Abstract
A machine learning algorithm (MLA) is an approach or tool to help in big data analytics (BDA) of applications. This tool is suitable to analyze a large amount of amount generated by an application for effective and efficient utilization of the data. Machine learning algorithms considered to find out meaningful data and information for industrial applications. It is one of the services under big data analytics (BDA). Big data analytics (BDA) is suitable for identifying risk management, cause of failure, identifying the customer based on their procurement detail records, detection of fraud, etc. So, this paper deals with the work done in this field to analyze the importance of machine learning tools and techniques, identify the field where it is suitable to use including industries such as marketing, human resource, healthcare, insurance, banking, automobile, etc. This paper identifies different challenges of machine learning tools and technologies including the current status of adoption in industries.
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References
https://www.newtechdojo.com/list-machine-learning-algorithms
MartÃn Noguerol T, Paulano-Godino F, MartÃn-Valdivia MT, Menias CO, Luna A (2019) Strengths, weaknesses, opportunities, and threats analysis of artificial intelligence and machine learning applications in radiology. J Am Coll Radiol 16:1239–1247
Shaikh TA, Ali R (2019) Big data for better Indian healthcare. Int J Inf Tecnol 11:735–741. https://doi.org/10.1007/s41870-019-00342-6
Qiu J, Wu Q, Ding G, Xu Y, Feng S (2016) A survey of machine learning for big data processing. EURASIP J Adv Signal Process 1:2016
Baum J, Laroque C, Oeser B, Skoogh A, Subramaniyan M (2018) Applications of big data analytics and related technologies in maintenance—literature-based research. Machines 6:54
Dorepalli S (2018) Machine learning and statistical approaches for big data: issues, challenges and research directions. Int J App Eng Res 12
Doupe P, Faghmous J, Basu S (2019) Machine learning for health services researchers. Value Health 22(7):808–815
Dean J (2014) Big data, data mining, and machine learning: value creation for business leaders and practitioners. Wiley and SAS Business Series
Athmaja S, Hanumanthappa M, Kavitha V (2017) A survey of machine learning algorithms for big data analytics. International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Coimbatore, 2017, pp 1–4. https://doi.org/10.1109/ICIIECS.2017.8276028
Singh SP, Jaiswal UC (2018) Machine learning for big data: a new perspective. Int J Appl Eng Res 13(5):2753–2762
Hadioui A, El Faddouli N, Touimi YB, Bennani S (2017) Machine learning based on big data extraction of massive educational knowledge. Hadioui 12(11):151–167. https://doi.org/10.3991/ijet.v12.i11.7460
Núñez Reiz A, Armengol de la Hoz MA, Sánchez GarcÃa M (2019) Big data analysis and machine learning in intensive care units. Big Data Analysis y Machine Learning en medicina intensiva. Med Intensiva 43(7):416-426. https://doi.org/10.1016/j.medin.2018.10.007
Zhou T, Song Z, Sundmacher K (2019) Big data creates new opportunities for materials research: a review on methods and applications of machine learning for materials design. Engineering. https://doi.org/10.1016/j.eng.2019.02.011
https://emerj.com/ai-sector-overviews/machine-learning-in-pharma-medicine
Dash S, Shakyawar SK, Sharma M, Kaushik S (2019) Big data in healthcare: management, analysis and future prospects. J Big Data
Bagula A, Mandava M, Bagula H (2018) A framework for healthcare support in the rural and low income areas of the developing world. J Netw Comput Appl 120(May):17–29
Nevin L, On behalf of the PLOS Medicine Editors (2018) Advancing the beneficial use of machine learning in health care and medicine: toward a community understanding. PLoS Med 15(11): e1002708. https://doi.org/10.1371/journal.pmed.1002708
Shen D, Wee C, Zhang D, Zhou L, Yap P (2014) Machine learning techniques for AD/MCI diagnosis and prognosis. In: Dua S, Rajendra U, Acharya, Dua P (eds) Machine Learning in Healthcare Informatics, pp. 147–179. Springer-Verlag Berlin Heidelberg.Intelligent Systems Reference Library 56, New York, United States. https://doi.org/10.1007/978-3-642-40017-9_8
Stewart WF, Roy J, Sun J, Ebadollahi S (2014) Clinical utility of machine learning and longitudinal EHR data. In: Machine learning in healthcare informatics, Intelligent Systems Reference Library 56, pp 209–227. https://doi.org/10.1007/978-3-642-40017-9_10
Fairley M, Scheinker D, Brandeau ML (2019) Improving the efficiency of the operating room environment with an optimization and machine learning model. Health Care Manag Sci 22(4):756–767. https://doi.org/10.1007/s10729-018-9457-3
https://medium.com/sciforce/top-ai-algorithms-for-healthcare-aa5007ffa330
https://mytechdecisions.com/facility/industrial-machine-learning-systems
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Rahul, K., Banyal, R.K., Goswami, P., Kumar, V. (2021). Machine Learning Algorithms for Big Data Analytics. In: Singh, V., Asari, V., Kumar, S., Patel, R. (eds) Computational Methods and Data Engineering. Advances in Intelligent Systems and Computing, vol 1227. Springer, Singapore. https://doi.org/10.1007/978-981-15-6876-3_27
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DOI: https://doi.org/10.1007/978-981-15-6876-3_27
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