Results 131 to 140 of about 804,833 (294)
VIEW, EarlyView.Lymphoma is a group of blood cancers that can appear in lymph nodes, blood, bone marrow, spleen, liver, or the central nervous system, which makes drug delivery and disease monitoring difficult. This review summarizes how nanomedicine technologies may improve targeted treatment and imaging, while carefully separating approved or guideline‐supported ...
Mohd Ahmar Rauf +5 more
wiley +1 more source
CMDN: Pre-Trained Visual Representations Boost Adversarial Robustness for UAV Tracking
DronesVisual object tracking is widely adopted to unmanned aerial vehicle (UAV)-related applications, which demand reliable tracking precision and real-time performance.
Ruilong Yu +5 more
doaj +1 more source
World Journal of Otorhinolaryngology - Head and Neck Surgery, EarlyView.ABSTRACT Objective To provide an overview of potential biases resulting from the utilization of artificial intelligence (AI) in otolaryngology and techniques to mitigate them. Data Sources Literature review and expert opinion. Conclusions AI promises to fundamentally transform medicine.
Matthew T. Ryan, David A. Gudis
wiley +1 more source
Report of Secretary of Defense to the Congress on the ... Defense budget and ... Defense program.
Includes transition budgets, authorization request and defense programs.Title varies slightly.Report year ends June 30.Mode of access ...
United States. Dept. of Defense.
core
Credit‐Driven Adaptive Grouping for Refined Cooperative Multi‐Agent Reinforcement Learning
CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT Policy heterogeneity is crucial for achieving sophisticated coordination in complex collaborative tasks, which has emerged as one of the key challenges in multi‐agent reinforcement learning (MARL) in recent years. Notably, the grouping paradigm has made remarkable progress in addressing policy heterogeneity.
Yirui Liu +6 more
wiley +1 more source
Generalization of Defense Effects Learned from a Single Adversarial Attack
ComputationAdversarial attacks misled deep neural networks by injecting perturbations into input images. Training networks with adversarial examples defended against adversarial attacks. However, training with specific adversarial examples only defended against the
Dongxian Niu, Lin Shi
doaj +1 more source
CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT Accurate load forecasting and reliable anomaly detection are critical for the stable operation of modern smart grids (SGs), which increasingly rely on cyber‐connected infrastructures. However, the integration of smart metres and two‐way communication exposes SGs to data integrity attacks that can manipulate consumption measurements, degrade ...
Murad Ali Khan +4 more
wiley +1 more source
Detection and Defense: Student-Teacher Network for Adversarial Robustness
IEEE AccessDefense against adversarial attacks is critical for the reliability and safety of deep neural networks (DNNs). Current state-of-the-art defense methods achieve significant robustness against adversarial attacks.
Kyoungchan Park, Pilsung Kang
doaj +1 more source
Robust Multi‐Source Batch Normalisation for Test‐Time Batch Adaptation
CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT Test‐Time Batch Adaptation (TTBA) aims to adapt a pre‐trained source model to small, unlabelled target batches at test time. The TTBA methods focus on adapting the pre‐trained model or the target data in a one‐to‐one alignment paradigm. However, these one‐to‐one alignment paradigms assume that the source domain may share the same knowledge ...
Xinlin Xiao +3 more
wiley +1 more source
IEEE Open Journal of the Industrial Electronics SocietyIntegrating machine learning into Automated Control Systems (ACS) enhances decision-making in industrial process management. One of the limitations to the widespread adoption of these technologies in industry is the vulnerability of neural networks to ...
Vitaliy Pozdnyakov +4 more
doaj +1 more source