An Intrusion Detection System Using Machine Learning for Internet of Medical Things

The COVID-19 pandemic has impacted everyday life, the global economy, travel, and commerce. In many cases, the tight measures put in place to stop COVID-19 have caused depression and other diseases. As many medical systems over the world are unable to hospitalize all the patients, some of them may get home healthcare assistance, while the government and healthcare organizations have access to substantial sickness management data. It allows patients to routinely update their health status and have it sent to distant hospitals. In certain cases, the medical authorities may designate quarantine stations and provide supervision equipment and platforms (such as Internet of Medical Things (IoMT) devices) for performing an infection-free treatment, whereas IoMT devices often lack enough protection, making them vulnerable to many threats. In this paper, we present an intrusion detection system (IDS) for IoMTs based on the following gradient boosting machines approaches: XGBoost, LightGBM, and CatBoost. With more than 99% in many evaluation measures, these approaches had a high detection rate and could be an effective solution in preventing attacks on IoMT devices. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A novel COVID-19 predictor model inspired by Flower Pollination Algorithm

The COVID-19 outbreak turned the world upside down by infecting hundred million people, killing more than five million and disrupting everyday life across the planet. The Wuhan virus shattered the global economy and brought daily life to a grinding halt in much of the world. The second largest populated country India had no escape as well. Since the very beginning of 20th century, machine learning based methodologies have been largely applied in epidemiological data analysis in order to control diseases and other health issues. In this regard, researchers have come up with various predictor models to forecast the future impact of the Wuhan virus, so that further spreading of virus can be controlled by implementing precautionary measures. The purpose behind this work is to investigate the prediction capability of Legendre Polynomial Neural Network (LEPNN) trained using the very popular bio-inspired Flower Pollination Algorithm on the real data set of three categories of COVID cases in India as well as Odisha. The three types are the confirmed, deceased and recovery cases of daily basis. The prediction performance of the LEPNN-FPA model has been assessed with respect to the performance of two other models. © 2022 IEEE.

A Novel Approach to the Two-Dimensional Cargo Load Problem

The transport and logistics sector, which include freight forwarders companies, constitutes a vast network of entities that are central to a good performance in services. With the COVID-19 pandemic and its effects on the global economy, there was a huge shortage in the number of containers available, thus creating the need to optimize the loading of available equipment to avoid waste and maximize profits from each export. The present work presents a novel approach where a set of restrictions were created that, applied in synergy with the Non-Linear GRG algorithm, aim to allocate the boxes in different consecutive lines until forming a wall, and, therefore, the walls complete the container, in order to maximize the occupancy on it. To validate the proposed approach a prototype was developed and studied in real-world problem where the solutions resulted in occupations around 80% to 90%. Thus, we can foresee the importance of the proposed approach in decision-making regarding container consolidation services. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Prediction and Analysis of COVID-19 Epidemic Based on Improved GEP Algorithm to Optimize SEIR Mode

In 2019, COVID-19 (CoronaVirus Disease 2019) broke out all over the world. COVID-19 is an infectious disease, which has a huge impact on the global economy. It is very difficult to prevent and control the epidemic situation of this infectious disease. At present, many SEIR(Susceptible Exposed Infected Recovered)models are used to predict the number of infectious diseases, which has the shortcomings of low prediction accuracy and inaccurate inflection point prediction. Therefore, this paper proposes that the prediction and analysis of COVID-19 based on improved GEP algorithm and optimized SEIR model can improve the prediction accuracy and inflection point prediction accuracy, and provide a theoretical basis for epidemic prevention of large-scale infectious diseases in the future. The algorithm. First, establish SEIR (Susceptible Exposed Infected Recovered) model to analyze the epidemic trend, and then use improved GEP (Gene Expression Programming) algorithm to analyze the infection coefficient of SEIR model beta And coefficient of restitution y, perform parameter estimation to optimize the initial value I and recovery coefficient of the infected population y and so on to improve the accuracy of model prediction. The experimental data take the number of COVID-19 infected people in the United States, China, the United Kingdom and Italy as examples. The results show that the SEIR model optimized based on the improved GEP algorithm conforms to the inflection point of the actual data, and the average error value is 1.32%. The algorithm provides a theoretical basis for the future epidemic prevention. © 2022 IEEE.

