A Weighted DTW Approach for Similarity Matching over Uncertain Time Series

To measure uncertain time series similarity effectively and efficiently, in this paper, we propose a weighted DTW distance-based approach for uncertain time series with the expected distance. We introduce a weight function to assign weights to a reference point and a testing point. With this function and the WDTW, the accuracy of calculating uncertain time series similarity can be improved. Also, to reduce the storage space and time-consuming, we extend the lower bound function LB_Keogh for DTW into ULB_Keogh for our approach

Surveillance Rapid Detection of Signs of Traffic Services in Real Time

This study aims to compare the efficiency of Intuitionistic Fuzzy Neural Network with Genetic Algorithm (IFNN-GA) in detecting real-time traffic signs in different road conditions compared with the human eyes in order to prove that they are more efficient in understanding the surrounding environment, support safe driving, and overcome human defects. Highlighting the driver's obstacles such as weather conditions, uneven lighting, shadows everywhere, intensity and brightness fluctuations and contrast in shape and texture of traffic signs and time spent for entry and exit. In order to realize all aspects of the road, a model was proposed: Intuitionistic Fuzzy Neural Network with Genetic Algorithm (IFNN-GA)