad de Guanajuato;Universidad de Guanajuato;Universidad de Guanajuato;Universidad de Guanajuato;Universidad de Guanajuato,Multiple Active Contours Guided by Differential Evolution for Medical Image Segmentation,2013,I. Cruz-Aceves;J. G. Avina-Cervantes;J. M. Lopez-Hernandez;H. Rostro-Gonzalez;C. H. Garcia-Capulin;M. Torres-Cisneros;R. Guzman-Cabrera,0,https://doi.org/10.1155/2013/190304,sinteticos;reales;reales,3;1;1,creados;Proporcionado por el IMSS;Auckland MRI Research Group Universidad de Auckland,no:con permiso;con permiso,no;si;no,None;None;None,https://www.hindawi.com/journals/cmmm/2013/190304/,0
44,Universidad de Guanajuato;Universidad de Guanajuato;Universidad de Guanajuato;Universidad de Guanajuato;Universidad de Guanajuato;Universidad de Guanajuato,Unsupervised Cardiac Image Segmentation via Multiswarm Active Contours with a Shape Prior,2013,I. Cruz-Aceves;J. G. Avina-Cervantes;J. M. Lopez-Hernandez;M. G. Garcia-Hernandez;M. A. Ibarra-Manzano,4,https://doi.org/10.1155/2013/909625,reales;reales,144,"Auckland MRI Research Group, University of Auckland;Cardiac Atlas Website",con permiso;si,no;no,None:https://www.cardiacatlas.org/,https://www.hindawi.com/journals/cmmm/2013/909625/,1
45,CIMAT;CIMAT;Universidad de Guanajuato,A flocking based method for brain tractography,2014,Ramon Aranda;Mariano Rivera;Alonso Ramirez-Manzanares,0,https://doi.org/10.1016/j.media.2014.01.009,sinteticos;reales,64;,creados;HARDI Reconstruction Challenge 2013,no;si,no;no,None;http://hardi.epfl.ch/static/events/2013_ISBI/,https://sci-hub.se/https://doi.org/10.1016/j.media.2014.01.009#,0
46,Universidad de Houston;CIMAT;CIMAT,Segmentation of the Luminal Border in Intravascular Ultrasound B-mode Images Using a Probabilistic Approach,2013,E. Gerardo Mendizabal-Ruiz;Mariano Rivera;Ioannis A. Kakadiaris,0,,reales,12,creados,no,no,None,https://www.cimat.mx/~mrivera/journals/mendizaval_ivus_mia13.pdf,0
47,CIMAT;CIMAT;CIMAT,Estimation of individual axon bundle properties by a Multi-Resolution Discrete-Search method,2017,Ricardo Coronado-Leija;Alonso Ramirez-Manzanares;Jose Luis Marroquin,0,https://doi.org/10.1016/j.media.2017.06.008,sinteticos;sinteticos;reales,80;NA;100,creados;reconstruidos;MASSIVE Brain Dataset,no;si;si,no;no;no,None;http://hardi.epfl.ch/event/isbi_challenge_2012;http://massive-data.org/,https://sci-hub.se/https://doi.org/10.1016/j.media.2017.06.008,0
48,INAOE;INAOE;IPN;INAOE;INAOE,Surrogate-assisted multi-objective model selection for support vector machines,2014,Alejandro Rosales-Pérez;Jesus A. Gonzalez;Carlos A. Coello Coello;Hugo Jair Escalante;Carlos A. Reyes-Garcia,0,https://doi.org/10.1016/j.neucom.2014.08.075,reales,1300,IDA benchmark dataset,si,no,http://www.raetschlab.org/Members/raetsch/benchmark,https://sci-hub.se/https://doi.org/10.1016/j.neucom.2014.08.075,0
49,INAOE;INAOE;IPN;INAOE;INAOE,Multi-Objective Model Type Selection,2014,Alejandro Rosales-Pérez;Jesus A. Gonzalez; Carlos A. Coello Coello;Hugo Jair Escalante;Carlos A. Reyes-Garcia,0,https://doi.org/10.1016/j.neucom.2014.05.077,reales,1300,IDA benchmark dataset,si,no,http://www.raetschlab.org/Members/raetsch/benchmark,https://sci-hub.se/https://doi.org/10.1016/j.neucom.2014.05.077,0
50,INAOE;INAOE;Universidad de las Americas;INAOE;INAOE;INAOE,Gyroscope-Driven Mouse Pointer with an EMOTIV® EEG Headset and Data Analysis Based on Empirical Mode Decomposition,2013,Gerardo Rosas-Cholula;Juan Manuel Ramirez-Cortes;Vicente Alarcon-Aquino;Pilar Gomez-Gil;Jose de Jesus Rangel-Magdaleno;Carlos Reyes-Garcia,4,10.3390/s130810561,reales,256,creados,no,si,None,https://sci-hub.se/10.3390/s130810561,1
51,INAOE;INAOE;INAOE;BUAP,Implementing a Fuzzy Inference System in a Multi-Objective EEG Channel Selection Model for Imagined Speech Classification,2016,Alejandro A. Torres-García;Carlos A. Reyes-García;Luis Villaseñor-Pineda;Gregorio García-Aguilar,0,10.1016/j.eswa.2016.04.011,reales,4455,"Torres-García et al.,2012",con permiso,si,None,,0
52,BUAP;INAOE;INAOE;INAOE;BUAP;UPAERP,Fusing Affective Dimensions and Audio-Visual Features from Segmented Video for Depression Recognition,2014,Humberto Pérez Espinosa;Hugo Jair Escalante; Luis Villaseñor Pineda;Manuel Montes y Gomez; David Pinto Avedaño; Veronica Reyes Meza,6,10.1145/2661806.2661815,reales,300,AVEC’14’s DRS challenge,si,no,Entrando al concurso,https://sci-hub.se/10.1145/2661806.2661815,1
53,INAOE;INAOE;INAOE,Bilingual Acoustic Feature Selection for Emotion Estimation Using a 3D Continuous Mode,2011,Humberto Perez Espinosa;Carlos A. Reyes García;Luis Villasenor Pineda,0,10.1109/FG.2011.5771349,reales,401;947,IEMOCAP;VAM,si;si,No;No,https://sail.usc.edu/iemocap/;https://sail.usc.edu/VAM/,https://www.scopus.com/record/display.uri?eid=2-s2.0-79958756535&origin=inward&txGid=2fa0fc071b4ea5d7a6be5d164a43f817,0
54,INAOE;INAOE;INAOE,Mass segmentation of mammograms using Markov models associated with constrained clustering,2020,Raul Cruz-Barbosa;Saiveth Hernandez-Hernández;Luis Enrique Sucar,0,https://doi.org/10.1007/s11517-020-02221-w,reales,143, Breast Cancer Digital Repository (BCDR),si,no,https://www.bcdr.eu/information/about,https://link.springer.com/article/10.1007/s11517-020-02221-w,0
55,INAOE;INAOE;INAOE,Toward asynchronous EEG-based BCI: Detecting imagined words segments in continuous EEG signals,2020,Tonatiuh Hernández-Del-Toro;Carlos A. Reyes-García;Luis Villaseñor-Pineda,0,10.1016/j.bspc.2020.102351,reales;reales;reales,100;160;160,creados;creados;creados,no,si,None,https://www.researchgate.net/publication/347418882_Toward_asynchronous_EEG-based_BCI_Detecting_imagined_words_segments_in_continuous_EEG_signals,0
56,University of Firenze;University of Liege;Holland Bloorview Kids Rehabilitation Hospital;INAOE;University of Firenze;INAOE,Automated analysis of newborn cry: relationships between melodic shapes and native language,2019,C. Manfredi;R. Viellevoye;S. Orlandi;A. Torres-García;G. Pieraccini;C.A. Reyes-García,1;3,10.1016/j.bspc.2019.101561,reales,7500,creados,no,no,None,https://www.researchgate.net/publication/333312082_Automated_analysis_of_newborn_cry_relationships_between_melodic_shapes_and_native_language,2
57,INAOE;INAOE;INAOE;INAOE,Selección de parámetros en el enfoque de bolsa de características para clasificación de habla imaginada en electroencefalogramas,2017,Jesús S. García-Salinas;Luis Villaseñor-Pineda;Carlos Alberto Reyes-Garcia;Alejandro A. Torres-García,0,10.13053/rcs-140-1-10,reales,4455,Misma de Applying Brain Signals Sonification for Automatic Classification,con permiso,si,None,https://rcs.cic.ipn.mx/2017_140/Seleccion%20de%20parametros%20en%20el%20enfoque%20de%20bolsa%20de%20caracteristicas%20para%20clasificacion%20de%20habla.pdf,0
