Sunday Olusanya Olatunji

Permeability is a crucial property that can be used to indicate whether a material can hold fluids or not. Predicting the permeability of carbonate reservoirs is always a challenging and expensive task while using traditional techniques. Traditional methods often demand a significant amount of time, resources, and manpower, which are sometimes beyond the limitations of under developing countries. However, predicting permeability with precision is crucial to characterize hydrocarbon deposits and explore oil and gas successfully. To contribute to this regard, the current study offers some permeability prediction models centered around ensemble machine learning techniques, e.g., the gradient boost (GB), random forest (RF), and a few others. In this regard, the prediction accuracy of these schemes has significantly been enhanced using feature selection and ensemble techniques. Importantly, the authors utilized actual industrial datasets in this study while evaluating the proposed models. These datasets were gathered from five different oil wells (OWL) in the Middle Eastern region when a petroleum exploration campaign was conducted. After carrying out exhaustive simulations on these datasets using ensemble learning schemes, with proper tuning of the hyperparameters, the resultant models achieved very promising results. Among the numerous tested models, the GB- and RF-based algorithms offered relatively better performance in terms of root means square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) while predicting permeability of the carbonate reservoirs. The study can potentially be helpful for the oil and gas industry in terms of permeability prediction in carbonate reservoirs.

Multiple Sclerosis (MS) is characterized by chronic deterioration of the nervous system, mainly the brain and the spinal cord. An individual with MS develops the condition when the immune system begins attacking nerve fibers and the myelin sheathing that covers them, affecting the communication between the brain and the rest of the body and eventually causing permanent damage to the nerve. Patients with MS (pwMS) might experience different symptoms depending on which nerve was damaged and how much damage it has sustained. Currently, there is no cure for MS; however, there are clinical guidelines that help control the disease and its accompanying symptoms. Additionally, no specific laboratory biomarker can precisely identify the presence of MS, leaving specialists with a differential diagnosis that relies on ruling out other possible diseases with similar symptoms. Since the emergence of Machine Learning (ML) in the healthcare industry, it has become an effective tool for uncovering hidden patterns that aid in diagnosing several ailments. Several studies have been conducted to diagnose MS using ML and Deep Learning (DL) models trained using MRI images, achieving promising results. However, complex and expensive diagnostic tools are needed to collect and examine imaging data. Thus, the intention of this study is to implement a cost-effective, clinical data-driven model that is capable of diagnosing pwMS. The dataset was obtained from King Fahad Specialty Hospital (KFSH) in Dammam, Saudi Arabia. Several ML algorithms were compared, namely Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Extra Trees (ET). The results indicated that the ET model outpaced the rest with an accuracy of 94.74%, recall of 97.26%, and precision of 94.67%.