The Approach of Machine Learning to Optimize the Bank-Customer Interaction at Pandemic Epochs

Along the pandemic created by the Corona virus 2019 (Covid-19 in shorthand), the global economy was observed to experience various turbulent months that were reflected by the increasing of unemployment and the apparition of a procrastinator behavior in all those customers that received a loan at the months before the beginning of pandemic. Because the apparition of pandemic was totally random, it had effects on the micro-economy that in most cases have turned out on the cuts of salaries. From a basic modeling of loan and Gaussian approach, the criteria of Mitchell are employed. The resulting simulations have yielded that up to a 50% of loaned volume of cash would be recovery. It was found that entropic situations would be in part a cause for the deficient management of loans in epochs of pandemic and crisis. © 2022 IEEE.

From Digital Economy to Global Economy—A Cybernetics Approach

The Digital Economy is a new form of economy that has grown rapidly in recent years, becoming a real oxymoron for more and more specialists. In this research, we aimed to make a systematic review of the existing literature in the field, to present the differences between digitization, digitalization and digital transformation and to define the Global Economy as a Cybernetic System in the Digital Age. Moreover, we will investigate what the role of the COVID-19 pandemic was in accelerating the digitalization of the Digital Economy and what the future post-COVID-19 actions of digital transformation could be. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Impact of Drug Repurposing on SARS-Cov-2 Main Protease

Abstract: The recent emergence of the severe acute respiratory disease caused by a novel coronavirus remains a concern posing many challenges to public health and the global economy. The resolved crystal structure of the main protease of SARS-CoV-2 or SCV2 (Mpro) has led to its identification as an attractive target for designing potent antiviral drugs. Herein, we provide a comparative molecular impact of hydroxychloroquine (HCQ), remdesivir, and β-D-N4-Hydroxycytidine (NHC) binding on SCV2 Mpro using various computational approaches like molecular docking and molecular dynamics (MD) simulation. Data analyses showed that HCQ, remdesivir, and NHC binding to SARS-CoV-2 Mpro decrease the protease loop capacity to fluctuate. These binding influences the drugs' optimum orientation in the conformational space of SCV2 Mpro and produce noticeable steric effects on the interactive residues. An increased hydrogen bond formation was observed in SCV2 Mpro–NHC complex with a decreased receptor residence time during NHC binding. The binding mode of remdesivir to SCV2 Mpro differs from other drugs having van der Waals interaction as the force stabilizing protein–remdesivir complex. Electrostatic interaction dominates in the SCV2 Mpro−HCQ and SCV2 Mpro–NHC. Residue Glu166 was highly involved in the stability of remdesivir and NHC binding at the SCV2 Mpro active site, while Asp187 provides stability for HCQ binding. © 2022, Pleiades Publishing, Ltd.

Design of Covid19 Detection Based on Relative Eccentric Feature Selection Using Deep Vectored Regressive Neural Network for Corona Virus

The world has seen various diseases in different variants, numerous pandemics in the twentieth century like covid-19. Fly infections are the fundamental driver of contaminations. An epidemic known as COVID-19 has been declared, and it has had a significant impact on society and the global economy. The diagnosis of Covid19 or non-Covid-19 cases early detection at the correct separation at the lowest cost early stages of the disease is one of the major problems in the current coronavirus pandemic. To address this problem, the proposed Deep learning and Design of covid19 detection based on Relative Eccentric Feature Selection (REFS) Using Deep Vectorized Regressive Neural Network (DVRNN) for corona virus the early detection of the Covid19 virus. Initially collects the covid19 sample test dataset, then the raw dataset trained into preliminary process is used to remove unwanted noise. After that preliminary processed dataset trained into the feature selection process is done to identify the best features of covid19 using Ensemble recursive feature selection. Further, the proposed DVRNN algorithm is done to classify the accurate detection of coronavirus. The proposed model would be useful for the timely and accurate identification of the coronavirus at different stages. Therefore it can detect the accurate results of covid19 effectively and accomplish good performance compared with previous methods. © 2022 IEEE.

