Machine learning in structural engineering

References
1. Vera-Olmos, F.J., Pardo, E., Melero, H., and
Malpica, N. DeepEye: Deep convolutional network
for pupil detection in real environments", Integrated
Computer-Aided Engineering, 26(1), pp. 85{95
(2019).
2. Yang, T., Cappelle, C., Ruichek, Y., and El Bagdouri,
M. Multi-object tracking with discriminant correlation
lter based deep learning tracker", Integrated
Computer-Aided Engineering, 26(3), pp. 273{284
(2019).
3. Benito-Picazo, J., Domnguez, E., Palomo, E.J.,
and Lopez-Rubio, E. Deep learning-based video
surveillance system managed by low cost hardware
and panoramic cameras", Integrated Computer-Aided
Engineering, 27(4), pp. 373{387 (2020).
4. Reyes, O. and Ventura, S. Performing multi-target
regression via a parameter sharing-based deep network",
International Journal of Neural Systems,
29(9), 1950014 (22 pages) (2019).
5. Wang, P. and Bai, X. Regional parallel structure
based CNN for thermal infrared face identication",
Integrated Computer-Aided Engineering, 25(3), pp.
247{260 (2018).
6. Rodriguez-Lera, F.J., Rico, F.M., and Olivera, V.M.
Neural networks for recognizing human activities in
home-like environments", Integrated Computer-Aided
Engineering, 26(1), pp. 37{47 (2019).
7. Hua, C., Wang, H., Wang, H., Lu, S., Liu, C., and
Khalid, S.M. A novel method of building functional
brain network using deep learning algorithm with
application in prociency detection", International
Journal of Neural Systems, 29(1), 1850015 (17 pages)
(2019).
8. Gaur, P., McCreadie, K., Pachori, R.B., Wang,
H., and Prasad, G. Tangent space feature-based
transfer learning classication model for two-class
motor imagery brain-computer interface", International
Journal of Neural Systems, 29(10), 19500215
(17 pages) (2019).
9. Lozano, A., Suarez, J.S., Soto-Sanchez, C., Garrigos,
J., Martinez-Alvarez, J.J., Ferrandez, J.M., and
Fernandez, E. Neurolight: a deep learning neural
interface for cortical visual prostheses", International
Journal of Neural Systems, 30(9), 2050045 (19 pages)
(2020).
10. Antoniades, A., Spyrou, L., Martin-Lopez, D.,
Valentin, A., Alarcon, G., Sanei, S., and Took,
C.C. Deep neural architectures for mapping scalp to
intracranial EEG", International Journal of Neural
Systems, 28(8), 1850009 (2018).
2652 J.P. Amezquita-Sanchez et al./Scientia Iranica, Transactions A: Civil Engineering 27 (2020) 2645{2656
11. Ansari, A.H., Cherian, P.J., Caicedo, A., Naulaers,
G., De Vos, M., and Van Huel, S. Neonatal
seizure detection using deep convolutional neural
networks", International Journal of Neural Systems,
29(4), 1850011 (20 pages) (2019).
12. Manzanera, M.O., Meles, S.K., Leenders, K.L.,
Renken, R.J., Pagani, M., Arnaldi, D., Nobili, F.,
Obeso, J., Oroz, M.R., Morbelli, S., and Maurits,
N.M. Scaled subprole modeling and convolutional
neural networks for the identication of Parkinson's
disease in 3D nuclear imaging data", International
Journal of Neural Systems, 29(9), 1950010 (15 pages)
(2019).
13. Park, S.E., Laxpati, N.G., Gutekunst, C.A., Connolly,
M.J., Tung, J., Berglund, K., Mahmoudi,
B., and Gross, R.E. A machine learning approach
to characterize the modulation of the hippocampal
rhythms via optogenetic stimulation of the medial
septum", International Journal of Neural Systems,
29(10), 1950020 (21 pages) (2019).
14. Zhang, J., Xiao, M., Gao, L., and Chu, S. Probability
and interval hybrid reliability analysis based on
adaptive local approximation of projection outlines
using support vector machine", Computer-Aided Civil
and Infrastructure Engineering, 34(11), pp. 991{1009
(2019).
15. Yu, B., Wang, H., Shan, W., and Yao, B. Prediction
of bus travel time using random forests based on near
neighbors", Computer-Aided Civil and Infrastructure
Engineering, 33(4), pp. 333{350 (2018).
