462
4 CONCLUSIONS
The proposed study attempts to develop an MLP-based deep learning network model for
prediction of quality of iron ores. The performance of the network is investigated for the different number of hidden layers (1, 2, 3, 4, and 5) and the different number of nodes in each
hidden layer (2, 3, 4, 5, 10, 50, 100, and 500). It is observed that the RMSE is lowest for the
model having 5-nodes and 3-hidden layers. The performance of the models was evaluated
using four indices including RMSE, normalized mean square error (NMSE), R-squared (R
2 ),
and bias. The RMSE, NMSE, and R-squared of the optimized model were obtained as 8.77,
0.0026, and 0.87 respectively. The results indicate that the model gives satisfactory performance in quality predictions of iron ores. The study was performed with less number of image
samples, and the performance can be further improved by increasing the number of samples.
The proposed technology of quality inspection may be more economical and robust. Thus,
the system can play a significant role in automation in the mineral industries. The bias of the
optimized model is found to be relatively high (1.14) and negative. A negative bias indicates
that the model performs with over prediction and thus the models need to be trained and
tested using more number of image samples.
REFERENCES
Aliper, A., Plis, S., Artemov, A., Ulloa, A., Mamoshina, P., & Zhavoronkov, A. (2016). Deep learning
applications for predicting pharmacological properties of drugs and drug repurposing using
transcriptomic data. Molecular pharmaceutics, 13(7): 2524–2530.
Baccouche, M., Mamalet, F., Wolf, C., Garcia, C., & Baskurt, A. (2011). Sequential Deep Learning for
Human Action Recognition. In: Salah A.A., Lepri B. (eds) Human Behavior Understanding. HBU
2011. Lecture Notes in Computer Science, vol. 7065. Springer, Berlin, Heidelberg.
Dahl, G.E., Sainath, T.N., & Hinton, G.E. (2013). Improving deep neural networks for LVCSR using
rectified linear units and dropout. IEEE conference on Acoustics, Speech and Signal Processing
(ICASSP), 26–31 May 2013, Vancouver, BC, Canada. Doi. 10.1109/ICASSP.2013.6639346.
Hosmer, D.W., Lemeshow, S., & Sturdivant, R.X. (2013). Applied logistic regression, Third Edition,
John Wiley & Sons Inc. DOI:10.1002/9781118548387.
Huang, P-S., Kim, M., Hasegawa-Johnson, M., & Smaragdis, P. (2014). Deep learning for monaural
speech separation. IEEE conference on Acoustics, Speech and Signal Processing (ICASSP), 4–9 May
2014, Florence, Italy.
Huval, B., Wang, T., Tandon, S., Kiske, J., Song, W., Pazhayampallil, J., Andriluka, M., Rajpurkar, P.,
Migimatsu, T., Cheng-Yue, R., Mujica, F., Coates, A., & Ng, A.Y. (2015). An empirical evaluation of
deep learning on highway driving. arXiv preprint arXiv:1504.01716.
Ivanov, O.P. (1986). Methodological aspects of ore quality control. Soviet Mining Science, 22(3):
207–212.
Kingma, D.P., & Ba, J.L. (2014). Adam: A method for stochastic optimization. arXiv preprint
arXiv:1412.6980.
Kunter, M.H., Nachtsheim, C.J., Neter, John, Li, W. (2005). Applied linear statistical models, 5th
Edition, McGraw Hill-Irwin, New York, NY, 10020.
Levine, S., Pastor, P., Krizhevsky, A., Ibarz, J., & Quillen, D. (2018). Learning hand-eye coordination
for robotic grasping with deep learning and large-scale data collection. The International Journal of
Robotics Research, 37(4–5): 421–436.
Ruck, D.W., Rogers, S.K., Kabrisky, M., Oxley, M.E., Suter, B.W. (1990). The multilayer perceptron as
an approximation to a Bayes optimal discriminant function. IEEE Transactions on Neural Networks
1(4): 296–298.
Shen, D., Wu, G., & Suk, H.I. (2017). Deep learning in medical image analysis. Annual review of
biomedical engineering, 19: 221–248.
Yu, D., & Deng, L. (2011). Deep learning and its applications to signal and information processing.
IEEE Signal Processing Magazine, 28(1): 145–154.
