Results 281 to 290 of about 2,268,403 (339)

Assessing Capability Complexity Using Enterprise Architecture Framework

Systems Engineering, EarlyView.
ABSTRACT This study proposes a structured and quantitative methodology to evaluate the holistic complexity of system‐of‐systems (SoSs), employing the Zachman Architecture Framework (ZAF) as its foundational analytical tool. A five‐phase analytical procedure is developed and empirically validated, encompassing: (1) refinement of complexity measures, (2)
Javad Bakhshi, Mahmoud Efatmaneshnik
wiley   +1 more source

Semi-Supervised Seven-Segment LED Display Recognition with an Integrated Data-Acquisition Framework. [PDF]

Sensors (Basel)
Xiang X, Zhu C, Ou Z, Zhang Q, Zheng S, Chen Z.   +5 more
europepmc   +1 more source

Pickin' up good vibrations: a systematic review of footfall detection and analysis in the realm of wildlife surveying

Wildlife Biology, EarlyView.
Exploration of new wildlife surveying methodologies that leverage advances in sensor technology and machine learning has led to tentative research into the application of seismology techniques. This, most commonly, involves the deployment of a footfall trap – a seismic sensor and data logger customised for wildlife footfall.
Benjamin J. Blackledge, Luigi La Spada, Robert A. Briers, Niamh M. McHugh, Patrick J. C. White   +4 more
wiley   +1 more source

Adversarial Defense without <i>Adversarial Defense</i>: Enhancing Language Model Robustness via Instance-level Principal Component Removal. [PDF]

Trans Assoc Comput Linguist
Wang Y, Xiao C, Li Y, Middleton SE, Moubayed NA, Lin C.   +5 more
europepmc   +1 more source

Pixel Lens: A Granular Assessment of Saliency Explanations

Artificial Intelligence for Engineering, EarlyView.
We propose a pipeline that detects shortcut‐dominated classifiers by comparing predictions on clean and shortcut‐perturbed images and checking dominance via a Shapley‐based ground‐truth explainer. The workflow quantifies the explanation quality of different explainable artificial intelligence (XAI) methods.
Kanglong Fan, Chen Ma, Yifan Peng, Jinglong Yang, Qilin Sun, Yuming Fang   +5 more
wiley   +1 more source

Calibration-free sEMG intention recognition via self-supervised pretraining and adversarial domain alignment for upper-limb rehabilitation. [PDF]

Sci Rep
Yang Y, Teo HH, King YJ, Zhang Y, Wang G, Qu X.   +5 more
europepmc   +1 more source

SSEL: spike-based structural entropic learning for spiking graph neural networks. [PDF]

Front Neurosci
Yang S, Wu Y, Chen B.
europepmc   +1 more source

Enhancing tumor deepfake detection in MRI scans using adversarial feature fusion ensembles. [PDF]

Sci Rep
Ali A, Basha HA, Thanuja K, Puneet, Gupta SK, Kim S.   +5 more
europepmc   +1 more source

Deviation‐Guided Attention for Semi‐Supervised Anomaly Detection With Contrastive Regularisation

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Anomaly detection (AD) aims to identify abnormal patterns that deviate from normal behaviour, playing a critical role in applications such as industrial inspection, medical imaging and autonomous driving. However, AD often faces a scarcity of labelled data. To address this challenge, we propose a novel semi‐supervised anomaly detection method,
Guanglei Xie, Xiaochang Hu, Yi Sun, Wenzhuo Zhang, Yafeng Bu, Hao Fu, Xin Xu   +6 more
wiley   +1 more source

Domain-Adaptive MRI Learning Model for Precision Diagnosis of CNS Tumors. [PDF]

Biomedicines
Abdelbaki W, Alshaya H, Nasir IM, Tehsin S, Said S, Bouchelligua W.   +5 more
europepmc   +1 more source