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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]

Statisticians in both academia and industry have encountered problems with high-dimensional data. The rapid feature increase has caused the feature count to outstrip the instance count. There are several established methods when selecting features from massive amounts of breast cancer data. Even so, overfitting continues to be a problem. The challenge of choosing important features with minimum loss in a different sample size is another area with room for development. As a result, the feature selection technique is crucial for dealing with high-dimensional data classification issues. This paper proposed a new architecture for high-dimensional breast cancer data using filtering techniques and a logistic regression model. Essential features are filtered out using a combination of hybrid chi–square and hybrid information gain (hybrid IG) with logistic regression as classifier. The results showed that hybrid IG performed the best for high-dimensional breast and prostate cancer data. The top 50 and 22 features outperformed the other configurations, with the highest classification accuracies of 86.96% and 82.61%, respectively, after integrating the hybrid information gain and logistic function (hybrid IG+LR) with a sample size of 75. In the future, multiclass classification of multidimensional medical data to be evaluated using data from a different domain. [ABSTRACT FROM AUTHOR]

Breast cancer is a serious disease that requires data analysis for diagnosis and treatment. Clustering is a data mining technique that is often used in breast cancer research to assess the level of malignancy at an early stage. However, clustering categorical data can be challenging because different levels in categorical variables can impact the clustering process. This research proposes a modified entropy measure (MEM) to enhance clustering performance. MEM aims to address the issue of distance-based measures in clustering categorical data. It is also a useful tool for assessing data loss in categorical clustering, which helps to understand the patterns and relationships by quantifying the information lost during clustering. An evaluation compares k-modes+MEM, k-means+MEM, DBSCAN+MEM, and affinity+MEM with conventional clustering algorithms. The assessment metrics of clustering accuracy, intracluster distance and fowlkes-mallow index (FMI) are employed to evaluate the algorithm performance. Experimental results show significant improvements. k-modes+MEM algorithm achieves a reduction in average intra-cluster distance and outperforms other algorithms in accuracy, intracluster distance, and FMI. The proposed algorithm can be extended to heterogeneous datasets in various domains such as healthcare, finance, and marketing. [ABSTRACT FROM AUTHOR]

The global pandemic of the coronavirus disease COVID-19 has impacted a variety of operations. This dilemma is also attributable to the lockdown measures taken by the afflicted nations. The entire or partial shutdown enacted by nations across the globe affected the majority of hospitals and clinics until the pandemic was contained. The judgements made by the authorities of each impacted nation vary based on a number of variables, including the nation's severity of reported cases, the availability of vaccines, beds in intensive care unit (ICU), staff number, patient number, and medicines. Consequently, this work offers a thorough analysis of the most recent machine learning (ML) and deep learning (DL) approaches for COVID-19 that can assist the medical field in offering quick and exact COVID-19 diagnosis in Malaysia. This research aims to review the machine learning and deep learning methods that were used to help diagnose COVID-19 in Malaysia. [ABSTRACT FROM AUTHOR]

UIGreenMetric is a guide for higher education institutions to raise awareness of sustainable development, sustainable research, building a green campus, and social influence. There are six assessment categories in UI GreenMetric. One of them is energy and climate change. The assessment point in energy and climate change is the implementation of smart buildings within the campus area. There is often pollution on campus. The campus still uses groundwater for daily sanitation; workshops' waste is discharged directly into the ground without any sewage treatment process; many private vehicles are in the campus area; and 30% of campus land has been used as vehicle parking lots. It is necessary to carry out a monitoring process to determine the concentration of CO2 in the air. For this reason, further study is needed on smart features that will be built to support UI GreenMetric concepts. It is expected to help monitor water, soil, and air environmental parameters. This smart would later be monitored remotely using the Internet of Things (IoT) method. The maximum result of air temperature is 32,3°C, the maximum level of CO2 is 526,8 ppm, the minimum humidity level is 47,2%RH, and the maximum level of PAR is 589,3 µ*mol/m2*s. The noise maximum level is 84 dB, and pH water maximum is 7,01. The density of students also caused an increase in some parameters. POLMED must concentrate on environmental sustainability. Therefore, we should pay for internal recycling water treatment, reducing the use of private vehicles, and expanding green open space. [ABSTRACT FROM AUTHOR]