4 CONCLUSIONS
The proposed study attempts to develop an MLP-based deep learning network model for
prediction of quality of iron ores. The performance of the network is investigated for the different number of hidden layers (1, 2, 3, 4, and 5) and the different number of nodes in each
hidden layer (2, 3, 4, 5, 10, 50, 100, and 500). It is observed that the RMSE is lowest for the
model having 5-nodes and 3-hidden layers. The performance of the models was evaluated
using four indices including RMSE, normalized mean square error (NMSE), R-squared (R
2 ),
and bias. The RMSE, NMSE, and R-squared of the optimized model were obtained as 8.77,
0.0026, and 0.87 respectively. The results indicate that the model gives satisfactory performance in quality predictions of iron ores. The study was performed with less number of image
samples, and the performance can be further improved by increasing the number of samples.
The proposed technology of quality inspection may be more economical and robust. Thus,
the system can play a significant role in automation in the mineral industries. The bias of the
optimized model is found to be relatively high (1.14) and negative. A negative bias indicates
that the model performs with over prediction and thus the models need to be trained and
tested using more number of image samples.
REFERENCES
Aliper, A., Plis, S., Artemov, A., Ulloa, A., Mamoshina, P., & Zhavoronkov, A. (2016). Deep learning
applications for predicting pharmacological properties of drugs and drug repurposing using
transcriptomic data. Molecular pharmaceutics, 13(7): 2524–2530.
Baccouche, M., Mamalet, F., Wolf, C., Garcia, C., & Baskurt, A. (2011). Sequential Deep Learning for
Human Action Recognition. In: Salah A.A., Lepri B. (eds) Human Behavior Understanding. HBU
2011. Lecture Notes in Computer Science, vol. 7065. Springer, Berlin, Heidelberg.
Dahl, G.E., Sainath, T.N., & Hinton, G.E. (2013). Improving deep neural networks for LVCSR using
rectified linear units and dropout. IEEE conference on Acoustics, Speech and Signal Processing
(ICASSP), 26–31 May 2013, Vancouver, BC, Canada. Doi. 10.1109/ICASSP.2013.6639346.
Hosmer, D.W., Lemeshow, S., & Sturdivant, R.X. (2013). Applied logistic regression, Third Edition,
John Wiley & Sons Inc. DOI:10.1002/9781118548387.
Huang, P-S., Kim, M., Hasegawa-Johnson, M., & Smaragdis, P. (2014). Deep learning for monaural
speech separation. IEEE conference on Acoustics, Speech and Signal Processing (ICASSP), 4–9 May
2014, Florence, Italy.
Huval, B., Wang, T., Tandon, S., Kiske, J., Song, W., Pazhayampallil, J., Andriluka, M., Rajpurkar, P.,
Migimatsu, T., Cheng-Yue, R., Mujica, F., Coates, A., & Ng, A.Y. (2015). An empirical evaluation of
deep learning on highway driving. arXiv preprint arXiv:1504.01716.
Ivanov, O.P. (1986). Methodological aspects of ore quality control. Soviet Mining Science, 22(3):
207–212.
Kingma, D.P., & Ba, J.L. (2014). Adam: A method for stochastic optimization. arXiv preprint
arXiv:1412.6980.
Kunter, M.H., Nachtsheim, C.J., Neter, John, Li, W. (2005). Applied linear statistical models, 5th
Edition, McGraw Hill-Irwin, New York, NY, 10020.
Levine, S., Pastor, P., Krizhevsky, A., Ibarz, J., & Quillen, D. (2018). Learning hand-eye coordination
for robotic grasping with deep learning and large-scale data collection. The International Journal of
Robotics Research, 37(4–5): 421–436.
Ruck, D.W., Rogers, S.K., Kabrisky, M., Oxley, M.E., Suter, B.W. (1990). The multilayer perceptron as
an approximation to a Bayes optimal discriminant function. IEEE Transactions on Neural Networks
1(4): 296–298.
Shen, D., Wu, G., & Suk, H.I. (2017). Deep learning in medical image analysis. Annual review of
biomedical engineering, 19: 221–248.
Yu, D., & Deng, L. (2011). Deep learning and its applications to signal and information processing.
IEEE Signal Processing Magazine, 28(1): 145–154.