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2022 | OriginalPaper | Chapter

You Got Data‥‥ Now What: Building the Right Solution for the Problem

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Abstract

The demands placed upon the agri-food industry are becoming ever greater and ever more urgent, and food producers are turning to technology to provide solutions to maximizing production and productivity. In the last decade, there has been a rapid expansion in Information and Communications Technology that is now capable of answering these challenges and breaking free of dependence upon manual labor and levels of human skill and experience that would take years or even decades to develop. This expansion has taken place at two essential levels. At a first level, it is possible to design and engineer individual sensors that can supply accurate measurements wherever they are placed and whenever they are in place. However, it is only when an array of sensors is deployed in a spatial network over an extended period, does the power of technology become apparent as through these networks remote and automatic control of production processes becomes realized. At a second level, it is now possible to design and engineer computing hardware and software to process the enormous datasets that these sensor networks generate. Furthermore, advancements in machine learning have facilitated the creation of predictive models to divine accurate process control decisions from these datasets.

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Literature
1.
go back to reference United Nations. (2020a). Global issues: Our growing population. United Nations Neutral Zone. United Nations. (2020a). Global issues: Our growing population. United Nations Neutral Zone.
2.
go back to reference United Nations. (2020b). Looking ahead in world food and agriculture. United Nations Neutral Zone. United Nations. (2020b). Looking ahead in world food and agriculture. United Nations Neutral Zone.
3.
go back to reference Gerland, P., Raftery, A. E., Ševcíková, H., Li, N., Gu, D., Spoorenberg, T., Alkema, L., Fosdick, B. K., Chunn, J., Lalic, N., Bay, G., Buettner, T., Heilig, G. K., & Wilmoth, J. (2014). World population stabilization unlikely this century. Science (New York, N.Y.), 346(6206), 234–237.CrossRef Gerland, P., Raftery, A. E., Ševcíková, H., Li, N., Gu, D., Spoorenberg, T., Alkema, L., Fosdick, B. K., Chunn, J., Lalic, N., Bay, G., Buettner, T., Heilig, G. K., & Wilmoth, J. (2014). World population stabilization unlikely this century. Science (New York, N.Y.), 346(6206), 234–237.CrossRef
4.
go back to reference Kummu, M., Guillaume, J. H. A., de Moel, H., Eisner, S., Flörke, M., Porkka, M., Siebert, S., Veldkamp, T. I., & Ward, P. J. (2016). The world’s road to water scarcity: Shortage and stress in the 20th century and pathways towards sustainability. Scientific reports, 6, 38495.CrossRef Kummu, M., Guillaume, J. H. A., de Moel, H., Eisner, S., Flörke, M., Porkka, M., Siebert, S., Veldkamp, T. I., & Ward, P. J. (2016). The world’s road to water scarcity: Shortage and stress in the 20th century and pathways towards sustainability. Scientific reports, 6, 38495.CrossRef
5.
go back to reference Agriland. (2020). Labour availability is now a critical issue within agri-food. Agriland Media. Agriland. (2020). Labour availability is now a critical issue within agri-food. Agriland Media.
6.
go back to reference Food Manufacture. (2020). Labour shortage reaching crisis point for agricultural sector. William Reed Business Media. Food Manufacture. (2020). Labour shortage reaching crisis point for agricultural sector. William Reed Business Media.
7.
go back to reference Food Processing Technology. (2020). UK food industry suffers from labour shortage. Global Data. Food Processing Technology. (2020). UK food industry suffers from labour shortage. Global Data.
8.
go back to reference Henriksen, A. V., Edwards, G. T. C., Pesonen, L. A., Green, O., & Sorensen, C. A. G. (2020). Internet of Things in arable farming: Implementation, applications, challenges and potential. Biosystems Engineering, 191(1), 60–84.CrossRef Henriksen, A. V., Edwards, G. T. C., Pesonen, L. A., Green, O., & Sorensen, C. A. G. (2020). Internet of Things in arable farming: Implementation, applications, challenges and potential. Biosystems Engineering, 191(1), 60–84.CrossRef
9.
go back to reference Jamieson, J. A. (1976). Passive Infrared Sensors: Limitations on Performance. Journal of Applied Optics, 15(4), 891–909.CrossRef Jamieson, J. A. (1976). Passive Infrared Sensors: Limitations on Performance. Journal of Applied Optics, 15(4), 891–909.CrossRef
10.
go back to reference Maxbotix. (2019). Ultrasonic sensors: Advantages and limitations. Maxbotix Inc.. Maxbotix. (2019). Ultrasonic sensors: Advantages and limitations. Maxbotix Inc..
