@conference {8784003,
	title = {On the Use of LSTM Networks for Predictive Maintenance in Smart Industries},
	booktitle = {2019 IEEE International Conference on Smart Computing (SMARTCOMP)},
	year = {2019},
	month = {June},
	pages = {241-248},
	keywords = {Deep Learning, Engines, History, Industry 4.0, Keras, learning (artificial intelligence), Logic gates, LSTM networks hyperparameters, Machine learning, machine learning approach, machine learning techniques, maintenance engineering, maintenance scheduling, predictive maintenance, production engineering computing, recurrent neural nets, remaining life assessment, remaining useful life, RUL, Sensor systems, smart industries, Smart Industry, TensorFlow},
	issn = {null},
	doi = {10.1109/SMARTCOMP.2019.00059},
	author = {D. Bruneo and F. De Vita}
}
@conference {8421336,
	title = {A Deep Learning Approach for Indoor User Localization in Smart Environments},
	booktitle = {2018 IEEE International Conference on Smart Computing (SMARTCOMP)},
	year = {2018},
	month = {June},
	pages = {89-96},
	keywords = {Buildings, cloud computing, Deep Learning, deep learning approach, Global Positioning System, Indoor environments, indoor localization, indoor navigation, indoor user localization, indoor user location, integral part, learning (artificial intelligence), Machine learning, radionavigation, smart environments, smart services, telecommunication computing, TensorFlow, user position, Wi-Fi fingerprint, Wireless communication, Wireless fidelity, wireless LAN},
	doi = {10.1109/SMARTCOMP.2018.00078},
	author = {F. De Vita and D. Bruneo}
}
