Results 91 to 100 of about 1,185,392 (332)

Adversarial Machine Learning Attacks on Condition-Based Maintenance Capabilities [PDF]

, 2021
Hamidreza Habibollahi Najaf Abadi
openalex   +1 more source

In Situ Liquid‐Cell Transmission Electron Microscopy Insights Into Lithium‐Ion Battery Materials Degradation: Challenges and Emerging Solutions

Advanced Science, EarlyView.
Lithium‐ion battery degradation arises from complex, localized processes during operation, limiting long‐term performance. In situ electrochemical liquid cell TEM provides unique access to these mechanisms. This review summarizes degradation phenomena revealed by liquid cell TEM, traces the evolution of the three main cell designs, compares their ...
Walid Dachraoui, Rolf Erni
wiley   +1 more source

Bilevel Models for Adversarial Learning and a Case Study

Mathematics
Adversarial learning has been attracting more and more attention thanks to the fast development of machine learning and artificial intelligence. However, due to the complicated structure of most machine learning models, the mechanism of adversarial ...
Yutong Zheng, Qingna Li
doaj   +1 more source

Attack-agnostic Adversarial Detection on Medical Data Using Explainable\n Machine Learning [PDF]

, 2021
Matthew Watson, Noura Al Moubayed
openalex   +1 more source

Probabilistic Modeling for Prediction Errors to Enhance Balancing Market Participation of Photovoltaic Systems: Error Threshold Estimation, Multisite Aggregation, and Overloading Effects

Advanced Energy and Sustainability Research, EarlyView.
This study proposes a method to increase the value of solar power in balancing markets by managing prediction errors. The approach models prediction uncertainties and quantifies reserve requirements based on a probabilistic model. This enables the more reliable participation of photovoltaic plants in balancing markets across multiple sites, especially ...
Jindan Cui, Xue Fang, Takashi Oozeki, Yuzuru Ueda   +3 more
wiley   +1 more source

Generative Artificial Intelligence Shaping the Future of Agri‐Food Innovation

AgriFood: Journal of Agricultural Products for Food, EarlyView.
Emerging use cases of generative artificial intelligence in agri‐food innovation. ABSTRACT The recent surge in generative artificial intelligence (AI), typified by models such as GPT, diffusion models, and large vision‐language architectures, has begun to influence the agri‐food sector.
Jun‐Li Xu, Dimitrios Argyropoulos, Tamíris da Costa   +2 more
wiley   +1 more source

Classical autoencoder distillation of quantum adversarial manipulations

Physical Review Research
Quantum neural networks have been proven robust against classical adversarial attacks, but their vulnerability against quantum adversarial attacks is still a challenging problem.
Amena Khatun, Muhammad Usman
doaj   +1 more source

Adversarial Challenges in Network Intrusion Detection Systems: Research Insights and Future Prospects

IEEE Access
Machine learning has brought significant advances in cybersecurity, particularly in the development of Intrusion Detection Systems (IDS). These improvements are mainly attributed to the ability of machine learning algorithms to identify complex ...
Sabrine Ennaji, Fabio de Gaspari, Dorjan Hitaj, Alicia Kbidi, Luigi Vincenzo Mancini   +4 more
doaj   +1 more source

AI‐Enhanced Surface‐Enhanced Raman Scattering for Accurate and Sensitive Biomedical Sensing

Advanced Intelligent Discovery, EarlyView.
AI‐SERS advances spectral interpretation with greater precision and speed, enhancing molecular detection, biomedical analysis, and imaging. This review explores its essential contributions to biofluid analysis, disease identification, therapeutic agent evaluation, and high‐resolution biomedical imaging, aiding diagnostic decision‐making.
Seungki Lee, Rowoon Park, Ho Sang Jung
wiley   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source