16. Abbasi, H., Bennet, L., Gunn, A.J., and Unsworth,
C.P. Latent phase detection of hypoxic-ischemic
spike transients in the EEG of preterm fetal sheep using
reverse biorthogonal wavelets & fuzzy classier",
International Journal of Neural Systems, 29(10),
1950013, (17 pages) (2019).
17. Chen, Z. and Liu, X.C. Roadway asset inspection
sampling using high-dimensional clustering and
locality-sensitivity hashing", Computer-Aided Civil
and Infrastructure Engineering, 34(2), pp. 116{129
(2019).
18. Lopez-Rubio, E., Molina-Cabello, M.A., Luque-
Baena, R.M., and Dominguez, E. Foreground detection
by competitive learning for varying input distributions",
International Journal of Neural Systems,
28(5), 1750056 (16 pages) (2018).
19. Sutton, R. and Barto, A., Reinforcement Learning,
2nd Ed., MIT Press (2018).
20. Adeli, H. and Yeh, C. Perceptron learning in engineering
design", Computer-Aided Civil and Infrastructure
Engineering, 4(4), pp. 247{256 (1989).
21. Amezquita-Sanchez, J.P., Valtierra-Rodriguez, M.,
Aldwaik, M., and Adeli, H. Neurocomputing in civil
infrastructure", Scientia Iranica, 23(6), pp. 2417{
2428 (2016).
22. Yuen, K.V. and Mu, H.Q. Real-time system identi-
cation: an algorithm for simultaneous model class
selection and parametric identication", Computer-
Aided Civil and Infrastructure Engineering, 30(10),
pp. 785{801 (2015).
23. Perez-Ramirez, C.A., Amezquita-Sanchez, J.P.,
Adeli, H. Valtierra-Rodriguez, M., Camarena-
Martinez, D., and Rene Romero-Troncoso, R.J. New
methodology for modal parameters identication of
smart civil structures using ambient vibrations and
synchrosqueezed wavelet", Engineering Applications
of Articial Intelligence, 48, pp. 1{16 (2016).
24. Jiang, X., Mahadevan, S., and Yuan, Y. Fuzzy
stochastic neural network model for structural system
identication", Mechanical Systems and Signal
Processing, 82, pp. 394{411 (2017).
25. Amezquita-Sanchez, J.P., Park, H.S., and Adeli, H.
A novel methodology for modal parameters identi
cation of large smart structures using MUSIC,
empirical wavelet transform, and Hilbert transform",
Engineering Structures, 147, pp. 148{159 (2017).
26. Karami, K., Fatehi, P., and Yazdani, A. On-line
system identication of structures using wavelet-
Hilbert transform and sparse component analysis",
Computer-Aided Civil and Infrastructure Engineering,
35(8), pp. 870{886 (2020).
27. Perez-Ramirez, C.A., Amezquita-Sanchez, J.P.,
Valtierra-Rodriguez, M., and Adeli, H. Recurrent
neural network model with Bayesian training and
mutual information for response prediction of large
buildings", Engineering Structures, 178, pp. 603{615
(2019).
28. Wu, W.H., Chen, C.C., Jhou, J.W., and Lai, G.
A rapidly convergent empirical mode decomposition
method for analyzing the environmental temperature
eects on stay cable force", Computer-Aided Civil
and Infrastructure Engineering, 33(8), pp. 672{690
(2018).
29. Zhang, S., Zhou, L., Chen, X., Zhang, L., Li, L.,
and Li, M. Network-wide trac speed forecasting:
3D convolutional neural network with ensemble empirical
mode decomposition", Computer-Aided Civil
and Infrastructure Engineering, 35(10), pp. 1132{
1147 (2020).
30. Saadaoui, F. and Ben Messaoud, O. Multiscaled
neural autoregressive distributed lag: a new empirical
mode decomposition model for nonlinear time
series forecasting", International Journal of Neural
Systems, 30(8), 2050039 (15 pages) (2020).
31. Wang, J., Liu, X.Z., and Ni, Y.Q. A Bayesian
probabilistic approach for acoustic emission-based
rail condition assessment", Computer-Aided Civil and
Infrastructure Engineering, 33(1), pp. 21{34 (2018).
32. Yao, X.J., Yi, T.H., Qu, C., and Li, H.N.
Blind modal identication using limited sensors
through modied sparse component analysis by timefrequency
method", Computer-Aided Civil and Infrastructure
Engineering, 33(9), pp. 769{782 (2018).