Optimized Layout: A Genetic Algorithm for Industrial and Business Application

Covid-19 epidemic has harmed the global economy. Particularly, the restaurant sector has been severely impacted by the rapid spread of the virus. The use of digital technology (DT) has been utilized to execute risk-reduction methods as service innovation tools. In this work, a genetic algorithm optimization is used to cope with the problems caused by the restrictions due to Covid-19 to optimize the management of the spaces in commercial and industrial structures. The approach through the GA involves the selection of the best members of a population that change genome based on the epochs. The digitization of a commercial or industrial environment becomes an optimal methodology for carrying out virtual design of work environments. Digitization thus becomes a strategy for the reduction of both monetary and time cost. Focusing on the case study, satisfactory results emerge supported by tests that reveal an appreciable robustness and open new scenarios for different applications of the methodology developed in this work, always in the context of optimal management of industrial and commercial spaces. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

EVALUATION OF AIR TRAFFIC IN THE CONTEXT OF THE COVID-19 PANDEMIC

The Covid-19 pandemic unexpectedly shook the entire global economy, causing it to destabilize over a long period of time. One of the sectors that was particularly hit hard was air traffic, and the changes that have taken place in it have been unmatched by any other crisis in history. The purpose of this article was to identify the time series describing the number of airline flights in Poland in the context of the Covid-19 pandemic. The article first presents selected statistics and indicators showing the situation of the global and domestic aviation market during the pandemic. Then, based on the data on the number of flights in Poland, the identification of the time series describing the number of flights by airlines was made. The discrete wavelet transformation (DWT) was used to determine the trend, while for periodicity verification, first statistical tests (Kruskal-Wallis test and Friedman test) and then spectral analysis were used. The confirmation of the existence of weekly seasonality allowed for the identification of the studied series as the sum of the previously determined trend and the seasonal component, as the mean value from the observations on a given day of the week. The proposed model was compared with the 7-order moving average model, as one of the most popular in the literature. As the obtained results showed, the model developed by the authors was better at identifying the studied series than the moving average. The errors were significantly lower, which made the presented solution more effective. This confirmed the validity of using wavelet analysis in the case of irregular behaviour of time series, and also showed that both spectral analysis and statistical tests (Kruskal-Walis and Fridman) proved successful in identifying the seasonal factor in the time series. The method used allowed for a satisfactory identification of the model for empirical data, however, it should be emphasized that the aviation services market is influenced by many variables and the forecasts and scenarios created should be updated and modified on an ongoing basis. © 2022 Warsaw University of Technology. All rights reserved.

How to Prepare for the Next Pandemic - Investigation of Correlation Between Food Prices and COVID-19 From Global and Local Perspectives

The coronavirus disease (COVID-19) has caused enormous disruptions to not only the United States, but also the global economy. Due to the pandemic, issues in the supply chain and concerns about food shortage drove up the food prices. According to the U.S. Bureau of Labor Statistics, the prices for food increased 4.1% and 3.7% over the year ended in August 2020 and August 2021, respectively, while the amount of annual increase in the food prices prior to the COVID-19 pandemic is less than 2.0%. Previous studies show that such kinds of exogenous disasters, including the 2011 Tohoku Earthquake, 9/11 terrorist attacks, and major infectious diseases, and the resulted unusual food prices often led to subsequent changes in people's consumption behaviors. We hypothesize that the COVID-19 pandemic causes food price changes and the price changes alter people's grocery shopping behaviors as well. To thoroughly explore this, we formulate our analysis from two different perspectives, by collecting data both globally, from China, Japan, United Kingdom, and United States, and locally, from different groups of people inside the US. In particular, we analyze the trends between food prices and COVID-19 as well as between food prices and spending, aiming to find out their correlations and the lessons for preparing the next pandemic. © 2022 IEEE.

