Results 101 to 110 of about 452,858 (310)

FACADE SEGMENTATION WITH A STRUCTURED RANDOM FOREST [PDF]

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017
In this paper we present a bottom-up approach for the semantic segmentation of building facades. Facades have a predefined topology, contain specific objects such as doors and windows and follow architectural rules.
K. Rahmani, H. Huang, H. Mayer
doaj   +1 more source

Random-effects model forest plots showing the impact of autologous BT on PCa survival after RP.

, 2017
Random-effects model forest plots showing the impact of autologous BT on PCa survival after RP.
Su-Liang Li (3704542), Yun Ye (232429), Xiao-Hua Yuan (3704545)   +2 more
core   +1 more source

Risk of Non‐Arteritic Anterior Ischemic Optic Neuropathy in Idiopathic Intracranial Hypertension Patients Treated with GLP‐1 Receptor Agonists

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Introduction Glucagon‐like peptide‐1 receptor agonists (GLP‐1 RAs) have demonstrated significant weight‐reducing effects and may offer benefits in idiopathic intracranial hypertension (IIH); however, recent concerns about the risk of non‐arteritic anterior ischemic optic neuropathy (NAION) have emerged.
Faisal A. Al‐Harbi, Mohanad A. Alkuwaiti, Yazeed B. Alaql, Ahmed K. Alsaif, Ahmed A. Alessa, Meshari Ayed Alharbi, Mohammed Alfalah, Saud A. Alnaaim, Sajjad M. AlHaddad, Ahmed Y. Azzam   +9 more
wiley   +1 more source

Screening Routine Clinical Notes for Epilepsy Surgery Candidates Using Large Language Models

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Epilepsy surgery is severely underutilized despite proven efficacy, with substantial under‐referral of eligible patients in routine clinical practice. This study evaluated the potential role of large language models (LLMs) as decision‐support tools for screening unstructured clinical notes to identify epilepsy surgery candidates and ...
Uriel Fennig, Nadav Amir, Maya Schiller, Roni Loebenstein, Johnatan Nissan, Marina Boxer, Shany Guly Gofrit, Gal Goshen, Sándor Beniczky, Nicola Maggio   +9 more
wiley   +1 more source

Uncovering G Protein‐Coupled Receptors: Novel Targets and Biomarkers for Predicting Glioma Prognosis

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Low‐grade gliomas (LGG) exhibit significant heterogeneity and recurrence risk. G protein‐coupled receptors (GPCR) contribute to glioma malignant progression, but their prognostic value remains unclear. This work attempts to formulate a GPCR‐based outcome‐predicting model for LGG. Methods Based on TCGA LGG data, the enrichment scores
Jun Yang, Dongxu Zhang, Dongyuan Liu, Da Lin, Hao Wang   +4 more
wiley   +1 more source

A Two‐Stage Questionnaire and Actigraphy Screening for iRBD in a Multicenter Retrospective Cohort

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Isolated rapid‐eye‐movement sleep behavior disorder is a prodromal marker of synucleinopathies. However, most cases remain undiagnosed due to the insufficient predictive value of questionnaires and limited access to confirmatory video‐polysomnography. We assessed a two‐stage screening strategy combining a brief questionnaire on rapid‐
Caleb A. Massimi, Giorgio Ricciardiello Mejia, Andre Metzger, Kang Hyun Ryu, Salonee Marwaha, Eva Grzegorczyk, Li Zhou, Eliana Jacobs, Boris Gilyadov, Claudia Kunney, Liqhwa Ncube, Ankit Parekh, Emmanuel Mignot, Fanny M. Elahi, Joseph Winer, Kathleen Poston, Andreas Brink‐Kjaer, Emmanuel H. During   +17 more
wiley   +1 more source

Neurochemical Endpoints to Inform Early‐Stage Trials of Spinocerebellar Ataxia 2 and 3 in a Multisite Setting

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Neurochemical levels measured by brain MR spectroscopy (MRS) have been proposed as endpoints for clinical trials in early‐stage spinocerebellar ataxia (SCA) trials. We tested their trial‐readiness by quantifying neurochemicals in three affected brain regions in early‐stage cohorts of SCA2 and SCA3, examining their reproducibility in ...
James M. Joers, Yaqiao Wei, Dinesh K. Deelchand, Adam Berrington, Young Woo Park, Guita Banan, Michal Povazan, Jens T. Rosenberg, André J. W. van der Kouwe, Tetsuo Ashizawa, Khalaf O. Bushara, Chiadi U. Onyike, Sub H. Subramony, Jeremy D. Schmahmann, Brian J. Soher, Peter B. Barker, Thomas H. Mareci, Eva‐Maria Ratai, Lynn E. Eberly, Gülin Öz   +19 more
wiley   +1 more source

Seed classification with random forest models

Applications in Plant Sciences
Premise To improve forest conservation monitoring, we developed a protocol to automatically count and identify the seeds of plant species with minimal resource requirements, making the process more efficient and less dependent on human operators. Methods
Josephine Elena Reek, Janneke Hille Ris Lambers, Eléonore Perret, Alana R. O. Chin   +3 more
doaj   +1 more source

Variational mode decomposition based random forest model for solar radiation forecasting: New emerging machine learning technology

, 2021
Forecasting of solar radiation (Radn) can provide an insight vision for the amount of green and friendly energy sources. Owing to the non-linearity and non-stationarity challenges caused by meteorological variables in forecasting Radn, a variational mode
Yaseen, Zaher Mundher, Zong, Tianrui, Prasad, Ramendra, Ali, Mumtaz, Xiang, Yong, Khan, Mohsin, Farooque, Aitazaz Ahsan   +6 more
core   +1 more source

Boosted Random Forest

IEICE Transactions on Information and Systems, 2015
Machine learning is used in various fields and demand for implementations is increasing. Within machine learning, a Random Forest is a multi-class classifier with high-performance classification, achieved using bagging and feature selection, and is capable of high-speed training and classification. However, as a type of ensemble learning, Random Forest
MISHINA, Yohei, MURATA, Ryuei, YAMAUCHI, Yuji, YAMASHITA, Takayoshi, FUJIYOSHI, Hironobu   +4 more
openaire   +2 more sources