Results 121 to 130 of about 75,080 (277)

Performance of a Deep Learning Algorithm for Melanoma Classification Across Diverse Dermoscopic and Tele‐Dermatology Datasets

JEADV Clinical Practice, EarlyView.
ABSTRACT Background Early detection of melanoma significantly boosts patient survival rates. Deep learning has demonstrated dermatologist‐level accuracy in assessing pigmented skin lesions by analysing images at the pixel level. However, these neural networks may face challenges with ‘real‐life’ images due to limited training data and image artefacts ...
Marianne Zanchetta, Fanny Gohard, Casandra Coltoiu, Sarah Le Naour, Marie Le Moigne, Valérie Pham‐Huu, Nicole Cochelin, Edmond Demoulins, Bryan Boule, Thibault Maho, Léonie Schroder, Nicolas Diris, Gaëlle Quereux, Brigitte Dreno, Lise Boussemart, Javier Saenz   +15 more
wiley   +1 more source

Machine learning model identifies tibial anatomical variables as potential risk factors for anterior cruciate ligament injury

Knee Surgery, Sports Traumatology, Arthroscopy, EarlyView.
Abstract Purpose The tibial slope is a well‐known risk factor for anterior cruciate ligament (ACL) injury. As machine learning continues to progress, it has become an increasingly explored tool for clinical screening and risk factor analysis. This study aims to develop and validate a prognostic machine learning model to predict the outcome of ACL ...
Cheng‐Hao Kao, Javad Hashemi, James Slauterbeck, Naveen Chandrashekar   +3 more
wiley   +1 more source

Single‐Shot Full‐Stokes Analysis of Partially‐Polarized Light With a Photonic Deep Random Neural Network

Laser &Photonics Reviews, EarlyView.
Photonic neural networks are emerging as a powerful tool for analyzing light. The article reports a photonic deep random neural network that performs single‐shot low‐latency measurements of the state and degree of polarization with high accuracy. Using only a stack of glass diffusers on top of a small camera sensor, this neuromorphic polarization ...
Alessandro Petrini, Claudio Conti, Davide Pierangeli   +2 more
wiley   +1 more source

Optimizing deep neural network architectures for renewable energy forecasting

Discover Sustainability
An accurate renewable energy output forecast is essential for energy efficiency and power system stability. Long Short-Term Memory(LSTM), Bidirectional LSTM(BiLSTM), Gated Recurrent Unit(GRU), and Convolutional Neural Network-LSTM(CNN-LSTM) Deep Neural ...
Sunawar khan, Tehseen Mazhar, Tariq Shahzad, Wajahat Waheed, Ahsen Waheed, Mamoon M. Saeed, Habib Hamam   +6 more
doaj   +1 more source

Optical Coherence Tomography for High‐Precision Industrial Inspection in Industry 4.0: Advances, Challenges, and Future Trends

Laser &Photonics Reviews, EarlyView.
This review examines how optical coherence tomography transforms industrial inspection by delivering real‐time, micrometer‐resolution, depth‐resolved imaging. It surveys applications across display manufacturing, thin films, microelectronics, laser processing, and coatings, evaluates performance against conventional techniques, and highlights emerging ...
Nipun Shantha Kahatapitiya, Sm Abu Saleah, Daewoon Seong, Naresh Kumar Ravichandran, Sangyeob Han, Ruchire Eranga Wijesinghe, Mansik Jeon, Jeehyun Kim   +7 more
wiley   +1 more source

Machine Learning Prediction of Raster Angle Effects on Mechanical Properties of Extrusion‐Based Additively Manufactured Conductive Thermoplastic Polyurethane Composites

Macromolecular Materials and Engineering, EarlyView.
This study integrates machine learning (ML) and high‐fidelity experiments to model how raster angle influences the mechanical properties of fused filament fabricated conductive TPU composites. Using Poly6 and SVR models, the approach accurately predicts stiffness, strength, and ductility.
Imran Khan, Ans Al Rashid, Muammer Koç
wiley   +1 more source

Machine Learning for Predictive Modeling in Nanomedicine‐Based Cancer Drug Delivery

Med Research, EarlyView.
The integration of AI/ML into nanomedicine offers a transformative approach to therapeutic design and optimization. Unlike conventional empirical methods, AI/ML models (such as classification, regression, and neural networks) enable the analysis of complex clinical and formulation datasets to predict optimal nanoparticle characteristics and therapeutic
Rohan Chand Sahu, Sanchit Arora, Dinesh Kumar, Ashish Kumar Agrawal   +3 more
wiley   +1 more source

An analysis of the influence of deep neural network (DNN) topology in bottleneck feature based language recognition. [PDF]

PLoS One, 2017
Lozano-Diez A, Zazo R, Toledano DT, Gonzalez-Rodriguez J.   +3 more
europepmc   +1 more source

Deep Learning‐Based Prediction of Controllable Stress/Strain‐Rate Loading and Design for Loading Conditions

Materials Genome Engineering Advances, EarlyView.
This study establishes the relationship between loading conditions and loading results in graded density impactor (GDI)‐based controllable stress/strain‐rate loading via deep learning, and ultimately realizing the prediction of loading curves and design of loading conditions.
Yiwei Zhang, Ruizhi Zhang, Junbang Jiang, Yahui Huang, Han Chen, Jian Zhang, Guoqiang Luo, Qiang Shen   +7 more
wiley   +1 more source

Enhancing Lipidomics With High‐Resolution Ion Mobility‐Mass Spectrometry

PROTEOMICS, EarlyView.
ABSTRACT Lipids, indispensable yet structurally intricate biomolecules, serve as critical regulators of cellular function and disease progression. Conventional lipidomics, constrained by limited resolution for isomeric and low‐abundance species, has been transformed by ion mobility‐mass spectrometry (IM‐MS).
Gaoyuan Lu, Shuling Xu, Penghsuan Huang, Lingjun Li   +3 more
wiley   +1 more source