參考文獻 |
[1] G. Ding et al., "Fish recognition using convolutional neural network," in OCEANS 2017, pp. 1-4, 2017.
[2] M. M. M. Fouad, H. M. Zawbaa, N. El-Bendary, and A. E. Hassanien, "Automatic Nile Tilapia fish classification approach using machine learning techniques," in 13th International Conference on Hybrid Intelligent Systems 2013, pp. 173-178, 2013.
[3] D.-J. Lee, R. B. Schoenberger, D. Shiozawa, X. Xu, and P. Zhan, "Contour matching for a fish recognition and migration-monitoring system," in Optics East, pp. 12, 2004.
[4] B. P. Ruff, J. A. Marchant, and A. R. Frost, "Fish sizing and monitoring using a stereo image analysis system applied to fish farming," Aquacultural Engineering, pp. 155-173, 1995.
[5] L. Jin and H. Liang, "Deep learning for underwater image recognition in small sample size situations," in OCEANS 2017 - Aberdeen, pp. 1-4, 2017.
[6] Y. Nagaoka, T. Miyazaki, Y. Sugaya, and S. Omachi, "Mackerel Classification Using Global and Local Features," in 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1209-1212, 2018.
[7] M. Chuang, J. Hwang, and K. Williams, "A Feature Learning and Object Recognition Framework for Underwater Fish Images," IEEE Transactions on Image Processing, pp. 1862-1872, 2016.
[8] L. Li and J. Hong, "Identification of fish species based on image processing and statistical analysis research," in 2014 IEEE International Conference on Mechatronics and Automation, pp. 1155-1160, 2014.
[9] M. N. Rachmatullah and I. Supriana, "Low Resolution Image Fish Classification Using Convolutional Neural Network," in 2018 5th International Conference on Advanced Informatics: Concept Theory and Applications (ICAICTA), pp. 78-83, 2018.
[10] P. H. Patrick, N. Ramani, W. G. Hanson, and H. Anderson, "The potential of a neural network based sonar system in classifying fish," in [1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering, pp. 207-213, 1991.
[11] L. Xiu, S. Min, H. Qin, and C. Liansheng, "Fast accurate fish detection and recognition of underwater images with Fast R-CNN," in OCEANS 2015 - MTS/IEEE Washington, pp. 1-5, 2015.
[12] S. Mohorovičić, "Implementing responsive web design for enhanced web presence," in Information & Communication Technology Electronics & Microelectronics (MIPRO), 2013 36th International Convention on, pp. 1206-1210, 2013.
[13] N. Marangunić and A. Granić, "Technology acceptance model: a literature review from 1986 to 2013," Universal Access in the Information Society, pp. 81-95, 2015.
[14] D. Sirdeshmukh, N. B. Ahmad, M. S. Khan, and N. J. Ashill, "Drivers of user loyalty intention and commitment to a search engine: An exploratory study," Journal of Retailing and Consumer Services, pp. 71-81, 2018.
[15] A. Hussain and E. O. Mkpojiogu, "The effect of responsive web design on the user experience with laptop and smartphone devices," Jurnal Teknologi (Sciences & Engineering), pp. 41-47, 2015.
[16] C. Peterson, Learning responsive web design: a beginner′s guide. " O′Reilly Media, Inc.", 2014.
[17] C. Sharkie and A. Fisher, Jump Start Responsive Web Design. SitePoint, 2013.
[18] M. Bean, Laravel 5 essentials. Packt Publishing Ltd, 2015.
[19] S. McCool, Laravel Starter. Packt Publishing Ltd, 2012.
[20] M. Stauffer, Laravel: up and running: a framework for building modern PHP apps. " O′Reilly Media, Inc.", 2016.
[21] D. Powers, PHP solutions: dynamic web design made easy. Apress, 2014.
[22] K. Tatroe, P. MacIntyre, and R. Lerdorf, Programming PHP: Creating Dynamic Web Pages. " O′Reilly Media, Inc.", 2013.
[23] L. Welling and L. Thomson, PHP and MySQL Web development. Sams Publishing, 2003.
[24] H. E. Williams and D. Lane, Web Database Applications with PHP and MySQL: Building Effective Database-Driven Web Sites. " O′Reilly Media, Inc.", 2004.