58,Universidad de Guadalajara;Universidad de Guadalajara;Universidad de Guadalajara;Universidad de Guadalajara;INAOE;Universidad de Guadalajara;Universidad de Guadalajara;Instituo Tecnologico de Aguascalientes,Artificial Visual System Used for Dental Fluorosis Discrimination,2016,Miguel Mora-Gonzalez;Evelia Martinez-Cano;Francisco J. Casillas-Rodriguez;Francisco G. Peña-Lecona;Carlos A. Reyes-García;Jesús Muñoz-Maciel;H. Ulises Rodríguez-Marmolejo,2,10.1007/978-3-319-28513-9_23,reales,15,creados,no,si,None,https://www.researchgate.net/publication/309150083_Artificial_Visual_System_Used_for_Dental_Fluorosis_Discrimination,1
59,University of Firenze;University of Firenze;INAOE;University of Bologna;University of Bologna;INAOE;University of Bologna;University of Bologna;University of Firenze,Analysis of Facial Expressions in Parkinson’s Disease Through Video-Based Automatic Methods,2017,Andrea Bandini;Silvia Orlandi;Hugo Jair Escalante;Fabio Giovannelli;Massimo Cincotta;Carlos A. ReyesGarcia;Paola Vanni;Gaetano Zaccara;Claudia Manfredi,1;2;7;9,10.1016/j.jneumeth.2017.02.006,reales,153,creados,no,no,None,https://www.researchgate.net/publication/313901575_Analysis_of_Facial_Expressions_in_Parkinson's_Disease_Through_Video-Based_Automatic_Methods,4
60,INAOE;INAOE;INAOE;Instituto Nacional de Rehabilitación,Infant Cry Classification Using Genetic Selection of a Fuzzy Model,2012,Alejandro Rosales-Pérez; Carlos A. Reyes-García;Jesus A. Gonzalez;Emilio Arch-Tirado,0,10.1007/978-3-642-33275-3_26,reales,1918,creados,no,si,None,https://www.researchgate.net/publication/290009266_Infant_Cry_Classification_Using_Genetic_Selection_of_a_Fuzzy_Model,0
61,INAOE;INAOE;INAOE,Genetic Fuzzy Relational Neural Network for Infant Cry Classification,2011,Alejandro Rosales-Pérez;Carlos A. Reyes-García;Pilar-Gomez-Gil,3,10.1007/978-3-642-21587-2_31,reales,1918, classification of infant cry databases built by the computer science deparment of the INAOE,con permiso,si,None,https://www.researchgate.net/publication/220827301_Genetic_Fuzzy_Relational_Neural_Network_for_Infant_Cry_Classification,1
62,INAOE;INAOE;INAOE;INAOE,Sonification and textification: Proposing methods for classifying unspoken words from EEG signals,2017,Erick F. González-Castaneda;Alejandro A. Torres-García;Carlos A. Reyes-García;Luis Villasenor-Pineda,0,https://doi.org/10.1016/j.bspc.2016.10.012,reales,4455,Análisis de señales electroencefalográficas para la clasificación de habla imaginada,con permiso,si,None,https://www.sciencedirect.com/science/article/abs/pii/S1746809416301744?via%3Dihub,0
63,BUAP;BUAP;INAOE;INAOE,Hacia un método de transferencia de aprendizaje en señales de EEG de habla imaginada ,2017,Jessica Nayeli López Espejel;Maya Carrillo Ruíz;Luis Villaseñor Pineda;Alejandro Torres García,1;2,10.13053/rcs-140-1-13,reales,4455,Análisis de señales electroencefalográficas para la clasificación de habla imaginada,con permirso,si,None,https://www.researchgate.net/publication/339236283_Hacia_un_metodo_de_transferencia_de_aprendizaje_en_senales_de_EEG_de_habla_imaginada,2
64,Universidad de Carabobo;Instituto Nacional de Neurología y Neurocirugía;Instituto Nacional de Neurología y Neurocirugía;BUAP;University College of London;INAOE;INAOE,"Automatic Recognition of Pain, Anxiety, Engagement and Tiredness for Virtual Rehabilitation from Stroke: A Marginalization Approach",2017,Jesus Joel Rivas;Lorena Palafox;Jorge Hernandez-Franco;María del Carmen Lara;Nadia Bianchi-Berthouze;Felipe Orihuela-Espina; Luis Enrique Sucar,2;4;5,10.1109/ACIIW.2017.8272607,reales,79,Detecting affective states in virtual rehabilitation,con permiso,si,None,https://www.researchgate.net/publication/338037194_Automatic_Recognition_of_Multiple_Affective_States_in_Virtual_Rehabilitation_by_Exploiting_the_Dependency_Relationships,3
65,INAOE;INAOE;INAOE;Instituto Nacional de Neurología y Neurocirugía;Instituto Nacional de Neurología y Neurocirugía;University College of London,Detecting affective states in virtual rehabilitation,2015,Jesús J.Rivas;Felipe Orihuela-Espina;L.Enrique Sucar;Lorena Palafox;Jorge Hernández-Franco;Nadia Bianchi-Berthouze,4;6,10.4108/icst.pervasivehealth.2015.259250,reales,79,creados,con permiso,si,None,https://www.researchgate.net/publication/301449553_Detecting_affective_states_in_virtual_rehabilitation,2
66,University of Trento;INAOE;INAOE;CREATE-NET;INAOE;CREATE-NET,Stress Modelling and Prediction in Presence of Scarce Data,2016,Alban Maxhuni;Pablo Hernandez-Leal;L. Enrique Sucar;Venet Osmani;Eduardo F. Morales;Oscar Mayora,0,10.1016/j.jbi.2016.08.023,reales,30,creados,no,no,None,https://www.researchgate.net/publication/304380472_Stress_Modelling_and_Prediction_in_Presence_of_Scarce_Data,0
67,INAOE;INAOE;INAOE;INAOE,Causal Probabilistic Graphical Models for Decoding Effective Connectivity in Functional Near InfraRed Spectroscopy,2016,Samuel Antonio Montero-Hernandez;Felipe Orihuela-Espina;Javier Herrera-Vega;Luis Enrique Sucar,0,https://www.aaai.org/ocs/index.php/FLAIRS/FLAIRS16/paper/viewPaper/12913,reales,310,(Leff et al. 2007) Imperial College London,con permiso,no,None,https://www.aaai.org/ocs/index.php/FLAIRS/FLAIRS16/paper/view/12913/12653,0
68,INAOE;INAOE;INAOE;INAOE,A naïve Bayes baseline for early gesture recognition,2016,"Hugo Jair Escalante;Eduardo F. Morales;L.Enrique Sucar",0,10.1016/j.patrec.2016.01.013,reales;reales;reales,240;2843;13883,MSRDaily3D;MAD;Montalbano,si;si;si,no;no;no,https://sites.google.com/view/wanqingli/data-sets/msr-dailyactivity3;http://humansensing.cs.cmu.edu/mad/ ;http://chalearnlap.cvc.uab.es/dataset/13/description/,https://www.researchgate.net/publication/293046015_A_naive_Bayes_baseline_for_early_gesture_recognition,0
69,INAOE;CRATE-NET;INAOE;CRATE-NET;INAOE;CREATE-NET,Stress Modelling Using Transfer Learning in Presence of Scarce Data,2015,Pablo Hernandez-Leal;Alban Maxhuni;L. Enrique Sucar;Venet Osmani;Eduardo F. Morales;Oscar Mayora,2,10.1007/978-3-319-26508-7_22,reales,1465,creados,no,no,None,https://www.researchgate.net/publication/295860589_Stress_Modelling_Using_Transfer_Learning_in_Presence_of_Scarce_Data,1
70,INAOE;INAOE;INAOE,User Modelling for Patient Tailored Virtual Rehabilitation,2015,Luis Enrique Sucar;Shender Maria Ávila-Sansores;Felipe Orihuela-Espina,2,10.1007/978-3-319-28007-3_17,reales,200,Adaptación en línea de una política de decisión utilizando aprendizaje por refuerzo y su aplicación en rehabilitación virtual.,con permiso,si,None,https://www.researchgate.net/publication/307934152_User_Modelling_for_Patient_Tailored_Virtual_Rehabilitation,1