The Graph Convolutional Networks Framework for Predicting Pandemic Impact on Stock Prices

Covid-19 has dealt an unprecedented hit to the global economy and all industries, with varying degrees of decline from retail to real estate. This volatility is most evident in stock prices. Previous stock price forecasting methods typically used historical data for each stock as a separate input into the system. This paper proposes an attention-based parallel graph convolutional network framework, which consists of two parallel GCNs. The first GCN takes stock features as input, and the second GCN takes other industry features as input, and sets an attention model to reflect the pairwise interactions between networks. Experimental results on selected stock data show that the model outperforms both the LSTM model and the GCN model in accuracy and F1 score. © 2022 IEEE.

Implementation of Digital Technologies and Automation: Key Strategy of Improving Project Efficiency During Challenging Phase

The current outbreak and the financial crisis occurred due to Coronavirus (COVID‐19);the global economy is melting like an ice-cream. This current pandemic and the market condition have affected not only the human but also greatly impacted the commodities prices, demand & supply especially into the industry those which believes on the traditional way of working such as oil & gas and other energy sectors. If I will talk about only the oil and Gas or Petroleum industry, then based on the current market information and statistics then the short team impact is nearly 25% to 30% decrease in the petroleum consumptions, but the long-term impact can be even more than 35% to 40%. The CAPEX and OPEX investment for research and development have been slashed like anything. When the world started investing into the other source of energy then it has started forcing oil and gas industry to think out of the box and industry must change rapidly prior to losing a substantial market share because of orthodox thinking in terms of utilizing the available technology or investing in the future technologies. This paper will discuss about the way how to shift the whole industry from man oriented to machine oriented, uses of traditional technologies to the modern technologies and implementation of digitization and automation of running plant as well as upcoming projects starting in the earliest phase e.g., Feasibility study, Pre-Feed, FEED and EPC stage (including Pre-Commissioning/Commissioning) and the operation phase of the projects. Copyright © 2022, International Petroleum Technology Conference.

Privacy-preserving COVID-19 contact tracing solution based on blockchain

The COVID-19 pandemic has severely affected daily life and caused a great loss to the global economy. Due to the very urgent need for identifying close contacts of confirmed patients in the current situation, the development of automated contact tracing app for smart devices has attracted more attention all over the world. Compared with expensive manual tracing approach, automated contact tracing apps can offer fast and precise tracing service, however, over-pursing high efficiency would lead to the privacy-leaking issue for app users. By combing with the benign properties (e.g., anonymity, decentralization, and traceability) of blockchain, we propose an efficient privacy-preserving solution in automated tracing scenario. Our main technique is a combination of non-interactive zero-knowledge proof and multi-signature with public key aggregation. By means of aggregating multiple signatures from different contacts at the mutual commitment phase, we only need fewer zero-knowledge proofs to complete the task of identifying contacts. It inherently leads to the benefits of saving storage and consuming less time for running verification algorithm on blockchain. Furthermore, we perform an experimental comparison by timing the execution of signature verification with and without aggregate signature, respectively. It shows that our solution can actually preserve the full-fledged privacy protection property with a lower computational cost. © 2022

