Results 111 to 120 of about 8,712 (199)

AI‐Enabled Imaging for Pathogen Detection Under Stress Conditions: A Systematic Review

Comprehensive Reviews in Food Science and Food Safety, Volume 25, Issue 3, May 2026.
ABSTRACT Advances in pathogen detection that incorporate artificial intelligence (AI) may capture microbial signals under challenging environmental conditions that traditional methods miss. This systematic review evaluates the application, performance, and methodological characteristics of AI‐enabled imaging for pathogen detection, including its impact
MeiLi Papa, Gillian Kuehnle, Yoo Jung (Erika) Oh, Jiyoon Yi   +3 more
wiley   +1 more source

Automated Melanocytic Lesion Classification: Capsule Networks Trained With Synthetic Images Can Outperform Networks Trained With Real Images

Australasian Journal of Dermatology, Volume 67, Issue 3, Page e169-e175, May 2026.
ABSTRACT Background/Objectives Convolutional neural networks (CNNs) are known, due to inherent flaws in their design, to be subject to classification error. Many of these shortcomings in classification performance were addressed in 2017 with the introduction of capsule networks (CNs).
Hayley Chai, Stephen Gilmore
wiley   +1 more source

On account of doomsday

American Ethnologist, Volume 53, Issue 2, Page 159-170, May 2026.
Abstract In recent years, Berlin has emerged as an epicenter of climate activism in Germany. There, a range of groups have mobilized in opposition to the role of the German state and the EU in accelerating the climate crisis. Many activists now see conventional political responses as exhausted and have turned to increasingly radical forms of civil ...
Max Jack
wiley   +1 more source

URAdv: A Novel Framework for Generating Ultra-Robust Adversarial Patches Against UAV Object Detection

Mathematics
In recent years, deep learning has been extensively deployed on unmanned aerial vehicles (UAVs), particularly for object detection. As the cornerstone of UAV-based object detection, deep neural networks are susceptible to adversarial attacks, with ...
Hailong Xi, Le Ru, Jiwei Tian, Bo Lu, Shiguang Hu, Wenfei Wang, Xiaohui Luan   +6 more
doaj   +1 more source

Strategies and Tools to Aid the Transition Between Paediatric and Adult Health Services for Young Adults With Neurodevelopmental Conditions: A Scoping Review

Journal of Paediatrics and Child Health, Volume 62, Issue 5, Page 711-729, May 2026.
ABSTRACT Introduction The global rise in neurodevelopmental conditions (NDD), alongside increased life expectancy for affected individuals, underscores the critical importance of effective transition from paediatric to adult healthcare. The transition between paediatrics and adults is an important stage to consider for emotional, psychosocial and ...
Yattheesh Thanalingam, Lisa Hamm, Jin Russell, Colette Muir, Fiona Langridge   +4 more
wiley   +1 more source

An Adaptive Physical-World Adversarial Patch for Remote Sensing Image Object Detection Models Considering the Structural Characteristics

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Remote sensing image object detection represents a typical application in the field of remote sensing image processing. Rapid advancements in artificial intelligence have established deep learning as a prevalent method for detecting critical targets ...
Xichen Xing, Xiong Xu, Qian Shi, Yanmin Jin, Chao Wang   +4 more
doaj   +1 more source

Defending Against Physical Adversarial Patch attacks On Infrared Human Detection

2024 IEEE International Conference on Image Processing (ICIP)
Infrared detection is an emerging technique for safety-critical tasks owing to its remarkable anti-interference capability. However, recent studies have revealed that it is vulnerable to physically-realizable adversarial patches, posing risks in its real-world applications.
Lukas Strack, Futa Waseda, Huy H. Nguyen, Yinqiang Zheng, Isao Echizen   +4 more
openaire   +2 more sources

RNAF: ResilienceNet Adversarial Framework Using Deep Reinforcement Learning for Adversarial Attacks on Digital Images

IEEE Access
Adversarial attack is a key concern for state-of-the-art artificial intelligence (AI), especially those used in image classification and computer vision.
Kainat Rizwan, Muhammad Asif Habib, Salman Raza, Mudassar Ahmad   +3 more
doaj   +1 more source

Benchmarking Adversarial Patch Selection and Location

Mathematics
Adversarial patch attacks threaten the reliability of modern vision models. We present PatchMap, the first spatially exhaustive benchmark of patch placement, built by evaluating over 1.5×108 forward passes on ImageNet validation images.
Shai Kimhi, Moshe Kimhi, Avi Mendelson
doaj   +1 more source

PapMOT: Exploring Adversarial Patch Attack Against Multiple Object Tracking

Tracking multiple objects in a continuous video stream is crucial for many computer vision tasks. It involves detecting and associating objects with their respective identities across successive frames. Despite significant progress made in multiple object tracking (MOT), recent studies have revealed the vulnerability of existing MOT methods to ...
Jiahuan Long, Tingsong Jiang, Wen Yao, Shuai Jia, Weijia Zhang, Weien Zhou, Chao Ma, Xiaoqian Chen   +7 more
openaire   +2 more sources