J.P. Amezquita-Sanchez et al./Scientia Iranica, Transactions A: Civil Engineering 27 (2020) 2645{2656 2653
33. Yuen, K.X. and Huang, K. Identiability-enhanced
Bayesian frequency-domain substructure identication",
Computer-Aided Civil and Infrastructure Engineering,
33(9), pp. 800{812 (2018).
34. Yuen, K.V., Kuok, S.C., and Dong, L. Selfcalibrating
Bayesian real-time system identication",
Computer-Aided Civil and Infrastructure Engineering,
34(9), pp. 806{821 (2019).
35. Tian, Y., Zhang, J., and Yu, S. Rapid impact
testing and system identication of footbridges using
particle image velocimetry", Computer-Aided Civil
and Infrastructure Engineering, 34(2), pp. 130{145
(2019).
36. Kostic, B. and Gul, M. Vibration-based damage
detection of bridges under varying temperature effects
using time-series analysis and articial neural
networks", Journal of Bridge Engineering, 22(10),
04017065 (2017).
37. Pan, H., Azimi, M., Yan, F., and Lin, Z. Timefrequency-
based data-driven structural diagnosis and
damage detection for cable-stayed bridges", Journal
of Bridge Engineering, 23(6), 04018033 (2018).
38. Yanez-Borjas, J.J., Valtierra-Rodriguez, M.,
Camarena-Martinez, D., and Amezquita-Sanchez,
J.P. Statistical time features for global corrosion
assessment in a truss bridge from vibration signals",
Measurement, 160, 107858 (2020).
39. Amezquita-Sanchez, J.P. Entropy algorithms for
detecting incipient damage in high-rise buildings subjected
to dynamic vibrations", Journal of Vibration
and Control (In press) (2020).
DOI: 10.1177/1077546320929145
40. Ettouney, M.M. and Alampalli, S., Infrastructure
Health in Civil Engineering: Theory and Components,
CRC press (2016).
41. Torres, J.F., Galicia, A., Troncoso, A., and Martnez-
Alvarez, F. A scalable approach based on deep
learning for big data time series forecasting", Integrated
Computer-Aided Engineering, 25(4), pp. 335{
348 (2018).
42. Valtierra-Rodriguez, M., Rivera-Guillen, J.R.,
Basurto-Hurtado, J.A. De-Santiago-Perez, J.J.,
Granados-Lieberman, D., and Amezquita-Sanchez,
J.P. Convolutional neural network and motor
current signature analysis during the transient state
for detection of broken rotor bars in induction
motors", Sensors, 20(13), 3721 (2020).
43. Abdeljaber, O., Avci, O., Kiranyaz, M.S., Boashash,
B., Sodano, H., and Inman, D.J. 1-D CNNs for structural
damage detection: Verication on a structural
health monitoring benchmark data", Neurocomputing,
275, pp. 1308{1317 (2018).
44. Krishnasamy, L. and Arumulla, R.M.R. Baselinefree
hybrid diagnostic technique for detection of
minor incipient damage in the structure", Journal
of Performance of Constructed Facilities, 33(2),
04019018 (2019).
45. Raei, M.H. and Adeli, H. A new neural dynamic
classication algorithm", IEEE Transactions on Neural
Networks and Learning Systems, 28(12), pp.
3074{3083 (2017).
46. Ibrahim, A., Eltawil, A., Na, Y., and El-Tawil, S.
A machine learning approach for structural health
monitoring using noisy data sets", IEEE Transactions
on Automation Science and Engineering, 17(2), pp.
900{908 (2019).
47. Zhang, Y., Miyamori, Y., Mikami, S., and Saito,
T. Vibration-based structural state identication
by a 1-dimensional convolutional neural network",
Computer-Aided Civil and Infrastructure Engineering,
34(9), pp. 822{839 (2019).
48. Huang, Y., Beck, J.L., and Li, H. Multitask sparse
Bayesian learning with applications in structural
health monitoring", Computer-Aided Civil and Infrastructure
Engineering, 34(9), pp. 732{754 (2019).
49. Wang, F., Song, G., and Mo, Y.L. Shear loading
detection of through bolts in bridge structures
using a percussion-based one-dimensional
memory-augmented convolutional neural network",
Computer-Aided Civil and Infrastructure Engineering
(In press) (2020). DOI: 10.1111/mice.12602
50. Naranjo-Perez, J., Infantes, M., Jimenez-Alonso,
J.F., and Saez, A. A collaborative machine learningoptimization
algorithm to improve the nite element
model updating of civil engineering structures", Engineering.