71,INAOE;INAOE;Instituto Nacional de Enfermedades Respiratorias;Instituto Nacional de Enfermedades Respiratorias;INAOE;INAOE;INAOE;INAOE;INAOE,Discovering human immunodeficiency virus mutational pathways using temporal Bayesian networks,2013,Pablo Hernandez-Leal; Alma Rios-Flores;Santiago Ávila-Rios;Gustavo Reyes-Terán;Jesus A. Gonzalez;Lindsey Fiedler-Cameras;Felipe Orihuela-Espina;Eduardo F. Morales;L. Enrique Sucar,2;6,10.1016/j.artmed.2013.01.005,reales,2373,HIV Stanford database,si,no,https://hivdb.stanford.edu/,https://www.researchgate.net/publication/236126237_Discovering_human_immunodeficiency_virus_mutational_pathways_using_temporal_Bayesian_networks,2
72,INAOE;INAOE;INAOE;University College of London;University College of London;University College of London;Universisty College of London,Estimating Functional Connectivity Symmetry between Oxy- and Deoxy-Haemoglobin: Implications for fNIRS Connectivity Analysis,2018,Samuel Montero-Hernandez;Felipe Orihuela-Espina;Luis Enrique Sucar;Paola Pinti;Antonia Hamilton;Paul Burgess;Ilias Tachtsidis ,4;5,10.3390/a11050070,reales,96,"Using Fiberless, Wearable fNIRS to Monitor Brain Activity in Real-world Cognitive Tasks",con permismo,no,None,https://www.researchgate.net/publication/325125312_Estimating_Functional_Connectivity_Symmetry_between_Oxy-_and_Deoxy-Haemoglobin_Implications_for_fNIRS_Connectivity_Analysis#pff,2
73,INAOE;INAOE;INAOE;University College of London;University College of London,Automatic Recognition of Multiple Affective States in Virtual Rehabilitation by Exploiting the Dependency Relationships,2019,Jesús Joel Rivas;Felipe Orihuela Espina;Luis Enrique Sucar;Amanda Williams;Nadia Bianchi Berthouze,4;5,,reales,79,Unobtrusive inferenceof affective states in virtual rehabilitation from upper limb motions: Afeasibility study,con permiso,si,None,https://www.researchgate.net/publication/338037194_Automatic_Recognition_of_Multiple_Affective_States_in_Virtual_Rehabilitation_by_Exploiting_the_Dependency_Relationshipss,2
74,Instituto Tecnologico de Culiacan;Instituto Tecnologico de Culiacan;Instituto Tecnologico de Orizaba;INAOE,Emotion Recognition In Intelligent Tutoring Systems for Android-Based Mobile Devices,2017,Ramón Zatarain-Cabada;María Lucía Barrón-Estrada;Giner Alor-Hernández;Carlos A. Reyes-García,2,,reales;reales,8040;33,RAFD;creados,si;no,no;si,http://www.socsci.ru.nl:8180/RaFD2/RaFD?p=main;None,https://www.researchgate.net/publication/313851343_PaperGinerKargaCamera-Ready,1
75,INAOE;INAOE;INAOE;INAOE,Posture Based Detection of Attention in Human Computer Interaction,2014,Patrick Heyer;Javier Herrera-Vega;Dan-El N. Vila Rosado;Luis Enrique Sucar;Felipe Orihuela-Espina,0,10.1007/978-3-642-53842-1_19,reales,377,creados,no,si,None,https://www.researchgate.net/publication/259982775_Posture_Based_Detection_of_Attention_in_Human_Computer_Interaction,0
76,UNAM;Imperial College London;Imperial College London;Imperial College London;Imperial College London,Automatic optic disc detection in colour fundus images by means of multispectral analysis and information content,2019,M. Elena Martinez Perez;NIcholas Witt;Kim H. Parker; Alun D. Hughes; Simon A.M Thom,1,10.7717/peerj.7119,reales;reales;reales,1131;40;1200,SABRE;DRIVE;MESSIDOR,con permiso;si:si,no;no;no,None;https://drive.grand-challenge.org/;http://www.adcis.net/en/third-party/messidor/,https://peerj.com/articles/7119/,1
77,UNAM;Imperial College London;Imperial College London;Imperial College London;Imperial College London,AUTOMATIC CLASSIFICATION OF ARTERIAL AND VENULAR TREES IN COLOUR FUNDUS IMAGES,2018,M Elena Martinez-Perez;Kim Parker;Nick Witt;S.A.McG. Thom;Alun Hughes,1,https://doi.org/10.1016/j.artres.2018.10.192,reales;reales;reales;reales,20;17;20;40,St.Mary's Hospital;St.Mary's Hospital;HOOVER;DRIVE;Segmentation of blood vessels from red-free and fluorescein retinal images,con permiso;con permiso;si;si,no;no;no;no,None;None;www.ces.clemson.edu/∼ahoover/stare;https://drive.grand-challenge.org/,https://www.researchgate.net/publication/329409874_Automatic_classification_of_arterial_and_venular_trees_in_colour_fundus_images,1
78,UNAM;UNAM;Fundacion de Asistencia Privada Conde de Valenciana IAP;UNAM,Mean Shift Based Automatic Detection of Exudates in Retinal Images,2013,Juan Martin Cárdenas;M Elena Martinez Perez;Francesc March;Nidiyare Hevia-Montiel,2;4,10.1007/978-3-642-32384-3_10,reales,20,creados,no,si,None,https://www.researchgate.net/publication/278695894_Mean_Shift_Based_Automatic_Detection_of_Exudates_in_Retinal_Images,2
79,UNAM;UNAM;Asociación para Evitar la Ceguera en México I.A.P;Asociación para Evitar la Ceguera en México I.A.P;Asociación para Evitar la Ceguera en México I.A.P,Optic disc and macula detection in fundus images by means of template matching,2014,Tzolkin Garduno-Alvarado;M. Elena Martinez-Perez;Maria A. Martinez-Castellanos;Luvia Rodriguez-Quinones;Samantha M. Salinas-Longoria,2;3;4;5,10.1109/EMBC.2014.6943547,reales,38,creados,no,si,None,https://pubmed.ncbi.nlm.nih.gov/25569915/,4
80,UNAM;UNAM;UAM,Deep Learning Spatial Compounding from Multiple Fetal Head Ultrasound Acquisitions,2020,Jorge Perez-Gonzalez;Nidiyare Hevia Montiel;Verónica Medina Bañuelos,2;3,10.1007/978-3-030-60334-2_30,reales,18,creados,no,si,None,https://www.researchgate.net/publication/345999208_Deep_Learning_Spatial_Compounding_from_Multiple_Fetal_Head_Ultrasound_Acquisitions,2
81,UNAM;UNAM;UNAM;UNAM;UNAM;UNAM;Insitituto Nacional de Rehabilitacion,Mammographic image analysis and computer assisted biopsy of breast tumors,2011,Fernando Arámbula Cosío;Nidiyare Hevia;Eric Lira;Cresencio García;Rosa Ma. Quispe;Bartolome Reyes;Eric Hazan Lasri,2;5,10.1109/BMEI.2011.6098250,sinteticos,5,creados,no,no,None,https://www.researchgate.net/publication/220714946_Mammographic_image_analysis_and_computer_assisted_biopsy_of_breast_tumors,2
82,UNAM;UNAM;UNAM;UAM;UAM,P300 Detection Based on EEG Shape Features,2016,Montserrat Alvarado-González;Edgar Garduño;Ernesto Bribiesca;Oscar Yáñez-Suárez;Verónica Medina-Bañuelos,1;5,10.1155/2016/2029791,reales,21,Dataset creado por el LINI de la UAM (P300 Akimpech Database),si,si,https://akimpech.izt.uam.mx/p300db/p300db.html,https://www.researchgate.net/publication/289984682_P300_Detection_Based_on_EEG_Shape_Features,1
83,UNAM;UNAM;Centro Medico ABC;Instituto Nacional de Neurología y Neurocirugía;UNAM;Centro Medico ABC,Neuromorphometry of primary brain tumors by magnetic resonance imaging,2015,Nidiyare Hevia-Montiel;Pedro I. Rodriguez-Perez;Paul J. Lamothe-Molina;Alfonso Arellano-Reynoso;Ernesto Bribiesca;Marco A. Alegria-Loyola,1,10.1117/1.JMI.2.2.024503,reales;reales,11;9,Instituto Nacional de Neurología y Neurocirugía;Montreal Neurological Institutes Brain Images of Tumors for Evaluation,si;no,no;si,None;http://www.bic.mni.mcgill.ca/~laurence/data/data.html,https://www.researchgate.net/publication/276466402_Neuromorphometry_of_primary_brain_tumors_by_magnetic_resonance_imaging,1
