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Online learning is essential in today’s world. The COVID-19 has resulted in shutting down all the universities across the globe. Countries like India and Turkey (lower-income countries) are suffering a lot in giving the best classroom practice to their students through online mode. The entire way of teaching-learning has changed drastically, and it is a need of an hour. Research suggests that online learning has been shown to increase retention of information, and take less time, meaning the changes coronavirus have caused might be here to stay. It is therefore important to understand from student’s perspectives about learning online. The paper systematically surveys the perception of learning online for Indian and Turkan students.

To achieve this goal, 594 samples of students (from India and Turkey country) have been taken into considerations, and through statistical measures, the results were analyzed. The set of four research questions comprising of effect of study on COVID-19 pandemic, perception of learning online in COVID-19 pandemic, perception of different genders in learning online and perception of Indians over Turkan students in learning online were analyzed through statistical measures such as mean, standard deviation and so on.

The descriptive statistics of various responses across various dimensions (gender, country) reveals that there is no effect in learning online as compared to classroom-based teaching. On the other hand, there is no significant difference in gender and country in learning online.

Online learning has become crucial in higher education as far as pandemic situation is concerned. Many higher education institutions across different countries are suffering various problems from student point of view. Middle-income countries who are with limited assets and less advancements in higher education need to adhere to certain guidelines in online learning. This empirical study will help to understand the perception of students in online learning across India and Turkey.

Since December 2019, global attention has been drawn to the rapid spread of COVID-19. Corona was discovered in India on 30 January 2020. To date, in India, 178,014 disease cases were reported with 14,011 deaths by the Indian Government. In the meantime, with an increasing spread speed, the COVID-19 epidemic occurred in other countries. The survival rate for COVID-19 patients who suffer from a critical illness is efficiently and precisely predicted as more fatal cases can be affected in advanced cases. However, over 400 laboratories and clinically relevant survival rates of all present critically ill COVID-19 patients are estimated manually. The manual diagnosis inevitably results in high misdiagnosis and missed diagnosis owing to a lack of experience and prior knowledge. The chapter presents an option for developing a machine-based prognostic model that exactly predicts the survival of individual severe patients with clinical data from different sources such as Kaggle data.gov and World Health Organization with greater than 95% accuracy. The data set and attributes are shown in detail. The reasonableness of such a mere three elements may depend, respectively, on their representativeness in the indices of tissue injury, immunity and inflammation. The purpose of this paper is to provide detailed study from the diagnostic aspect of COVID-19, the work updates the cost-effective and prompt criticality classification and prediction of survival before the targeted intervention and diagnosis, in particular the triage of the vast COVID-19 explosive epidemic.

Automated machine learning (ML) provides resources and platforms to render ML available to non-ML experts, to boost efficiency in ML and to accelerate research in machine learning. H2O AutoML is used to generate the results (Dulhare et al., 2020). ML has achieved major milestones in recent years, and it is on which an increasing range of disciplines depend. But this performance is crucially dependent on specialists in human ML to perform the following tasks: preprocess the info and clean it; choose and create the appropriate apps; choose a family that fits the pattern; optimize hyperparameters for layout; and models of computer learning post processes. Review of the findings collected is important.

These days, the concept of automated ML techniques is being used in every field and domain, for example, in the stock market, education institutions, medical field, etc. ML tools play an important role in harnessing the massive amount of data. In this paper, the data set relatively holds a huge amount of data, and appropriate analysis and prediction are necessary to track as the numbers of COVID cases are increasing day by day. This prediction of COVID-19 will be able to track the cases particularly in India and might help researchers in the future to develop vaccines. Researchers across the world are testing different medications to cure COVID; however, it is still being tested in various labs. This paper highlights and deploys the concept of AutoML to analyze the data and to find the best algorithm to predict the disease. Appropriate tables, figures and explanations are provided.

