Results 61 to 70 of about 112,589 (145)

Applied Artificial Intelligence in Materials Science and Material Design

Advanced Intelligent Systems, Volume 7, Issue 8, August 2025.
AI‐driven methods are transforming materials science by accelerating material discovery, design, and analysis, leveraging large datasets to enhance predictive modeling and streamline experimental techniques. This review highlights advancements in AI applications across spectroscopy, microscopy, and molecular design, enabling efficient material ...
Emigdio Chávez‐Angel, Martin Børstad Eriksen, Alejandro Castro‐Alvarez, Jose H. Garcia, Marc Botifoll, Oscar Avalos‐Ovando, Jordi Arbiol, Aitor Mugarza   +7 more
wiley   +1 more source

Hybrid CNN-GCN Network for Hyperspectral Image Classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
In recent years, convolutional neural networks (CNNs) have been impressive due to their excellent feature representation abilities, but it is difficult to learn long-distance spatial structures information. Unlike CNN, graph convolutional networks (GCNs)
Cuiping Shi, Diling Liao, Liguo Wang
doaj   +1 more source

Detecting Changes in Space‐Varying Parameters of Local Poisson Point Processes

Environmetrics, Volume 36, Issue 5, July 2025.
ABSTRACT Recent advances in local models for point processes have highlighted the need for flexible methodologies to account for the spatial heterogeneity of external covariates influencing process intensity. In this work, we introduce tessellated spatial regression, a novel framework that extends segmented regression models to spatial point processes,
Nicoletta D'Angelo
wiley   +1 more source

Enhanced Leaf Disease Segmentation Using U‐Net Architecture for Precision Agriculture: A Deep Learning Approach

Food Science &Nutrition, Volume 13, Issue 7, July 2025.
This study proposes a deep learning‐based approach for leaf disease identification using the U‐Net architecture for precise segmentation. By training on a dataset of 7056 annotated leaf images, the model effectively distinguishes between healthy and diseased regions, achieving 99.70% training accuracy and 98.99% validation accuracy in 40 epochs.
Gurpreet Singh, Asma A. Al‐Huqail, Ahmad Almogren, Sukhdeep Kaur, Kapil Joshi, Ajay Singh, Salil Bharany, Seada Hussen, Ateeq Ur Rehman   +8 more
wiley   +1 more source

Superpixel Based Segmentation of Historical Document Images Using a Multiscale Texture Analysis

, 2019
International audienceIn this paper, a superpixel based segmentation of Historical Document Images (HDIs) using multiscale texture analysis is proposed. A Simple Linear Iterative Clustering (SLIC) superpixel technique and Kmeans classifier are applied in
Chaieb, Ramzi, Ramzi Chaieb, Karim Kalti, Kalti, Karim, Emna Soyed, Soyed, Emna   +5 more
core   +1 more source

Multiscale Feature Search-Based Graph Convolutional Network for Hyperspectral Image Classification

Remote Sensing
With the development of hyperspectral sensors, the availability of hyperspectral images (HSIs) has increased significantly, prompting advancements in deep learning-based hyperspectral image classification (HSIC) methods.
Ke Wu, Yanting Zhan, Ying An, Suyi Li
doaj   +1 more source

Deep‐Learning‐Enhanced Electron Microscopy for Earth Material Characterization

Journal of Geophysical Research: Machine Learning and Computation, Volume 2, Issue 2, June 2025.
Abstract Rocks, as Earth materials, contain intricate microstructures that reveal their geological history. These microstructures include grain boundaries, preferred orientation, twinning and porosity, holding critical significance in the realm of the energy transition.
Hans van Melick, Richard Taylor, Oliver Plümper   +2 more
wiley   +1 more source

Hyperspectral Image Classification Based on Multiscale Feature Search Graph Convolutional Network With Meta Pseudolabels

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
In recent years, graph convolutional networks (GCNs) have been introduced for hyperspectral image (HSI) classification due to their ability to effectively process the inherent graph structure of HSI data.
Suyi Li, Ke Wu, Huize Liu, Dandan Zhou, Ying Xu   +4 more
doaj   +1 more source

Cutting‐Edge Deep Learning Methods for Image‐Based Object Detection in Autonomous Driving: In‐Depth Survey

Expert Systems, Volume 42, Issue 4, April 2025.
ABSTRACT Object detection is a critical aspect of computer vision (CV) applications, especially within autonomous driving systems (AVs), where it is fundamental to ensuring safety and reducing traffic accidents. Recent advancements in computational resources have enabled the widespread adoption of Deep Learning (DL) techniques, significantly enhancing ...
Narges Saeedizadeh, Seyed Mohammad Jafar Jalali, Burhan Khan, Shady Mohamed   +3 more
wiley   +1 more source

Content-driven superpixels and their applications

, 2013
This thesis develops a new superpixel algorithm that displays excellent visual reconstruction of the original image. It achieves high stability across multiple random initialisations, achieved by producing superpixels directly corresponding to local ...
Lowe, Richard
core   +1 more source