11.
go back to reference National Safety Council. (2020). The pros and cons of electrochemical sensors. National Safety Council Congress & Expo. National Safety Council. (2020). The pros and cons of electrochemical sensors. National Safety Council Congress & Expo.
12.
go back to reference Donald, N. (1988). The design of everyday things. Basic Books. Donald, N. (1988). The design of everyday things. Basic Books.
13.
go back to reference Compton, M., Barnaghi, P., Bermudez, L., Garcia-Castro, R., Corcho, O., Cox, S., Graybeal, J., Hauswirth, M., Henson, C., Herzog, A., Huang, V., Janowich, K., Kelsey, W. D., Le Phouc, D., LeFort, L., Leggieri, M., Neuhaus, H., Nikolov, A., Page, K., … Taylor, K. (2012). The SSN ontology of the W3C semantic sensor network incubator group. Journal of Web Semantics, 17(1), 25–32.CrossRef Compton, M., Barnaghi, P., Bermudez, L., Garcia-Castro, R., Corcho, O., Cox, S., Graybeal, J., Hauswirth, M., Henson, C., Herzog, A., Huang, V., Janowich, K., Kelsey, W. D., Le Phouc, D., LeFort, L., Leggieri, M., Neuhaus, H., Nikolov, A., Page, K., … Taylor, K. (2012). The SSN ontology of the W3C semantic sensor network incubator group. Journal of Web Semantics, 17(1), 25–32.CrossRef
14.
go back to reference Liu, H., & Tang, Z. (2013). Metal oxide gas sensor drift compensation using a dynamic classifier ensemble based on fitting. Sensors, 13(7), 9160–9173.CrossRef Liu, H., & Tang, Z. (2013). Metal oxide gas sensor drift compensation using a dynamic classifier ensemble based on fitting. Sensors, 13(7), 9160–9173.CrossRef
15.
go back to reference Irish, J. (2005). Ocean instrumentation – Instrumentation specifications. Massachusetts Institute of Technology. Irish, J. (2005). Ocean instrumentation – Instrumentation specifications. Massachusetts Institute of Technology.
16.
go back to reference Loock, H. P., & Wentzell, P. D. (2012). Detection limits of chemical sensors: Applications and misapplications. Sensors and Actuators B: Chemical, 173(2), 157–163.CrossRef Loock, H. P., & Wentzell, P. D. (2012). Detection limits of chemical sensors: Applications and misapplications. Sensors and Actuators B: Chemical, 173(2), 157–163.CrossRef
17.
go back to reference Dang, Q. K., & Suh, Y. S. (2014). Sensor saturation compensated smoothing algorithm for inertial sensor based motion tracking. Sensors, 14(5), 8167–8188.CrossRef Dang, Q. K., & Suh, Y. S. (2014). Sensor saturation compensated smoothing algorithm for inertial sensor based motion tracking. Sensors, 14(5), 8167–8188.CrossRef
18.
go back to reference Palmisano, V., Weidner, E., Boon-Brett, L., Bonato, C., Harskamp, F., Moretto, P., Post, M. B., Burgess, R., Rivkin, C., & Buttner, W. J. (2015). Selectivity and resistance to poisons of commercial hydrogen sensors. International Journal of Hydrogen Energy, 40(35), 11740–11747.CrossRef Palmisano, V., Weidner, E., Boon-Brett, L., Bonato, C., Harskamp, F., Moretto, P., Post, M. B., Burgess, R., Rivkin, C., & Buttner, W. J. (2015). Selectivity and resistance to poisons of commercial hydrogen sensors. International Journal of Hydrogen Energy, 40(35), 11740–11747.CrossRef
19.
go back to reference Sparkfun Electronics. (2020). FLIR Radiometric Lepton Dev Kit V2. Sparkfun Electronics. Sparkfun Electronics. (2020). FLIR Radiometric Lepton Dev Kit V2. Sparkfun Electronics.
