Results 91 to 100 of about 4,047,749 (292)

UC Food Waste: A Multimodal Robotic Perception Dataset for Robust Contaminant Detection in Complex Biowaste Scenes

open access: yesAdvanced Robotics Research, EarlyView.
A balanced multimodal food‐waste dataset and a novel Split Spatial‐Spectral Attention fusion framework are presented for robotic contaminant detection. Separate processing of RGB, thermal, and multispectral inputs preserves subtle material signatures before feature integration, reducing modality interference and improving classification and ...
Matthew Vestal   +5 more
wiley   +1 more source

Solid Harmonic Wavelet Bispectrum for Image Analysis

open access: yesAdvanced Science, EarlyView.
The Solid Harmonic Wavelet Bispectrum (SHWB), a rotation‐ and translation‐invariant descriptor that captures higher‐order (phase) correlations in signals, is introduced. Combining wavelet scattering, bispectral analysis, and group theory, SHWB achieves interpretable, data‐efficient representations and demonstrates competitive performance across texture,
Alex Brown   +3 more
wiley   +1 more source

Gender Classification Using a Convolutional Neural Network (CNN)

open access: yes
Abstract The purpose of this paper is to demonstrate an innovative convolutional neural network (also known as CNN) methodology for real-time categorization of gender via face photos. The suggested CNN architecture boasts much reduced computational complexity than the current methodologies used in pattern recognition applications.
Vyshnavi, Cherukuri   +4 more
openaire   +2 more sources

TI-CNN: Convolutional Neural Networks for Fake News Detection

open access: yesCoRR, 2018
With the development of social networks, fake news for various commercial and political purposes has been appearing in large numbers and gotten widespread in the online world. With deceptive words, people can get infected by the fake news very easily and will share them without any fact-checking.
Yang Yang 0122   +5 more
openaire   +2 more sources

Directly Connected Convolutional Neural Networks

open access: yes, 2018
Convolutional neural networks (CNNs) have better performance in feature extraction and classification. Most of the applications are based on a traditional structure of CNNs.
Zhanji Gui   +3 more
core   +1 more source

Recent Advances in Laser‐Induced Graphene‐Based Gas Sensors: From Sensing Mechanisms to Biomedical Applications

open access: yesAdvanced Science, EarlyView.
Laser‐induced graphene (LIG) provides a scalable, laser‐direct‐written route to porous graphene architecture with tunable chemistry and defect density. Through heterojunction engineering, catalytic functionalization, and intrinsic self‐heating, LIG achieves highly sensitive and selective detection of NOX, NH3, H2, and humidity, supporting next ...
Md Abu Sayeed Biswas   +6 more
wiley   +1 more source

Atomic Defects in Layered Transition Metal Dichalcogenides for Sustainable Energy Storage and the Intelligent Trends in Data Analytics

open access: yesAdvanced Science, EarlyView.
This review comprehensively summarizes the atomic defects in TMDs for their applications in sustainable energy storage devices, along with the latest progress in ML methodologies for high‐throughput TEM data analysis, offering insights on how ML‐empowered microscopy facilitates bridging structure–property correlation and inspires knowledge for precise ...
Zheng Luo   +6 more
wiley   +1 more source

Longitudinal Morphomolecular Monitoring of Head and Neck Carcinogenesis

open access: yesAdvanced Science, EarlyView.
A miniaturized endoscopic probe that integrates Raman spectroscopy and optical coherence tomography, enabling simultaneous molecular and structural imaging of living tissue, was developed. Applied longitudinally in a mouse model of head and neck cancer, the system tracks disease progression from precancerous lesions to invasive cancer.
Jianrong Qiu   +7 more
wiley   +1 more source

Physics‐Embedded Neural Network: A Novel Approach to Design Polymeric Materials

open access: yesAdvanced Science, EarlyView.
Traditional black‐box models for polymer mechanics rely solely on data and lack physical interpretability. This work presents a physics‐embedded neural network (PENN) that integrates constitutive equations into machine learning. The approach ensures reliable stress predictions, provides interpretable parameters, and enables performance‐driven, inverse ...
Siqi Zhan   +8 more
wiley   +1 more source

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