Structures, 225, 111327 (2020).
51. Wang, Z. and Cha, Y.J. Unsupervised deep learning
approach using a deep auto-encoder with a one-class
support vector machine to detect structural damage",
Structural Health Monitoring (In press) (2020).
DOI: 10.1177/1475921720934051
52. Sajedi, S.O. and Liang, X. Vibration-based semantic
damage segmentation for large-scale structural health
monitoring", Computer-Aided Civil and Infrastructure
Engineering, 35(6), pp. 579{596 (2020).
53. Cha, Y.J., Choi, W., Suh, G., Mahmoudkhani,
S., and Buyukozturk, O. Autonomous structural
visual inspection using region-based deep learning for
detecting multiple damage types", Computer-Aided
Civil and Infrastructure Engineering, 33(9), pp. 731{
747 (2018).
54. Gao, Y. and Mosalam, K.M. Deep transfer learning
for image-based structural damage recognition",
Computer-Aided Civil and Infrastructure Engineering,
33(9), pp. 748{768 (2018).
55. Zhang, X., Rajan, D., and Story, B. Concrete crack
detection using context-aware deep semantic segmentation
network", Computer-Aided Civil and Infrastructure
Engineering, 34(11), pp. 951{971 (2019)
56. Wu, R.T., Ankush Singla, A., Jahanshahi, M.R.,
Bertino, E., Ko, B.J., and Verma, D. Pruning
deep convolutional neural networks for ecient edge
computing in condition assessment of civil infrastructures",
Computer-Aided Civil and Infrastructure
Engineering, 34(9), pp. 774{789 (2019).
2654 J.P. Amezquita-Sanchez et al./Scientia Iranica, Transactions A: Civil Engineering 27 (2020) 2645{2656
57. Nayyeri, F., Hou, L., Zhou, J., and Guan, H.
Foreground-background separation technique for
road and bridge crack detection", Computer-Aided
Civil and Infrastructure Engineering, 34(6), pp. 457{
470 (2019).
58. Deng, J., Lu, Y., and Lee, V.C.S. Concrete crack
detection with handwriting script interferences using
faster region-based convolutional neural network",
Computer-Aided Civil and Infrastructure Engineering,
35(4), pp. 373{388 (2020).
59. Pan, X. and Yang, T.Y. Post-disaster imaged-based
damage detection and repair cost estimation of reinforced
concrete buildings using dual convolutional
neural networks", Computer-Aided Civil and Infrastructure
Engineering, 35(5), pp. 495{510 (2020).
60. Liu, Y.F., Nie, X., Fan, J.S., and Liu, X.G. Imagebased
crack assessment of bridge piers using unmanned
aerial vehicles and 3D scene reconstruction",
Computer-Aided Civil and Infrastructure Engineering,
35(5), pp. 511{529 (2020).
61. Jiang, S. and Zhang, J. Real-time crack assessment
using deep neural networks with wall-climbing unmanned
aerial system", Computer-Aided Civil and Infrastructure
Engineering, 35(6), pp. 549{564 (2020).
62. Athanasiou, A., Ebrahimkhanlou, A., Zaborac, J.,
Hrynyk, T., and Salamone, S. A machine learning
approach based on multifractal features for crack
assessment of reinforced concrete shells", Computer-
Aided Civil and Infrastructure Engineering, 35(6),
pp. 565{578 (2020).
63. Ying, Z. and Ni, Y. Advances in structural vibration
control application of magneto-rheological
visco-elastomer", Journal of Theoretical and Applied
Mechanics, 7(2), pp. 61{66 (2017).
64. Lu, Z., Wang, Z., Zhou, Y., and Lu, X. Nonlinear
dissipative devices in structural vibration control: A
review", Journal of Sound and Vibration, 423, pp.
18{49 (2018).
65. Deng, Z. and Dapino, M.J. Review of magnetostrictive
materials for structural vibration control", Smart
Materials and Structures, 27(11), 113001 (2018).
66. Elias, S. and Matsagar, V. Research developments
in vibration control of structures using passive tuned
mass dampers", Annual Reviews in Control, 44, pp.
129{156 (2017).
