Results 221 to 230 of about 2,268,403 (339)
Explainable human‐in‐the‐loop healthcare image information quality assessment and selection
Abstract Smart healthcare applications cannot be separated from healthcare data analysis and the interactive interpretability between data and model. A human‐in‐the‐loop active learning approach is introduced to reduce the cost of healthcare data labelling by evaluating the information quality of unlabelled medical data and then screening the high ...
Yang Li, Sezai Ercisli
wiley +1 more source
Adversarial robustness of artificial intelligence
Leon Bungert +2 more
doaj +1 more source
Imbalanced Gradients: A Subtle Cause of Overestimated Adversarial Robustness [PDF]
Xingjun Ma +5 more
openalex +1 more source
AI‐based localization of the epileptogenic zone using intracranial EEG
Abstract Artificial intelligence (AI) is rapidly transforming our lives. Machine learning (ML) enables computers to learn from data and make decisions without explicit instructions. Deep learning (DL), a subset of ML, uses multiple layers of neural networks to recognize complex patterns in large datasets through end‐to‐end learning.
Atsuro Daida +5 more
wiley +1 more source
Abstract Autonomous vehicles are required to operate in an uncertain environment. Recent advances in computational intelligence techniques make it possible to understand driving scenes in various environments by using a semantic segmentation neural network, which assigns a class label to each pixel.
Yining Hua +4 more
wiley +1 more source
An enhanced ensemble defense framework for boosting adversarial robustness of intrusion detection systems. [PDF]
Awad Z, Zakaria M, Hassan R.
europepmc +1 more source
Adversarial Framework with Certified Robustness for Time-Series Domain via Statistical Features
Taha Belkhouja, Janardhan Rao Doppa
openalex +2 more sources
Towards Achieving Adversarial Robustness by Enforcing Feature\n Consistency Across Bit Planes [PDF]
Sravanti Addepalli +4 more
openalex +1 more source
Neural Network Models for Solar Irradiance Forecasting in Polluted Areas: A Comparative Study
Pollution‐aware hybrid ensemble model is proposed to forecast solar irradiance across eight diverse cities. The model integrates MLP, RNN, and NARX to handle varying atmospheric pollution levels. The model outperforms state‐of‐the‐art methods with enhanced accuracy and interpretability on standard solar irradiance data set.
Mujtaba Ali +6 more
wiley +1 more source

