GASVR- A Model to Predict and Analyze Crude Oil Price

The variations in the price of crude oil are very erratic, nonlinear, and dynamic with a high degree of uncertainty making it much more difficult to predict accurately. As a result, the opacity and intricacy in determining the crude oil price have been a significant topic of interest for researchers. This paper develops an efficient Genetic Algorithm(GA) based fine-tuned Support Vector Regression(SVR) model for predicting crude oil prices. The strategy utilizes key economic factors that ascertain the price per barrel, which serves as the input. The NASDAQ dataset used in this work encompasses ten years of daily data. The GA technique fine-tunes the parameters of the SVR model to boost the model's ability to foresee crude oil price fluctuations. The proposed model's performance is evaluated by employing various major criteria that compare our model to its counterparts, such as SVR and Long Short-Term Memory (LSTM) approaches. In light of these criteria, the findings of root mean square error (RMSE) and mean absolute percentage error (MAPE) indicate that this model surpasses others in predicting crude oil prices more accurately. Finally, this study also analyzes the impact of persistent uncertainness concerning the COVID-19 outbreak on crude oil price trends. © 2022 IEEE.

The impact of COVID–19 on ESL learning in India: Problems, challenges, and coping strategies

COVID-19 has significantly affected the teaching-learning continuum in India. Most of the educational institutes had adopted online mode for the delivery of content and pedagogies enabled by digital technology, devices, and platforms. The pandemic has adversely affected English language learning in India, for learners used to learning ESL in a real-life situation through regular face-to-face mode experienced challenges in earning ESL through virtual mode. Learning English to develop the required language skills in virtual classrooms was anything but easy. Nonetheless, Indian students, as this study finds, took up the challenge by their stride and survived the altered learning conditions forced by the pandemic. This student-centric study aims to explore the impact of COVID-19 on language learning, the problems and challenges faced by student-learners, and the strategies to overcome them. An online survey was conducted to collect data from a group of students (n=400) using a survey questionnaire and mixed methods were used for data analysis and interpretation. The results indicate that the students experienced moderate to high-level difficulty in language learning and their coping strategies worked out. © 2022 International Journal of Health Sciences.

Omicron Virus Data Analytics Using Deep Learning Technique

The man-made brainpower (AI) methods overall and convolutional brain organizations (CNNs) specifically have achieved victories in clinical picture examination and grouping. A profound CNN design partakesprojected into this research article for the analysis of OMICRONgroundedonto clinical radiography analysis (X-ray). As matter of the fact, thenon-availability in adequate scope and excellent X-ray picture database, a compelling and exact Convolutional NN (CNN) characterization remained anexamination. Managingthose intricacies, for example, accessibility with avery-little measured and contrastdatabaseof picture resolutionchallenges, the database has been pre-processed into various stages utilizing various strategies to accomplish a powerful preparation databaseof the appliedConvolutional NN (CNN)prototypical to achieve itsfinest presentation. Preprocessing phases in the database acted intoresearch incorporate database adjusting, clinical specialists' picture investigation, and information expansion. The exploratory outcomes reveal general precision up to 98.08% that exhibits its great capacity of the prototypicalConvolutional NN (CNN)systemof the ongoing application space. Convolutional NN (CNN)prototype has been tried into 2 (two) situations. The primary situation explains that it hasbeen tried utilizing the 7762 X-ray pictures as database,it accomplished a precision of 98.08 percent. To the subsequent situation, the prototypical hastried been utilizing the autonomous database of Omicron X-ray pictures from Kaggle. The execution intocurrentassessment the situation remained just about 98.08%. It additionally demonstrates that the prototypicalsystem beats different systems, asa similar examination has finished been thru a portion of AIcalculations. The proposed model has superseded every one of the models by and large and explicitly when the model testing was finished utilizing a free testing set.