As the difficulty of such activities frequently goes beyond non-ML-experts, the exponential growth of ML implementations has generated a market for off-the-shelf ML solutions that can be used quickly and without experience. We name the resulting work field which is oriented toward the radical automation of AutoML machine learning. The third class is that of the individuals who have illnesses such as diabetes, high BP, asthma, malignant growth, cardiovascular sickness and so forth. As their safe frameworks have been undermined effectively because of a common ailment, these individuals become obvious objectives. Diseases experienced by the third classification of individuals can be lethal (Shinde et al., 2020). Examining information is fundamental in having the option to comprehend the spread and treatment adequacy. The world needs a lot more individuals investigating the information. The understanding from worldwide data on the spread of the infection and its conduct will be key in limiting the harm. The main contributions of this study are as follows: predicting COVID-19 pandemic in India using AutoML; analyzing the data set predicting the patterns of the virus; and comparative analysis of predictive algorithms. The organization of the paper is as follows, Sections I and II describe the introduction and the related work in the field of analyzing the COVID pandemic. Section III describes the workflow/framework for AutoML using the components with respect to the data set used to analyze the patterns of COVID-19 patients.

The purpose of this paper is to improve the existing paradigm of edge computing to maintain a balanced energy usage.

The new greedy algorithm is proposed to balance the energy consumption in edge computing.

The new greedy algorithm can balance energy more efficiently than the random approach by an average of 66.59 percent.

The results are shown in this paper which are better as compared to existing algorithms.

The latest 2019 coronavirus (COVID-2019), which first appeared in December 2019 in Wuhan's city in China, rapidly spread around the world and became a pandemic. It has had a devastating impact on daily lives, the public's health and the global economy. The positive cases must be identified as soon as possible to avoid further dissemination of this disease and swift care of patients affected. The need for supportive diagnostic instruments increased, as no specific automated toolkits are available. The latest results from radiology imaging techniques indicate that these photos provide valuable details on the virus COVID-19. User advanced artificial intelligence (AI) technologies and radiological imagery can help diagnose this condition accurately and help resolve the lack of specialist doctors in isolated areas. In this research, a new paradigm for automatic detection of COVID-19 with bare chest X-ray images is displayed. Images are presented. The proposed model DarkCovidNet is designed to provide correct binary classification diagnostics (COVID vs no detection) and multi-class (COVID vs no results vs pneumonia) classification. The implemented model computed the average precision for the binary and multi-class classification of 98.46% and 91.352%, respectively, and an average accuracy of 98.97% and 87.868%. The DarkNet model was used in this research as a classifier for a real-time object detection method only once. A total of 17 convolutionary layers and different filters on each layer have been implemented. This platform can be used by the radiologists to verify their initial application screening and can also be used for screening patients through the cloud.

This study also uses the CNN-based model named Darknet-19 model, and this model will act as a platform for the real-time object detection system. The architecture of this system is designed in such a way that they can be able to detect real-time objects. This study has developed the DarkCovidNet model based on Darknet architecture with few layers and filters. So before discussing the DarkCovidNet model, look at the concept of Darknet architecture with their functionality. Typically, the DarkNet architecture consists of 5 pool layers though the max pool and 19 convolution layers. Assume as a convolution layer, and as a pooling layer.

The work discussed in this paper is used to diagnose the various radiology images and to develop a model that can accurately predict or classify the disease. The data set used in this work is the images bases on COVID-19 and non-COVID-19 taken from the various sources. The deep learning model named DarkCovidNet is applied to the data set, and these have shown signification performance in the case of binary classification and multi-class classification. During the multi-class classification, the model has shown an average accuracy 98.97% for the detection of COVID-19, whereas in a multi-class classification model has achieved an average accuracy of 87.868% during the classification of COVID-19, no detection and Pneumonia.

One of the significant limitations of this work is that a limited number of chest X-ray images were used. It is observed that patients related to COVID-19 are increasing rapidly. In the future, the model on the larger data set which can be generated from the local hospitals will be implemented, and how the model is performing on the same will be checked.

Deep learning technology has made significant changes in the field of AI by generating good results, especially in pattern recognition. A conventional CNN structure includes a convolution layer that extracts characteristics from the input using the filters it applies, a pooling layer that reduces calculation efficiency and the neural network's completely connected layer. A CNN model is created by integrating one or more of these layers, and its internal parameters are modified to accomplish a specific mission, such as classification or object recognition. A typical CNN structure has a convolution layer that extracts features from the input with the filters it applies, a pooling layer to reduce the size for computational performance and a fully connected layer, which is a neural network. A CNN model is created by combining one or more such layers, and its internal parameters are adjusted to accomplish a particular task, such as classification or object recognition.

