384
A. Jmal et al.
6. Ara´ ujo, M., Pereira, A., Benevenuto, F.: A comparative study of machine translation for multilingual sentence-level sentiment analysis. Inf. Sci. 512, 1078–1102
(2020). https://doi.org/10.1016/j.ins.2019.10.031
7. Mohamed, M.B., Makhlouf, A.M., Fakhfakh, A.: Intrusion cancellation for anomaly
detection in healthcare applications. In: International Wireless Communications
and Mobile Computing Conference (IWCMC) (2019). https://doi.org/10.1109/
IWCMC.2019.8766592
8. Rashid, K.M., Joseph, L.: Times-series data augmentation and deep learning for
construction equipment activity recognition. Adv. Eng. Inform. 42, 100944 (2019).
https://doi.org/10.1016/j.aei.2019.100944
9. Joselin, J., et al.: A review on neural networks. Int. J. Trend Sci. Res. Dev. (IJTSRD) 2, 565–569 (2018). ISSN 2456-6470
10. Nwankpa, C.E., Ijomah, W., Gachagan, A., Marshall, S.: Activation Functions: Comparison of Trends in Practice and Research for Deep Learning.
arXiv:1811.03378v1, pp. 1–20, 8 November 2018
11. Alkittawi, H.: A deep-learning-based fall-detection system to support aging-inplace. Texas A&M University-Corpus Christi Corpus Christi, Texas (2017)
12. Smagulova, K., James, A.P.: Overview of long short-term memory neural networks.
In: James, A.P. (ed.) Deep Learning Classifiers with Memristive Networks. MOST,
vol. 14, pp. 139–153. Springer, Cham (2020). https://doi.org/10.1007/978-3-03014524-8 11
13. Pascanu, R., Mikolov, T., Bengio, Y.: On the difficulty of training recurrent neural networks. In: Proceedings of the 30th International Conference on Machine
Learning, Atlanta, Georgia, USA, vol. 28, no. 2 (2013)
14. Pienaar, S.W., Malekian, R.: Human activity recognition using LSTM-RNN deep
neural network architecture. In: IEEE 2nd Wireless Africa Conference (WAC), Pretoria, South Africa, 18–20 August 2019 (2019). https://doi.org/10.1109/AFRICA.
2019.8843403
15. Jmal, A., Barioul, R., Makhlouf, A.M., Fakhfakh, A., Kanoun, O.: Human activity
recognition from BlueNRG-tile sensor using recurrent neural network. In: 12th
International Workshop on Impedance Spectroscopy (IWIS), Chemnitz, Germany,
pp. 1–2. IEEE (2019)
16. Shoaib, M., Bosch, S., Incel, O.D., Scholten, H., Havinga, P.J.M.: Fusion of smartphone motion sensors for physical activity recognition. Sensors 14, 10146–10176
(2014). https://doi.org/10.3390/s140610146
17. Oscar, D.L., Miguel, A.: Labrador: a survey on human activity recognition using
wearable sensors. IEEE Commun. Surv. Tutor. 15(3), 1192–1209 (2013). https://
doi.org/10.1109/SURV.2012.110112.00192
A. Jmal et al.
6. Ara´ ujo, M., Pereira, A., Benevenuto, F.: A comparative study of machine translation for multilingual sentence-level sentiment analysis. Inf. Sci. 512, 1078–1102
(2020). https://doi.org/10.1016/j.ins.2019.10.031
7. Mohamed, M.B., Makhlouf, A.M., Fakhfakh, A.: Intrusion cancellation for anomaly
detection in healthcare applications. In: International Wireless Communications
and Mobile Computing Conference (IWCMC) (2019). https://doi.org/10.1109/
IWCMC.2019.8766592
8. Rashid, K.M., Joseph, L.: Times-series data augmentation and deep learning for
construction equipment activity recognition. Adv. Eng. Inform. 42, 100944 (2019).
https://doi.org/10.1016/j.aei.2019.100944
9. Joselin, J., et al.: A review on neural networks. Int. J. Trend Sci. Res. Dev. (IJTSRD) 2, 565–569 (2018). ISSN 2456-6470
10. Nwankpa, C.E., Ijomah, W., Gachagan, A., Marshall, S.: Activation Functions: Comparison of Trends in Practice and Research for Deep Learning.
arXiv:1811.03378v1, pp. 1–20, 8 November 2018
11. Alkittawi, H.: A deep-learning-based fall-detection system to support aging-inplace. Texas A&M University-Corpus Christi Corpus Christi, Texas (2017)
12. Smagulova, K., James, A.P.: Overview of long short-term memory neural networks.
In: James, A.P. (ed.) Deep Learning Classifiers with Memristive Networks. MOST,
vol. 14, pp. 139–153. Springer, Cham (2020). https://doi.org/10.1007/978-3-03014524-8 11
13. Pascanu, R., Mikolov, T., Bengio, Y.: On the difficulty of training recurrent neural networks. In: Proceedings of the 30th International Conference on Machine
Learning, Atlanta, Georgia, USA, vol. 28, no. 2 (2013)
14. Pienaar, S.W., Malekian, R.: Human activity recognition using LSTM-RNN deep
neural network architecture. In: IEEE 2nd Wireless Africa Conference (WAC), Pretoria, South Africa, 18–20 August 2019 (2019). https://doi.org/10.1109/AFRICA.
2019.8843403
15. Jmal, A., Barioul, R., Makhlouf, A.M., Fakhfakh, A., Kanoun, O.: Human activity
recognition from BlueNRG-tile sensor using recurrent neural network. In: 12th
International Workshop on Impedance Spectroscopy (IWIS), Chemnitz, Germany,
pp. 1–2. IEEE (2019)
16. Shoaib, M., Bosch, S., Incel, O.D., Scholten, H., Havinga, P.J.M.: Fusion of smartphone motion sensors for physical activity recognition. Sensors 14, 10146–10176
(2014). https://doi.org/10.3390/s140610146
17. Oscar, D.L., Miguel, A.: Labrador: a survey on human activity recognition using
wearable sensors. IEEE Commun. Surv. Tutor. 15(3), 1192–1209 (2013). https://
doi.org/10.1109/SURV.2012.110112.00192