Deep Learning: Parameter Optimization Using Proposed Novel Hybrid Bees Bayesian Convolutional Neural Network

References

• Al-Musawi, A. 2019. The development of new artificial intelligence based hybrid techniques combining bees algorithm, data mining and genetic algorithm for detection, classification and prediction of faults in induction motors. Doctoral dissertation, Cardiff University.
   Google Scholar
• Badan, F. 2019. Evolutionary algorithms in convolutional neural network design. Online Accessed March 16, 2020. http://excel.fit.vutbr.cz/submissions/2019/033/33.pdf
   Google Scholar
• Badem, H., A. Basturk, A. Caliskan, and M. E. Yuksel. 2017. A new efficient training strategy for deep neural networks by hybridization of artificial bee colony and limited–memory BFGS optimization algorithms. Neurocomputing 266:506–1745. doi:10.1016/j.neucom.2017.05.061.
   Web of Science ®Google Scholar
• Baldominos, A., Y. Saez, and P. Isasi. 2018. Evolutionary convolutional neural networks: An application to handwriting recognition. Neurocomputing 283:38–52. doi:10.1016/j.neucom.2017.12.049.
   Web of Science ®Google Scholar
• Baronti, L. (2020). Analysis and development of the bees algorithm for primitive fitting in point cloud models. Doctoral dissertation, University of Birmingham.
   Google Scholar
• Bernard, N., and F. Leprévost.2018. Evolutionary algorithms for convolutional neural network visualisation. In Latin American High Performance Computing Conference, Springer, Cham, September.
   Google Scholar
• Brownlee, J. (2020). Understand the impact of learning rate on neural network performance. Online [Accessed November 29, 2021. https://machinelearningmastery.com/understand-the-dynamics-of-learning-rate-on-deep-learning-neural-networks/
   Google Scholar
• Bullinaria, J. A., and K. AlYahya. 2014. Artificial bee colony training of neural networks: Comparison with back-propagation. Memetic Computing 6 (3):171–82. doi:10.1007/s12293-014-0137-7.
   Web of Science ®Google Scholar
• Chandrayan, P. 2017. Deep learning: deep belief network fundamentals. Online Accessed October 8, 2019. https://codeburst.io/deep-learning-deep-belief-network-fundamentals-d0dcfd80d7d4
   Google Scholar
• Chiroma, H., A. Y. U. Gital, N. Rana, M. A. Shafi’i, A. N. Muhammad, A. Y. Umar, and A. I. Abubakar. 2019. Nature inspired meta-heuristic algorithms for deep learning: recent progress and novel perspective. In Science and Information Conference. Springer, Cham, April.
   Google Scholar
• Chung, H., & Shin, K. S. 2019. Genetic algorithm-optimized multi-channel convolutional neural network for stock market prediction. Neural Computing and Applications 1–18.
   PubMedGoogle Scholar
• Ciortea, E. M. 2018. IoT analysis of manufacturing using petri nets. In IOP Conference Series: Materials Science and Engineering Vol. 400, No. 4, IOP Publishing, Constanta, Romania, August.
   Google Scholar
• Cogswell, M., F. Ahmed, R. Girshick, L. Zitnick, and D. Batra. 2015. Reducing overfitting in deep networks by decorrelating representations. arXiv preprint arXiv:1511.06068.‏.
   Google Scholar
• Davydova, O. 2017. 7 types of artificial neural networks for natural language. Online Accessed October 8, 2019. https://medium.com/@datamonsters/artificial-neural-networks-fornatural-language-processing-part-1-64ca9ebfa3b2
   Google Scholar
• De Filippis, L. A. C., L. M. Serio, F. Facchini, and G. Mummolo. 2017. ANN Modelling to Optimize Manufacturing Process. In Advanced Applications for Artificial Neural Networks, ed. Adel El-Shahat, (pp. 201–226). IntechOpen.
   Google Scholar
• Ding, L., W. Fang, H. Luo, P. E. Love, B. Zhong, and X. Ouyang. 2018. A deep hybrid learning model to detect unsafe behavior: Integrating convolution neural networks and long short-term memory. Automation in Construction 86:118–24.
   Web of Science ®Google Scholar
• Ebubekir, K. 2010. The bees algorithm theory, improvements and applications. Cardiff United Kingdom.‏: Manufacturing Engineering Centre School of Engineering University of Wales.
   Google Scholar
• Ekins, S. 2016. The next era: Deep learning in pharmaceutical research. Pharmaceutical Research 33 (11):2594–603. doi:10.1007/s11095-016-2029-7.
   PubMed Web of Science ®Google Scholar
• Fu, K., D. Cheng, Y. Tu, and L. Zhang. 2016. Credit card fraud detection using convolutional neural networks. In International Conference on Neural Information Processing, Springer, Cham, October.
