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Enhancing the management and monitoring of oil and gas processes demands the development of precise predictive analytic techniques. Over the past two years, oil and its prediction have advanced significantly using conventional and modern machine learning techniques. Several review articles detail the developments in predictive maintenance and the technical and non-technical aspects of influencing the uptake of big data. The absence of references for machine learning techniques impacts the effective optimization of predictive analytics in the oil and gas sectors. This review paper offers readers thorough information on the latest machine learning methods utilized in this industry's predictive analytical modeling. This review covers different forms of machine learning techniques used in predictive analytical modeling from 2021 to 2023 (91 articles). It provides an overview of the details of the papers that were reviewed, describing the model's categories, the data's temporality, field, and name, the dataset's type, predictive analytics (classification, clustering, or prediction), the models' input and output parameters, the performance metrics, the optimal model, and the model's benefits and drawbacks. In addition, suggestions for future research directions to provide insights into the potential applications of the associated knowledge. This review can serve as a guide to enhance the effectiveness of predictive analytics models in the oil and gas industries. [ABSTRACT FROM AUTHOR]

Forecasting with a precise evaluation of new cases and the rate of occurrence is essential for the effective implementation of governmental initiatives and early prevention of any infectious illness. Despite the extensive research done on the severe acute respiratory syndrome coronavirus 2 (SARSCoV-2) virus since the outbreak, not enough knowledge could be gained about the virus in terms of immune function, virus-host interactions, pathogenesis, propagation, and mutations. In this paper, various statistical models, namely Supervised Machine Learning techniques (ML), are being discussed for previous diseases and for the recent COVID-19 pandemic. Namely, the use of the Support Vector Machine (SVM) model and a variety of time series regression models is demonstrated for several infectious diseases, including COVID-19. As infectious diseases evolve throughout time, they provide data on a single variable, that is, "the figure of contaminations that occurred over time"; thus, researchers tend to use time series models to fit the data and make predictions using different evaluation metrics to find the best-fitting model. This review developed ideas about how to enhance the current modeling techniques. Furthermore, findings of current Machine Learning Techniques are being evaluated, which attempts to estimate the COVID-19 spread. Researchers looking for approaches to advance SARS-CoV-2 research as well as individuals curious about the field's current condition will find this review to be helpful. [ABSTRACT FROM AUTHOR]