Results 51 to 60 of about 2,415 (194)
Comparison of the performance of the DNet model with other models.
, 2019 A. Mean CCnorm of linear-nonlinear (LN), network receptive field (NRF) and dynamic network (DNet) models. B. Time course of predictions by best performing versions of each of the LN, NRF and DNet models, compared with real neural responses for an example
Ben D. B. Willmore (483582) +3 more
core +1 more source
, 2021 In the OpenAIRE context, research organizations are aggregated from several datasources. This often leads to a duplication problem because an organization can be provided by multiple datasources. Deduplication is a fundamental task to solve this problem.
Manghi P. +11 more
core +1 more source
Histopathological Evidence of Neurodegenerative Pathology in Epilepsy: A Systematic Review
Annals of Clinical and Translational Neurology, Volume 13, Issue 8, Page 1530-1542, August 2026.ABSTRACT Epilepsy affects > 50 million people worldwide and is associated with a disproportionate burden of cognitive impairment. Emerging evidence suggests that neurodegenerative proteinopathies, particularly hyperphosphorylated tau (p‐tau) and amyloid‐β (Aβ), may contribute to cognitive dysfunction in people with epilepsy (PWE), even in the absence ...
Syeda Amrah Hashmi +7 more
wiley +1 more source
Prediction of neural responses using a dynamic network (DNet) model.
, 2019 A. The DNet model. The cochleagram is passed through a set of linear-nonlinear filters, whose response is then integrated over time using an exponentially-decaying impulse response to produce the hidden units’ outputs.
Ben D. B. Willmore (483582) +3 more
core +1 more source
Dysembryoplastic neuroepithelial tumors and refractory epilepsy: a case report and literature review
Journal de la Faculté de Médecine d'Oran, 2022 IntroductionNeuroepithelial dysembryoplastic tumors are grade I benign tumors, typicallylocalized in the suprententorial cortex and usually revealed by drug-resistantepilepsy.
Souad DAOUD +5 more
doaj
Sensors, 2022 Because the number of air quality measurement stations governed by a public authority is limited, many methodologies have been developed in order to integrate low-cost sensors and to improve the spatial density of air quality measurements.
Ivan Popović +4 more
doaj +1 more source
Pediatric Blood &Cancer, Volume 73, Issue 7, July 2026.ABSTRACT Background This study investigated how neighborhood‐level social determinants of health (SDOH), including redlining and neurological risk, interact to influence cognitive outcomes in children treated for brain tumors (CTBT). Methods A retrospective chart review of 161 CTBT aged 5–17 was conducted.
Alannah R. Srsich +5 more
wiley +1 more source
, 2021 Dnet Hadoop Software implements The OpenAIRE Graph (formerly known as the OpenAIRE Research Graph), one of the largest open scholarly record collections worldwide, key in fostering Open Science and establishing its practices in daily research activities.
Andrea Mannocci +8 more
core
Advanced Intelligent Systems, Volume 8, Issue 7, July 2026.Four decades of retinal vessel segmentation research (1982–2025) are synthesized, spanning classical image processing, machine learning, and deep learning paradigms. A meta‐analysis of 428 studies establishes a unified taxonomy and highlights performance trends, generalization capabilities, and clinical relevance.
Avinash Bansal +6 more
wiley +1 more source
Developing Relative Humidity and Temperature Corrections for Low-Cost Sensors Using Machine Learning
Sensors, 2021 Existing government air quality monitoring networks consist of static measurement stations, which are highly reliable and accurately measure a wide range of air pollutants, but they are very large, expensive and require significant amounts of maintenance.
Ivan Vajs +4 more
doaj +1 more source