[
1. A. W. Senior, R. Evans, J. Jumper, J. Kirkpatrick, L. Sifre, T. Green, C. Qin, A. ˇ Z´ıdek, A. W. R. Nelson, A. Bridgland, H. Penedones, S. Petersen, K. Simonyan, S. Crossan, P. Kohli, D. T. Jones, D. Silver, K. Kavukcuoglu, and D. Hassabis, Improved protein structure prediction using potentials from deep learning, Nature, vol. 577, pp. 706–710, Jan 2020.10.1038/s41586-019-1923-731942072
]Search in Google Scholar
[
2. G. Carleo, I. Cirac, K. Cranmer, L. Daudet, M. Schuld, N. Tishby, L. Vogt-Maranto, and L. Zdeborová, Machine learning and the physical sciences, Rev. Mod. Phys., vol. 91, p. 045002, Dec 2019.10.1103/RevModPhys.91.045002
]Search in Google Scholar
[
3. Darmatasia and M. I. Fanany, Handwriting recognition on form document using convolutional neural network and support vector machines (cnn-svm), 2017 5th International Conference on Information and Communication Technology (ICoIC7), pp. 1–6, 2017.10.1109/ICoICT.2017.8074699
]Search in Google Scholar
[
4. N. H. Tandel, H. B. Prajapati, and V. K. Dabhi, Voice recognition and voice comparison using machine learning techniques: A survey, 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), pp. 459–465, 2020.10.1109/ICACCS48705.2020.9074184
]Search in Google Scholar
[
5. S. Ahlawat, A. Choudhary, A. Nayyar, S. Singh, and B. Yoon, Improved handwritten digit recognition using convolutional neural networks (cnn), Sensors, vol. 20, no. 12, 2020.10.3390/s20123344734960332545702
]Search in Google Scholar
[
6. K. Han, D. Yu, and I. Tashev, Speech emotion recognition using deep neural network and extreme learning machine, in Interspeech 2014, September 2014.10.21437/Interspeech.2014-57
]Search in Google Scholar
[
7. P. Hadikhani, N. Borhani, S. Hashemi, and D. Psaltis, Learning from droplet flows in microfluidic channels using deep neural networks, Scientific Reports, vol. 9, p. 8114, 2019.10.1038/s41598-019-44556-x654461131148559
]Search in Google Scholar
[
8. Y. Mahdi and K. Daoud, Microdroplet size prediction in microfluidic systems via artificial neural network modeling for water-in-oil emulsion formulation, Journal of Dispersion Science and Technology, vol. 38, no. 10, pp. 1501–1508, 2017.10.1080/01932691.2016.1257391
]Search in Google Scholar
[
9. J. W. Khor, N. Jean, E. S. Luxenberg, S. Ermon, and S. K. Y. Tang, Using machine learning to discover shape descriptors for predicting emulsion stability in a microfluidic channel, Soft Matter, vol. 15, pp. 1361–1372, 2019.10.1039/C8SM02054J
]Search in Google Scholar
[
10. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016. http://www.deeplearningbook.org.
]Search in Google Scholar
[
11. T. Osman, S. S. Psyche, J. M. Shafi Ferdous, and H. U. Zaman, Intelligent traffic management system for cross section of roads using computer vision, 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC), pp. 1–7, 2017.10.1109/CCWC.2017.7868350
]Search in Google Scholar
[
12. A. Montessori, A. Tiribocchi, M. Bogdan, F. Bonaccorso, M. Lauricella, J. Guzowski, and S. Succi, Translocation dynamics of high-internal phase double emulsions in narrow channels, Langmuir, vol. 37, pp. 9026–9033, Aug 2021.10.1021/acs.langmuir.1c01026
]Search in Google Scholar
[
13. A. Montessori, M. L. Rocca, P. Prestininzi, A. Tiribocchi, and S. Succi, Deformation and breakup dynamics of droplets within a tapered channel, Physics of Fluids, vol. 33, no. 8, p. 082008, 2021.10.1063/5.0057501
]Search in Google Scholar
[
14. M. Bogdan, A. Montessori, A. Tiribocchi, F. Bonaccorso, M. Lauricella, L. Jurkiewicz, S. Succi, and J. Guzowski, Stochastic jetting and dripping in confined soft granular flows, Phys. Rev. Lett., vol. 128, p. 128001, Mar 2022.10.1103/PhysRevLett.128.128001
]Search in Google Scholar
[
15. M. Costantini, C. Colosi, J. Guzowski, A. Barbetta, J. Jaroszewicz, W. Swieszkowski, M. Dentini, and P. Garstecki, Highly ordered and tunable polyhipes by using microfluidics, J. Mater. Chem. B, vol. 2, pp. 2290–2300, 2014.10.1039/c3tb21227k
]Search in Google Scholar
[
16. Durve, Mihir, Bonaccorso, Fabio, Montessori, Andrea, Lauricella, Marco, Tiribocchi, Adriano, and Succi, Sauro, Tracking droplets in soft granular flows with deep learning techniques, Eur. Phys. J. Plus, vol. 136, no. 8, p. 864, 2021.10.1140/epjp/s13360-021-01849-3838011734458055
]Search in Google Scholar
[
17. A. S. Utada, E. L. Lorenceau, D. R. Link, P. D. Kaplan, H. A. Stone, and D. A. Weitz, Monodisperse double emulsions generated from a microcapillary device, Science, vol. 308, pp. 537–541, 2005.10.1126/science.110916415845850
]Search in Google Scholar
[
18. A. Montessori, P. Prestininzi, M. La Rocca, and S. Succi, Lattice boltzmann approach for complex nonequilibrium flows, Physical Review E, vol. 92, no. 4, p. 043308, 2015.10.1103/PhysRevE.92.043308
]Search in Google Scholar
[
19. C. Coreixas, B. Chopard, and J. Latt, Comprehensive comparison of collision models in the lattice boltzmann framework: Theoretical investigations, Physical Review E, vol. 100, no. 3, p. 033305, 2019.10.1103/PhysRevE.100.033305
]Search in Google Scholar
[
20. S. Succi, The lattice boltzmann equation: For complex states of flowing matter, Oxford University Press, 2018.10.1093/oso/9780199592357.001.0001
]Search in Google Scholar
[
21. M. Durve, F. Bonaccorso, A. Montessori, M. Lauricella, A. Tiribocchi, and S. Succi, A fast and efficient deep learning procedure for tracking droplet motion in dense microfluidic emulsions, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 379, no. 2208, p. 20200400, 2021.10.1098/rsta.2020.0400