20.
go back to reference Ward, W. K., Engle, J. M., Branigan, D., El Youssef, J., Massoud, R. G., & Castle, J. R. (2012). The effect of rising vs. falling glucose level on amperometric glucose sensor lag and accuracy in type 1 diabetes. Journal of Diabetic Medicine, 29(8), 1067–1073.CrossRef Ward, W. K., Engle, J. M., Branigan, D., El Youssef, J., Massoud, R. G., & Castle, J. R. (2012). The effect of rising vs. falling glucose level on amperometric glucose sensor lag and accuracy in type 1 diabetes. Journal of Diabetic Medicine, 29(8), 1067–1073.CrossRef
21.
go back to reference World Nuclear Association. (2019). RBMK reactors – Appendix to nuclear power reactors. World Nuclear Association. World Nuclear Association. (2019). RBMK reactors – Appendix to nuclear power reactors. World Nuclear Association.
22.
go back to reference Corrigan, T. E., & Beavers, W. O. (1968). Dead space interaction in continuous stirred tank reactors. Chemical Engineering Science, 23(9), 1003–1006.CrossRef Corrigan, T. E., & Beavers, W. O. (1968). Dead space interaction in continuous stirred tank reactors. Chemical Engineering Science, 23(9), 1003–1006.CrossRef
23.
go back to reference Hilbert, M., & Lopez, P. (2011). The World’s technological capacity to store, communicate, and compute information. Science, 332(2), 60–65.CrossRef Hilbert, M., & Lopez, P. (2011). The World’s technological capacity to store, communicate, and compute information. Science, 332(2), 60–65.CrossRef
24.
go back to reference Fidanova, S., Shindarov, M., & Marinov, P. (2017). Wireless sensor positioning using ACO algorithm. In Recent contributions in intelligent systems (pp. 33–44). Springer.CrossRef Fidanova, S., Shindarov, M., & Marinov, P. (2017). Wireless sensor positioning using ACO algorithm. In Recent contributions in intelligent systems (pp. 33–44). Springer.CrossRef
25.
go back to reference Abbas, N., Yu, F., & Fan, Y. (2018). Intelligent video surveillance platform for wireless multimedia sensor networks. Journal of Applied Sciences, 348(8), 1–14. Abbas, N., Yu, F., & Fan, Y. (2018). Intelligent video surveillance platform for wireless multimedia sensor networks. Journal of Applied Sciences, 348(8), 1–14.
26.
go back to reference Cisco Systems. (2020). What is a Wi-Fi or wireless network vs. a wired network? Cisco Systems. Cisco Systems. (2020). What is a Wi-Fi or wireless network vs. a wired network? Cisco Systems.
27.
go back to reference MacDonald, J. M., Korb, P., & Hoppe, R. A. (2016). Farm size and the organization of U.S (Crop Farming). United States Department of Agriculture Economic Research Service. MacDonald, J. M., Korb, P., & Hoppe, R. A. (2016). Farm size and the organization of U.S (Crop Farming). United States Department of Agriculture Economic Research Service.
28.
go back to reference Zigbee Alliance. (2020). What is Zigbee? Zigbee Alliance. Zigbee Alliance. (2020). What is Zigbee? Zigbee Alliance.
29.
go back to reference Jackman, P., Gray, A. J. G., Brass, A., Stevens, R., Shi, M., Scuffell, D., Hammersley, S., & Grieve, B. (2012). Processing online crop disease warning information via sensor networks using ISA ontologies. CIGR Journal, 15(3), 243–251. Jackman, P., Gray, A. J. G., Brass, A., Stevens, R., Shi, M., Scuffell, D., Hammersley, S., & Grieve, B. (2012). Processing online crop disease warning information via sensor networks using ISA ontologies. CIGR Journal, 15(3), 243–251.
30.
go back to reference West, J., & Kimber, R. B. E. (2015). Innovations in air sampling to detect plant pathogens. Annals of Applied Biology, 166(1), 4–17.CrossRef West, J., & Kimber, R. B. E. (2015). Innovations in air sampling to detect plant pathogens. Annals of Applied Biology, 166(1), 4–17.CrossRef
31.
go back to reference He, Y., Peng, J., Liu, F., Zhang, C., & Kong, W. (2015). Critical review of fast detection of crop nutrient and physiological information with spectral and imaging technology. Transactions of the Chinese Society of Agricultural Engineering, 31(3), 174–189. He, Y., Peng, J., Liu, F., Zhang, C., & Kong, W. (2015). Critical review of fast detection of crop nutrient and physiological information with spectral and imaging technology. Transactions of the Chinese Society of Agricultural Engineering, 31(3), 174–189.
