References

• 1. 1)
     • 39. Rehman, O.U., Yang, S., Khan, S., et al: ‘A quantum particle swarm optimizer with enhanced strategy for global optimization of electromagnetic devices’, IEEE Trans. Magn., 2019, 55, pp. 1–4.
  2. 2)
     • 27. Yang, J., Yu, K., Gong, Y., et al: ‘Linear spatial pyramid matching using sparse coding for image classification’. 2009 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR 2009), Miami, FL, USA., 2009, pp. 1794–1801.
  3. 3)
     • 19. Yamasaki, T., Honma, T., Aizawa, K.: ‘Efficient optimization of convolutional neural networks using particle swarm optimization’. 2017 IEEE Third Int. Conf. on Multimedia Big Data (BigMM), Laguna Hills, California, USA., 2017, pp. 70–73.
  4. 4)
     • 34. Tang, Y.: ‘Deep learning using linear support vector machines’, arXiv preprint arXiv:1306.0239, 2013.
  5. 5)
     • 33. Hinton, G.E., Srivastava, N., Krizhevsky, A., et al: ‘Improving neural networks by preventing co-adaptation of feature detectors’, arXiv preprint arXiv:1207.0580, 2012.
  6. 6)
     • 55. Higashi, N., Iba, H.: ‘Particle swarm optimization with Gaussian mutation’. Proc. 2003 IEEE Swarm Intelligence Symp. (SIS'03), Indiana, USA., 2003, pp. 72–79.
  7. 7)
     • 40. Rehman, O.U., Rehman, S.U., Tu, S., et al: ‘A quantum particle swarm optimization method with fitness selection methodology for electromagnetic inverse problems’, IEEE Access, 2018, 6, pp. 63 155–63 163.
  8. 8)
     • 31. Jarrett, K., Kavukcuoglu, K., LeCun, Y., et al: ‘What is the best multi-stage architecture for object recognition?'. 2009 IEEE 12th Int. Conf. on Computer Vision, Kyoto, Japan, 2009, pp. 2146–2153.
  9. 9)
     • 14. Liu, H., Tian, H.-q., Chen, C., et al: ‘An experimental investigation of two wavelet-MLP hybrid frameworks for wind speed prediction using GA and PSO optimization’, Int. J. Electr. Power Energy Syst., 2013, 52, pp. 161–173.
  10. 10)
      • 26. Zeiler, M.D., Fergus, R.: ‘Visualizing and understanding convolutional networks’. European Conf. on Computer Vision, Zurich, Switzerland, 2014, pp. 818–833.
  11. 11)
      • 2. Rehman, S.U., Tu, S., Huang, Y., et al: ‘CSFL: a novel unsupervised convolution neural network approach for visual pattern classification’, AI Commun., 2017, 30, (5), pp. 311–324.
  12. 12)
      • 44. Ngiam, J., Chen, Z., Chia, D., et al: ‘Tiled convolutional neural networks’, in Lafferty, J.D., Williams, C.K.I., Shawe-Taylor, J., et al (Eds.): Advances in Neural Information Processing Systems 23, (Curran Associates, Inc., 2010), pp. 1279–1287.
  13. 13)
      • 22. ur Rehman, S., Tu, S., Waqas, M., et al: ‘Unsupervised pre-trained filter learning approach for efficient convolution neural network’, Neurocomputing, 2019, 365, pp. 171–190.
  14. 14)
      • 48. Coates, A., Ng, A., Lee, H.: ‘An analysis of single-layer networks in unsupervised feature learning’. Proc. 14th Int. Conf. on Artificial Intelligence and Statistics, FL, USA., 2011, pp. 215–223.
  15. 15)
      • 28. Boureau, Y.-L., Ponce, J., LeCun, Y.: ‘A theoretical analysis of feature pooling in visual recognition’. Proc. 27th Int. Conf. on Machine Learning (ICML-10), Haifa, Israel, 2010, pp. 111–118.
  16. 16)
      • 4. Ding, C., Tao, D.: ‘Robust face recognition via multimodal deep face representation’, IEEE Trans. Multimed., 2015, 17, (11), pp. 2049–2058.
  17. 17)
      • 21. LeCun, Y., Bottou, L., Bengio, Y., et al: ‘Gradient-based learning applied to document recognition’, Proc. IEEE, 1998, 86, (11), pp. 2278–2324.
  18. 18)
      • 54. Zhan, Z.-H., Zhang, J., Li, Y., et al: ‘Adaptive particle swarm optimization’, IEEE Trans. Syst. Man Cybern. B, Cybern., 2009, 39, (6), pp. 1362–1381.
