The fingerprint identification system is nowadays the biometric sector that is most exploited. Segmentation of the fingerprint image is considered as one of its first stage of processing.This stage thus typically affects the extraction and matching process of the feature, resulting in a high accuracy fingerprint recognition system.Three important steps are proposed in this paper. First, to improve the quality of the fingerprint images, Soble and TopHat filtering method were used.K-means clustering for combining 5-dimensional vector characteristics (variance, mean difference, gradient coherence, ridge direction, and energy spectrum) then accurately separates the foreground and background region for each local block in a fingerprint image.Also, local variance thresholding is used in our approach to reducing computing time for segmentation. Finally, we are combined with our DBSCAN clustering system that was performed to overcome the disadvantages of classifying K-means in the segmentation of fingerprint images.In four different databases, the proposed algorithm is tested. Experimental results show that our approach is significantly effective in the separation between the ridge and non-ridge region against some recently published techniques.

Traffic management has been and remains a significant issue, particularly in urban regions with elevated vehicle density. Adoption of an intelligent transport system (ITS) has extensively experimented to curb the traffic threat with blended experiences as a result. By communicating with other vehicles traveling on the same road in the form of clusters, an ad hoc vehicle network (VANET) forms and ITS that can allow vehicles with less human input to cooperate. By considering the elevated mobility nature of cars in VANET, this article provides a solution to the primary threat of VANET clustering by embracing the flexibility of fuzzy logics for cluster formation on a multilane urban high way. It also demonstrates that cluster stability is enhanced by performing the cluster head (CH) selection process based on a mixture of fuzzy logic, lane weighting, and utility function with the fuzzy membership function adjusted to boost cluster stability.