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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.

Vibrio cholerae is a non-invasive enteric pathogen known to cause a major public health problem called cholera. The pathogen inhabits the aquatic environment while outside the human host, it is transmitted into the host easily through ingesting contaminated food and water containing the vibrios, thus causing diarrhoea and vomiting. V. cholerae must resist several layers of colonization resistance mechanisms derived from the host or the gut commensals to successfully survive, grow, and colonize the distal intestinal epithelium, thus causing an infection. The colonization resistance mechanisms derived from the host are not specific to V. cholerae but to all invading pathogens. However, some of the gut commensal-derived colonization resistance may be more specific to the pathogen, making it more challenging to overcome. Consequently, the pathogen has evolved well-coordinated mechanisms that sense and utilize the anti-colonization factors to modulate events that promote its survival and colonization in the gut. This review is aimed at discussing how V. cholerae interacts and resists both host- and microbe-specific colonization resistance mechanisms to cause infection., Competing Interests: Conflict of interest: All authors declare no competing interests., (© 2023 the Author(s), licensee AIMS Press.)

Breast cancer is diagnosed using histopathological imaging. This task is extremely time-consuming due to high image complexity and volume. However, it is important to facilitate the early detection of breast cancer for medical intervention. Deep learning (DL) has become popular in medical imaging solutions and has demonstrated various levels of performance in diagnosing cancerous images. Nonetheless, achieving high precision while minimizing overfitting remains a significant challenge for classification solutions. The handling of imbalanced data and incorrect labeling is a further concern. Additional methods, such as pre-processing, ensemble, and normalization techniques, have been established to enhance image characteristics. These methods could influence classification solutions and be used to overcome overfitting and data balancing issues. Hence, developing a more sophisticated DL variant could improve classification accuracy while reducing overfitting. Technological advancements in DL have fueled automated breast cancer diagnosis growth in recent years. This paper reviewed studies on the capability of DL to classify histopathological breast cancer images, as the objective of this study was to systematically review and analyze current research on the classification of histopathological images. Additionally, literature from the Scopus and Web of Science (WOS) indexes was reviewed. This study assessed recent approaches for histopathological breast cancer image classification in DL applications for papers published up until November 2022. The findings of this study suggest that DL methods, especially convolution neural networks and their hybrids, are the most cutting-edge approaches currently in use. To find a new technique, it is necessary first to survey the landscape of existing DL approaches and their hybrid methods to conduct comparisons and case studies.