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Applications of machine learning

Dr. Tamaro Green

Applications of machine learning

2020-11-12

Machine learning has applications in software testing and software development (Zhang, Jin, Xing, & Gong, 2020).  Zhang et al. (2020) explore applying path analysis machine learning to automate detection of defects in software applications.  Richter, Hüllermeier, Jakobs, and Wehrheim (2020) examine graph representation for algorithm selection for software testing machine learning models.  Zou and Magoulès (2020) identify how communication lag can slow the performance of parallel algorithms in large scale systems.

Machine learning algorithms assist in the securing of complex network systems (Kotulski et al., 2018).  Kotulski et al. (2018) discuss network slicing as an approach to improving security in fifth generation networks, 5G. Hasan, Islam, Zarif, and Hashem (2019) describe implementations of machine learning in the security of Internet of Things with anomaly detection algorithms.  Xuan et al. (2020) develop a model for balancing blockchains for smart contracts with data sharing.

Machine learning also has applications in health care.  Vashisht and Prakash (2020) apply support vector machine, neural network, linear regression, and polynomial regression models for predicting the rate of spread of the novel coronavirus.  Majeed and Rauf (2020) discuss applications of graph theory such as network modelling, network analysis, image analysis, scheduling optimization, and content analysis.  Paparizos, Tsafas, and Birbas (2020) list some of the challenges for developing robots for health care applications amid the coronavirus pandemic such as cost, efficiency, trainability, and mobility.

 

 

 

Hasan, M., Islam, M. M., Zarif, M. I. I., & Hashem, M. M. A. (2019). Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches. Internet of Things, 7, 100059. doi:https://doi.org/10.1016/j.iot.2019.100059

Kotulski, Z., Nowak, T. W., Sepczuk, M., Tunia, M., Artych, R., Bocianiak, K., . . . Wary, J.-P. (2018). Towards constructive approach to end-to-end slice isolation in 5G networks. EURASIP Journal on Information Security, 2018(1), 2. doi:10.1186/s13635-018-0072-0

Majeed, A., & Rauf, I. (2020). Graph theory: A comprehensive survey about graph theory applications in computer science and social networks. Inventions, 5(1). doi:10.3390/inventions5010010

Paparizos, C., Tsafas, N., & Birbas, M. (2020). A zynq-based robotic system for treatment of contagious diseases in hospital isolated environment. Technologies, 8(2). doi:10.3390/technologies8020028

Richter, C., Hüllermeier, E., Jakobs, M.-C., & Wehrheim, H. (2020). Algorithm selection for software validation based on graph kernels. Automated Software Engineering, 27(1), 153-186. doi:10.1007/s10515-020-00270-x

Vashisht, G., & Prakash, R. (2020). Predicting the rate of growth of the novel corona virus 2020. International Journal on Emerging Technologies, 11(3), 19-25.

Xuan, S., Zheng, L., Chung, I., Wang, W., Man, D., Du, X., . . . Guizani, M. (2020). An incentive mechanism for data sharing based on blockchain with smart contracts. Computers & Electrical Engineering, 83, 106587. doi:https://doi.org/10.1016/j.compeleceng.2020.106587

Zhang, Y., Jin, D., Xing, Y., & Gong, Y. (2020). Automated defect identification via path analysis-based features with transfer learning. Journal of Systems and Software, 166, 110585. doi:https://doi.org/10.1016/j.jss.2020.110585

Zou, Q., & Magoulès, F. (2020). Reducing the effect of global synchronization in delayed gradient methods for symmetric linear systems. Advances in Engineering Software, 147, 102837. doi:https://doi.org/10.1016/j.advengsoft.2020.102837



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IDJC 2020

International Data Journalism Conference

IDJC 2020

International Data Journalism Conference

IDJC 2020

International Data Journalism Conference