84,IMAGINA Laboratory DIMAp - UFRN;IMAGINA Laboratory DIMAp - UFRN;UNAM,Texture Fuzzy Segmentation using Adaptive Affinity Functions,2012,Bruno M. Carvalho;Tiago S. Souza;Edgar Garduño,0,10.1145/2245276.2245288,sinteticos,10,Brodatz Texture Dataset,si,no,https://www.ux.uis.no/~tranden/brodatz.html,https://www.researchgate.net/publication/254005594_Texture_fuzzy_segmentation_using_adaptive_affinity_functions,0
85,Hospital Civil de Guadalajara;UNAM;UNAM;Hospital Infantil de México;Hospital Civil de Guadalajara,Temporary morphological changes in plus disease induced during contact digital imaging,2011,LC Zepeda-Romero;ME Martinez-Perez;S Ruiz-Velasco;MA Ramirez-Ortiz;JA Guitierrez-Padilla,2,10.1038/eye.2011.170,reales,10,Hospital Civil de Guadalajara,no,si,None,https://pubmed.ncbi.nlm.nih.gov/21760627/,1
86,UNAM;Universidad de Papaloapan;IPN,Early Breast Cancer Detection by Magnetic Induction Spectroscopy,2013,Nidiyare Hevia Montiel;Eduardo Sanchez Soto;Cesar Gonzales Diaz,1,https://doi.org/10.11239/jsmbe.51.R-196,reales;reales,22;21,creados;creados,no,si,None,https://www.jstage.jst.go.jp/article/jsmbe/51/Supplement/51_R-196/_article/-char/ja/,1
87,INAOE;INAOE;INAOE,An Algorithm for Computing Goldman Fuzzy Reducts,2017,J. Ariel Carrasco-Ochoa;Manuel S. Lazo-Cortes;Jose Fco. Martinez-Trinidad,0,10.1007/978-3-319-59226-8_1,reales;reales;reales;reales;reales;reales;relaes;reales;reales;reales,625;1473;150;1484;336;303;57;345;214;768,Balance Scale;Contraceptive Method Choice;Iris;Yeast;Ecoli;Heart Disease Cleveland;Labor;Liver Disorder;Glass;Pima Indians Diabetes,si;si;si;si;si;si;si;si;si;si,no;no,https://archive.ics.uci.edu/ml/datasets/Balance+Scale;https://archive.ics.uci.edu/ml/datasets/Contraceptive+Method+Choice;,https://sci-hub.se/10.1007/978-3-319-59226-8_1,0
88,BUAP;BUAP;BUAP;BUAP;BUAP,Parameter Experimentation for Epileptic Seizure Detection in EEG Signals using Short-Time Fourier Transform,2019,Ricardo Ramos-Aguilar;J. Arturo Olvera-Lopez;Ivan Olmos-Pineda;Susana Snchez-Urrieta;Manuel Martin-Ortiz,4,10.13053/rcs-148-9-7,reales,4097,Bonn University EGG Dataset,si,no,https://sccn.ucsd.edu/~arno/fam2data/publicly_available_EEG_data.html,https://www.researchgate.net/publication/336510084_Parameter_Experimentation_for_Epileptic_Seizure_Detection_in_EEG_Signals_using_Short-Time_Fourier_Transform,1
89,BUAP;BUAP;BUAP;BUAP,Time-Frequency Analysis of EEG Spectrograms using 2-D Gabor Filters for Epileptic Seizure Classification,2018,Ricardo Ramos-Aguilar;J. Arturo Olvera-Lopez;Ivan Olmos-Pineda;Manuel Martín-Ortíz,0,10.13053/rcs-147-11-3,reales,4097,Bonn University EGG Dataset,si,no,https://sccn.ucsd.edu/~arno/fam2data/publicly_available_EEG_data.html,https://www.researchgate.net/publication/326449741_Time-Frequency_Analysis_of_EEG_Spectrograms_using_2-D_Gabor_Filters_for_Epileptic_Seizure_Classification,0
90,BUAP;BUAP;BUAP;Universidad Politecnica de Sinaloa,Hand Vein Infrared Image Segmentation for Biometric Recognition,2014,Ignacio Irving Morales-Montiel;J. Arturo Olvera-López;Manuel Martín-Ortiz;Eber E. Orozco-Guillen,0,10.13053/rcs-80-1-5,reales,6000,Tecnocampus Hand Image Database,no,no,http://splab.cz/en/download/databaze/tecnocampus-hand-image-database,https://www.researchgate.net/publication/324068541_Hand_Vein_Infrared_Image_Segmentation_for_Biometric_Recognition,0
91,BUAP;BUAP;BUAP;BUAP,Mouth and eyebrow segmentation for emotion recognition using interpolated polynomials,2018,Jesus García-Ramírez;J. Arturo Olvera-López;Ivan Olmos-Pineda;Manuel Martín-Ortíz,0,10.3233/JIFS-169496,reales;reales;reales,474;213;43,MMI;Jaffe;VidTIMIT,si;si;si,no;no;no,https://mmifacedb.eu/;https://zenodo.org/record/3451524;https://www.conradsanderson.id.au/vidtimit/,https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs169496,0
92,BUAP;BUAP;Laboratorios Clinicos de Puebla;BUAP,An Algorithm to Classify DNA Sequences of Hepatitis C Virus Based on Localized Conserved Regions and Heuristic Search,2019,Sarahi Zúñiga-Herrera;Ivan Olmos-Pineda;Javier Garcés-Eisele;Mario Rossainz-López,1,"ISSN 1870-4069 Research in Computing Science 148(9), 2019",reales,reales,GenBank,si,no,https://www.ncbi.nlm.nih.gov/genbank/,https://www.rcs.cic.ipn.mx/2019_148_9/An%20Algorithm%20to%20Classify%20DNA%20Sequences%20of%20Hepatitis%20C%20Virus%20Based%20on%20Localized%20Conserved.pdf,1
93,INAOE;INAOE;INAOE;INAOE;INAOE;BUAP;IMSS;IMSS,Segmentation and Classification of Bone Marrow Cells Images Using Contextual Information for Medical Diagnosis of Acute Leukemias,2015,Carolina Reta;Leopoldo Altamirano;Jesus A. Gonzalez;Raquel Diaz-Hernandez;Hayde Peregrina;Ivan Olmos;Jose E. Alonso;Ruben Lobato,1;4;5,https://doi.org/10.1371/journal.pone.0130805,reales,633,Coleccion de imagenes del IMSS,con permiso,si,None,https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0130805,3
94,BUAP;INAOE;INAOE;IMSS;IMSS,P-088 A medical support diagnosis tool for morphological acuteleukemia identification,2013,I. Olmos;J. Gonzalez;L. Altamirano;J. Alonso;R. Lobato,0,10.1016/S0145-2126(13)70137-1,reales,1028,Dataset de UMAE – IMSS Leucemia,con permiso,si,None,https://www.infona.pl/resource/bwmeta1.element.elsevier-20521e09-0a80-361d-a2a6-c1c3de9bfc1d,0
95,INAOE;BUAP;INAOE;INAOE;INAOE;INAOE;IMSS;IMSS,Leukemia identification from bone marrow cells images using a machine vision and data mining strategy,2013,Jesus A. Gonzalez;Ivan Olmos;Leopoldo Altamirano;Blanca A. Morales;Carolina Reta;Martha C. Galindo;Jose E. Alonso;Ruben Lobato,4;5;6,10.3233/IDA-2010-0476,reales,1028,Dataset de UMAE – IMSS Leucemia,con permiso,si,None,https://www.researchgate.net/publication/220571549_Leukemia_identification_from_bone_marrow_cells_images_using_a_machine_vision_and_data_mining_strategy,3
96,INAOE;INAOE;University of Texas;INAOE;INAOE,Leukocytes Segmentation Using Markov Random Fields,2011,Carolina Reta;Leopoldo Altamirano;Jesus A. Gonzalez;Raquel Diaz-Hernandez;Jose S. Guichard,1;4,10.1007/978-1-4419-7046-6_35,reales,200,Coleccion de imagenes del IMSS San Jose,con permiso,si,None,https://www.researchgate.net/publication/50832902_Leukocytes_Segmentation_Using_Markov_Random_Fields,2
97,INAOE;University of Texas;INAOE;INAOE;INAOE,Microcalcifications Detection Using Fisher's Linear Discriminant and Breast Density,2011,"G.A Rodriguez;Jesus A. Gonzales; Leopoldo Altamirano Robles;Jose S. Guichard;R. Diaz",5,10.1007/978-1-4419-7046-6_45,reales;reales,322;336,Mammographic Image Analysis Society (MIAS);ISSSTEP Mammograms,si;con permiso,no;si,https://www.mammoimage.org/databases/;None,https://www.researchgate.net/publication/50832912_Microcalcifications_Detection_Using_Fisher's_Linear_Discriminant_and_Breast_Density,1