32.
go back to reference Henrich, V., Krauss, G., Gotze, C., & Sandow, C. (2020). Index database: A database for remote sensing indices. University of Bonn. Henrich, V., Krauss, G., Gotze, C., & Sandow, C. (2020). Index database: A database for remote sensing indices. University of Bonn.
33.
go back to reference Ahamed, T., Tian, L., Jiang, Y., Zhao, B., Liu, H., & Ting, K. C. (2012). Tower remote-sensing system for monitoring energy crops; image acquisition and geometric corrections. Biosystems Engineering, 112(2), 93–107.CrossRef Ahamed, T., Tian, L., Jiang, Y., Zhao, B., Liu, H., & Ting, K. C. (2012). Tower remote-sensing system for monitoring energy crops; image acquisition and geometric corrections. Biosystems Engineering, 112(2), 93–107.CrossRef
34.
go back to reference CLAAS. (2020). Forage harvesters – Jaguar. CLAAS Harsewinkel. CLAAS. (2020). Forage harvesters – Jaguar. CLAAS Harsewinkel.
35.
go back to reference John Deere. (2020). HarvestLab 3000. John Deere. John Deere. (2020). HarvestLab 3000. John Deere.
36.
go back to reference YARA. (2020). N-Sensor ALS – to variably apply nitrogen. YARA. YARA. (2020). N-Sensor ALS – to variably apply nitrogen. YARA.
37.
go back to reference Oerke, E. C., Mahlein, A. K., & Steiner, U. (2014). Proximal sensing of plant diseases. In Detection and diagnostics of plant pathogens. Springer. Oerke, E. C., Mahlein, A. K., & Steiner, U. (2014). Proximal sensing of plant diseases. In Detection and diagnostics of plant pathogens. Springer.
38.
go back to reference European Parliament. (2020). Chemicals and pesticides, factsheets on the European Union. . European Parliament. (2020). Chemicals and pesticides, factsheets on the European Union. .
39.
go back to reference European Space Imaging. (2020). Our satellites: Earths most advanced constellation. European Space Imaging. European Space Imaging. (2020). Our satellites: Earths most advanced constellation. European Space Imaging.
40.
go back to reference Partel, V., Kakarla, S. C., & Ampatzidis, Y. (2019). Development and evaluation of a low-cost and smart technology for precision weed management utilizing artificial intelligence. Computers and Electronics in Agriculture., 157(3), 339–350.CrossRef Partel, V., Kakarla, S. C., & Ampatzidis, Y. (2019). Development and evaluation of a low-cost and smart technology for precision weed management utilizing artificial intelligence. Computers and Electronics in Agriculture., 157(3), 339–350.CrossRef
41.
go back to reference Benke, K., & Tompkins, B. (2017). Future food-production systems: vertical farming and controlled-environment agriculture. Journal of Sustainability: Science, Practice & Policy., 13(1), 13–26. Benke, K., & Tompkins, B. (2017). Future food-production systems: vertical farming and controlled-environment agriculture. Journal of Sustainability: Science, Practice & Policy., 13(1), 13–26.
42.
go back to reference Jha, M. K., Pakira, S. S., & Sahu, M. R. (2019). Protected cultivation of horticulture crops. Educreation Publishing. Jha, M. K., Pakira, S. S., & Sahu, M. R. (2019). Protected cultivation of horticulture crops. Educreation Publishing.
43.
go back to reference Rouse, J. W., Haas, R. H., Scheel, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the great plains with ERTS. In: Proceedings, 3rd earth resource technology satellite (ERTS) symposium, vol. 1, p. 48–62. Rouse, J. W., Haas, R. H., Scheel, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the great plains with ERTS. In: Proceedings, 3rd earth resource technology satellite (ERTS) symposium, vol. 1, p. 48–62.
