COVID-19 is a profoundly contagious pandemic that has taken the world by storm and has afflicted different fields of life with negative effects. It has had a substantial impact on education which is evident from the transition from Face-to-Face (F2F) teaching to online mode of education and the rigid implementation of lockdown across the globe. This study examines the factors impacting the performance of teachers during the lockdown period of COVID-19 using various feature selection algorithms and Artificial Intelligence techniques. In this paper, we have proposed a novel multilevel feature selection for the prediction of the factors affecting the teachers’ satisfaction with online teaching and learning in COVID-19. The proposed multilevel feature selection is composed of the Fast Correlation Based Filter (FCBF), Mutual Information (MI), Relieff, and Particle Swarm Optimization (PSO) feature selection. The performance of the proposed feature selection approach is evaluated through the teachers’ benchmark dataset. We used a range of measures like accuracy, precision, f-measure, and recall to evaluate the performance of the proposed approach. We applied different machine learning approaches (SVM, LGBM, and ANN) with the proposed multilevel feature selection technique. The performance of the proposed approach is also compared with existing feature selection algorithms, and the results show the improvement in the performance of feature selection in terms of accuracy, precision, recall, and F-Measure. Proposed feature selection provides more than 80% accuracy with Light Weight Gradient Boosting Machine (LGBM).

In forensic anthropology, age estimation is one of the most important aspects of analysis. The use of radiographic data for age estimation based on the epiphyseal union is a scientifically approved and widely accepted method. X-ray images of the right wrist joint of 281 patients (143 males and 138 females) aged 12-22 years were retrospectively examined and the stages of union at the distal epiphyses of radius and ulna were assessed, to estimate age in a Qassim population, Saudi Arabia. The distal epiphyses of radius and ulna fused completely at 18-19 years in females and 19-20 years in males. Mean age gradually increased and varied with each stage of union, with a statistically significant difference of the females' and males' comparable means. Females experienced epiphyseal union one year earlier than males. The ROC curve showed 90.9% accuracy in males and 90.6% in females. The findings of this study indicate that radiographic analysis of the union at distal epiphyses of radius and ulna is a helpful technique for estimation of chronological age. When compared to other studies, the results showed limited variance among different population groups.

Worldwide, COVID-19 is a highly contagious epidemic that has affected various fields. Using Artificial Intelligence (AI) and particular feature selection approaches, this study evaluates the aspects affecting the health of students throughout the COVID-19 lockdown time. The research presented in this paper plays a vital role in indicating the factor affecting the health of students during the lockdown in the COVID-19 pandemic. The research presented in this article investigates COVID-19’s impact on student health using feature selections. The Filter feature selection technique is used in the presented work to statistically analyze all the features in the dataset, and for better accuracy. ReliefF (TuRF) filter feature selection is tuned and utilized in such a way that it helps to identify the factors affecting students’ health from a benchmark dataset of students studying during COVID-19. Random Forest (RF), Gradient Boosted Decision Trees (GBDT), Support Vector Machine (SVM), and 2- layer Neural Network (NN), helps in identifying the most critical indicators for rapid intervention. Results of the approach presented in the paper identified that the students who maintained their weight and kept themselves busy in health activities in the pandemic, such student’s remained healthy through this pandemic and study from home in a positive manner. The results suggest that the 2- layer NN machine-learning algorithm showed better accuracy (90%) to predict the factors affecting on health issues of students during COVID-19 lockdown time.

Objective: Epilepsy is a chronic neurological disorder that occurs due to irregular neuronal activity in the central nervous system. The main job of a radiologist is to investigate the structural etiology in epilepsy patients. This study was undertaken to find out the importance of magnetic resonance imaging (MRI) in the screening of intractable epilepsy through a systematic search of literature. Methods: A systematic review was performed using the PRISMA guidelines. Peer reviewed studies on MRI and intractable epilepsy were retrieved from MEDLINE, ScienceDirect and Google Scholar. Moreover, studies cited in the key articles were also screened to increase the sensitivity and specificity of the systematic search. Results: The database search till March 2022 found a total of 112610 articles. Out of them, only 10 highly selected articles were included in the study. The pooled data point out that the rapid development in MRI techniques and the functional MRI (fMRI) has now become more and more critical in the diagnosis and management of patients with epilepsy. In addition, the data also pointed out that MRI-based approaches are also very useful for post-operative epilepsy patients as it gives information about the quality of the surgery. The data collected showed that the MRI is the choice technique for the evaluation of patients with epilepsy. Conclusions: The applicability of MRI in epilepsy diagnosis is highly accessible in all over the globe. The pooled data concluded that the MRI-based surgical approaches are extremely useful for the surgeons to provide three-dimensional imaging with superimposed real-time pointer details that have proved successful for epilepsy patients.

BACKGROUND: The complication of the bile duct is quite a typical case of indisposition after liver transplant from a deceased or a live donor. The morbidity rates of the patients that underwent orthotopic liver transplantation were reported. In this case report, the intrahepatic stone formation was studied in post liver transplanted patients. CASE REPORT: This report presents a sporadic case of 53-year-old male patient in post-operative liver transplant. The initial post-transplant course of the patient was quite favorable, but after a week post-surgery, the patient developed the case of jaundice and came on with yellow discoloration in the skin. The endoscopic retrograde cholangiopancreatography (ERCP) was initially performed and the common bile duct (CBD) stent was inserted. Routine post-operative examination and follow-ups showed that the onset of jaundice. Further investigations with ERCP and the magnetic resonance cholangiopancreatography (MRCP) to determine the complete mapping of the biliary region showed the presence the presence of biliary stones in the CBD. In short, this is a rare finding that presents a sporadic case of post-operative liver transplant complications of biliary stones. The patient underwent a liver transplant but came back with post-surgery complications. MRCP and ERCP techniques were used for the imaging and providing an accurate diagnosis, indicating the presence of biliary stones in the CBD. CONCLUSION: Intrahepatic stone was found in patients with post liver transplanted patient.