The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
IoT for Smart Cities: Machine Learning Approaches in Smart Healthcare—A Review
Open accessAbstract
Smart city is a collective term for technologies and concepts that are directed toward making cities efficient, technologically more advanced, greener and more socially inclusive. These concepts include technical, economic and social innovations. This term has been tossed around by various actors in politics, business, administration and urban planning since the 2000s to establish tech-based changes and innovations in urban areas. The idea of the smart city is used in conjunction with the utilization of digital technologies and at the same time represents a reaction to the economic, social and political challenges that post-industrial societies are confronted with at the start of the new millennium. The key focus is on dealing with challenges faced by urban society, such as environmental pollution, demographic change, population growth, healthcare, the financial crisis or scarcity of resources. In a broader sense, the term also includes non-technical innovations that make urban life more sustainable. So far, the idea of using IoT-based sensor networks for healthcare applications is a promising one with the potential of minimizing inefficiencies in the existing infrastructure. A machine learning approach is key to successful implementation of the IoT-powered wireless sensor networks for this purpose since there is large amount of data to be handled intelligently. Throughout this paper, it will be discussed in detail how AI-powered IoT and WSNs are applied in the healthcare sector. This research will be a baseline study for understanding the role of the IoT in smart cities, in particular in the healthcare sector, for future research works.
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Cites
Artificial intelligence in healthcare: past, present and future
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017) · Stroke and Vascular Neurology · doi:10.1136/svn-2017-000101 · Cited by 4,837
Artificial intelligence in medicine
Hamet, P., & Tremblay, J. (2017) · Metabolism · doi:10.1016/j.metabol.2017.01.011 · Cited by 2,334
Introduction to Machine Learning
Yu, F. R., & He, Y. (2019) · SpringerBriefs in Electrical and Computer Engineering · doi:10.1007/978-3-030-10546-4_1 · Cited by 1,594
On big data, artificial intelligence and smart cities
Allam, Z., & Dhunny, Z. A. (2019) · Cities · doi:10.1016/j.cities.2019.01.032 · Cited by 1,178
Applications of artificial intelligence and machine learning in smart cities
Ullah, Z., Al-Turjman, F., Mostarda, L., & Gagliardi, R. (2020) · Computer Communications · doi:10.1016/j.comcom.2020.02.069 · Cited by 756
Blockchain and IoT Integration: A Systematic Survey
Panarello, A., Tapas, N., Merlino, G., Longo, F., & Puliafito, A. (2018) · Sensors · doi:10.3390/s18082575 · Cited by 748
Advances in Smart Environment Monitoring Systems Using IoT and Sensors
Ullo, S. L., & Sinha, G. R. (2020) · Sensors · doi:10.3390/s20113113 · Cited by 730
Iot-based smart cities: A survey
Arasteh, H., Hosseinnezhad, V., Loia, V., Tommasetti, A., Troisi, O., Shafie-khah, M., & Siano, P. (2016) · 2016 IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC) · doi:10.1109/eeeic.2016.7555867 · Cited by 464
Survey on Collaborative Smart Drones and Internet of Things for Improving Smartness of Smart Cities
Alsamhi, S. H., Ma, O., Ansari, M. S., & Almalki, F. A. (2019) · IEEE Access · doi:10.1109/access.2019.2934998 · Cited by 440
Li, W., Chai, Y., Khan, F., Jan, S. R. U., Verma, S., Menon, V. G., Kavita, & Li, X. (2021) · Mobile Networks and Applications · doi:10.1007/s11036-020-01700-6 · Cited by 413
Cited by
Significance of machine learning in healthcare: Features, pillars and applications
Javaid, M., Haleem, A., Pratap Singh, R., Suman, R., & Rab, S. (2022) · International Journal of Intelligent Networks · doi:10.1016/j.ijin.2022.05.002 · Cited by 641
Evaluation of artificial intelligence techniques in disease diagnosis and prediction
Ghaffar Nia, N., Kaplanoglu, E., & Nasab, A. (2023) · Discover Artificial Intelligence · doi:10.1007/s44163-023-00049-5 · Cited by 426
Blockchain Technology: Security Issues, Healthcare Applications, Challenges and Future Trends
Wenhua, Z., Qamar, F., Abdali, T.-A. N., Hassan, R., Jafri, S. T. A., & Nguyen, Q. N. (2023) · Electronics · doi:10.3390/electronics12030546 · Cited by 282
Heidari, A., Jafari Navimipour, N., Unal, M., & Zhang, G. (2023) · ACM Computing Surveys · doi:10.1145/3571728 · Cited by 195
Reviewing Federated Machine Learning and Its Use in Diseases Prediction
Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A. (2023) · Sensors · doi:10.3390/s23042112 · Cited by 114
Metadata
- Authors
- Ghazal, T. M., Hasan, M. K., Alshurideh, M. T., Alzoubi, H. M., Ahmad, M., Akbar, S. S., Al Kurdi, B., & Akour, I. A.
- Venue
- Future Internet
- Publisher
- MDPI AG
- Published
- 2021-08-23
- Volume
- 13
- Issue
- 8
- Pages
- 218
- ISSN
- 1999-5903 (ISSN-L)
- OpenAlex
- W3193630181
- Cited by
- 674
- References
- 32
- FWCI
- 120.68
- Type
- journal article
- Language
- en
- Open access
- gold
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- Ghazal, T. M., Hasan, M. K., Alshurideh, M. T., Alzoubi, H. M., Ahmad, M., Akbar, S. S., Al Kurdi, B., & Akour, I. A. (2021). IoT for Smart Cities: Machine Learning Approaches in Smart Healthcare—A Review. Future Internet. https://doi.org/10.3390/fi13080218
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