The global pandemic of the coronavirus disease COVID-19 has impacted a variety of operations. This dilemma is also attributable to the lockdown measures taken by the afflicted nations. The entire or partial shutdown enacted by nations across the globe affected the majority of hospitals and clinics until the pandemic was contained. The judgements made by the authorities of each impacted nation vary based on a number of variables, including the nation's severity of reported cases, the availability of vaccines, beds in intensive care unit (ICU), staff number, patient number, and medicines. Consequently, this work offers a thorough analysis of the most recent machine learning (ML) and deep learning (DL) approaches for COVID-19 that can assist the medical field in offering quick and exact COVID-19 diagnosis in Malaysia. This research aims to review the machine learning and deep learning methods that were used to help diagnose COVID-19 in Malaysia. [ABSTRACT FROM AUTHOR]

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 wellcoordinated 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Clustering methods in data mining aim to group a set of patterns based on their similarity. In a data survey, heterogeneous information is established with various types of data scales like nominal, ordinal, binary, and Likert scales. A lack of treatment of heterogeneous data and information leads to loss of information and scanty decision-making. Although many similarity measures have been established, solutions for heterogeneous data in clustering are still lacking. The recent entropy distance measure seems to provide good results for the heterogeneous categorical data. However, it requires many experiments and evaluations. This article presents a proposed framework for heterogeneous categorical data solution using a mini batch k-means with entropy measure (MBKEM) which is to investigate the effectiveness of similarity measure in clustering method using heterogeneous categorical data. Secondary data from a public survey was used. The findings demonstrate the proposed framework has improved the clustering’s quality. MBKEM outperformed other clustering algorithms with the accuracy at 0.88, v-measure (VM) at 0.82, adjusted rand index (ARI) at 0.87, and Fowlkes-Mallow’s index (FMI) at 0.94. It is observed that the average minimum elapsed time-varying for cluster generation, k at 0.26 s. In the future, the proposed solution would be beneficial for improving the quality of clustering for heterogeneous categorical data problems in many domains. [ABSTRACT FROM AUTHOR]

The ability to create "structured models" of biological simulations is becoming more and more commonplace. Although computer simulations can be used to estimate the model, they are restricted by the lack of experimentally available parameter values, which must be approximated. In this study, an Enhanced Segment Particle Swarm Optimization (ESe-PSO) algorithm that can estimate the values of small-scale kinetic parameters is described and applied to E. coli's main metabolic network as a model system. The glycolysis, phosphotransferase system, pentose phosphate, the TCA cycle, gluconeogenesis, glyoxylate pathways, and acetate formation pathways of Escherichia coli are represented by the Differential Algebraic Equations (DAE) system for the metabolic network. However, this algorithm uses segments to organize particle movements and the dynamic inertia weight (ω) to increase the algorithm's exploration and exploitation potential. As an alternative to the state-of-the-art algorithm, this adjustment improves estimation accuracy. The numerical findings indicate a good agreement between the observed and predicted data. In this regard, the result of the ESe-PSO algorithm achieved superior accuracy compared with the Segment Particle Swarm Optimization (Se-PSO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution (DE) algorithms. As a result of this innovative approach, it was concluded that small-scale and even entire cell kinetic model parameters can be developed. [ABSTRACT FROM AUTHOR]

Glycolipids prepared from palm kernel oil, PKO, in tropical regions represent an excellent opportunity to develop new surfactant formulations for cosmeceutical and pharmaceutical applications while contributing to sustainable economies. Here we report the synthesis of a new mannoside obtained from palm kernel oil, aManPKO, and we characterise its thermotropic properties by using DSC, POM, SAXS, WAXS, FT-IR and broadband dielectric spectroscopy. aManPKO exhibits a monotropic smectic phase with a partially interdigitated bilayer structure, similar to its major unsaturated component, the oleyl mannoside with a C18:1 monounsaturated alkyl chain. The smectic phase of aManPKO vitrifies below room temperature, which is associated to the C18:1 component, but also to the activation of short-range motions, ~4 Å, by cleavage of strong hydrogen bonding between the polar heads of the mannosides. The stability of the liquid crystal phase, on the other hand, is linked to the presence of weak hydrogen bonding, whose breakage leads to the activation of long rate motions in the range of the layer distances, ~39 Å. [ABSTRACT FROM AUTHOR]