COVId-19 Vaccine Ranking Using ANP Method

Wuhan Province in China reported the first case of novel corona virus as pneumonia outbreak during December 2019. The novel coronavirus was soon declared a pandemic by the World Health Organization. On 16th of July 2021, the number of COVID-19 confirmed cases was 188,128,952 globally, out of which 4,059,339 individuals succumbed to this deadly virus. In a short span of time, eight vaccines were approval for emergency use in different nations. The selection of vaccine depends upon many criteria. Concepts from multi-criteria decision making (MCDM) are appropriate to compare and rank them. The paper proposes analytical network processing (ANP) method to rank the eight vaccines according to seven criteria. The study proposes a decision tool to select the best vaccine among the candidate vaccines. A mathematical model based on ANP approach with three clusters having interrelationships within and among the clusters is proposed. © International Journal of Operations Research and Information Systems. All rights reserved.

Student Performance Prediction Using Classification Models

The education industry has gone through major changes amidst the recent COVID-19 pandemic. Facing unforeseen circumstances, educational institutions were forced to shift to an online learning model rather than an offline, classroom-based learning model. The sudden change in the learning model impacted not only students but also the teaching faculty. Even though many resources are available online, simulating a classroom-like study environment is not an easy task. Hence mapping student performance in the new learning model is an essential task. The main goal of our work is to predict the student performance in the online learning model implemented by many colleges and universities amidst the COVID-19 pandemic. Unlike the previous work in this domain, we are purely focusing on an online study system. An online survey was conducted to collect the data from the students who had undergone the aforementioned learning model for at least one semester. The data set for the research includes features that would have an impact on a student’s performance having various attributes. The model strives to predict a student’s performance with good accuracy and help infer where the online learning model can be improved. Several classifiers such as KNN, Gradient boost, Adaboost, Decision tree, SVM, Gaussian NB were used to classify the student data. To validate the performance of these classifiers we have compared them with the latest state-of-the-art works. The Gradient Boost, Xgboost Classifier, and SVM classifiers returned the highest accuracies, in essence, 97.46, 97.45, and 97.45%, respectively. This indicates that the performance of the students is predictable with the given features. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

From Chalk Boards to Smart Boards: An Integration of IoT into Educational Environment During Covid-19 Pandemic

Internet of Things (IoT) is a unique paradigm shift in the domain of Information Technology. It converts the real-life things into intelligent virtual devices to ensure a machine-to-machine transmission of information. With its increasingly technological magnitude, it ascertains an imperative role in almost all spheres of life, and the global education markets have incredibly reaped benefits. The present paper gives an insight into the radical evolution and integration of IoT trends in education sector—the transition from conventional chalk boards to modern smart boards, especially at the outset of Covid-19 pandemic when the exigencies of global education demanded it the most. The paper also lists out opportunities, obstructions, scalability of tools and technology, and services allied to IoT expertise, while at the same time bridges the gap between educational and technical applications amid Covid-19 pandemic. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Doctor's Dilemma: Evaluating an Explainable Subtractive Spatial Lightweight Convolutional Neural Network for Brain Tumor Diagnosis

In Medicine Deep Learning has become an essential tool to achieve outstanding diagnosis on image data. However, one critical problem is that Deep Learning comes with complicated, black-box models so it is not possible to analyze their trust level directly. So, Explainable Artificial Intelligence (XAI) methods are used to build additional interfaces for explaining how the model has reached the outputs by moving from the input data. Of course, that's again another competitive problem to analyze if such methods are successful according to the human view. So, this paper comes with two important research efforts: (1) to build an explainable deep learning model targeting medical image analysis, and (2) to evaluate the trust level of this model via several evaluation works including human contribution. The target problem was selected as the brain tumor classification, which is a remarkable, competitive medical image-based problem for Deep Learning. In the study, MR-based pre-processed brain images were received by the Subtractive Spatial Lightweight Convolutional Neural Network (SSLW-CNN) model, which includes additional operators to reduce the complexity of classification. In order to ensure the explainable background, the model also included Class Activation Mapping (CAM). It is important to evaluate the trust level of a successful model. So, numerical success rates of the SSLW-CNN were evaluated based on the peak signal-to-noise ratio (PSNR), computational time, computational overhead, and brain tumor classification accuracy. The objective of the proposed SSLW-CNN model is to obtain faster and good tumor classification with lesser time. The results illustrate that the SSLW-CNN model provides better performance of PSNR which is enhanced by 8%, classification accuracy is improved by 33%, computation time is reduced by 19%, computation overhead is decreased by 23%, and classification time is minimized by 13%, as compared to state-of-the-art works. Because the model provided good numerical results, it was then evaluated in terms of XAI perspective by including doctor-model based evaluations such as feedback CAM visualizations, usability, expert surveys, comparisons of CAM with other XAI methods, and manual diagnosis comparison. The results show that the SSLW-CNN provides good performance on brain tumor diagnosis and ensures a trustworthy solution for the doctors.

Analysis of COVID-19 pandemic - Origin, global impact and Indian therapeutic solutions for infectious diseases

The first case of COVID-19 was reported in China in December 2019(ref. 1) and almost 213 countries have reported around 5,350,000 COVID-19 cases all over the world, with the mortality rate up to 3.4% as of May 23,2020. On March 11, 2020, the WHO (World Health Organization) declared COVID-19 as a global pandemic. Moving towards from epidemic to global pandemic situation just in two months, COVID-19 has caused tremendous adverse effects on people's well being and the economy all over the world. Scientists and researchers around the globe have a vested interest in researching and mitigating to handle the dire situation. This paper covers the COVID-19's origin, characteristics of the virus and reasons behind the outbreak, and precautionary measures that have to be followed to handle the critical situation. Several therapeutic solutions in the Indian healing tradition have been discussed to improve the immune system in order to equip ourselves to deal with the outbreak of COVID-19.