COVID-19 Triggers a Paradigm Shift in Technology for Insurance Industry

As the global economy grapples with the advent of novel coronavirus and its variants, the aftermath has left all industries with ongoing uncertainties and incalculable loss of life and livelihood in most countries worldwide. In such unpredictable situations, the insurance industry and governments worldwide have become the prominent source of optimism to sail through the situation. This applies to the insurance industry globally, which is currently in the grip of fear due to the COVID-19 outbreak and anticipating significant economic slowdown and hardship because insurance rides on the back of other Industries. Therefore, to overcome a few of the tenacious roadblocks due to the COVID outbreak, Insurers will be forced to reassess all aspects of their business life cycle and take necessary steps to continue operations with minimum disruption. Precisely, the impact of COVID on General Insurers and Life and Health Insurers varied depending on the lines of business, product lines, and a bouquet of benefits offered by the insurers. The pandemic has taken a hit on new gross written premiums on specific lines of business, such as medical, travel, commercial, and business insurance. Few lines of business such as motor and home have remained muted during the COVID timeframe. However, the claims volumes for personal insurance (e.g., motor) have significantly decreased due to the lockdown and travel restriction;the industry has witnessed the highest claims volumes in life and health compared to the past several decades. They say, "As every dark cloud has a silver lining,” it has given an opportunity to many insurers to develop new products (e.g., Pay Mile Auto insurance) and push toward greater productivity, i.e., digital capability across product range which will result in an elevated position to understand and address to the customer and intermediary self-service (such as Portals) and implicit and explicit needs. Notably, the Insurance industry is likely to lean toward offering personalized yet custom-made products and services, which are sharply focused on preventative care and embracing digitalization across the value chain. Besides enabling scalability and connectivity, insurers are strategically focused on digitizing the core of the business and cloud implementation;automation across the insurance value chain is necessary to compete successfully with new innovative product development or inclusive business models. Around the globe, the insurance industry is continuously putting a deep focus on revitalizing the technology paradigm to grow and strive to achieve cost-effectiveness amid emerging markets, rapidly changing economic conditions and stiff competition from Insurtech. According to industry experts across geographies, growth may be a balanced blend of preventative and protective approaches, with a gamut of new and improved services and products, and insurers are deeply fostering redefining service-oriented strategies and innovative products. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Empirical Study on Intention to Use of Touch'n Go e-Wallet among Millennial in Malaysia during Covid-19 Endemic

The Covid-19 epidemic has wreaked havoc on the global economy indeed, Malaysia was unavoidable from this. Nevertheless, this has created a new transformation and renovation for the FinTech industry to develop tremendously. The users of digital wallets are anticipated to grow and hastened the shift from physical payment to digital payment. Touch' n Go e-wallet is one of the FinTech innovation platforms that growing quickest rate ever in Malaysia's emerging market, owing to the upward demand for contactless payments throughout Southeast Asia due to the epidemic. The adoption of digital wallets only grew extensively during the Covid-19 pandemic periods in Malaysia. Hence, the main purpose of this study is to determine the predictors that influence the intention to use Touch' n Go e-wallet among Millennial in Malaysia, particularly during the Covid-19 endemic. This study has proposed to extend the TAM model (perceived usefulness, perceived ease of use) with perceived risk and satisfaction predictors. A sample of a total of 150 millennial in Malaysia who use Touch' n Go e-wallet to participate in the survey. Millennial account for approximately one-third of Malaysia's population and roughly 50 percent of the labour force in Malaysia. The hypotheses were further carried out by employing the ordinary least squares regression analysis. The findings revealed that among Malaysia's millennials, perceived ease of use and consumers satisfaction have a substantial impact on their desire to use Touch' n Go e-wallets app. In order to increase the usage rates of digital payments transaction, the digital wallet service providers in Malaysia must focus on the usability and potential risks that might occur during the transactions are held. More so, a good customer experience will lead to a satisfactory level in using the e-wallet of Touch' n Go. This empirical study provides a model for government to stimulate and foster digital payment in the coming years. © 2022 IEEE.

