%% This BibTeX bibliography file was created using BibDesk.
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@book{alford2007optimisation,
  title={Optimisation in underground mining},
  author={Alford, Christopher and Brazil, Marcus and Lee, David H},
  booktitle={Handbook of operations research in natural resources},
  pages={561-577},
  year={2007},
  publisher={Springer}
}

@book{zaki2014data,
  title={Data mining and analysis: fundamental concepts and algorithms},
  author={Zaki, Mohammed J and Meira Jr, Wagner and Meira, Wagner},
  year={2014},
  publisher={Cambridge University Press}
}

@article{rendu,
  title={Proceedings Orebody Modelling and Strategic Mine Planning},
  author={J-M. Rendu},
  year={2004},
  publisher={Conference Proceedings SMP 2014},
  journaltitle={Conference Proceedings SMP}
}

@article{estimatingvehicle,
  title={Estimating Vehicle Movement Direction from Smartphone Accelerometers Using Deep Neural Networks},
  author={{HERNANDEZ} Sanchez S., {FERNANDEZ} Pozo R., {HERNANDEZ GOMEZ} L.},
  year={2018},
  publisher={Sensors (Basel)},
  journaltitle={Sensors (Basel). 2018;18(8):2624.}
}

@article{largescale,
  title={Large scale transport mode classification from mobile sensor data},
  author={{STAVRIANOS} S},
  year={2017},
  publisher={Sentiance at https://www.sentiance.com/2017/12/04/transport-mode-classification/},
  journaltitle={Sentiance}
}

@article{datafusion,
  title={Data Fusion on Motion and Magnetic Sensors embedded on Mobile Devices for the Identification of Activities of Daily Living},
  author={PIRES, Ivan Miguel et al.},
  year={2017},
  publisher={arXiv preprint arXiv},
  doi={1711.07328},
  journaltitle={arXiv}
}

@article{identification,
  title={Identification of activities of daily living through data fusion on motion and magnetic sensors embedded on mobile devices},
  author={PIRES, Ivan Miguel et al.},
  year={2018},
  publisher={Pervasive and Mobile Computing v. 47},
  pages={78-93},
  journaltitle={Pervasive and Mobile Computing}
}

@article{conv,
author={X. {LIANG} and G. {WANG}},
booktitle={2017 IEEE 14th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)},
title={A Convolutional Neural Network for Transportation Mode Detection Based on Smartphone Platform},
year={2017},
volume={},
number={},
pages={338-342},
keywords={accelerometers;mobile computing;neural nets;smart phones;transportation;automatic transportation mode detection;energy-efficient transportation mode detection application;convolutional neural network;transportation modes;smartphone platform;urban transportation planning;accelerometer sensor;Transportation;Acceleration;Gravity;Accelerometers;Neural networks;Smoothing methods;Mobile communication;transportation mode detection;accelerometer;deep learning},
doi={10.1109/MASS.2017.81},
ISSN={2155-6814},
month={Oct}}