Core Concepts
CapsNets are effective noise stabilizers for time series data, outperforming CNNs in robustness against manual and adversarial attacks.
Abstract
Standalone Note here
Abstract
CapsNets utilize capsules to learn position-equivariant features, enhancing robustness.
Investigated CapsNets' effectiveness on noisy time series sensor data, outperforming CNNs.
Introduction
CapsNet maintains object invariance in position-equivariant data.
Hypothesis: CapsNets resist noisy time-series sensor data and attacks from defective sensors.
Background
DR-CapsNets enhance fundamental temporal features while inhibiting noisy ones.
Experiments
Conducted experiments on ECG dataset comparing DR-CapsNets with CNN under various noise baselines.
Results
DR-CapsNets outperformed CNN in all noise baselines, achieving high accuracy and f1-score.
Conclusions
CapsNets effectively analyze noisy time series sensor data, functioning as noise stabilizers and improving robustness.
References
Stats
Our method achieved an accuracy of 98.7%, 97.5%, 97.2%, and 94.8% for different types of noise attacks.
DR-Caps achieved an accuracy of 98.22% compared to CNN's 94.17%.