98,BUAP;BUAP;BUAP,Automatic Segmentation in Breast Thermographic Images Based on Local Pattern Variations,2018,Daniel Sanchez Ruiz;Ivan Olmos Pineda;J. Arturo Olvera Lopez,0,10.13053/rcs-147-11-5,reales,287,DMR (Database for Mastology Research),si,no,http://visual.ic.uff.br/dmi/,https://www.researchgate.net/publication/339207298_Automatic_Segmentation_in_Breast_Thermographic_Images_Based_on_Local_Pattern_Variations,0
99,INAOE;INAOE;INAOE;Universidad de Colombia,Image Classification through Text Mining techniques: a Proposal,2014,Adrain Pastor López-Monroy;Manuel Montes-y-Gómez;Hugo Jai Escalante;Fabio A. Gonzáles,0,10.13053/rcs-71-1-7,reales,1417,Data set de Micro-structural tissue analysis for automatic histopathological image annotation 10.1002/jemt.21063 ,con permiso,no,None,https://rcs.cic.ipn.mx/2014_71/Image%20Classi_cation%20through%20Text%20Mining%20techniques_%20a%20Proposal.pdf,0
100,INAOE;INAOE;INAOE;Georgia State University,Hybrid feature selection method for biomedical datasets,2012,Saul Solorio Fernandez;Jose Francisco Marínez Trinidad;Jesus Ariel Carrasco Ochoa;Yan Quing Zhang,0,10.1109/CIBCB.2012.6217224,reales;reales;reales;reales,45;38;144;60,reduced lymphoma;leukemia;Global Cancer MAP;dataset C of embryonal tumors of the central nervous system,si;si;si;si,no,http://www.upo.es/eps/aguilar/datasets.htm;http://www.upo.es/eps/aguilar/datasets.htm;http://www.upo.es/eps/aguilar/datasets.htm;http://www.upo.es/eps/aguilar/datasets.htm,https://www.researchgate.net/publication/237151372_Hybrid_feature_selection_method_for_biomedical_datasets,0
101,INAOE;INAOE;INAOE,A systematic evaluation of filter Unsupervised Feature Selection methods,2020,Saúl Solorio-Fernández;J. Ariel Carrasco-Ochoa;José Fco. Martínez-Trinidad,0,https://doi.org/10.1016/j.eswa.2020.113745,reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales,120;226;205;286;6597;1473;690;1000;540;366;336;194;303;294;270;155;368;3772;351;150;12960;345;32;148;106;124;169;122;8124;5620;164;10992;768;90;2310;208;683;4601;3190;76;151;470;958;846;435;990;5000;569;178;101;1560;165;400;130;62;2010;203;1440;96;50;72;102;171;72;69;174;200;100;100;1427;1943;1993;111;85;85;187,Inflamations;Audiology;Automovile;Breast-Cancer;Clean-2;Contraception;Credit-aprova;Credit-German;Cylinder-bands;Dermatology;Ecoli;Flags;Heart-c;Heart-h;Heart-statlog;Hepatitis;Horse-colic;Hypothyroid;Ionosphere;Iris;Nursery; Liver-disorders; Lung-cancer;Lymphography;Promoters;Monks-1-train;Monks-2-train;Monks-3-train;Mushroom;Optdigits;Parkinsons;Pendigits;Pima;Post-operative;Segment;Sonar;Soybean;Spambase;Splice;Sponge;Tae;Thoracic; Tic-tac-toe;Silhouettes;Vote;Vowel; Waveform-5000;Wdbc;Wine;Zoo;Isolet;Yale;OLR;WarpAR10P;Colon;WarpPIE10P;Lung;COIL20;Lymphoma;GLIOMA;ALLAML;Prostate_GE;TOX_171;Leukemia;Nci9;Carcinom;Arcene;Orlraws10P;Pixraw10P;RELATHE;PCMAC;BASEHOCK;CLL_SUB_111;GLI_85;SMK_CAN_187,si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;si;,no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no;no,50 * https://archive.ics.uci.edu/ml/index.php;25 * https://jundongl.github.io/scikit-feature/datasets.html,https://www.sciencedirect.com/science/article/abs/pii/S0957417420305698,0
102,INAOE;INAOE,Standardization-refinement domain adaptation method for cross-subject EEG-based classification in imagined speech recognition,2020,Magdiel Jimenez-Guarneros;Pilar Gómez-Gil ,2, https://doi.org/10.1016/j.patrec.2020.11.013,reales;reales,495;200,Dataset de Implementing a fuzzy inference system in a multiobjective EEG channel selection model for imagined speech classification;Dataset de Inferring imagined speech using EEG signals: a new approach using Riemannian manifold features,con permiso;con permiso,si;no,None;None,https://www.sciencedirect.com/science/article/abs/pii/S0167865520304244,1
103,INAOE;INAOE,Segmentation of MRI brain scans using spatial constraints and 3D features.,2020,Jonas Grande-Barreto;Pilar Gomez-Gil ,2,10.1007/s11517-020-02270-1,sinteticos;reales,20;38,BrainWeb dataset;IBSR dataset,si;si,no;no,https://brainweb.bic.mni.mcgill.ca/brainweb/;https://www.nitrc.org/projects/ibsr/,https://europepmc.org/article/MED/33155095,1
104,INAOE;INAOE;INAOE;Universidad Veracruzana,Characterization of glycated hemoglobin based on Raman spectroscopy and artificial neural networks,2020,N. Gonzalez-Viveros;J. Castro-Ramos;P. Gomez-Gil;H. H. Cerecedo-Núñez,1;3,https://doi.org/10.1016/j.saa.2020.119077,sinteticos,10,creados,no,no,None,https://www.sciencedirect.com/science/article/abs/pii/S1386142520310568,2
105,INAOE;INAOE;INAOE;INAOE;New Mexico Tech;INAOE,Classifiers Ensemble of HMM and d-Vectors in Biometric Speaker Verification,2020,Juan Carlos Atenco-Vazquez;Juan C. Moreno-Rodriguez;Israel Cruz-Vega;Pilar Gomez-Gil;Rene Arechiga;Juan Manuel Ramirez-Cortes,4,10.1007/978-3-030-60884-2,reales,1020,Creados BIOMEX-DB,no,si,None,https://www.semanticscholar.org/paper/Classifiers-Ensemble-of-HMM-and-d-Vectors-in-Atenco-Vazquez-Moreno-Rodriguez/76d4bac5c8ff571a8e5d649a861e93159e6af7e4,1
106,INAOE;INAOE,"Custom Domain Adaptation: A New Method for Cross-Subject, EEG-Based Cognitive Load Recognition",2020,Magdiel Jimenez-Guarneros;Pilar Gomez-Gil,2,10.1109/LSP.2020.2989663,reales;reales,2670;3600,Dataset de Spectrotemporal dynamics of the EEG during working memory encoding and maintenance predicts individual behavioral capacity; Dataset de Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks ,con permiso;con permiso,no;no,None;None,https://ieeexplore.ieee.org/abstract/document/9076296,1
107,Universidad Autonoma de Tlaxcala;Universidad De las Americas;INAOE;INAOE;UNAM,A New Wavelet-Based Neural Network for Classification of Epileptic-Related States using EEG,2019,E. Juarez-Guerra;V. Alarcon-Aquino;P. Gomez-Gil;J. M. Ramírez-Cortes;E. S. García-Treviño,3,https://doi.org/10.1007/s11265-019-01456-7,reales,500,EGG Epileptic University of Bonn dataset,si,no,http://epileptologie-bonn.de/cms/front content.php?idcat=495&idart=855,https://link.springer.com/article/10.1007/s11265-019-01456-7,1
108,INAOE;INAOE;INAOE;INAOE;Universidad de las Americas,Segmentation and Classification of Noisy Thermographic Images as an Aid for Identifying Risk Levels of Breast Cancer,2020,Pilar Gomez-Gil;Daniela Reynoso-Armenta;Jorge Castro-Ramos;Juan Manuel Ramirez-Cortes;Vicente Alarcon-Aquino,1;2,10.1007/978-3-030-35445-9_21,reales,600,TH-CEPREC,con permiso,si,None,https://link.springer.com/chapter/10.1007/978-3-030-35445-9_21,2
109,INAOE;INAOE,Unsupervised brain tissue segmentation in MRI images,2018,Jonas Grande-Barreto;Pilar Gomez-Gil,2,10.1109/ROPEC.2018.8661425,sinteticos,20,BrainWeb,si,no,https://brainweb.bic.mni.mcgill.ca/,https://ieeexplore.ieee.org/abstract/document/8661425,1