The study proposes to determine the impending challenges to lean integration with Industry 4.0 (I4.0) in manufacturing that aims at achieving desired operational performance. Integrating lean and Industry 4.0 as the two industrial approaches is synergetic in providing operational benefits such as increasing flexibility, improving productivity, reducing cost, reducing delivery time, improving quality and value stream mapping (VSM). There is an urgent need to understand the integrated potential of OPEX strategies like lean manufacturing and also to determine the challenges for manufacturing SMEs and further suggest a strategic roadmap for the future.

The current work has used a combined approach on interpretative structural modeling (ISM) and fuzzy Matrice d'impacts croisés multiplication appliquée á un classment (MICMAC) approach to structure the multiple level analysis for the implementation challenges to integrate OPEX strategies with Industry 4.0.

The research has found that the indulgence of various implementation issues like lack of standardization, lack of vision and lack of trained support, all are the major challenges that inhibit the integration of OPEX strategies with I4.0 technologies in manufacturing.

The research has investigated the internal factors acting as a roadblock to lean and Industry 4.0 adoption. Further studies may consider external factors to lean and Industry 4.0 implementation. Also, further research may consider other operational excellence approaches and extend further to relevant sectors.

This study provides the analysis of barriers that is useful for the managers to take strategic actions for implementing OPEX strategies with I4.0 in smart manufacturing.

The research determines the adoption challenges towards the integrated framework. This is the first study to explore challenges in integrating OPEX strategies with I4.0 technologies in manufacturing SMEs.

This purpose of this study is to perfrom the analysis of COVID-19 with the help of blood samples. The blood samples used in the study consist of more than 100 features. So to process high dimensional data, feature reduction has been performed by using the genetic algorithm.

In this study, the authors will implement the genetic algorithm for the prediction of COVID-19 from the blood test sample. The sample contains records of around 5,644 patients with 111 attributes. The genetic algorithm such as relief with ant colony optimization algorithm will be used for dimensionality reduction approach.

The implementation of this study is done through python programming language and the performance evaluation of the model is done through various parameters such as accuracy, sensitivity, specificity and area under curve (AUC).

The implemented model has achieved an accuracy of 98.7%, sensitivity of 96.76%, specificity of 98.80% and AUC of 92%. The results have shown that the implemented algorithm has performed better than other states of the art algorithms.

The purpose of this paper is to propose a novel resource availability assessment for supply chain (SC) configuration. This approach involves understanding both local resource availability and the demand-side implications of supplying global/regional markets as part of a more holistic SC design activity that incorporates local environmental factors.

The proposed framework was derived from literature analysis, bridging relevant literature domains – natural capital theory, industrial ecology and SC configuration – in order to develop design rules for future resource-constrained industrial systems. In order to test the proposed framework, an exploratory case study, based on secondary data, was conducted.

Research findings suggest that this approach might better identify relationships and vulnerabilities between natural resource availability and the viability of regional/global SCs. The research suggests that natural resource availability depends upon three elements – local resource consumption, global resource demand and external environmental factors.

The framework has two main limitations. The current work is focussed on a single industry case study used to exemplify the approach. Second, the framework does not consider other possible industries, which might enter or leave the specific location during the company’s operation. Furthermore, no assessment was made of the migration of populations within the area.

For practitioners, such as those in the agri-food sector, the resource availability assessment framework informs SC configuration design. For policymakers, the research aims to provide policy guidelines, which can help to improve water-saving strategies for a particular region. At a broader societal level, the research raises awareness of resource scarcity amongst industrial players and the wider public.

A resource availability assessment framework has been proposed, suggesting that the dynamics of both global and local resource demand, in conjunction with changing local environmental factors, can over time significantly deteriorate a firm’s natural resource impact on the local environment. Thus, the framework seeks to deliver mechanisms to evaluate potential vulnerabilities and solutions available to firms using a more proactive SC design method and to apply reconfiguration processes that account for natural resources, based primarily on network and resource attributes.