67. Rahimi, F., Aghayari, R., and Samali, B. Application
of tuned mass dampers for structural vibration
control: a state-of-the-art review", Civil Engineering
Journal, 6(8), pp. 1622{1651 (2020).
68. Chiew, F.H., Ng, C.K., Chai, K.C., and Tay, K.M.
A fuzzy adaptive resonance theory-based model
for mix proportion estimation of high performance
concrete", Computer-Aided Civil and Infrastructure
Engineering, 32(9), pp. 772{786 (2017).
69. Padillo, F., Luna, J.M., Herrera, F., and Ventura, S.
Mining association rules on big data through mapreduce
genetic programming", Integrated Computer-
Aided Engineering, 25(1), pp. 31{48 (2018).
70. Wang, N. and Adeli, H. Self-constructing wavelet
neural network algorithm for nonlinear control of
large structures", Engineering Applications of Arti-
cial Intelligence, 41, pp. 249{258 (2015).
71. Chen, H.Y., and Liang, J.W. Adaptive wavelet neural
network controller for active suppression control
of a diaphragm-type pneumatic vibration isolator",
International Journal of Control, Automation and
Systems, 15(3), pp. 1456{1465 (2017).
72. Bui, H.L., Le, T.A., and Bui, V.B. Explicit formula
of hedge-algebras-based fuzzy controller and applications
in structural vibration control", Applied Soft
Computing, 60, pp. 150{166 (2017).
73. Wang, J., Huang, Y., Wang, T., Zhang, C., and Liu,
Y. Fuzzy nite-time stable compensation control for
a building structural vibration system with actuator
failures", Applied Soft Computing, 93, 106372 (2020).
74. Fu, J., Lai, J., Yang, Z., Bai, J., and Yu, M. Fuzzyneural
network control for a magnetorheological elastomer
vibration isolation system", Smart Materials
and Structures, 29(7), 074001 (2020).
75. Rahmani, H.R., Chase, G., Wiering, M., and Konke,
C. A framework for brain learning-based control of
smart structures", Advanced Engineering Informatics,
42, 100986 (2019).
76. Lu, X., Liao, W., Huang, W., Xu, Y., and
Chen, X. An improved linear quadratic
regulator control method through convolutional
neural network-based vibration identication",
Journal of Vibration and Control (In press)
https://doi.org/10.1177/1077546320933756 (2020).
DOI: 10.1177/1077546320933756
77. Adeli, H. and Yeh, C. Explanation-based machine
learning in engineering design", Engineering Applications
of Articial Intelligence, 3(2), pp. 127{137
(1990).
78. Raei, M.H., Khushefati, W.H., Demirboga, R., and
Adeli, H. Novel approach for concrete mix design
using neural dynamics model and the virtual lab
concept", ACI Materials Journal, 114(1), pp. 117{
127 (2017).
79. Adeli, H. and Park, H.S., Neurocomputing for Design
Automation, Boca Raton, Florida: CRC Press (1998).
80. Zheng, H., Moosavi, V., and Akbarzadeh, M. Machine
learning assisted evaluations in structural design
and construction", Automation in Construction,
119, 103346 (2020).
81. Greco, A., Cannizzaro, F., and Pluchino, A. Seismic
collapse prediction of frame structures by means of
genetic algorithms", Engineering Structures, 143, pp.
152{168 (2017).
82. Asteris, P.G., and Nikoo, M. Articial bee colonybased
neural network for the prediction of the fundamental
period of inlled frame structures", Neural
Computing and Applications, 31(9), pp. 4837{4847
(2019).
J.P. Amezquita-Sanchez et al./Scientia Iranica, Transactions A: Civil Engineering 27 (2020) 2645{2656 2655
83. Luo, H. and Paal, S.G. A locally weighted machine
learning model for generalized prediction of
drift capacity in seismic vulnerability assessments",
Computer-Aided Civil and Infrastructure Engineering,
34(11), pp. 935{950 (2019).
84. Nie, Z., Jiang, H., and Kara, L.B. Stress eld
prediction in cantilevered structures using convolutional
neural networks", Journal of Computing and
Information Science in Engineering, 20(1), pp. 1{16
(2020).
85. Nguyen, T., Kashani, A., Ngo, T., and Bordas, S.
Deep neural network with high-order neuron for the
prediction of foamed concrete strength", Computer-
Aided Civil and Infrastructure Engineering, 34(4),
pp. 316{332 (2019).
