Abstract
Human-like thinking abilities can be simulated in machines to achieve artificial intelligence (AI) for making predictions, helping make decisions, classification, etc. Algorithms for machine learning (ML) can be employed to train and deploy models into various applications. Deep learning is an ML consisting of artificial neural networks (ANNs) augmented with multiple abstraction layers, useful for processes of pattern recognition or classification, supported by large datasets. In the ongoing COVID-19 pandemic situation, one such application of AI and deep learning is the detection of face masks to help impede the transmission of infection. In this paper, firstly, the concepts of AI, data analytics for AI, ML, deep learning, neural networks, and use of technologies for smart cities are discussed in detail. This is followed by highlighting the application of these technologies in the event of a pandemic—face mask detection, using OpenCV (Open source Computer Vision), TensorFlow, and Keras, and achieving up to 99% accuracy in detection.
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References
Russel S, Norvig P (2013) Artificial intelligence: a modern approach. Pearson Education Limited
The Four Components of trusted Artificial Intelligence (2018) Retrieved from towards data science: https://towardsdatascience.com/the-four-components-of-trusted-artificial-intelligence-14a1e36562ea
Applications of AI—Real Life Use Cases in Different Sectors (2018) Retrieved from data-flair training: https://data-flair.training/blogs/applications-of-artificial-intelligence/
Mitchell RS, Michalski JG, Carbonell TM (2013) An artificial intelligence approach. Springer, Berlin
Das S, Dey A, Pal A, Roy N (2015) Applications of artificial intelligence in machine learning: review and prospect. Int J Comput Appl 115(9):31–41
Jasim NA, Th H, Rikabi SA (2021) Design and implementation of smart city applications based on the internet of things. Int J Interact Mob Technol 15(13):1–15
Islam MS, Hasan MM, Wang X, Germack HD (2018) A systematic review on healthcare analytics: application and theoretical perspective of data mining. In: Healthcare, vol 6, No 2. Multidisciplinary Digital Publishing Institute, p. 54
Kohavi R, Rothleder NJ, Simoudis E (2002) Emerging trends in business analytics. Commun ACM 45(8):45–48
I.T. Glossary: Analytics (2018) Retrieved from Gartner: https://www.gartner.com/en/information-technology/glossary/analytics
Jong Yd (2019) Levels of data analytics. Retrieved from it happens: http://www.ithappens.nu/levels-of-data-analytics/
Tayo BO (2019) 4 Components of an analytics model. Retrieved from towards data science: https://towardsdatascience.com/4-components-of-an-analytics-model-b087797f9a7a#:~:text=This%20article%20will%20discuss%204,Data%20Component%20and%20Algorithm%20Component
Sciforce (2020) A Layman’s guide to data science: data science workflow. Retrieved from KD Nuggets: https://www.kdnuggets.com/2020/07/laymans-guide-data-science-workflow.html
Sharma R (2019) Prerequisite for data science: it’s not what you think it is. Retrieved from UpGrad: https://www.upgrad.com/blog/prerequisite-for-data-science/
Boisseau J, Wilson L (2019) Enterprise AI: data analytics, data science and machine learning. Retrieved from CIO: https://www.cio.com/article/3342421/enterprise-ai-data-analytics-data-science-and-machine-learning.html
SAS Institute (2020) Machine learning: what it is and why it matters. Retrieved from SAS insights: https://www.sas.com/en_in/insights/analytics/machine-learning.html
Haykin S (2001) Neural networks a comprehensive foundation, 2nd ed. Pearson Prentice Hall
Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, Cambridge
Browniee J (2019) How to improve deep learning performance. Retrieved from machine learning mastery: https://machinelearningmastery.com/improve-deep-learning-performance/
Deland S (2019) When to use machine learning or deep learning? Retrieved from embedded computing design: https://www.embedded-computing.com/guest-blogs/when-to-use-machine-learning-or-deep-learning
Gibson JP (2017) Deep learning a practitioner’s approach. O’Reilly Media, USA
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Advances in neural information processing systems (NIPS), vol 25, pp 1106–1114. Tahoe
Nicholson C (2020) A beginner's guide to Restricted Boltzmann Machines (RBMs). Retrieved from Pathmind: https://wiki.pathmind.com/restricted-boltzmann-machine
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S et al (2014) Generative adversarial nets. Adv Neural Inf Process Syst 27:2672–2680
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444
Nash KO (2015) An introduction to convolutional neural networks. arXiv:1511.08458v2[cs.NE]
Rota PA, Oberste MS, Monroe SS, Nix WA, Campagnoli R, Icenogle JP et al (2003) Characterization of a novel coronavirus associated with severe acute respiratory syndrome. Science 300(5624):1394–1399
Inamdar M, Mehendale N (2020) Real-time face mask identification using facemasknet deep learning network. SSRN(3663305)
Yadav S (2020) Deep learning based safe social distancing and face mask detection in public areas for COVID-19 safety guidelines adherence. Int J Res Appl Sci Eng Technol 8(7):1368–1375
Memish ZA, Zumla AI, Al-Hakeem RF, Al-Rabeeah AA, Stephens GM (2013) Family cluster of middle east respiratory syndrome coronavirus infections. N Engl J Med 368(26):2487–2494
Sarkar D, Bali R, Ghosh T (2018) Hands-on transfer learning with python: implement advanced deep learning and neural network models using TensorFlow and Keras. Packt Publishing Limited
Arel I, Rose DC, Karnowski TP (2010) Deep machine learning-a new frontier in artificial intelligence research [research frontier]. IEEE Comput Intell Mag 5(4):13–18
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Dahiya, M., Malik, N. (2022). Application for Smart Cities During Pandemic—Face Mask Detection. In: Srinivasa, K.G., Siddesh, G.M., Manisekhar, S.R. (eds) Society 5.0: Smart Future Towards Enhancing the Quality of Society. Advances in Sustainability Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-19-2161-2_13
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