Výsledky vyhledávání - "Górriz, Juan Manuel"

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RESISTO Project: Automatic detection of operation temperature anomalies for power electric transformers using thermal imaging

Autor: López-García, David, Segovia, Fermín, Rodríguez-Rivero, Jacob, Ramírez, Javier, Pérez, David, Serrano, Raúl, Górriz, Juan Manuel

Publikováno v: (2024) International Work-Conference on the Interplay Between Natural and Artificial Computation (pp. 225-245). Cham: Springer Nature Switzerland

The RESISTO project represents a pioneering initiative in Europe aimed at enhancing the resilience of the power grid through the integration of advanced technologies. This includes artificial intelligence and thermal surveillance systems to mitigate

Externí odkaz: http://arxiv.org/abs/2410.19800

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RESISTO Project: Safeguarding the Power Grid from Meteorological Phenomena

Autor: Rodríguez-Rivero, Jacob, López-García, David, Segovia, Fermín, Ramírez, Javier, Górriz, Juan Manuel, Serrano, Raúl, Pérez, David, Maza, Iván, Ollero, Aníbal, Solà, Pol Paradell, Selga, Albert Gili, Domínguez-García, José Luis, Romero, A., Berro, A., Domínguez, Rocío, Prieto, Inmaculada

The RESISTO project, a pioneer innovation initiative in Europe, endeavors to enhance the resilience of electrical networks against extreme weather events and associated risks. Emphasizing intelligence and flexibility within distribution networks, RES

Externí odkaz: http://arxiv.org/abs/2410.19799

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EEG Connectivity Analysis Using Denoising Autoencoders for the Detection of Dyslexia

Autor: Martinez-Murcia, Francisco Jesus, Ortiz, Andrés, Górriz, Juan Manuel, Ramírez, Javier, Lopez-Perez, Pedro Javier, López-Zamora, Miguel, Luque, Juan Luis

Publikováno v: INT J NEURAL SYST 30 (7), 2020, 2050037

The Temporal Sampling Framework (TSF) theorizes that the characteristic phonological difficulties of dyslexia are caused by an atypical oscillatory sampling at one or more temporal rates. The LEEDUCA study conducted a series of Electroencephalography

Externí odkaz: http://arxiv.org/abs/2311.13876

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An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works

Autor: Shoeibi, Afshin, Moridian, Parisa, Khodatars, Marjane, Ghassemi, Navid, Jafari, Mahboobeh, Alizadehsani, Roohallah, Kong, Yinan, Gorriz, Juan Manuel, Ramírez, Javier, Khosravi, Abbas, Nahavandi, Saeid, Acharya, U. Rajendra

Publikováno v: Computers in Biology and Medicine, 2022, 106053

Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand movements. Epileptic seizure detection methods involve neurological exams, blood

Externí odkaz: http://arxiv.org/abs/2105.14278

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Applications of Deep Learning Techniques for Automated Multiple Sclerosis Detection Using Magnetic Resonance Imaging: A Review

Autor: Shoeibi, Afshin, Khodatars, Marjane, Jafari, Mahboobeh, Moridian, Parisa, Rezaei, Mitra, Alizadehsani, Roohallah, Khozeimeh, Fahime, Gorriz, Juan Manuel, Heras, Jónathan, Panahiazar, Maryam, Nahavandi, Saeid, Acharya, U. Rajendra

Publikováno v: Computers in Biology and Medicine,Volume 136,2021,104697

Multiple Sclerosis (MS) is a type of brain disease which causes visual, sensory, and motor problems for people with a detrimental effect on the functioning of the nervous system. In order to diagnose MS, multiple screening methods have been proposed

Externí odkaz: http://arxiv.org/abs/2105.04881

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Deep Learning in current Neuroimaging: a multivariate approach with power and type I error control but arguable generalization ability

Autor: Jiménez-Mesa, Carmen, Ramírez, Javier, Suckling, John, Vöglein, Jonathan, Levin, Johannes, Górriz, Juan Manuel, ADNI, Alzheimer's Disease Neuroimaging Initiative, DIAN, Dominantly Inherited Alzheimer Network

Discriminative analysis in neuroimaging by means of deep/machine learning techniques is usually tested with validation techniques, whereas the associated statistical significance remains largely under-developed due to their computational complexity.

Externí odkaz: http://arxiv.org/abs/2103.16685

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A connection between the pattern classification problem and the General Linear Model for statistical inference

Autor: Gorriz, Juan Manuel, group, SIPBA, Suckling, John

A connection between the General Linear Model (GLM) in combination with classical statistical inference and the machine learning (MLE)-based inference is described in this paper. Firstly, the estimation of the GLM parameters is expressed as a Linear

Externí odkaz: http://arxiv.org/abs/2012.08903

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Ensemble Deep Learning on Large, Mixed-Site fMRI Datasets in Autism and Other Tasks

Autor: Leming, Matthew, Górriz, Juan Manuel, Suckling, John

Deep learning models for MRI classification face two recurring problems: they are typically limited by low sample size, and are abstracted by their own complexity (the "black box problem"). In this paper, we train a convolutional neural network (CNN)

Externí odkaz: http://arxiv.org/abs/2002.07874

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