Results 261 to 270 of about 155,699 (303)

Deep learning enables automatic detection of joint damage progression in rheumatoid arthritis-model development and external validation. [PDF]

Rheumatology (Oxford)
Venäläinen MS, Biehl A, Holstila M, Kuusalo L, Elo LL.   +4 more
europepmc   +1 more source

Toward High‐Performance Electrochemical Energy Storage Systems: A Case Study on Predicting Electrochemical Properties and Inverse Material Design of MXene‐Based Electrode Materials with Automated Machine Learning (AutoML)

Advanced Electronic Materials, EarlyView.
This study demonstrates PyCaret's AutoML framework for predicting the electrochemical and structural properties of MXene‐based electrodes, including intercalation voltage, capacity, and lattice constants. AutoML streamlines workflows, ranks key elemental descriptor, and enables inverse molecular formula prediction based on performance targets.
Berna Alemdag, Görkem Saygili, Matthias Franzreb, Gözde Kabay   +3 more
wiley   +1 more source

Depth‐Customizable 3D Electrode Array for Recording Functional Connectivity in the Brain

Advanced Electronic Materials, EarlyView.
In this study, a depth‐adjustable, flexible 3D multi‐shank electrode array that produces precise neural recordings at various brain depths is developed. Integrating 2D flexible electrode arrays with a modular supporting board allowed the insertion depth to be easily adjusted without re‐fabrication.
Minseok Kim, Sehwan Park, Haeyun Lee, Jimin Lee, Namsun Chou, Hyogeun Shin   +5 more
wiley   +1 more source

A fully automated, expert-perceptive image quality assessment system for whole-body [18F]FDG PET/CT. [PDF]

EJNMMI Res
Zhang C, Gao X, Zheng X, Xie J, Feng G, Bao Y, Gu P, He C, Wang R, Tian J.   +9 more
europepmc   +1 more source

A Detailed Comprehensive Role of Digital Technologies in Green Finance Initiative for Net‐Zero Energy Transition

Advanced Energy and Sustainability Research, EarlyView.
This study aims to provide actionable recommendations for leveraging digital innovation for the achievement of scalable, equitable, and transparent Net Zero Energy Transition by offering actionable recommendations. As a result of this comprehensive analysis, the review highlights the critical interplay between digital technologies and GF as vital ...
Furkan Ahmad, Ameni Boumaiza, Antonio Sanfilippo, Luluwah Al‐Fagih   +3 more
wiley   +1 more source

The use of a convolutional neural network to automate radiologic scoring of computed tomography of paranasal sinuses. [PDF]

Biomed Eng Online
Lee DJ, Hamghalam M, Wang L, Lin HM, Colak E, Mamdani M, Simpson AL, Lee JM.   +7 more
europepmc   +1 more source

Integrating Aggregate Materials and Machine Learning Algorithms: Advancing Detection of Pathogen‐Derived Extracellular Vesicles

Aggregate, EarlyView.
Pathogen‐derived extracellular vesicle detection: theoretical insights and advanced methodologies: This review explores the theoretical foundations and practical applications of pEV detection, emphasizing the challenges and innovative strategies involving novel aggregate materials and machine learning for enhanced diagnostic accuracy.
Lihan Lai, Yun Su, Cong Hu, Zehong Peng, Wei Xue, Liang Dong, Tony Y. Hu   +6 more
wiley   +1 more source

High-throughput screening data generation, scoring and FAIRification: a case study on nanomaterials. [PDF]

J Cheminform
Tancheva G, Hongisto V, Patyra K, Iliev L, Kochev N, Nymark P, Kohonen P, Jeliazkova N, Grafström R.   +8 more
europepmc   +1 more source

AInsectID Version 1.1: An Insect Species Identification Software Based on the Transfer Learning of Deep Convolutional Neural Networks

Advanced Intelligent Discovery, EarlyView.
This paper describes the basis for AInsectID Version 1, a GUI‐operable open‐source insect species identification, color processing, and image analysis software. This paper discusses our methods of algorithmic development, coupled to rigorous machine training used to enable high levels of validation accuracy.
Haleema Sadia, Parvez Alam
wiley   +1 more source

Multicentre prospective risk analysis of a fully automated radiotherapy workflow. [PDF]

Phys Imaging Radiat Oncol
De Kerf G, Barragán-Montero A, Brouwer CL, Pisciotta P, Biston MC, Fusella M, Herbin G, Kneepkens E, Marrazzo L, Mason J, Nielsen CP, Snijders K, Tanadini-Lang S, Vaandering A, Janssen TM.   +14 more
europepmc   +1 more source