Self-Trained Convolutional Neural Network (CNN) for Tuberculosis Diagnosis in Medical Imaging. [PDF]
Sarawagi K +3 more
europepmc +1 more source
Combining Multi-Dimensional Convolutional Neural Network (CNN) With Visualization Method for Detection of Aphis gossypii Glover Infection in Cotton Leaves Using Hyperspectral Imaging. [PDF]
Yan T +6 more
europepmc +1 more source
Revealing Protein–Protein Interactions Using a Graph Theory‐Augmented Deep Learning Approach
This study presents a fast, cost‐efficient approach for classifying protein–protein interactions by integrating graph‐theory parametrization with deep learning (DL). Multiscale features extracted from graph‐encoded polarized‐light microscopy (PLM) images enable accurate prediction of binding strengths.
Bahar Dadfar +5 more
wiley +1 more source
An enhanced lightweight T-Net architecture based on convolutional neural network (CNN) for tomato plant leaf disease classification. [PDF]
Batool A +4 more
europepmc +1 more source
Harnessing Machine Learning to Understand and Design Disordered Solids
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
A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet. [PDF]
Gunduz H, Gunal S.
europepmc +1 more source
This article reviews the current state of bioinspired soft robotics. The article discusses soft actuators, soft sensors, materials selection, and control methods used in bioinspired soft robotics. It also highlights the challenges and future prospects of this field.
Abhirup Sarker +2 more
wiley +1 more source
Performance of convolutional neural network (CNN) and performance influencing factors for wood species classification of Lepidobalanus growing in Korea. [PDF]
Kim JH, Park WG, Kim NH.
europepmc +1 more source
Fruit Classification using Convolutional Neural Network(CNN)
openaire +1 more source
A Hybrid Transfer Learning Framework for Brain Tumor Diagnosis
A novel hybrid transfer learning approach for brain tumor classification achieves 99.47% accuracy using magnetic resonance imaging (MRI) images. By combining image preprocessing, ensemble deep learning, and explainable artificial intelligence (XAI) techniques like gradient‐weighted class activation mapping and SHapley Additive exPlanations (SHAP), the ...
Sadia Islam Tonni +11 more
wiley +1 more source

