The whole world faces a pandemic situation due to the deadly virus, namely COVID-19. It takes considerable time to get the virus well-matured to be traced, and during this time, it may be transmitted among other people. To get rid of this unexpected situation, quick identification of COVID-19 patients is required. We have designed and optimized a machine learning-based framework using inpatient’s facility data that will give a user-friendly, cost-effective, and time-efficient solution to this pandemic. The proposed framework uses Bayesian optimization to optimize the hyperparameters of the classifier and ADAptive SYNthetic (ADASYN) algorithm to balance the COVID and non-COVID classes of the dataset. Although the proposed technique has been applied to nine state-of-the-art classifiers to show the efficacy, it can be used to many classifiers and classification problems. It is evident from this study that eXtreme Gradient Boosting (XGB) provides the highest Kappa index of 97.00%. Compared to without ADASYN, our proposed approach yields an improvement in the kappa index of 96.94%. Besides, Bayesian optimization has been compared to grid search, random search to show efficiency. Furthermore, the most dominating features have been identified using SHapely Adaptive exPlanations (SHAP) analysis. A comparison has also been made among other related works. The proposed method is capable enough of tracing COVID patients spending less time than that of the conventional techniques. Finally, two potential applications, namely, clinically operable decision tree and decision support system, have been demonstrated to support clinical staff and build a recommender system.

A photonic crystal fiber (PCF) for sensing of sulfuric acid is designed and analyzed using Comsol Multiphysics. To analyze the sensor performance, 0%, 10%, 20%, 30%, 40% H₂SO₄ solution is placed into the fiber separately and then relative sensitivity, confinement loss, birefringence, effective area etc. are investigated for each solution over wavelength ranging from 0.8 to 1.8 µm. The sensor structure affords moderately high relative sensitivity and around 63.4% sensitivity is achieved for the highest concentration of H₂SO₄ at the wavelength 1.5 µm in x polarization direction. This PCF model also shows zero confinement loss for all solutions of H₂SO₄ over wavelength ranging from 1 to 1.35 µm and later on approximately 1.422 × 10⁻¹⁷ dB/km confinement loss is found for the highest concentration of H₂SO₄ at 1.5 µm wavelength. Besides, higher birefringence is attained when the concentration of sulfuric acid is lower and it is achieved 7.5 × 10⁻⁴ at 1.5 µm wavelength. Moreover, higher sensing area is achieved at high concentration of sulfuric acid.