44.
go back to reference Ryu, K. H., Kim, G. Y., & Chae, H. Y. (2000). Monitoring greenhouse plants using thermal imaging. IFAC Proceedings Volumes, 33(29), 181–186.CrossRef Ryu, K. H., Kim, G. Y., & Chae, H. Y. (2000). Monitoring greenhouse plants using thermal imaging. IFAC Proceedings Volumes, 33(29), 181–186.CrossRef
45.
go back to reference Li, L., Zhang, Q., & Huang, D. (2014). A Review of Imaging Techniques for Plant Phenotyping. Journal of Sensors, 14(11), 20078–20111.CrossRef Li, L., Zhang, Q., & Huang, D. (2014). A Review of Imaging Techniques for Plant Phenotyping. Journal of Sensors, 14(11), 20078–20111.CrossRef
46.
go back to reference Corkery, G., Ward, S., Kenny, C., & Hemmingway, P. (2013). Incorporating smart sensing technologies into the poultry industry. World Poultry Research, 3(4), 106–128. Corkery, G., Ward, S., Kenny, C., & Hemmingway, P. (2013). Incorporating smart sensing technologies into the poultry industry. World Poultry Research, 3(4), 106–128.
47.
go back to reference Jackman, P., Penya, H., & Ross, R. (2020). The role of information and communication technology in poultry broiler production process control: A review. Agricultural Engineering International (CIGR Journal), 22(3), 284–299 Jackman, P., Penya, H., & Ross, R. (2020). The role of information and communication technology in poultry broiler production process control: A review. Agricultural Engineering International (CIGR Journal), 22(3), 284–299
48.
go back to reference Ward, S. (2012). BOSCA – A smart networked sensing system in agriculture: A poultry industry focus. Science Foundation Ireland. Ward, S. (2012). BOSCA – A smart networked sensing system in agriculture: A poultry industry focus. Science Foundation Ireland.
49.
go back to reference Jackman, P., Ward, S., Brennan, L., Corkery, G., & McCarthy, U. (2015). Application of wireless technologies to forward predict crop yields in the poultry production chain. CIGR Journal, 17(2), 287–295. Jackman, P., Ward, S., Brennan, L., Corkery, G., & McCarthy, U. (2015). Application of wireless technologies to forward predict crop yields in the poultry production chain. CIGR Journal, 17(2), 287–295.
50.
go back to reference Astill, J., Dara, R. A., Fraser, E. D. G., & Sharif, S. (2018). Detecting and predicting emerging disease in poultry with the implementation of new technologies and big data: A focus on avian influenza virus. Frontiers in Veterinary Science, 5(1), 1–12. Astill, J., Dara, R. A., Fraser, E. D. G., & Sharif, S. (2018). Detecting and predicting emerging disease in poultry with the implementation of new technologies and big data: A focus on avian influenza virus. Frontiers in Veterinary Science, 5(1), 1–12.
51.
go back to reference Agrologic. (2017). Poultry products. Agrologic Online Service. Agrologic. (2017). Poultry products. Agrologic Online Service.
52.
go back to reference Fancom. (2017). Broiler climate controllers. Fancom Online Service. Fancom. (2017). Broiler climate controllers. Fancom Online Service.
53.
go back to reference Rotem. (2014). Platinum plus controller manual, rotem control and management online service. Petach-Tikva. Rotem. (2014). Platinum plus controller manual, rotem control and management online service. Petach-Tikva.
54.
go back to reference Ross, R. J. (2015). Precise poultry: Analytics supported decision systems in poultry farming. Enterprise Ireland. Ross, R. J. (2015). Precise poultry: Analytics supported decision systems in poultry farming. Enterprise Ireland.
55.
go back to reference Neves, D. P., Mehdizadeh, S. A., Tscharke, M., deAlancar-Naas, I., & Banhazi, T. M. (2015). Detection of flock movement and behaviour of broiler chickens at different feeders using image analysis. Information Processing in Agriculture, 2(2), 177–182.CrossRef Neves, D. P., Mehdizadeh, S. A., Tscharke, M., deAlancar-Naas, I., & Banhazi, T. M. (2015). Detection of flock movement and behaviour of broiler chickens at different feeders using image analysis. Information Processing in Agriculture, 2(2), 177–182.CrossRef
56.
go back to reference Ross, J. W., Hale, B. J., Gabler, N., & Rhoads, R. P. (2015). Physiological consequences of heat stress in pigs. Animal Production Science, 55(11), 1381–1390.CrossRef Ross, J. W., Hale, B. J., Gabler, N., & Rhoads, R. P. (2015). Physiological consequences of heat stress in pigs. Animal Production Science, 55(11), 1381–1390.CrossRef
57.
go back to reference Ter-Sarkisov, A., Ross, R., & Kelleher, J. (2017). Bootstrapping labelled dataset construction for cow tracking and behavior analysis. In: 14th Conference on computer and robot vision. Edmonton, AL, Canada. May 17–19, 2017. Ter-Sarkisov, A., Ross, R., & Kelleher, J. (2017). Bootstrapping labelled dataset construction for cow tracking and behavior analysis. In: 14th Conference on computer and robot vision. Edmonton, AL, Canada. May 17–19, 2017.
