An Intelligent Spatial Proximity System Using Neurofuzzy Classifiers and Contextual Information

Abstract

ABSTRACT:In this paper, we propose a novel approach to reason with spatial proximity. The approach is based on contextual information and uses a neurofuzzy classifier to handle the uncertainty aspect of proximity. Neurofuzzy systems are a combination of neural networks and fuzzy systems and incorporate the advantages of both techniques. Although fuzzy systems are focused on knowledge representation, they do not allow the estimation of membership functions. Conversely, neuronal networks use powerful learning techniques but they are not able to explain how results are obtained. Neurofuzzy systems benefit from both techniques by using training data to generate membership functions and by using fuzzy rules to represent expert knowledge. Moreover, contextual information is collected from a knowledge base. The complete solution that we propose is integrated in a GIS, enhancing it with proximity reasoning. From an application perspective, the proposed approach was used in the telecommunication domain and particularly in fiber optic monitoring systems. In such systems, a user needs to qualify the distance between a fiber break and the surrounding objects of the environment to optimize the assignment of emergency crews. The neurofuzzy classifier has been used to compute the membership function parameters of the contextual information inputs using a training data set and fuzzy rules.* Corresponding author. This is useful to know for communication with the appropriate person in cases with more than one author.

Robust Fuzzy Neuro System for Big Data Analytics

“…Monte-Carlo sampling is one of the ways to deal with uncertainty in spatial. Different ways to tackle uncertainty are to construct optimized algorithms and use advanced mathematics for calculation of probabilistic distributions [21] To solve complex machine learning problem a technique of combining Fuzzy System Neural network for big data came into existence named as ANFIS. This collective intelligence system plays crucial role by combining feature of both system and removes some of the limitations of each other by using few artificial intelligence technique and algorithm which makes system performance very well.…”