  19. 19)
      • 5. Rehman, S.u., Tu, S., Huang, Y., et al: ‘Optimization of CNN through novel training strategy for visual classification problems’, Entropy, 2018, 20, (4), p. 290.
  20. 20)
      • 1. Zhang, K., Zhang, Z., Li, Z., et al: ‘Joint face detection and alignment using multitask cascaded convolutional networks’, IEEE Signal Process. Lett., 2016, 23, (10), pp. 1499–1503.
  21. 21)
      • 29. Huang, F.J., Boureau, Y.-L., LeCun, Y., et al: ‘Unsupervised learning of invariant feature hierarchies with applications to object recognition’. 2007 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR'07), Minneapolis, USA., 2007, pp. 1–8.
  22. 22)
      • 13. Ludermir, T.B., De Oliveira, W.R.: ‘Particle swarm optimization of MLP for the identification of factors related to common mental disorders’, Expert Syst. Appl., 2013, 40, (11), pp. 4648–4652.
  23. 23)
      • 47. Yu, K., Zhang, T.: ‘Improved local coordinate coding using local tangents'. ICML'10: Proc. of the 27th Int. Conf. on Machine Learning, Haifa, Israel, 2010, pp. 1215–1222.
  24. 24)
      • 18. Wang, B., Sun, Y., Xue, B., et al: ‘A hybrid GA-PSO method for evolving architecture and short connections of deep convolutional neural networks’, arXiv preprint arXiv:1903.03893, 2019.
  25. 25)
      • 9. Liu, K.-C., Hsu, C.-C., Wang, W.-Y., et al: ‘Real-time facial expression recognition based on CNN’. 2019 Int. Conf. on System Science and Engineering (ICSSE), Dong Hoi, Vietnam, 2019, pp. 120–123.
  26. 26)
      • 3. Low, C.-Y., Teoh, A.B.-J., Toh, K.-A.: ‘Stacking PCANet+: an overly simplified ConvNets baseline for face recognition’, IEEE Signal Process. Lett, 2017, 24, pp. 1581–1585.
  27. 27)
      • 15. Warsito, B., Yasin, H., Prahutama, A.: ‘Particle swarm optimization versus gradient based methods in optimizing neural network’. J. Phys. Conf. Ser., Central Java, Indonesia, 2019, 1217, pp. 012101.
  28. 28)
      • 37. Kennedy, J.: ‘Particle swarm optimization’ in ‘Encyclopedia of machine learning’ (Springer, 2011), pp. 760–766.
  29. 29)
      • 36. Hatipoglu, N., Bilgin, G.: ‘Classification of histopathological images using convolutional neural network’. 2014 4th Int. Conf. on Image Processing Theory, Tools and Applications (IPTA), Paris, France, 2014, pp. 1–6.
  30. 30)
      • 7. Rehman, S.U., Tu, S., Huang, Y., et al: ‘A benchmark dataset and learning high-level semantic embeddings of multimedia for cross-media retrieval’, IEEE Access, 2018, 6, pp. 67 176–67 188.
  31. 31)
      • 12. LeCun, Y., Boser, B.E., Denker, J.S., et al: ‘Handwritten digit recognition with a back-propagation network’, in Touretzky, D.S. (Ed.): Advances in Neural Information Processing Systems 2, (Morgan-Kaufmann, 1990), pp. 396–404.
  32. 32)
      • 8. Rehman, S.U., Tu, S., Huang, Y., et al: ‘Face recognition: a novel un-supervised convolutional neural network method’. IEEE Int. Conf. of Online Analysis and Computing Science (ICOACS), Chongqing, China, 2016, pp. 139–144.
  33. 33)
      • 51. Seha, S.N.A., Hatzinakos, D.: ‘Human recognition using transient auditory evoked potentials: a preliminary study’, IET Biometrics, 2018, 7, (3), pp. 242–250.
  34. 34)
      • 24. Nair, V., Hinton, G.E.: ‘Rectified linear units improve restricted Boltzmann machines’. Proc. 27th int. Conf. on machine learning (ICML-10), Haifa, Israel, 2010, pp. 807–814.
  35. 35)
      • 25. Phung, S.L., Bouzerdoum, A.: ‘A pyramidal neural network for visual pattern recognition’, IEEE Trans. Neural Netw., 2007, 18, (2), pp. 329–343.
  36. 36)
      • 20. da Silva, G.L.F., Valente, T.L.A., Silva, A.C., et al: ‘Convolutional neural network-based PSO for lung nodule false positive reduction on CT images’, Comput. Methods Programs Biomed., 2018, 162, pp. 109–118.
  37. 37)
      • 42. Krizhevsky, A., Hinton, G.: ‘Learning multiple layers of features from tiny images’, Citeseer, Tech. Rep., 2009.