110,INAOE;INAOE;INAOE;INAOE;INAOE;INAOE,EEG motor imagery signals classification using maximum overlap wavelet transform and support vector machine,2017,Cesar E. Hernandez-Gonzalez;Juan M Ramirez-Cortes;Pilar Gomez-Gil;Jose Rangel-Magdaleno;Hayde Peregrina-Barreto;Israel Cruz-Vega,3,10.1109/ROPEC.2017.8261667,reales,48,BCI Competition IV,si,no,http://www.bbci.de/competition/iv/,https://ieeexplore.ieee.org/abstract/document/8261667,1
111,Universidad de las Americas;Universidad de las Americas;INAOE,Epilepsy Seizure Detection in EEG Signals Using Wavelet Transforms and Neural Networks,2014,E. Juarez-Guerra;V. Alarcon-Aquino;P. Gomez-Gil,3,10.1007/978-3-319-06764-3_33,reales,500,EGG Epileptic University of Bonn dataset,si,no,http://epileptologie-bonn.de/cms/front_content.php?idcat=193&lang=3&changelang=3,https://link.springer.com/chapter/10.1007/978-3-319-06764-3_33,1
112,INAOE;Universidad de las Americas;INAOE;INAOE,Identification of Epilepsy Seizures Using Multi-resolution Analysis and Artificial Neural Networks,2014,Pilar Gómez-Gil;Ever Juárez-Guerra;Vicente Alarcón-Aquino;Manuel Ramírez-Cortés;José Rangel-Magdaleno,1,10.1007/978-3-319-05170-3_23,reales,500,EGG Epileptic University of Bonn dataset,si,no,http://epileptologie-bonn.de/cms/front_content.php?idcat=193,https://link.springer.com/chapter/10.1007/978-3-319-05170-3_23,1
113,INAOE;INAOE;INAOE;Universidad de las Americas,Brain Computer Interface Development Based on Recurrent Neural Networks and ANFIS Systems,2013,Emanuel Morales-Flores;Juan Manuel Ramírez-Cortés;Pilar Gómez-Gil;Vicente Alarcón-Aquino,3,10.1007/978-3-642-35323-9_9,reales,20,Dataset from A new mode of communication between man and his surroundings,si,no,https://www.cs.colostate.edu/eeg/main/data/1989_Keirn_and_Aunon,https://link.springer.com/chapter/10.1007/978-3-642-35323-9_9,1
114,INAOE;INAOE;INAOE;Universidad de las Americas,Mental Tasks Temporal Classification Using an Architecture Based on ANFIS and Recurrent Neural Networks,2013,Emmanuel Morales-Flores;Juan Manuel Ramírez-Cortés;Pilar Gómez-Gil;Vicente Alarcón-Aquino,3,10.1007/978-3-642-33021-6_11,reales,20,Dataset from A new mode of communication between man and his surroundings,si,no,https://www.cs.colostate.edu/eeg/main/data/1989_Keirn_and_Aunon,https://link.springer.com/chapter/10.1007/978-3-642-33021-6_11,1
115,INAOE;INAOE;INAOE;INAOE;INAOE,Genetic Selection of Fuzzy Model for Acute Leukemia Classification,2011,Alejandro Rosales-Pérez;Carlos A. Reyes-García;Pilar Gómez-Gil;Jesus A. Gonzalez;Leopoldo Altamirano,3,10.1007/978-3-642-25324-9_46,reales,633,Dataset de Laboratorio de Especialidades IMSS Puebla,con permiso,si,None,https://link.springer.com/chapter/10.1007/978-3-642-25324-9_46,1
116,INAOE;INAOE;INAOE,Genetic Fuzzy Relational Neural Network for Infant Cry Classification,2011,Alejandro Rosales-Pérez;Carlos A. Reyes-Garcia;Pilar Gómez-Gil,3,10.1007/978-3-642-21587-2_31,reales,1918,Dataset de llantos de bebe del INAOE,con permiso,si,None,https://link.springer.com/chapter/10.1007/978-3-642-21587-2_31,1
117,INAOE;INAOE;Universidad de las Americas;Universidad de las Americas;INAOE,A Biometric System Based on Neural Networks and SVM Using Morphological Feature Extraction from Hand-Shape Images,2011,Juan-Manuel RAMIREZ-CORTES;Pilar GOMEZ-GIL;Vicente ALARCON-AQUINO;David BAEZ-LOPEZ;Rogerio ENRIQUEZ-CALDERA,2,10.15388/Informatica.2011.324,reales,400,creados,no,si,None,https://www.researchgate.net/publication/220073884_A_Biometric_System_Based_on_Neural_Networks_and_SVM_Using_Morphological_Feature_Extraction_from_Hand-Shape_Images,1
118,INAOE;Universidad de las Americas;INAOE;INAOE;IPN,Anfis-Based P300 Rhythm Detection Using Wavelet Feature Extraction on Blind Source Separated Eeg Signals,2011,Juan Manuel Ramirez-Cortes;Vicente Alarcon-Aquino;Gerardo Rosas-Cholula;Pilar Gomez-Gil;Jorge Escamilla-Ambrosio,4,10.1007/978-1-4614-0373-9_27,reales,8,creados,no,si,None,https://link.springer.com/chapter/10.1007/978-1-4614-0373-9_27,1
119,INAOE;INAOE;New Mexico Tech;INAOE;INAOE,Bimodal Biometrics Using EEG-Voice Fusion at Score Level Based on Hidden Markov Models,2020,Juan Carlos Moreno-Rodriguez;Juan Manuel Ramirez-Cortes;Rene Arechiga-Martinez;Pilar Gomez-Gil;Juan Carlos Atenco-Vazquez,4,10.1007/978-3-030-35445-9_44,reales,165,Open access database of EEG signals recorded during imagined speech,si,no,https://drive.google.com/file/d/0By7apHbIp8ENZVBLRFVlSFhzbHc/view,https://link.springer.com/chapter/10.1007/978-3-030-35445-9_44,1
120,INAOE;INAOE;BUAP;Instituto Nacional de Neurología y Neurocirugía;INAOE,Sensor Abstracted Extremity Representation for Automatic Fugl-Meyer Assessment,2016,Patrick Heyer;Felipe Orihuela-Espina;Luis R. Castrejón;Jorge Hernández-Franco;Luis Enrique Sucar,0,10.1007/978-3-319-49622-1_17,reales,750,creados,no,si,None,https://eudl.eu/pdf/10.1007/978-3-319-49622-1_17,0
121,INAOE;BUAP;INAOE;INAOE;University of Pavia;BUAP,Automated Intracellular Calcium Profiles Extraction from Endothelial Cells Using Digital Fluorescence Images,2018,Marcial Sanchez-Tecuatl;Ajelet Vargaz Guadarrama;Juan Manuel Ramirez Cirtes;Pilar Gomez Gil;Frencesoc Moccia;Roberto Berra Romani,2;4,https://doi.org/10.3390/ijms19113440,reales,na,creados,no,no,None,https://www.mdpi.com/1422-0067/19/11/3440/htm,2
122,INAOE;INAOE;INAOE;INAOE;BUAP,Head movement artifact removal in EEG signals using Empirical Mode Decomposition and Pearson Correlation,2013,Gerardo Rosas-Cholula;Juan Manuel Ramirez-Cortes;Jose Rangel-Magdaleno;Pilar Gomez-Gil;Vicente Alarcon-Aquino,4,,reales,25,creados,no,si,None,http://worldcomp-proceedings.com/proc/p2013/ICA3048.pdf,1
123,INAOE;INAOE;INAOE;Universidad de las Americas,Brain Computer Interface Development Based on Recurrent Neural Networks and ANFIS Systems,2013,Emanuel Morales-Flores;Juan Manuel Ramírez-Cortés;Pilar Gómez-Gil;Vicente Alarcón-Aquino,3,10.1007/978-3-642-35323-9_9,reales,20,Dataset from A new mode of communication between man and his surroundings,si,no,https://www.cs.colostate.edu/eeg/main/data/1989_Keirn_and_Aunon,https://link.springer.com/chapter/10.1007/978-3-642-35323-9_9,1
124,INAOE;INAOE;INAOE;University of Alcala;INAOE;INAOE,Embedded System for Bimodal Biometrics with Fiducial Feature Extraction on ECG and PPG Signals,2020,Denisse E. Mancilla-Palestina;Jose A. Jimenez-Duarte;Juan Manuel Ramirez-Cortes;Alvaro Hernandez;Pilar Gomez-Gil;Jose Rangel-Magdaleno,1;5,10.1109/I2MTC43012.2020.9128394,reales,31,creados,no,si,None,https://ieeexplore.ieee.org/abstract/document/9128394/authors#authors,2
125,UNAM;UNAM;UNAM,"NeuronGrowth, a software for automatic quantification of neurite and filopodial dynamics from time-lapse sequences of digital images",2011,Zian Fanti;M. Elena Martinez-Perez;Francisco F. De-Miguel,2,10.1002/dneu.20866,reales,24,creados,si,no,http://www.ifc.unam.mx/ffm/download.html,https://pubmed.ncbi.nlm.nih.gov/21913334/,1