   Google Scholar
• Geissbauer, R., J. Vedso, and S. Schrauf. 2016. Global industry 4.0 survey: Building the digital enterprise, 1. Retrieved from PwC Website: https://www. pwc. com/gx/en/industries/industries-4.0/landing-page/industry-4.0-building-your-digital-enterprise-april-2016. pdf
   Google Scholar
• Hui, J. 2017. Convolutional neural networks (CNN) tutorial. Online Accessed November 15, 2021. https://jhui.github.io/2017/03/16/CNN-Convolutional-neural-network
   Google Scholar
• Imanguliyev, A. 2013. Enhancements for the Bees Algorithm. Doctoral dissertation, Cardiff University.
   Google Scholar
• Joshi, S., D. K. Verma, G. Saxena, and A. Paraye. 2019. Issues in training a convolutional neural network model for image classification. In International Conference on Advances in Computing and Data Sciences, Springer, Singapore, April.
   Google Scholar
• Korshunova, K. P. 2018. A convolutional fuzzy neural network for image classification. In 2018 3rd Russian-Pacific Conference on Computer Technology and Applications (RPC) (pp. 1–4), IEEE, August.
   Google Scholar
• Le, Q. V. 2015. A tutorial on deep learning part 2: Autoencoders, convolutional neural networks and recurrent neural networks. Google Brain 20: 1–20.
   Google Scholar
• Lee, W. Y., S. M. Park, and K. B. Sim. 2018. Optimal hyperparameter tuning of convolutional neural networks based on the parameter-setting-free harmony search algorithm. Optik 172:359–67. doi:10.1016/j.ijleo.2018.07.044.
   Web of Science ®Google Scholar
• Li, B. H., B. C. Hou, W. T. Yu, X. B. Lu, and C. W. Yang. 2017. Applications of artificial intelligence in intelligent manufacturing: A review. Frontiers of Information Technology & Electronic Engineering 18 (1):86–96. doi:10.1631/FITEE.1601885.
   Web of Science ®Google Scholar
• Liang, J., and R. Liu. 2015. Stacked denoising autoencoder and dropout together to prevent overfitting in deep neural network. In 2015 8th International Congress on Image and Signal Processing (CISP) (pp. 697–701), IEEE‏, October.
   Google Scholar
• Lindfield, G., and J. Penny. 2017. Introduction to Nature-Inspired Optimization. Waltham, MA, United States: Academic Press.
   Google Scholar
• Ma, N., X. Zhang, H. T. Zheng, and J. Sun 2018. Shufflenet v2: Practical guidelines for efficient cnn architecture design. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 116–131).
   Google Scholar
• Mallawaarachchi, V. 2017. Introduction to genetic algorithms. Online Accessed April 2, 2020. https://towardsdatascience.com/introduction-to-genetic-algorithms-including-example-code-e396e98d8bf3
   Google Scholar
• Masko, D., and P. Hensman. 2015. The impact of imbalanced training data for convolutional neural networks‏. Degree Project in Computer Science, KTH Royal Institute of Technology.
   Google Scholar
• MathWorks-1. Convolutional neural network. Online Accessed October 7, 2019. https://uk.mathworks.com/solutions/deep-learning/convolutional-neural-network.html.
   Google Scholar
• MathWorks-2. Deep learning using bayesian optimization. Online Accessed April 4, 2020. https://www.mathworks.com/help/deeplearning/ug/deep-learning-using-bayesian-optimization.html.
   Google Scholar
• MathWorks-3. Bees algorithm (BeA) in MATLAB. Online Accessed April 9, 2020. https://uk.mathworks.com/matlabcentral/fileexchange/52967-bees-algorithm-bea-in-matlab.
   Google Scholar
• McDermott, J. 2021. Convolutional neural networks — image classification w Keras. Online Accessed November 15, 2021. https://www.learndatasci.com/tutorials/convolutional-neural-networks-image-classification.
   Google Scholar
• Motepe, S., A. N. Hasan, and R. Stopforth. 2019. Improving load forecasting process for a power distribution network using hybrid AI and deep learning algorithms. IEEE Access 7:82584–98. doi:10.1109/ACCESS.2019.2923796.
   Web of Science ®Google Scholar
• Ouf, H. 2017. Maxpooling vs minpooling vs average pooling. Online Accessed November 15, 2021. https://hany-ouf.blogspot.com/2020/08/maxpooling-vs-minpooling-vs-average.html.
   Google Scholar
• Packianather, M. S., A. K. Al-Musawi, and F. Anayi. 2019. Bee for mining (B4M)–A novel rule discovery method using the Bees algorithm with quality-weight and coverage-weight. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 233 (14): 5101–5112.