  38. 38)
      • 38. Rehman, O.U., Tu, S., Rehman, S.U., et al: ‘Design optimization of electromagnetic devices using an improved quantum inspired particle swarm optimizer', Appl. Comput. Electromagn. Soc. J., 2018, 33, (9).
  39. 39)
      • 45. Bo, L., Ren, X., Fox, D.: ‘Kernel descriptors for visual recognition’, in J.D., Lafferty, C.K.I., Williams, Shawe-Taylor, J., Shawe-Taylor, J., et al (Eds.): Advances in Neural Information Processing Systems 23, (Curran Associates, Inc., 2010, pp. 244–252.
  40. 40)
      • 41. Fletcher, R., Reeves, C.M.: ‘Function minimization by conjugate gradients’, Comput. J., 1964, 7, (2), pp. 149–154.
  41. 41)
      • 32. Simonyan, K., Zisserman, A.: ‘Very deep convolutional networks for large-scale image recognition’, arXiv preprint arXiv:1409.1556, 2014.
  42. 42)
      • 50. Khamsemanan, N., Nattee, C., Jianwattanapaisarn, N.: ‘Human identification from freestyle walks using posture-based gait feature’, IEEE Trans. Inf. Forensics Sec., 2017, 13, (1), pp. 119–128.
  43. 43)
      • 46. Chan, T.-H., Jia, K., Gao, S., et al: ‘PCANet: a simple deep learning baseline for image classification?’, IEEE Trans. Image Process., 2015, 24, (12), pp. 5017–5032.
  44. 44)
      • 23. Fukushima, K., Miyake, S.: ‘Neocognitron: a new algorithm for pattern recognition tolerant of deformations and shifts in position’, Pattern Recognit., 1982, 15, (6), pp. 455–469.
  45. 45)
      • 6. Bulan, O., Kozitsky, V., Ramesh, P., et al: ‘Segmentation- and annotation-free license plate recognition with deep localization and failure identification’, IEEE Trans. Intell. Transp. Syst., 2017, 18, (9), pp. 2351–2363.
  46. 46)
      • 17. Chhabra, Y., Varshney, S., Wadhwa, A.: ‘Hybrid particle swarm training for convolution neural network (CNN)’. 2017 Tenth Int. Conf. on Contemporary Computing (IC3), Noida, India, August 2017, pp. 1–3.
  47. 47)
      • 16. Zhu, F., Xu, C.: ‘Particle swarm hybridize with Gaussian process regression for displacement prediction’. 2010 IEEE Fifth Int. Conf. on Bio-Inspired Computing: Theories and Applications (BIC-TA), Liverpool, UK., 2010, pp. 522–525.
  48. 48)
      • 49. Krizhevsky, A.: ‘Cuda-convnet’, code.google.com/p/cudaconvnet, 2014.
  49. 49)
      • 10. Lu, Q., Liu, Y., Huang, J., et al: ‘License plate detection and recognition using hierarchical feature layers from CNN’, Multimedia Tools Appl., 2019, 78, (11), pp. 15 665–15 680.
  50. 50)
      • 35. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ‘ImageNet classification with deep convolutional neural networks’. Advances in Neural Information Processing Systems, NY, USA., 2012, pp. 1097–1105.
  51. 51)
      • 52. Damer, N., Opel, A., Nouak, A.: ‘CMC curve properties and biometric source weighting in multi-biometric score-level fusion’. 17th Int. Conf. on Information Fusion (FUSION), Salamanca, Spain, 2014, pp. 1–6.
  52. 52)
      • 30. Wang, T., Wu, D.J., Coates, A., et al: ‘End-to-end text recognition with convolutional neural networks’. 2012 21st Int. Conf. on Pattern Recognition (ICPR), Tsukuba, Japan, 2012, pp. 3304–3308.
  53. 53)
      • 43. Phung, S.L., Bouzerdoum, A., Chai, D.: ‘Skin segmentation using color pixel classification: analysis and comparison’, IEEE Trans. Pattern Anal. Mach. Intell., 2005, 27, (1), pp. 148–154.
  54. 54)
      • 11. Wang, L., Liao, J., Xu, C.: ‘Vehicle detection based on drone images with the improved faster R-CNN’. Proc. 2019 11th Int. Conf. on Machine Learning and Computing, Zhuhai, China, 2019, pp. 466–471.
  55. 55)
      • 53. DeCann, B., Ross, A.: ‘Relating ROC and CMC curves via the biometric menagerie’. 2013 IEEE Sixth Int. Conf. on Biometrics: Theory, Applications and Systems (BTAS), Washington, DC, USA., 2013, pp. 1–8.