126,IPN;IPN;IPN;IPN,Review on plantar data analysis for disease diagnosis,2018,Julian Andres Ramirez Bautista;Silvia Liliana Chaparro Cárdenas;Antonio Hernández Zavala;Jorge Adalberto Huerta-Ruelas,2,10.1016/j.bbe.2018.02.004,reales;reales;reales;reales;reales,48;236;25;35;5;172,Dataset de Differences in foot sensitivity and plantar pressure between young adults and elderly;Dataset de Plantar pressure distribution patterns of young school children in comparison to adults; Dataset de The evaluation of plantar pressure distribution in obese and non-obese adults;. Plantar pressure differences between obese and non-obese adults: a biomechanical analysis;Dataset de Characterization of plantar pressures in visually impaired individuals: a pilot study;Dataset de Clinical determinants of plantar forces and pressures during walking in older people,con permiso;con permiso;con permiso;con permiso;con permiso,no;no;no;no;no,None;None;None;None;None,https://www.researchgate.net/publication/324039598_Review_on_plantar_data_analysis_for_disease_diagnosis,1
127,IPN;IPN;IPN;IPN;Széchenyi István University;IPN;Budapest University of Technology and Economics,Detection of Human Footprint Alterations by Fuzzy Cognitive Maps Trained with Genetic Algorithm,2018,Julian Andres Ramirez Bautista;Antonio Hernandez Zavala;Jorge A. Huerta Ruelas;Miklos F.Hatwágner;Silvia L. Chaparro Cárdenas;Lászó T. Kóczy,5,10.1109/MICAI46078.2018.00013,reales,24,Pedica Centers,con pemiso,si,None,https://www.researchgate.net/publication/340229397_Detection_of_Human_Footprint_Alterations_by_Fuzzy_Cognitive_Maps_Trained_with_Genetic_Algorithm,1
128,IPN;IPN;IPN,Measuring concept semantic relatedness through common spatial pattern feature extraction on EEG signals,2018,Hiram Calvo;José Luis Paredes;Jesús Figueroa-Nazuno,0,https://doi.org/10.1016/j.cogsys.2018.03.004,reales,18,creados,no,si,None,https://www.saber.cic.ipn.mx/SABERv3/publicacions/webView/3148,0
129,IPN;Universite de Tulouse;IPN;Universite de Toulouse,Beyond the paradigm of nanomechanical measurements on cells using AFM: An automated methodology to rapidly analyse thousands of cells.,2019,S. Proa-Coronado;C. Séverac;A. Martinez-Rivas;E. Dague,0,10.1039/C9NH00438F,reales;reales;reales;reales,1021;957;1000;574,creados;creados;creados;creados,no,no,None,https://www.researchgate.net/publication/335196386_Beyond_the_paradigm_of_nanomechanical_measurements_on_cells_using_AFM_An_automated_methodology_to_rapidly_analyse_thousands_of_cells,0
130,IPN;IPN;IPN;IPN;IPN,Adaptive Filtering Approach With Forgetting Factor for Stochastic Signals Applied to EEG,2020,Karen Alicia Aguilar-Cruz;José de Jesús Medel-Juárez;María Teresa Zagaceta-Álvarez;Rosaura Palma-Orozco;Romeo Urbieta-Parrazales,1;3;4,10.1109/ACCESS.2020.2997850,reales,500,EGG Epileptic University of Bonn dataset,si,no,http://epileptologie-bonn.de/cms/front_content.php,https://www.saber.cic.ipn.mx/SABERv3/publicacions/webView/3486,3
131,IPN;ITESM;IPN;IPN,Classification of Motor Imagery EEG Signals with CSP Filtering Through Neural Networks Models,2018,Carlos Daniel Virgilio Gonzalez;Juan Humberto Sossa Azuela;Elsa Rubio Espino;Victor H. Ponce Ponce,3,10.1007/978-3-030-04491-6_10,reales,300,BCI Competition III,si,no,http://www.bbci.de/competition/iii/,https://www.saber.cic.ipn.mx/SABERv3/publicacions/webView/3179,1
132,IPN;IPN;IPN;IPN,n-Iterative Exponential Forgetting Factor for EEG Signals Parameter Estimation,2018,Karen Alicia Aguilar Cruz;María Teresa Zagaceta Álvarez;Rosaura Palma Orozco;José de Jesús Medel Juárez ,1;2;3, 10.1155/2018/4613740,sinteticos;reales,1;6,creados;Subset de A criterion for adaptive autoregressive models,no;con permiso,no,None;None,https://www.saber.cic.ipn.mx/SABERv3/publicacions/webView/3079,3
133,IPN;IPN;University of Michigan;IPN;IPN,Fuzzy inference model evaluating turn for Parkinson’s disease patients,2017,Christopher Ornelas-Vences;Luis Pastor Sanchez-Fernandez;Luis Alejandro SanchezPerez;Alejandro Garza-Rodriguez;Albino Villegas-Bastida,0,10.1016/j.compbiomed.2017.08.026,reales,94,creados ,no,si,None,https://www.saber.cic.ipn.mx/SABERv3/publicacions/webView/3104,0
134,IPN;IPN;IPN;IPN,Fast COVID-19 and Pneumonia Classification Using Chest X-ray Images,2020,Juan Eduardo Luján-García;Marco Antonio Moreno-Ibarra;Yenny Villuendas-Rey;Cornelio Yáñez-Márquez,3,10.3390/math8091423,reales;reales,287;5856,covid-chestxray-dataset;Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification,si,no,https://github.com/ieee8023/covid-chestxray-dataset;https://data.mendeley.com/datasets/rscbjbr9sj/2,https://www.saber.cic.ipn.mx/SABERv3/publicacions/webView/3359,1
135,IPN;IPN;IPN;IPN,A Transfer Learning Method for Pneumonia Classification and Visualization,2020,Juan Eduardo Luján-García;Cornelio Yáñez-Márquez;Yenny Villuendas-Rey;Oscar Camacho-Nieto,3,https://doi.org/10.3390/app10082908,reales,5856,Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification,si,no,https://data.mendeley.com/datasets/rscbjbr9sj/2,https://www.saber.cic.ipn.mx/SABERv3/publicacions/webView/3363,1
136,IPN;IPN;IPN;IPN;IPN,AISAC: An Artificial Immune System for Associative Classification Applied to Breast Cancer Detection,2020,David González-Patiño;Yenny Villuendas-Rey;Amadeo José Argüelles-Cruz;Oscar Camacho-Nieto;Cornelio Yáñez-Márquez,2,10.3390/app10020515,reales;reales;reales;reales;reales;reales;reales;reales;reales;reales,362;699;569;198;740506;961;286;32;306;470,Breast Cancer Digital Repository (BCDR);Breast Cancer Wisconsin (Original) Dataset;Breast Cancer Wisconsin (Diagnostic) Data Set;Breast Cancer Wisconsin (Prognostic) Data Set;Breast Cancer SEER (BCSEER);Mammographic Mass Data Set (MMDS);Breast Cancer Data Set (BCDS);Lung Cancer Data Set (LCDS);Haberman’s Survival Data Set (HSDS);Thoracic Surgery Data Set (TSDS),si;si;si:con permiso;si;si;si;si;si;si,no;no;no;no;no;no;no;no;no;no,http://bcdr.inegi.up.pt/;https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+%28original%29;https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic);https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(Prognostic);None;http://archive.ics.uci.edu/ml/datasets/mammographic+mass;https://archive.ics.uci.edu/ml/datasets/breast+cancer;http://archive.ics.uci.edu/ml/datasets/Lung+Cancer;https://archive.ics.uci.edu/ml/datasets/haberman%27s+survival;https://archive.ics.uci.edu/ml/datasets/Thoracic+Surgery+Data,https://www.saber.cic.ipn.mx/SABERv3/publicacions/webView/3368,1