[25] B. Leuf and W. Cunningham, "The Wiki way: quick collaboration on the Web," 2001.
[26] S. Hasija, M. J. Buragohain, and S. Indu, "Fish Species Classification Using Graph Embedding Discriminant Analysis," in 2017 International Conference on Machine Vision and Information Technology (CMVIT), pp. 81-86, 2017.
[27] X. Bai, X. Yang, and L. J. Latecki, "Detection and recognition of contour parts based on shape similarity," Pattern Recognition, pp. 2189-2199, 2008.
[28] Y. Nishida, T. Ura, T. Hamatsu, K. Nagahashi, S. Inaba, and T. Nakatani, "Fish recognition method using vector quantization histogram for investigation of fishery resources," in 2014 Oceans - St. John′s, pp. 1-5, 2014.
[29] S. Kumar, S. K. Singh, and A. K. Singh, "Muzzle point pattern based techniques for individual cattle identification," IET Image Processing, pp. 805-814, 2017.
[30] M. Kociolek, P. Bajcsy, M. Brady, and A. Cardone, "Interpolation-Based Gray-Level Co-Occurrence Matrix Computation for Texture Directionality Estimation," in 2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), pp. 146-151, 2018.
[31] G. Anders et al., "Density estimation of grey-level co-occurrence matrices for image texture analysis," Physics in Medicine & Biology, pp. 195017, 2018.
[32] Z. Qingfeng, X. Yang, and B. Leping, "Analysis of shape features of flame and interference image in video fire detection," in 2015 Chinese Automation Congress (CAC), pp. 633-637, 2015.
[33] N. Ghosh, S. Agrawal, and M. Motwani, "A Survey of Feature Extraction for Content-Based Image Retrieval System," in Proceedings of International Conference on Recent Advancement on Computer and Communication, pp. 305-313, 2018.
[34] P. A. Kowalski and M. Kusy, "Sensitivity analysis for probabilistic neural network structure reduction," IEEE transactions on neural networks and learning systems, pp. 1919-1932, 2018.
[35] C. Napoli, G. Pappalardo, E. Tramontana, R. K. Nowicki, J. T. Starczewski, and M. Woźniak, "Toward work groups classification based on probabilistic neural network approach," in International Conference on Artificial Intelligence and Soft Computing, pp. 79-89, 2015.
[36] C. Szegedy et al., "Going deeper with convolutions," in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1-9, 2015.
[37] J. Jin, A. Dundar, and E. Culurciello, "Flattened convolutional neural networks for feedforward acceleration," arXiv preprint arXiv:1412.5474, 2014.
[38] K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778, 2016.
[39] A. L. Lemos, F. Daniel, and B. Benatallah, "Web service composition: a survey of techniques and tools," ACM Computing Surveys (CSUR), pp. 33, 2016.
[40] Y. Takeuchi, K. Sengoku, and T. Shimada, "Information processing server, client, and information processing system," ed: Google Patents, 2017.
[41] J. van den Ende, L. Frederiksen, and A. Prencipe, "The front end of innovation: Organizing search for ideas," Journal of Product Innovation Management, pp. 482-487, 2015.
[42] M. Villamizar et al., "Evaluating the monolithic and the microservice architecture pattern to deploy web applications in the cloud," in Computing Colombian Conference (10CCC), 2015 10th, pp. 583-590, 2015.
[43] E. Braun, A. Radkohl, C. Schmitt, T. Schlachter, and C. Düpmeier, "A lightweight web components framework for accessing generic data services in environmental information systems," in From Science to Society: Springer, 2018, pp. 191-201.
[44] L. J. Mitchell, PHP Web Services: APIs for the Modern Web. " O′Reilly Media, Inc.", 2016.
[45] R. Connolly, Fundamentals of web development. Pearson Education, 2015.
[46] E. Pulier, F. Martinez, and D. C. Hill, "System and method for a cloud computing abstraction layer," ed: Google Patents, 2018.
[47] M. Y. Yi and Y. Hwang, "Predicting the use of web-based information systems: self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model," International Journal of Human-Computer Studies, pp. 431-449, 2003.
[48] F. Davis, R. Bagozzi, and P. R. Warshaw, User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. 1989, pp. 982-1003. |