Classification of Distorted Patterns by Feed-Forward Spiking Neural Networks

Sporea, Ioana; Grüning, André

doi:10.1007/978-3-642-33269-2_34

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…e output neuron's spike train contains a single spike, where the timing differs from each other, as given in Table 2. e process of building and training a Multi-ReSuMe-based classifier is given in Algorithm 2. e parameters are initialized as follows: A + � 1.2, A − � 0.5, τ + � τ − � 0.5, and a � 0.05 [45].…”

Section: Classification Using Snnsmentioning

confidence: 99%

Online Traffic Accident Spatial-Temporal Post-Impact Prediction Model on Highways Based on Spiking Neural Networks

Peng

2021

Journal of Advanced Transportation

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Traffic accident management as an approach to improve public security and reduce economic losses has received public attention for a long time, among which traffic accidents post-impact prediction (TAPIP) is one of the most important procedures. However, existing systems and methodologies for TAPIP are insufficient for addressing the problem. The drawbacks include ignoring the recovery process after clearance and failing to make comprehensive prediction in both time and space domain. To this end, we build a 3-stage TAPIP model on highways, using the technology of spiking neural networks (SNNs) and convolutional neural networks (CNNs). By dividing the accident lifetime into two phases, i.e., clean-up phase and recovery phase, the model extracts characteristics in each phase and achieves prediction of spatial-temporal post-impact variables (e.g., clean-up time, recovery time, and accumulative queue length). The framework takes advantage of SNNs to efficiently capture accident spatial-temporal features and CNNs to precisely represent the traffic environment. Integrated with an adaptation and updating mechanism, the whole system works autonomously in an online manner that continues to self-improve during usage. By testing with a new dataset CASTA pertaining to California statewide traffic accidents on highways collected in four years, we prove that the proposed model achieves higher prediction accuracy than other methods (e.g., KNN, shockwave theory, and ANNs). This work is the introduction of SNNs in the traffic accident prediction domain and also a complete description of post-impact in the whole accident lifetime.

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Section: Classification Using Snnsmentioning

confidence: 99%

Online Traffic Accident Spatial-Temporal Post-Impact Prediction Model on Highways Based on Spiking Neural Networks

Peng

2021

Journal of Advanced Transportation

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Generalisation and robustness investigation for facial and speech emotion recognition using bio-inspired spiking neural networks

Mansouri-Benssassi

2021

Soft Comput

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Emotion recognition through facial expression and non-verbal speech represents an important area in affective computing. They have been extensively studied from classical feature extraction techniques to more recent deep learning approaches. However, most of these approaches face two major challenges: (1) robustness—in the face of degradation such as noise, can a model still make correct predictions? and (2) cross-dataset generalisation—when a model is trained on one dataset, can it be used to make inference on another dataset?. To directly address these challenges, we first propose the application of a spiking neural network (SNN) in predicting emotional states based on facial expression and speech data, then investigate, and compare their accuracy when facing data degradation or unseen new input. We evaluate our approach on third-party, publicly available datasets and compare to the state-of-the-art techniques. Our approach demonstrates robustness to noise, where it achieves an accuracy of 56.2% for facial expression recognition (FER) compared to 22.64% and 14.10% for CNN and SVM, respectively, when input images are degraded with the noise intensity of 0.5, and the highest accuracy of 74.3% for speech emotion recognition (SER) compared to 21.95% of CNN and 14.75% for SVM when audio white noise is applied. For generalisation, our approach achieves consistently high accuracy of 89% for FER and 70% for SER in cross-dataset evaluation and suggests that it can learn more effective feature representations, which lead to good generalisation of facial features and vocal characteristics across subjects.

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Classification of Distorted Patterns by Feed-Forward Spiking Neural Networks

Cited by 2 publications

References 15 publications

Online Traffic Accident Spatial-Temporal Post-Impact Prediction Model on Highways Based on Spiking Neural Networks

Online Traffic Accident Spatial-Temporal Post-Impact Prediction Model on Highways Based on Spiking Neural Networks

Generalisation and robustness investigation for facial and speech emotion recognition using bio-inspired spiking neural networks

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