By reading and understanding this case study, students are expected to: 1.Able to understand and review the impact of unethical practices from accounting perspective; 2.Able to make an analysis of how one unethical act triggers a series of forced unethical acts (ripple effect); 3.Identify the unfair practices as well as be proactive in preventing unfair practices in the business day to day affairs; 4.Able to relate the function of various ratios (current ratio, quick ration, debt to asset ratio, debt to equity ratio etc.) and its impact on the business performance; and 5.Able to apply various lean quality tools, doing the root cause analysis in identifying and solving problems.

T.M. Exports (TME) was an India-based privately owned and operated enterprise. The company had a brilliant employee named Sanjay, who was a 12-year veteran. TME’s Business Intelligence (BI) department at TME head office, Kanpur, India, ostensibly learned on April 8, 2019, from the rumors about a brand-new vehicle dished out to Sanjay by his friend who made fortune worth of millions from certain transactions. To add fuel to the fire, another incident surfaced concerning a warehouse keeper, Mohit, who was also involved in embezzlement in one of the sales offices. On May 16, 2019, BI reported these two incidents to the internal auditor who launched an internal investigation to get to root of this case. Consequently, the company owner, Tariq Mahmood got himself caught up in a dilemma to fire both Sanjay and Mohit only or restructure the organization for better transparency and integrative approach in future. Moreover, the newly appointed Chief Executive Officer had the dilemma of keeping high safety stock to maximize service level or keeping conservative safety stock and rely on-spot market-buying if demand spiked. He decided and instructed all the warehouses to keep higher inventories to meet the forecasted demand, considering unexpected spikes in demand witnessed historically. Thus, increase in inventory caused panic in the sales department as demand was sluggish. He, therefore, offered high discounted prices to liquidate the stock. This study integrated the theories of accounting/financial ratio metrics, accounts reconciliation, business ethics and lean tools. It was demonstrated in this case that the irregularities in sales accounting and their inability of reconciliation had a serious impact on business performance. The concept of total reward was also invoked to understand the disruptive and unscrupulous practices.

This case has been particularly focused on undergraduate and postgraduate early-stage-level students pursuing business or commerce program, particularly those specializing in accounting (sales accounting) and human resource management courses.

Teaching notes are available for educators only.

CSS 1: Accounting and Finance.

For the first time in a decade, a new form of pneumonia virus, coronavirus, COVID-19, appeared in Wuhan, China. To date, it has affected millions of people, killed thousands and resulted in thousands of deaths around the world. To stop the spread of this virus, isolate the infected people. Computed tomography (CT) imaging is very accurate in revealing the details of the lungs and allows oncologists to detect COVID. However, the analysis of CT scans, which can include hundreds of images, may cause delays in hospitals. The use of artificial intelligence (AI) in radiology could help to COVID-19-positive cancer in this manner is the main purpose of the work.

CT scans are a medical imaging procedure that gives a three-dimensional (3D) representation of the lungs for clinical purposes. The volumetric 3D data sets can be regarded as axial, coronal and transverse data sets. By using AI, we can diagnose the virus presence.

The paper discusses the use of an AI for COVID-19, and CT classification issue and vaccination details of COVID-19 have been detailed in this paper.

Originality of the work is, all the data can be collected genuinely and did research work doneown methodology.

The purpose of this paper is to analyze and build a deep learning model that can furnish statistics of COVID-19 and is able to forecast pandemic outbreak using Kaggle open research COVID-19 data set. As COVID-19 has an up-to-date data collection from the government, deep learning techniques can be used to predict future outbreak of coronavirus. The existing long short-term memory (LSTM) model is fine-tuned to forecast the outbreak of COVID-19 with better accuracy, and an empirical data exploration with advanced picturing has been made to comprehend the outbreak of coronavirus.

This research work presents a fine-tuned LSTM deep learning model using three hidden layers, 200 LSTM unit cells, one activation function ReLu, Adam optimizer, loss function is mean square error, the number of epochs 200 and finally one dense layer to predict one value each time.

LSTM is found to be more effective in forecasting future predictions. Hence, fine-tuned LSTM model predicts accurate results when applied to COVID-19 data set.

The fine-tuned LSTM model is developed and tested for the first time on COVID-19 data set to forecast outbreak of pandemic according to the authors’ knowledge.