A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron

The Coronavirus (COVID-19) outbreak in December 2019 has drastically affected humans worldwide, creating a health crisis that has infected millions of lives and devastated the global economy. COVID-19 is ongoing, with the emergence of many new strains. Deep learning (DL) techniques have proven helpful in efficiently analysing and delineating infectious regions in radiological images. This survey paper draws a taxonomy of deep learning techniques for detecting COVID-19 infection in radiographic imaging modalities Chest X-Ray, and Computer Tomography. DL techniques are broadly categorised into classification, segmentation, and multi-stage approaches for COVID-19 diagnosis at the image and region-level analysis. These techniques are further classified as pre-trained and custom-made Convolutional Neural Network architectures. Furthermore, a discussion is drawn on radiographic datasets, evaluation metrics, and commercial platforms provided for detection. In the end, a brief look is paid to emerging ideas, gaps in existing research, and challenges in developing diagnostic techniques. This survey provides insight into the promising areas of research in DL and is likely to guide the research community on the upcoming development of deep learning techniques for COVID-19. This will pave the way to accelerate the research in designing customised DL-based diagnostic tools for effectively dealing with new variants of COVID-19 and emerging challenges. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Digital Opportunities in MSMEs Throughout Economic Disruptions: Entrepreneurs' Experiences and Challenges

The global trend in cases has had a significant impact on the social parts of society, which has had no impact on the global economy, which has been severely impacted by the unexpected public health risk, which has caused businesses to close down digital commercial companies. The pandemic's great challenges have turned into great opportunities for entrepreneurs from all over the world. Obeying the recovery, they see digital tools as solutions to help them survive and even thrive in the long run. This study investigates the drivers and barriers of digital innovation in micro, small, and medium enterprises (MSMEs) in Indonesia, particularly in Yogyakarta Province. Conducting in-depth interviews with 50 MSMEs in Yogyakarta, Indonesia, and comparing them to Inductive Content Analysis to generate themes. The study's findings revealed that the adverse consequences of the pandemic, which boosted entrepreneurial innovation by shifting business to digital platforms, were classified as internal and external motivation. Participants discussed the barriers to digital entrepreneurship, such as the skills required to conduct business online, market issues in digital platforms, the availability of high-quality internet infrastructure, and pandemic restrictions. The findings of this study pertain to the entrepreneurship literature and provide a focus for empirical research in order to develop programs that assist entrepreneurs during economic disruptions. This will serve as a guide in the development of government policies and strategies for economic recovery through digital entrepreneurship while keeping small entrepreneurs in mind. It also recommends future related research to empower entrepreneurs, particularly those in emerging economies, during and after the pandemic. Copyright 2022. © by the International Institute of Informatics and Systemics. All rights reserved.

A Rolling-Based Multipeak Learning Model for COVID-19 Pandemic Predicting

The COVID-19 pandemic has led to a dramatic loss of human life and the global economy, and presents an unprecedented challenge to public health management for all countries around the world. Access to an accurate epidemic prediction model plays a crucial role in epidemic prevention, infection scale control, and medical resource allocation. In this paper, we first propose a multipeak SEIYAQURD model by using the multipeak learning algorithm to predict the COVID-19 epidemic. The model separates the total population according to characteristics of COVID-19 and can capture trend changes in the epidemic. Then, the fitting period technique and the rolling prediction strategy are proposed to improve the prediction accuracy. Numerical experiments based on the data of COVID-19 in the United States are performed to demonstrate the effectiveness of our proposed method by comparing with two benchmark methods from the literature in two cases, one has a smooth trend and the other has a significant changing trend. © 2022 IEEE.

Towards a virtual business ecosystem in the Metaverse Era

The emergence of new business ecosystem such as virtual economy and digital economy has transformed traditional economic thinking and brought new opportunities for the development. Today, the global economy is gradually recovering from the COVID-19 pandemic, but the coexistence and collision of real-world and online activities still exist, so by analyzing the experience of using individuals, the trust of the community, and the conflict and integration between the two, it may provide a good business ecosystem for Metaverse Era. © 2022 IEEE.