58.
go back to reference Yukun, S., Pengju, H., Yujie, W., Ziqi, C., Yang, L., Baisheng, D., Runze, L., & Yonggen, Z. (2019). Automatic monitoring system for individual dairy cows based on a deep learning framework that provides identification via body parts and estimation of body condition score. Journal of Dairy Science, 102(11), 10140–10151.CrossRef Yukun, S., Pengju, H., Yujie, W., Ziqi, C., Yang, L., Baisheng, D., Runze, L., & Yonggen, Z. (2019). Automatic monitoring system for individual dairy cows based on a deep learning framework that provides identification via body parts and estimation of body condition score. Journal of Dairy Science, 102(11), 10140–10151.CrossRef
59.
go back to reference Bennett, S. (1993). Development of the PID controller. IEEE Control Systems Magazine, 13(6), 58–62.CrossRef Bennett, S. (1993). Development of the PID controller. IEEE Control Systems Magazine, 13(6), 58–62.CrossRef
60.
go back to reference Liu, C., Peng, J.-F., Zhao, F.-Y., & Li, C. (2009). Design and optimization of fuzzy-PID controller for the nuclear reactor power control. Nuclear Engineering and Design, 239(11), 2311–2316.CrossRef Liu, C., Peng, J.-F., Zhao, F.-Y., & Li, C. (2009). Design and optimization of fuzzy-PID controller for the nuclear reactor power control. Nuclear Engineering and Design, 239(11), 2311–2316.CrossRef
61.
go back to reference Lu, X., Duan, X., Mao, X., Li, Y., & Zhang, X. (2017). Feature extraction and fusion using deep convolutional neural networks for face detection. Mathematical Problems in Engineering, 1(1), 1–9.CrossRef Lu, X., Duan, X., Mao, X., Li, Y., & Zhang, X. (2017). Feature extraction and fusion using deep convolutional neural networks for face detection. Mathematical Problems in Engineering, 1(1), 1–9.CrossRef
62.
go back to reference Pereira, D. T., Aldarondo, D. E., Willmore, L., Kislin, M., Wang, S. S.-H., Murthy, M., & Shaevitz, J. W. (2019). Fast animal pose estimation using deep neural networks. Nature Methods, 16(1), 117–125.CrossRef Pereira, D. T., Aldarondo, D. E., Willmore, L., Kislin, M., Wang, S. S.-H., Murthy, M., & Shaevitz, J. W. (2019). Fast animal pose estimation using deep neural networks. Nature Methods, 16(1), 117–125.CrossRef
63.
go back to reference Shakoor, N., Lee, S., & Mockler, T. C. (2017). High throughput phenotyping to accelerate crop breeding and monitoring of diseases in the field. Current Opinion in Plant Biology, 38(1), 184–192.CrossRef Shakoor, N., Lee, S., & Mockler, T. C. (2017). High throughput phenotyping to accelerate crop breeding and monitoring of diseases in the field. Current Opinion in Plant Biology, 38(1), 184–192.CrossRef
64.
go back to reference Graves, A. (2012). Supervised sequence labelling with recurrent neural networks. Springer Press.MATHCrossRef Graves, A. (2012). Supervised sequence labelling with recurrent neural networks. Springer Press.MATHCrossRef
65.
go back to reference Trabesinger, A. (2017). Quantum computing: towards reality. Nature Outline, 543(1). Trabesinger, A. (2017). Quantum computing: towards reality. Nature Outline, 543(1).
Metadata
Title
You Got Data‥‥ Now What: Building the Right Solution for the Problem
Author
Patrick Jackman
Copyright Year
2022
DOI
https://doi.org/10.1007/978-3-030-84148-5_1

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