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Statistical Learning Based Framework for Random Networks Knowledge Extraction Applied in Smart Cities

  • Smail TiganiEmail author
  • Mohammed Ouzzif
  • Rachid Saadane
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 366)

Abstract

Smart Cities are a future reality that emerged recently. They became a wide research field around the world. These cities will combine the power of ubiquitous communication networks and wireless sensors with the efficient management systems to solve daily challenges and create exciting services. In this work, we involve the power of artificial intelligence to solve one of the serious challenges in big cities. This concerns the traffic management and prediction. This work proposes a statistical model serving the analysis of a random graph that represents, in reality, roads on map. Using those models and collected data from sensors or human agents, we can extract useful hidden knowledge for the best decision making. To prove the reliability of the approach, a Monte Carlo simulation algorithm is designed and results confirms the added-value of the approach.

Keywords

Learning theory Bayesian modeling Random graph Smart Cities Traffic prediction 

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© Springer Science+Business Media Singapore 2016

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Authors and Affiliations

  1. 1.Computer Science DepartmentENSEMCasablancaMorocco
  2. 2.Computer Science DepartmentESTCasablancaMorocco
  3. 3.Electrical DepartmentEHTPCasablancaMorocco

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