137,IPN;IPN;IPN;University of Waterloo,A Novel Bio-Inspired Method for Early Diagnosis of Breast Cancer through Mammographic Image Analysis,2019,David González-Patiño;Yenny Villuendas-Rey;Amadeo-José Argüelles-Cruz;Fakhri Karray,0,10.3390/app9214492,reales;reales;reales;reales;reales;reales,362;699;198;286;961;306,Breast Cancer Digital Repository (BCDR);Breast Cancer Wisconsin (Original) Data Set;Breast Cancer Wisconsin (Prognostic) Data Set;Lung Cancer Data Set (LCDS);Mammographic Mass Data Set (MMDS);Haberman’s Survival Data Set (HSDS),si;si;si:si:si:si,no;no;no;no;no;no,http://bcdr.inegi.up.pt/;https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+%28original%29;https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(Prognostic);http://archive.ics.uci.edu/ml/datasets/Lung+Cancer;http://archive.ics.uci.edu/ml/datasets/mammographic+mass;https://archive.ics.uci.edu/ml/datasets/haberman%27s+survival,https://www.saber.cic.ipn.mx/SABERv3/publicacions/webView/3403,0
138,IPN;IPN;IPN,Prediction of Cancer using Microarrays Analysis by Machine Learning Algorithms,2019,José Luis Velázquez-Rodríguez;Yenny Villuendas-Rey;Cornelio Yáñez-Márquéz,2,"ISSN 1870-4069 Research in Computing Science 148(10), 2019",reales;reales;reales;reales;reales;reales;reales;reales;relaes;reales,3051;2000;9868;5597;4869;4869;4023;5244;6033;2308,Leukemia;Colon;Adenocarcinoma;Brain tumor;Breast cancer 1;Breast cancer 2;Lymphoma;National Cancer Institute (NCI) ;Prostate cancer;SRBCT,si;si;si;si:si:si:si:si:si:si,no;no;no;no;no;no;no;no;no;no,,https://www.saber.cic.ipn.mx/SABERv3/publicacions/webView/3569,1
139,Universidad Iberoamericana Puebla;IPN;IPN;Universidad Autónoma de Chihuahua,Automatic electroencephalographic information classifier based on recurrent neural networks,2019,Mariel Alfaro‑Ponce;Amadeo Argüelles;Isaac Chairez;Arizbeth Pérez,4,10.1007/s13042-018-0867-9,reales;reales,500;600,EEG database [online];Dataset de Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals,si;con permiso,no;no,http://epilepsy.uni-freiburg.de/freiburg-seizure-prediction-project/eeg-database,https://www.saber.cic.ipn.mx/SABERv3/publicacions/webView/3223,1
140,Universidad Mexiquense del Bicentenario;IPN;IPN;IPN,A machine learning approach to medical image classification: Detecting age-related macular degeneration in fundus images,2019,Andrés García-Florianoa;Ángel Ferreira-Santiago;Oscar Camacho-Nieto;Cornelio Yáñez-Márquez,0,10.1016/j.compeleceng.2017.11.008,reales,397,STARE dataset,si,no,https://cecas.clemson.edu/~ahoover/stare/,https://www.saber.cic.ipn.mx/SABERv3/publicacions/webView/3119,0
141,IPN;IPN;IPN;IPN;IPN,System for Face Recognition under Different Facial Expressions Using a New Associative Hybrid Model Am alpha beta-KNN for People with Visual Impairment or Prosopagnosia,2019,Moisés Márquez-Olivera;Antonio-Gustavo Juárez-Gracia;Viridiana Hernández-Herrera;Amadeo-José Argüelles-Cruz;Itzamá López-Yáñez,3,10.3390/s19030578,reales;reales;reales,593;30900;24300,Extended Cohn-Kanade Dataset (CK+);CAS-PEAL-R1;creados,si;con permiso;no,no;no;si,https://www.kaggle.com/c/visum-facial-expression-analysis;None;None,https://www.saber.cic.ipn.mx/SABERv3/publicacions/webView/3402,1
142,IPN;IPN;IPN,Experimental Comparison of Bioinspired Segmentation Algorithms Applied to Segmentation of Digital Mammographies,2017,David González-Patiño;Yenny Villuendas-Rey;Amadeo J. Argüelles-Cruz,2,Research in Computing Science 138 (2017) ISSN 1870-4069,reales,200,Breast Cancer Digital Repository,con permiso,no,None,https://www.rcs.cic.ipn.mx/2017_138/Experimental%20Comparison%20of%20Bioinspired%20Segmentation%20Algorithms%20Applied%20to%20Segmentation.pdf,1
143,IPN;IPN;Universidad Ciego de Ávila;IPN;IPN,Medical diagnosis of chronic diseases based on novel Computational Intelligence algorithm,2018,Yenny Villuendas-Rey;Mariana-D. Alanis-Tamez;Carmen-F. Rey Benguría;Cornelio Yáñez-Márquez;Oscar Camacho-Nieto,1;2;3,10.3217/jucs-024-06-0775,reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales;reales,277;345;297;306;270;80;830;215;768;84;462;267;7200;569;683,Breast Cancer data set;Liver Disorders (BUPA) data set;Heart Disease (Cleveland) data set;Haberman's Survival data set;Statlog (Heart) data set;Hepatitis data set;Hepatitis data set;Thyroid Disease (New Thyroid) data set;Pima Indians Diabetes data set;Post-Operative data set;South African Hearth data set;SPECTF Heart data set;Thyroid Disease (thyroid0387) data set;Breast Cancer Wisconsin diagnosis (wdbc) data set;Breast Cancer Wisconsin original (wisconsin) data set,si;si;si;si;si;si;si;si;si;si;si;si;si;si;si,no;no;no;no;no;no;no;no;no;no;no;no;no;no;no,https://www.keel.es;https://archive.ics.uci.edu/ml/index.php,http://www.jucs.org/jucs_24_6/medical_diagnosis_of_chronic/jucs_24_06_0775_0796_rey.pdf,3
144,IPN;IPN;IPN,Mammogram image segmentation using bioinspired novel bat swarm clusternig,2016,David González-Patiño;Yenny Villuendas-Rey;Amadeo J. Argüelles-Cruz,2,Research in Computing Science 118 (2016) ISSN 1870-4069,reales,200,Breast Cancer Digital Repository,con permiso,no,None,https://www.rcs.cic.ipn.mx/2016_118/Mammogram%20Image%20Segmentation%20Using%20Bioinspired%20Novel%20Bat%20Swarm%20Clustering.pdf,1
145,Universidad Autonoma del Estado de Hidalgo;IPN;IPN,Pattern recognition for electroencephalographic signals based on continuous neural networks,2016,M. Alfaro-Ponce;A. Argüelles;I. Chairez,1, http://dx.doi.org/10.1016/j.neunet.2016.03.004,reales;reales,500;90,Seizure Prediction Project Freiburg;creados,si;no,no;si,http://epilepsy.uni-freiburg.de/freiburg-seizure-prediction-project/eeg-database;None,https://www.saber.cic.ipn.mx/SABERv3/publicacions/webView/2987,1
146,IPN;IPN;IPN,The Potential Use of Bioinspired Algorithms Applied in the Segmentation of Mammograms ,2018,David González-Patiño;Yenny Villuendas-Rey;Amadeo J. Argüelles-Cruz,2,10.5220/0006951103030306,reales,362,Breast Cancer Digital Repository database,si,no,https://www.bcdr.eu/information/about,https://ipn.elsevierpure.com/es/publications/the-potential-use-of-bioinspired-algorithms-applied-in-the-segmen,1
147,IPN;IPN;University of Michigan;IPN,Fuzzy inference model based on triaxial signals for pronation and supination assessment in Parkinson's disease patients,2020,Alejandro Garza-Rodríguez;Luis Pastor Sánchez-Fernández;Luis Alejandro Sánchez-Pérez;José Juan Carbajal Hernández,0,10.1016/j.artmed.2020.101873,reales,57,creados,no,si,None,https://www.saber.cic.ipn.mx/SABERv3/publicacions/webView/3427,0
148,IPN;IPN;University of Michigan;IPN;IPN,Pronation and supination analysis based on biomechanical signals from Parkinson’s disease patients,2018,Alejandro Garza-Rodríguez;Luis Pastor Sánchez-Fernández;Luis Alejandro Sánchez-Pérez;Christopher Ornelas-Vences;Mariane Ehrenberg-Inzunza,5,10.1016/j.artmed.2017.10.001,reales,210,creados,no,no,None,https://www.sciencedirect.com/science/article/pii/S0933365717301033,1