Results 91 to 100 of about 6,370 (239)

The transformative potential of artificial intelligence in pediatric medicine: Current applications, methodological challenges, and future directions

Pediatric Investigation, EarlyView.
Artificial intelligence (AI) offers transformative potential for paediatric diagnosis and treatment, yet implementation faces unique challenges, including data scarcity, algorithmic bias, and children's developmental physiology. This review examines current applications and charts a path toward transparent, equitable, and trustworthy AI in child health.
Ruisong Wang, Xiaoman Ding, Wanyue Zhang, Tieliu Shi   +3 more
wiley   +1 more source

Llm-ga: A gradient-based multi-label adversarial attack by large language models

Complex & Intelligent Systems
Deep neural networks (DNNs) are highly sensitive to small, meticulously crafted perturbations, which have been utilized in adversarial attacks, threatening the reliability of DNNs in practical applications. Current adversarial attack methods rely heavily
Yujiang Liu, Yamin Hu, Zhijian Chen, Shiyin Wang, Wenjian Luo   +4 more
doaj   +1 more source

Distillation-Based Cross-Model Transferable Adversarial Attack for Remote Sensing Image Classification

Remote Sensing
Deep neural networks have achieved remarkable performance in remote sensing image (RSI) classification tasks. However, they remain vulnerable to adversarial attack.
Xiyu Peng, Jingyi Zhou, Xiaofeng Wu
doaj   +1 more source

Using art history to explore society's changing connections with agriculture

PLANTS, PEOPLE, PLANET, EarlyView.
Food insecurity is a looming challenge that especially affects those least fortunate. Consumer food choices have a substantial impact on the sustainability of current food systems. Here, we use art as a lens through which to consider our contemporary and historical relationship to one of the world's most crucial crops, the potato, in the context of the
Edward F. Hill‐King, Martin R. McAinsh, Michael R. Roberts   +2 more
wiley   +1 more source

Physically structured adversarial patch inspired by natural leaves multiply angles deceives infrared detectors

Journal of King Saud University: Computer and Information Sciences
Researching infrared adversarial attacks is crucial for ensuring the safe deployment of security-sensitive systems reliant on infrared object detectors.
Zhiyang Hu, Xing Yang, Jiwen Zhao, Haoqi Gao, Haoli Xu, Hua Mu, Yangyang Wang   +6 more
doaj   +1 more source

RLXSS: Optimizing XSS Detection Model to Defend Against Adversarial Attacks Based on Reinforcement Learning

Future Internet, 2019
With the development of artificial intelligence, machine learning algorithms and deep learning algorithms are widely applied to attack detection models. Adversarial attacks against artificial intelligence models become inevitable problems when there is a
Yong Fang, Cheng Huang, Yijia Xu, Yang Li   +3 more
doaj   +1 more source

Recovering Localized Adversarial Attacks [PDF]

, 2019
Deep convolutional neural networks have achieved great successes over recent years, particularly in the domain of computer vision. They are fast, convenient, and -- thanks to mature frameworks -- relatively easy to implement and deploy. However, their reasoning is hidden inside a black box, in spite of a number of proposed approaches that try to ...
Göpfert, Jan Philip, Wersing, Heiko, Hammer, Barbara, Tetko, Igor V., Kůrková, Věra, Karpov, Pavel, Theis, Fabian   +6 more
openaire   +3 more sources

Stop Using Limiting Stimuli as a Measure of Sensitivities of Energetic Materials

Propellants, Explosives, Pyrotechnics, EarlyView.
ABSTRACT Accurately estimating the sensitivity of explosive materials is a potentially life‐saving task that requires standardised protocols across nations. One of the most widely applied procedures worldwide is the so‐called ‘1‐In‐6’ test from the United Nations (UN) Manual of Tests in Criteria, which estimates a ‘limiting stimulus’ for a material. In
Dennis Christensen, Geir Petter Novik
wiley   +1 more source

DIPA: Adversarial Attack on DNNs by Dropping Information and Pixel-Level Attack on Attention

Information
Deep neural networks (DNNs) have shown remarkable performance across a wide range of fields, including image recognition, natural language processing, and speech processing. However, recent studies indicate that DNNs are highly vulnerable to well-crafted
Jing Liu, Huailin Liu, Pengju Wang, Yang Wu, Keqin Li   +4 more
doaj   +1 more source

Integrating multimodal data and machine learning for entrepreneurship research

Strategic Entrepreneurship Journal, EarlyView.
Abstract Research Summary Extant research in neuroscience suggests that human perception is multimodal in nature—we model the world integrating diverse data sources such as sound, images, taste, and smell. Working in a dynamic environment, entrepreneurs are expected to draw on multimodal inputs in their decision making.
Yash Raj Shrestha, Vivianna Fang He
wiley   +1 more source