Abstract
Today every application like e-healthcare, agriculture, etc., is connected through smart devices to reduce workforce and enhance productivity. Many applications like defences, banking an utilities, media and entertainment, transportation, banking, retail, agriculture, education, manufacturing, etc., are using smart devices in their working-structure to improved growth of a business/production. These applications are generating a lot of data, which called as “big data” and this data is increasing at a huge rate. For example, most of the data (90%) is generated in last decade only. Together this, we required modern tools to analyses this data for generating useful results. But in near future, this analytics process may shift towards automation. How these automated analytics by deep learning (by robots/machines) will change future forever. Also, with this automated/advanced analytics process we need to provide a disruptive environment which is more towards to protecting nature. This article provide detail explanation regarding “how machines can be useful in learning process through its automate learning process” and “how machine/Artificial Intelligence (AI) can be useful in detecting vulnerabilities/intrusion without much human interaction instantly” and so on. In 21st century, most of tasks will be completed by machines or artificial intelligences. This work discusses several useful terms, scenarios (with many examples in several applications), tools, open issues with opportunities towards automated analytics, i.e., with discussing that “How AI will change near future”.
All authors have contributed in this work equally. Amit Kumar Tyagi has analysed, and approved this manuscript.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Rong, G., et al.: Artificial Intelligence in Healthcare: Review and Prediction Case Studies. Engineering 6, 409–413 (2016)
Chiang, R.H.L., et al.: Business intelligence and analytics education, and program development: a unique opportunity for the information systems discipline. ACM Trans. Manage. Inf. Syst. 3(3), 12 (2012)
Tyagi, A.K., Rekha, G.: Machine learning with big data. In: Proceedings of International Conference on Sustainable Computing in Science, Technology and Management (SUSCOM), Amity University Rajasthan, Jaipur, India, 26–28 February 2019, 20 March 2019
Davenport, T.H.: From analytics to artificial intelligence. J. Bus. Anal. 1(2), 73–80 (2018). ISSN: 2573-2358
Bagloee, S.A., et al.: Autonomous vehicles: challenges, opportunities, and future implications for transportation policies. J. Mod. Transp. 24(4), 284–303 (2016)
Kumar, P.S., Pranavi, S.: Performance analysis of machine learning algorithms on diabetes dataset using big data analytics. In: 2017 International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions) (ICTUS), Dubai, pp. 508–513 (2017). https://doi.org/10.1109/ICTUS.2017.8286062.
Ali, M., Mosa, A.H., Al Machot, F., Kyamakya, K.: EEG-based emotion recognition approach for e-healthcare applications. In: 2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN), Vienna, pp. 946–950 (2016). https://doi.org/10.1109/ICUFN.2016.7536936
Kibria, M.G., Nguyen, K., Villardi, G.P., Zhao, O., Ishizu, K., Kojima, F.: Big data analytics, machine learning, and artificial intelligence in next-generation wireless networks. IEEE Access 6, 32328–32338 (2018). https://doi.org/10.1109/ACCESS.2018.2837692
Aronson, S.J., Rehm, H.L.: Building the foundation for genomics in precision medicine. Nature 526(7573), 336–342 (2015). https://doi.org/10.1038/nature15816
Bose, R.: Advanced analytics: Opportunities and challenge. Ind. Manage. Data Syst. 109(2), 155–172 (2009)
Abbasi, A., Sarker, S., Chiang, R.H.L.: Big Data Research in Information Systems: Toward an Inclusive Research Agenda. J. Assoc. Inf. Syst. 17(2), 3 (2016)
Pramod, A., Naicker, H.S., Tyagi, A.K.: Machine learning and deep learning: open issues and future research directions for next ten years. In: Computational Analysis and Understanding of Deep Learning for Medical Care: Principles, Methods, and Applications. Wiley Scrivener (2020)
Joseph, N.P.S., et al.: Barebone cloud IaaS: revitalisation disruptive technology. Int. J. Bus. Inf. Syst. (IJBIS) 18(1), 107–126 (2015)
https://www.pewresearch.org/internet/2017/05/03/the-future-of-jobs-and-jobs-training/
Tyagi, A.K., Chahal, P.: Artificial intelligence and machine learning algorithms. In: Challenges and Applications for Implementing Machine Learning in Computer Vision. IGI Global (2020). https://doi.org/10.4018/978-1-7998-0182-5.ch008
Tyagi, A.K., Rekha, G.: Challenges of applying deep learning in real-world applications. In: Challenges and Applications for Implementing Machine Learning in Computer Vision, pp. 92–118. IGI Global (2020). https://doi.org/10.4018/978-1-7998-0182-5.ch004
Tyagi, A.K., Nair, M.M., Niladhuri, S., Abraham, A.: Security, Privacy Research issues in Various Computing Platforms: A Survey and the Road Ahead. J. Inf. Assur. Secur. 15(1), 1–16 (2020)
Acknowledgements
This research is funded by the Anumit Academy’s Research and Innovation Network (AARIN), India. The authors would like to thank AARIN India, an education foundation body and a research network for supporting the project through its financial assistance.
Author information
Authors and Affiliations
Contributions
The authors declare that they do not have any conflict of interest with respect to publication of this research work.
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Varsha, R., Nair, S.M., Tyagi, A.K., Aswathy, S.U., RadhaKrishnan, R. (2021). The Future with Advanced Analytics: A Sequential Analysis of the Disruptive Technology’s Scope. In: Abraham, A., Hanne, T., Castillo, O., Gandhi, N., Nogueira Rios, T., Hong, TP. (eds) Hybrid Intelligent Systems. HIS 2020. Advances in Intelligent Systems and Computing, vol 1375. Springer, Cham. https://doi.org/10.1007/978-3-030-73050-5_56
Download citation
DOI: https://doi.org/10.1007/978-3-030-73050-5_56
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-73049-9
Online ISBN: 978-3-030-73050-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)