]Search in Google Scholar
[
22. The pascal visual object classes homepage. http://host.robots.ox.ac.uk/pascal/VOC/.
]Search in Google Scholar
[
23. Coco dataset homepage. http://cocodataset.org.
]Search in Google Scholar
[
24. F. Zhou, H. Zhao, and Z. Nie, Safety helmet detection based on yolov5, in 2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA), pp. 6–11, 2021.10.1109/ICPECA51329.2021.9362711
]Search in Google Scholar
[
25. L. C. M. Junior and J. Alfredo C. Ulson, Real time weed detection using computer vision and deep learning, in 2021 14th IEEE International Conference on Industry Applications (INDUSCON), pp. 1131–1137, 2021.10.1109/INDUSCON51756.2021.9529761
]Search in Google Scholar
[
26. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, You only look once: Unified, real-time object detection, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788, 2016.10.1109/CVPR.2016.91
]Search in Google Scholar
[
27. J. Redmon and A. Farhadi, Yolov3: An incremental improvement, ArXiv:1804.02767v1, 2018.
]Search in Google Scholar
[
28. N. Wojke, A. Bewley, and D. Paulus, Simple online and realtime tracking with a deep association metric, 2017 IEEE International Conference on Image Processing (ICIP), pp. 3645–3649, 2017.10.1109/ICIP.2017.8296962
]Search in Google Scholar
[
29. A. Bewley, Z. Ge, L. Ott, F. Ramos, and B. Upcroft, Simple online and realtime tracking, in 2016 IEEE International Conference on Image Processing (ICIP), pp. 3464–3468, 2016.10.1109/ICIP.2016.7533003
]Search in Google Scholar
[
30. H. W. Kuhn, The hungarian method for the assignment problem, Naval Research Logistics Quarterly, vol. 2, no. 1-2, pp. 83–97, 1955.10.1002/nav.3800020109
]Search in Google Scholar
[
31. R. E. Kalman, A New Approach to Linear Filtering and Prediction Problems, Journal of Basic Engineering, vol. 82, pp. 35–45, 03 1960.10.1115/1.3662552
]Search in Google Scholar
[
32. A. Cavagna, L. Del Castello, I. Giardina, T. Grigera, A. Jelic, S. Melillo, T. Mora, L. Parisi, E. Silvestri, M. Viale, and A. M. Walczak, Flocking and turning: a new model for self-organized collective motion, Journal of Statistical Physics, vol. 158, pp. 601–627, Feb 2015.10.1007/s10955-014-1119-3
]Search in Google Scholar
[
33. M. Ballerini, N. Cabibbo, R. Candelier, A. Cavagna, E. Cisbani, I. Giardina, V. Lecomte, A. Orlandi, G. Parisi, A. Procaccini, M. Viale, and V. Zdravkovic, Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study, Proceedings of the National Academy of Sciences, vol. 105, no. 4, pp. 1232–1237, 2008.10.1073/pnas.0711437105
]Search in Google Scholar
[
34. T. Vicsek, A. Czir´ok, E. Ben-Jacob, I. Cohen, and O. Shochet, Novel type of phase transition in a system of self-driven particles, Phys. Rev. Lett., vol. 75, pp. 1226–1229, Aug 1995.10.1103/PhysRevLett.75.1226
]Search in Google Scholar
[
35. I. D. COUZIN, J. KRAUSE, R. JAMES, G. D. RUXTON, and N. R. FRANKS, Collective memory and spatial sorting in animal groups, Journal of Theoretical Biology, vol. 218, no. 1, pp. 1–11, 2002.10.1006/jtbi.2002.306512297066
]Search in Google Scholar
[
36. L. Barberis and F. Peruani, Large-scale patterns in a minimal cognitive flocking model: Incidental leaders, nematic patterns, and aggregates, Phys. Rev. Lett., vol. 117, p. 248001, Dec 2016.10.1103/PhysRevLett.117.248001
]Search in Google Scholar
[
37. M. Durve, A. Tiribocchi, F. Bonaccorso, A. Montessori, M. Lauricella, M. Bogdan, J. Guzowski, and S. Succi, Droptrack - automatic droplet tracking with yolov5 and deepsort for microfluidic applications, Physics of Fluids, vol. 34, no. 8, p. 082003, 2022.10.1063/5.0097597
]Search in Google Scholar