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Javier Bejar
Javier Bejar
Associate Professor of Artificial Intelligence, Universitat Politècnica de Catalunya
Verified email at cs.upc.edu
Title
Cited by
Cited by
Year
Clustering algorithm for determining community structure in large networks
JM Pujol, J Béjar, J Delgado
Physical Review E—Statistical, Nonlinear, and Soft Matter Physics 74 (1 …, 2006
1762006
Generality-based conceptual clustering with probabilistic concepts
L Talavera, J Béjar
IEEE Transactions on pattern analysis and machine intelligence 23 (2), 196-206, 2001
822001
Aprendizaje automático
A Moreno, E Armengol, J Béjar Alonso, LA Belanche Muñoz, ...
Edicions UPC, 1994
791994
Concept formation in WWTP by means of classification techniques: a compared study
M Sànchez, U Cortés, J Béjar, JD Grácia, J Lafuente, M Poch
Applied Intelligence 7 (2), 147-165, 1997
751997
DAI-DEPUR: an integrated and distributed architecture for wastewater treatment plants supervision
M Sànchez, U Cortés, J Lafuente, IR Roda, M Poch
Artificial Intelligence in Engineering 10 (3), 275-285, 1996
581996
Integrating declarative knowledge in hierarchical clustering tasks
L Talavera, J Béjar
Advances in Intelligent Data Analysis, 211-222, 1999
541999
Neuropsychological impairment in post-COVID condition individuals with and without cognitive complaints
M Ariza, N Cano, B Segura, A Adan, N Bargalló, X Caldú, A Campabadal, ...
Frontiers in aging neuroscience 14, 1029842, 2022
522022
K-means vs Mini Batch K-means: a comparison
J Béjar Alonso
452013
Wind energy forecasting with neural networks: A literature review
J Manero, J Béjar, U Cortés
Computación y Sistemas 22 (4), 1085-1098, 2018
412018
COVID-19 severity is related to poor executive function in people with post-COVID conditions
M Ariza, N Cano, B Segura, A Adan, N Bargalló, X Caldú, A Campabadal, ...
Journal of Neurology 270 (5), 2392-2408, 2023
402023
“Dust in the wind...”, deep learning application to wind energy time series forecasting
J Manero, J Béjar, U Cortés
Energies 12 (12), 2385, 2019
372019
Convolutional neural networks for classification of malware assembly code
D Gibert, J Béjar, C Mateu, J Planes, D Solis, R Vicens
Recent Advances in Artificial Intelligence Research and Development, 221-226, 2017
352017
Discovery of Spatio-Temporal Patterns from Location Based Social Networks.
J Bejar, S Álvarez-Napagao, D Garcia-Gasulla, I Gómez-Sebastià, L Oliva, ...
CCIA, 126-135, 2014
262014
LINNEO+: Herramienta para la adquisición de conocimiento y generación de reglas de clasificación en dominios poco estructurados
J Béjar, U Cortés
Actas del III Congreso Iberoamericano de Inteligencia Artificial (IBERAMIA92), 1992
251992
K-means vs mini batch k-means: A comparison
J Béjar
KEMLG-Grup d’Enginyeria del Coneixement i Aprenentatge Automàtic-Reports de …, 2013
232013
Nearest-neighbours for time series
JMG Illa, JB Alonso, MS Marré
Applied Intelligence 20 (1), 21-35, 2004
232004
A distributed control system based on agent architecture for wastewater treatment
J Baeza, D Gabriel, J Béjar, J Lafuente
Computer‐Aided Civil and Infrastructure Engineering 17 (2), 93-103, 2002
222002
Supraspinal modulation of neuronal synchronization by nociceptive stimulation induces an enduring reorganization of dorsal horn neuronal connectivity
E Contreras‐Hernández, D Chávez, E Hernández, E Velázquez, P Reyes, ...
The Journal of Physiology 596 (9), 1747-1776, 2018
172018
A visual embedding for the unsupervised extraction of abstract semantics
D Garcia-Gasulla, E Ayguadé, J Labarta, J Béjar, U Cortés, T Suzumura, ...
Cognitive Systems Research 42, 73-81, 2017
162017
A machine learning methodology for the selection and classification of spontaneous spinal cord dorsum potentials allows disclosure of structured (non-random) changes in …
M Martin, E Contreras-Hernández, J Béjar, G Esposito, D Chávez, ...
Frontiers in neuroinformatics 9, 21, 2015
142015
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Articles 1–20