Results 101 to 110 of about 53,216 (300)

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

Synchronous Response Analysis of Features for Remote Sensing Crop Classification Based on Optical and SAR Time-Series Data

Sensors, 2019
Accurate crop classification is the basis of agricultural research, and remote sensing is the only effective measuring technique to classify crops over large areas.
Yingwei Sun, Jiancheng Luo, Tianjun Wu, Ya’nan Zhou, Hao Liu, Lijing Gao, Wen Dong, Wei Liu, Yingpin Yang, Xiaodong Hu, Lingyu Wang, Zhongfa Zhou   +11 more
doaj   +1 more source

Schematic of RNN.

(a) Standard RNN without modular structure (b) RNN with modular structure.
Kohei Ichikawa (7343024), Kunihiko Kaneko (25945)   +1 more
core   +1 more source

DeepMapper: Attention‐Based AutoEncoder for System Identification in Wound Healing and Stage Prediction

Advanced Intelligent Discovery, EarlyView.
The authors develop a deep learning model for real‐time tracking of wound progression. The deep learning framework maps the nonlinear evolution of a time series of images to a latent space, where they learn a linear representation of the dynamics. The linear model is interpretable and suitable for applications in feedback control.
Fan Lu, Ksenia Zlobina, Michael Briden, Oscar Avalos, Héctor Carrión, Sebastian Osorio, Hsin‐ya Yang, Alexandra Nava, Michelle D. Bagood, Marco Rolandi, Roslyn Rivkah Isseroff, Marcella Gomez   +11 more
wiley   +1 more source

Breast Cancer Prediction Using Stacked GRU-LSTM-BRNN

Applied Computer Systems, 2020
Breast Cancer diagnosis is one of the most studied problems in the medical domain. Cancer diagnosis has been studied extensively, which instantiates the need for early prediction of cancer disease.
Dutta Shawni, Mandal Jyotsna Kumar, Kim Tai Hoon, Bandyopadhyay Samir Kumar   +3 more
doaj   +1 more source

Semi-supervised bidirectional RNN for misinformation detection

, 2022
Misinformation refers to inaccurate information created to misguide the readers. It spreads on social platforms like Twitter with various presentations such as fake news and rumors that usually contain numbers, categorical information, texts, images, etc.
Lijun Qian, Xishuang Dong
core   +1 more source

When Biology Meets Medicine: A Perspective on Foundation Models

Advanced Intelligent Discovery, EarlyView.
Artificial intelligence, and foundation models in particular, are transforming life sciences and medicine. This perspective reviews biological and medical foundation models across scales, highlighting key challenges in data availability, model evaluation, and architectural design.
Kunying Niu, Yuxing Wang, Limin Zou, Jing Huang   +3 more
wiley   +1 more source

Boosting Charge Storage in Ferri/Ferrocyanide‐Based Alkaline Redox Flow Batteries

Angewandte Chemie, EarlyView.
By modifying the solvation environment, cation engineering through Li+ substitution of traditional K+ or Na+ boosts the alkaline solubility of the ferri/ferrocyanide catholyte redox couple, providing a viable pathway to overcome the long‐standing challenge of energy density limitations for aqueous redox flow batteries (RBFs).
Mahla Sarfaraz Khabbaz, Kuldeepsinh Raj, Lucas Zhang, Sepideh Biabanialitappeh, J. David Bazak, Nav Nidhi Rajput, Xiaoliang Wei   +6 more
wiley   +2 more sources

Detecting Ineffective Efforts during Expiration for Neonates with Attention RNNs

Current Directions in Biomedical Engineering
Patient-ventilator asynchronies occur during mechanical ventilation when there is a mismatch between the patient’s needs and the ventilator’s settings.
Oprea Camelia, Stegemann Lara, Olivier Lena Sophie, Buglowski Mateusz, Becker Sabine, Orlikowsky Thorsten, Kowalewski Stefan, Schoberer Mark, Stollenwerk André   +8 more
doaj   +1 more source

AI‐BioMech: Deep Learning Prediction of Mechanical Behavior in Aperiodic Biological Cellular Materials

Advanced Intelligent Discovery, EarlyView.
AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia, Marcelo A. Dias, Parvez Alam   +2 more
wiley   +1 more source