86. Luo, X. and Kareem, A. Deep convolutional neural
networks for uncertainty propagation in random
elds", Computer-Aided Civil and Infrastructure Engineering,
34(12), pp. 1041{1054 (2019).
87. Oh, B.K., Glisic, B., Kim, Y., and Park, H.S. Convolutional
neural network-based wind-induced response
estimation model for tall buildings", Computer-Aided
Civil and Infrastructure Engineering, 34(10), pp.
843{858 (2019).
88. Oh, B.K., Park, Y., and Park, H.S. Seismic response
prediction method for building structures using convolutional
neural network", Structural Health Monitoring,
27(5), p. e2519 (2020).
89. Zhang, R., Chen, Z., Chen, S., Zheng, J.,
Buyukozturk, O., and Sun, H. Deep long short-term
memory networks for nonlinear structural seismic
response prediction", Computers and Structures, 220,
pp. 55{68 (2020).
90. Wang, J., Zhang, L., Chen, Y., and Yi, Z. A
new delay connection for long short-term memory
networks", International Journal of Neural Systems,
28(6), 1750061 (2018).
91. Gulgec, N.S., Takac, M., and Pakzad, S.N. Structural
sensing with deep learning: strain estimation
from acceleration data for fatigue assessment",
Computer-Aided Civil and Infrastructure Engineering,
35(12), pp. 1349{1364 (2020).
92. Ahmadlou, M. and Adeli, H. Enhanced probabilistic
neural network with local decision circles: a robust
classier", Integrated Computer-Aided Engineering,
17(3), pp. 197{210 (2010).
93. Hirschauer, T.J., Adeli, H., and Buford, J.A.
Computer-aided diagnosis of Parkinson's disease using
enhanced probabilistic neural network", Journal
of Medical Systems, 39(11), p. 179 (2015).
94. Amezquita-Sanchez, J.P., Adeli, A., and Adeli, H.
A new methodology for automated diagnosis of
mild cognitive impairment (MCI) using magnetoencephalography
(MEG)", Behavioural Brain Research,
305, pp. 174{180 (2016).
95. Amezquita-Sanchez, J.P., Valtierra-Rodriguez, M.,
Adeli, H., and Perez-Ramirez, C. A novel wavelet
transform-homogeneity model for sudden cardiac
death prediction using ECG signals", Journal of
Medical Systems, 42(10), p. 176 (2018).
96. Raei, M.H. and Adeli, H. NEEWS: A novel earthquake
early warning model using neural dynamic
classication and neural dynamic optimization", Soil
Dynamics and Earthquake Engineering, 100, pp. 417{
427 (2017).
97. Raei, M.H. and Adeli, H. A novel machine learningbased
algorithm to detect damage in high-rise building
structures", The Structural Design of Tall and
Special Buildings, 26(18), e1400 (2017).
98. Pereira, D.R., Piteri, M.A., Souza, A.N., Papa, J.P.,
and Adeli, H. FEMa: a nite element machine for
fast learning", Neural Computing and Applications,
32(10), pp. 6393{6404 (2019).
99. Schetinin, V., Jakaite, L., and Krzanowski, W.
Bayesian learning of models estimating uncertainty
in alert systems: application to aircraft collision
avoidance", Integrated Computer-Aided Engineering,
25(3), pp. 229{245 (2018).
100. Molina-Cabello, M.A., Luque-Baena, R.M., Lopez-
Rubio, E., and Thurnhofer-Hemsi, K. Vehicle
type detection by ensembles of convolutional neural
networks on super resolved images", Integrated
Computer-Aided Engineering, 25(4), pp. 321{333
(2018).
101. Reyes, O., Fardoun, H.M., and Ventura, S. An
ensemble-based method for the selection of instances
in the multi-target regression problem", Integrated
Computer-Aided Engineering, 25(4), pp. 305{320
(2018).
102. Hamreras, S., Boucheham, B., Molina-Cabello, M.A.,
Bentez-Rochel, R., and Lopez-Rubio, E. Contentbased
image retrieval by ensembles of deep learning
object classiers", Integrated Computer-Aided Engineering,
27(3), pp. 317{331 (2020).
103. Rokibul Alam, K.M., Siddique, N., and Adeli, H. A
dynamic ensemble learning algorithm for neural networks",
Neural Computing and Applications, 32(10),
pp. 6393{6404 (2020).