Adaptive Clutter-Aware Visualization for Mobile Data Stream Mining

Gaber, Mohamed Medhat; Krishnaswamy, Shonali; Gillick, Brett; Nicoloudis, Nicholas; Liono, Jonathan; AlTaiar, Hasnain; Zaslavsky, Arkady

doi:10.1109/ictai.2010.116

Cited by 8 publications

(1 citation statement)

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“…Dynamic adaptation to data rates and fine tuning of processing parameters can significantly enhance the longevity of continuous real-time processing of data streams in mobile environments [1].…”

Section: Background and Literature Surveymentioning

confidence: 99%

Crash Prediction System for Mobile Device on Android by Using Data Stream Minning Techniques

Singh

Dongre

2012

2012 Sixth Asia Modelling Symposium

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As an accident is very common scenario for today's world. This paper presents one more step towards safety of driving through an application called Crash Prediction System (CPS). That analyzes driver's behaviour in real time and predicts that whether driver is suitable or not for driving. This system requires various profile of driver's detail for accurate prediction and gives result like Fit, Unfit and Partially Fit. For this application a concept of Data Stream minning is used with Principle Component Analysis (PCA) and Hidden Markov Model (HMM) techniques. Other interactive feature for this application is that, it used for mobile device. A large range of application is running on mobile device which are facing problem like a memory, computational capability, battery and many more. For eliminating all these issues there is a platform known as Android which we used for this application. Experiment result of CPS on Google Android platform is presented, proving the effectiveness of the proposed system.

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Section: Background and Literature Surveymentioning

confidence: 99%