Results 181 to 190 of about 2,967,782 (299)

Explainable ensemble learning using SHAP for ERP anomaly detection. [PDF]

Sci Rep
Qazi A, Jadoon AAK.
europepmc   +1 more source

New High‐Tc Charge‐Transfer Multiferroicity in the Quasi‐2D Antiferromagnet CrSbS3

Advanced Science, EarlyView.
ABSTRACT Low‐dimensional magnets, particularly 2D systems, offer a rich platform for realizing unconventional multiferroic mechanisms, especially when multiple polarization channels coexist. In the quasi‐2D antiferromagnet CrSbS3, which crystallizes in the centrosymmetric orthorhombic space group Pnma and orders magnetically at TN ≈ 90 K, two ...
Hung‐Cheng Wu, Yu‐Hsin Liao, Chih‐Chung Tsai, Pei‐Ying Yang, Yu‐Hao Chang, Yu‐Hui Liang, D. Chandrasekhar Kakarla, Ajay Tiwari, C. Dhanasekhar, Chin‐Wei Wang, Daisuke Okuyama, Chia‐Nung Kuo, Chin‐Shan Lue, Tzu‐Hsuan Weng, Kung‐Hsuan Lin, Jau‐Wern Chiou, Chi‐Hung Lee, Hung‐Duen Yang   +17 more
wiley   +1 more source

Dual-domain self-supervised feature alignment via spectral-spatial representation learning for deepfake anomaly detection. [PDF]

PLoS One
He Y.
europepmc   +1 more source

Ethical Precision in Nanoscale Brain Interfacing

Advanced Science, EarlyView.
As brain interfaces approach the nanoscale, precision no longer only measures—it knows, predicts, and potentially reshapes the mind. This work argues that traditional ethics fails under such conditions and proposes a shift toward continuous, operation‐based governance using the recovery–discovery framework to track, constrain, and responsibly steer ...
Guilherme Wood
wiley   +1 more source

Interpretable Graph-Temporal-Spectral Fusion for Precursor-Related Anomaly Detection in Underground Mine Sensor Networks. [PDF]

Sensors (Basel)
Mao S, Liang Y, Li Q.
europepmc   +1 more source

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

GMoE-AD: Generalized Hyperspectral Anomaly Detection via Mixture-of-Experts and Domain-Invariant Learning. [PDF]

Sensors (Basel)
Hossain M, Robinson A, Preza C, Wang L.
europepmc   +1 more source

Unifying Composition and Process Design: A Heterogeneous Graph Neural Network for Discovering High‐Performance Cu Alloys

Advanced Science, EarlyView.
By overcoming the fixed‐path limitations of conventional machine learning, a heterogeneous graph neural network fundamentally reconstructs material data representation. Integrating variable processing sequences with intrinsic elemental features, this framework enables exploratory optimization across high‐dimensional spaces.
Jie Yin, Qian Lei, Wei Yan, Yue Li, Tong Xie, Shuang Zhou, Chong Wang, Bram Hoex, Qiang Long, Caoyang Jiang, Min Song, Zhou Li, Zhangwei Wang   +12 more
wiley   +1 more source

Federated Anomaly Detection for IoT-Enabled EV Charging Cybersecurity Under Aggressive and Stealth Cyberattacks. [PDF]

Sensors (Basel)
Al Qwaid M, Ramasamy G, Hossen MS.
europepmc   +1 more source

Anomaly Detection: Methods, Applications and Experimental Evaluation

openL'Anomaly Detection è un processo fondamentale nell'analisi dei dati, utile per distinguere comportamenti normali da errori, frodi o malfunzionamenti.
ANSALDO, GABRIELE
core