Bayesian Approach for Automotive Vehicle Data Analysis

Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 10)

Abstract

Streaming network data can be analyzed by advance machine data methods. Machine data methods are ideal for large scale and sensor concentrated applications. Prediction analytics can be used to support proactive complex event processing while probabilistic graphical model can be extensively used to ascertain data transmitted by sensors. The structure of probabilistic graphical models encompasses variety of different types of models and range of methods relating to them. In this paper, real time sensor (OBD ha-II) device data has been used from telematics competition organized by kaggle.com for driver signature. This device is highly equipped to extract sensors related information such as Accelerometer, Gyroscope, GPS, and Magnetometer. Data cleaning, pre-processing, and integration techniques are performed on data obtained from OBD-II device. We have performed various classification algorithms on sensor data using data mining and machine learning open source tool “WEKA 3.7.10” and have identified that Bayes Net classification technique generates best results.

Keywords

Probabilistic graphical model Machine learning OBD-II Sensor data analysis 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.School of Computer StudiesAhmedabad UniversityAhmedabadIndia
  2. 2.Maharaja Krishnakumarsinhji, Bhavnagar UniversityBhavnagarIndia

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