   Web of Science ®Google Scholar
• Packianather, M. S., B. Yuce, E. Mastrocinque, F. Fruggiero, D. T. Pham, and A. Lambiase. 2014. Novel genetic bees algorithm applied to single machine scheduling problem. In 2014 World Automation Congress (WAC), IEEE, Waikoloa, HI, United States, April.
   Google Scholar
• Pan, H. 2017. A Study on Deep Learning: Training, Models and Applications.
   Google Scholar
• Panwar, M., J. Padmini, A. Acharyya, and D. Biswas. 2017. Modified distributed arithmetic based low complexity CNN architecture design methodology. In 2017 European Conference on Circuit Theory and Design (ECCTD), IEEE, Catania, Italy, September.
   Google Scholar
• Popko, E. A., and I. A. Weinstein. 2016. Fuzzy logic module of convolutional neural network for handwritten digits recognition. In Journal of Physics: Conference Series, Vol. 738, No. 1 IOP Publishing, Athens, Greece, August.
   Google Scholar
• Qolomany, B., M. Maabreh, A. Al-Fuqaha, A. Gupta, and D. Benhaddou. 2017.Parameters optimization of deep learning models using particle swarm optimization. In 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC), IEEE, Bangkok, Thailand, June.
   Google Scholar
• Rashid, T. A., and S. M. Abdullah. 2018. A hybrid of artificial bee colony, genetic algorithm, and neural network for diabetic mellitus diagnosing. ARO-The Scientific Journal of Koya University 6 (1):55–64. doi:10.14500/aro.10368.
   Web of Science ®Google Scholar
• Shah, A., E. Kadam, H. Shah, S. Shinde, and S. Shingade. 2016. Deep residual networks with exponential linear unit. In Proceedings of the Third International Symposium on Computer Vision and the Internet, Jaipur, India, September.
   Google Scholar
• Singh, A. K., B. Ganapathysubramanian, S. Sarkar, and A. Singh. 2018. Deep learning for plant stress phenotyping: Trends and future perspectives. Trends in Plant Science 23 (10):883–98. doi:10.1016/j.tplants.2018.07.004.
   PubMed Web of Science ®Google Scholar
• Sinha, T., B. Verma, and A. Haidar. 2017. Optimization of convolutional neural network parameters for image classification. In 2017 IEEE Symposium Series on Computational Intelligence (SSCI), Honolulu, HI, United States, IEEE.
   Google Scholar
• Tch, A. 2017. The mostly complete chart of neural networks, explained. Online Accessed October 7, 2019. https://towardsdatascience.com/the-mostly-complete-chart-of-neural-networks-explained-3fb6f2367464.
   Google Scholar
• Wang, J., Y. Ma, L. Zhang, R. X. Gao, and D. Wu. 2018. Deep learning for smart manufacturing: Methods and applications. Journal of Manufacturing Systems 48:144–56. doi:10.1016/j.jmsy.2018.01.003.
   Web of Science ®Google Scholar
• Wu, B., Z. Liu, Z. Yuan, G. Sun, and C. Wu. 2017. Reducing overfitting in deep convolutional neural networks using redundancy regularizer. In International Conference on Artificial Neural Networks, Springer, Cham, September.
   Google Scholar
• Wu, J. 2017. Introduction to convolutional neural networks. China: National Key Lab for Novel Software Technology, Nanjing University. 5, 23.
   Google Scholar
• Wuest, T., D. Weimer, C. Irgens, and K. D. Thoben. 2016. Machine learning in manufacturing: Advantages, challenges, and applications. Production & Manufacturing Research 4 (1):23–45. doi:10.1080/21693277.2016.1192517.
   Web of Science ®Google Scholar
• Xu, F., C. M. Pun, H. Li, Y. Zhang, Y. Song, and H. Gao (2019). Training feed-forward artificial neural networks with a modified artificial bee colony algorithm. Neurocomputing
   Google Scholar
• Yamashita, R., M. Nishio, R. K. G. Do, and K. Togashi. 2018. Convolutional neural networks: An overview and application in radiology. Insights into Imaging 9 (4):611–29. doi:10.1007/s13244-018-0639-9.
   PubMed Web of Science ®Google Scholar
• Zeybek, S., D. T. Pham, E. Koç, and A. Seçer. 2021. An improved bees algorithm for training deep recurrent networks for sentiment classification. Symmetry 13 (8):1347. doi:10.3390/sym13081347.
   Google Scholar
• Zhang, H., S. Kiranyaz, and M. Gabbouj. 2018. Finding better topologies for deep convolutional neural networks by evolution. arXiv preprint arXiv:1809.03242.
   Google Scholar