Location Prediction in a Smart Environment

Abstract

The context prediction and especially the location prediction is an important feature for improving the performance of smart systems. Predicting the next location or context of the user make the system proactive, so the system will be capable to offer the suitable services to the user without his involving. In this paper, a new approach will be presented based on the combination of pattern technique and Bayesian network to predict the next location of the user. This approach was tested on real data set, our model was able to achieve 89% of the next location prediction accuracy.

Authors and Affiliations

Wael Ali Alosaimi, Ahmed Binmahfoudh, Roobaea Alroobaea, Atef Zaguia

Keywords

Related Articles

Interaction Protocols in Multi-Agent Systems based on Agent Petri Nets Model

This paper deals with the modeling of interaction between agents in Multi Agents System (MAS) based on Agent Petri Nets (APN). Our models are created based on communicating agents. Indeed, an agent initiating a conversat...

Towards Efficient Graph Traversal using a Multi-GPU Cluster

Graph processing has always been a challenge, as there are inherent complexities in it. These include scalability to larger data sets and clusters, dependencies between vertices in the graph, irregular memory accesses du...

Detection and Classification of Mu Rhythm using Phase Synchronization for a Brain Computer Interface

Phase synchronization in a brain computer interface based on Mu rhythm is evaluated by means of phase lag index and weighted phase lag index. In order to detect and classify the important features reflected in brain sign...

Design and Implementation for Multi-Level Cell Flash Memory Storage Systems

The flash memory management functions of write coalescing, space management, logical-to-physical mapping, wear leveling, and garbage collection require significant on-going computation and data movement. MLC flash memory...

A Novel Steganography Method for Hiding BW Images into Gray Bitmap Images via k-Modulus Method

This paper is to create a pragmatic steganographic implementation to hide black and white image which is known as stego image inside another gray bitmap image that known as cover image. First of all, the proposed techniq...

Download PDF file
  • EP ID EP498560
  • DOI 10.14569/IJACSA.2019.0100334
  • Views 83
  • Downloads 0

How To Cite

Wael Ali Alosaimi, Ahmed Binmahfoudh, Roobaea Alroobaea, Atef Zaguia (2019). Location Prediction in a Smart Environment. International Journal of Advanced Computer Science & Applications, 10(3), 264-269. https://europub.co.uk/articles/-A-498560