A Review based Investigation of Exploratory analysis in AI and Machine Learning for a Variety of Applications

Main Article Content

Sirisha. J
Sowjanya Vuddanti
J.V.N. Ramesh

Abstract

In recent years, the number of production settings that make use of machine learning (ML) and other types of AI has grown significantly. The research presents a comprehensive review of where machine learning (ML) applications stand in industrial contexts at present. The development of smart mining tools has allowed for the generation, collection, and exchange of data in near-real time. This is why there is so much interest in machine learning (ML) studies in the mining industry. Additionally, this study provided a thorough evaluation of data sciences and ML's applications in a variety of petroleum engineering and geosciences domains, such as petroleum exploration, reservoir characterization, oil well drilling, production, and well stimulation, with a focus on the rapidly developing area of unconventional reservoirs. Future directions for data science and ML in the oil and gas industry are discussed, and the properties of ML that are necessary to enhance prediction are analysed. This study provides a detailed comparison of various ML techniques that can be used in the oil and gas industry. New possibilities for analysing and predicting medical data have emerged thanks to the development of artificial intelligence and machine learning, which were covered in this article. Multiple recent studies have shown that AI and ML can be used to fight the COVID-19 pandemic. This article's goal is to offer reviewers with an overview of recent studies that have made use of AI and ML in a variety of contexts.

Article Details

How to Cite
J, S., Vuddanti, S. ., & Ramesh, J. (2022). A Review based Investigation of Exploratory analysis in AI and Machine Learning for a Variety of Applications. International Journal on Recent and Innovation Trends in Computing and Communication, 10(2s), 182–185. https://doi.org/10.17762/ijritcc.v10i2s.5926
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Articles

References

Bajic, B., Cosic, I., Lazarevic, M., Sremcev, N., Rikalovic, A., 2018. Machine Learning Techniques for Smart Manufacturing: Applications and Challenges in Industry 4.0. 9th International Scientific and Expert Conference TEAM 2018.

Leo Kumar, S.P., 2017. State of The Art-Intense Review on Artificial Intelligence Systems Application in Process Planning and Manufacturing. Engineering Applications of Artificial Intelligence 65, 294–329.

Ma, L., Xie, W., Zhang, Y., 2019. Blister Defect Detection Based on Convolutional Neural Network for Polymer Lithium-Ion Battery. Applied Sciences 9 (6), 1085.

Handelman, G.S.; Kok, H.K.; Chandra, R.V.; Razavi, A.H.; Huang, S.; Brooks, M.; Lee, M.J.; Asadi, H. Peering Into the Black Box of Artificial Intelligence: Evaluation Metrics of Machine Learning Methods. Am. J. Roentgenol. 2019, 212, 38–43. [CrossRef] [PubMed]

Spasi´c, I.; Nenadic, G. Clinical Text Data in Machine Learning: Systematic Review. JMIR Med. Inform. 2020, 8, e17984. [CrossRef] [PubMed]

Kitchenham, B., Pearl Brereton, O., Budgen, D., Turner, M., Bailey, J., Linkman, S., 2009. Systematic literature reviews in software engineering – A systematic literature review. Information and Software Technology 51 (1), 7–15.

Kim, D., Lee, T., Kim, S., Lee, B., Youn, H.Y., 2018. Adaptive Packet Scheduling in IoT Environment Based on Q-learning. Procedia Computer Science 141, 247–254

Walther, J., Spanier, D., Panten, N., Abele, E., 2019. Very short-term load forecasting on factory level – A machine learning approach. Procedia CIRP 80, 705–710.

Brik, B., Bettayeb, B., Sahnoun, M.’h., Duval, F., 2019. Towards Predicting System Disruption in Industry 4.0: Machine Learning-Based Approach. Procedia Computer Science 151, 667–674.

Lavrik, E., Panasenko, I., Schmidt, H.R., 2019. Advanced Methods for the Optical Quality Assurance of Silicon Sensors. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 922, 336–344

Pinto, R., Cerquitelli, T., 2019. Robot fault detection and remaining life estimation for predictive maintenance. Procedia Computer Science 151, 709–716.

Kuhnle, A., Schäfer, L., Stricker, N., Lanza, G., 2019. Design, Implementation and Evaluation of Reinforcement Learning for an Adaptive Order Dispatching in Job Shop Manufacturing Systems. Procedia CIRP 81, 234–239