ABSTRACT
People in the post-Covid situation consider this as a challenge for personal, social, and organisational goals. The pandemic due to Covid-19 has brought in a drastic change in the attitude preferences and expectations of consumers. To meet the changes in the expectations of consumers, the businesses are forced to bring changes in marketing strategies using the business intelligence (BI) tools. This chapter explores how BI tools can capitalise this social distancing environment into opportunities for holistic growth of organisations, societies, and individuals. It explores the theoretical and pragmatic perspectives of marketing strategies in the post-Covid Era that can be developed using the BI tools. Considering the advancement due to BI, the chapter gives a workable marketing model to help business organizations attain their goals. The research methodology adopted is exploratory in nature, which identifies the need for marketing activities of corporates. The research maps available BI tools and techniques to execute marketing activities more effectively in this virtually connected business environment. The findings disclosed the successful application of BI tools for developing marketing strategies according to the changing business environment and identifying the role of BI in developing marketing strategies accordingly. © 2023 selection and editorial matter, Deepmala Singh, Anurag Singh, Amizan Omar & S.B Goyal.
ABSTRACT
In social IoMT systems, resource-constrained devices face the challenges of limited computation, bandwidth, and privacy in the deployment of deep learning models. Federated learning (FL) is one of the solutions to user privacy and provides distributed training among several local devices. In addition, it reduces the computation and bandwidth of transferring videos to the central server in camera-based IoMT devices. In this work, we design an edge-based federated framework for such devices. In contrast to traditional methods that drop the resource-constrained stragglers in a federated round, our system provides a methodology to incorporate them. We propose a new phase in the FL algorithm, known as split learning. The stragglers train collaboratively with the nearest edge node using split learning. We test the implementation using heterogeneous computing devices that extract vital signs from videos. The results show a reduction of 3.6 h in the training time of videos using the split learning phase with respect to the traditional approach. We also evaluate the performance of the devices and system with key parameters, CPU utilization, memory consumption, and data rate. Furthermore, we achieve 87.29% and 60.26% test accuracy at the nonstragglers and stragglers, respectively, with a global accuracy of 90.32% at the server. Therefore, FedCare provides a straggler-resistant federated method for a heterogeneous system for social IoMT devices.
ABSTRACT
COVID-19, a brand-new coronavirus, was found in Wuhan, China, in December 2019 and has since spread to 24 additional nations as well as numerous locations in China. The number of confirmed cases continues to rise every day, reaching 34,598 on February 8, 2021. We present our findings a new method was used in this investigation, predictive framework, for such number of reported COVID-19 cases in the China. During the next 10 days, predicated on recently known cases in China. The suggested upgraded adaptable neuro-fuzzy powerful instrument (ANFIS) with an updated floral modeling is used in this model. The salp swarm algorithm (SSA) was used to implement the pollination algorithm (FPA). Generally, SSA is used to enhance FPA in order to minimize its shortcomings. The fundamental theme of the essay FPASSA-ANFIS seems to be a proposed paradigm of improving ANFIS effectiveness through determining FPASSA which was used to determine the ANFIS specifications. The world is also used to analyze the FPASSA-ANFIS model. Statistical figures from the World Health Organization (WHO) on the COVID-19 pandemic for forecast the cases reported these following are indeed the cases for the next 10 days. Most specifically, the FPASSA-ANFIS model in comparison to such a number of other models outperformed them in terms of computing time, root mean squared error (RMSE), and mean absolute percentage (MAP). Researchers also put the suggested model to the tests utilizing two distinct datasets of week pandemic confirmed cases from two or more countries: the USA and China. These results also indicated incredible performance. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Preventing the transmission of COVID-19 necessitates diagnosis and identification. Researchers have developed algorithms to detect the presence of COVID-19 in X-ray and CT scans and images. These methodologies produce skewed data and incorrect disease detection. So, in the case of COVID-19 forecasting utilizing CT scans in an IoT setting, the current study paper established an oppositional-based deep dense convolutional neural network (DDCNN) and chimp optimization algorithm. The framework proposed is divided into two stages: preprocessing and estimation. Previously, a CT scan pictures generated from anticipated COVID-19 are acquired utilizing IoT devices from an open-source system. After that, the photos are preprocessed with a Gaussian function. A Gaussian filter can be used to remove undesirable noise from CT scan pictures that have been obtained. The preprocessed photos are then transmitted to the prediction process. DDCNN is applied to the images preprocessed in this step. The recommended classifier is designed to be as efficient as possible using the oppositional-based chimp optimization algorithm (OCOA). This approach is used to choose the best classifier parameters under consideration. Furthermore, the suggested method is applied to forecast COVID-19 and categorizes the findings as COVID-19 or non-COVID-19. The proposed technique was used in Python, and results were assessed using statistical analysis. CNN-EPO and CNN-FA were compared to the new method. The results proved that the proposed model was optimal. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
The coronavirus, formerly known as COVID-19, has caused massive global disasters. As a precaution, most governments imposed quarantine periods ranging from months to years and postponed significant financial obligations. Furthermore, governments around the world have used cutting-edge technologies to track citizens’ activity. Thousands of sensors were connected to IoT (Internet of Things) devices to monitor the catastrophic eruption with billions of connected devices that use these novel tools and apps, privacy and security issues regarding data transmission and memory space abound. In this study, we suggest a block-chain-based methodology for safeguarding data in the billions of devices and sensors connected over the internet. Various trial secrecy and safety qualities are based on cutting-edge cryptography. To evaluate the proposed model, we recom-mend using an application of the system, a Raspberry Pi single-board computer in an IoT system, a laptop, a computer, cell phones and the Ethereum smart contract platform. The models ability to ensure safety, effectiveness and a suitable budget is proved by the Gowalla dataset results. © 2023, Tech Science Press. All rights reserved.
ABSTRACT
Background:The present coronavirus-related epidemic of respiratory and upper respiratory infections (SARS CoV-19) has worldwide mortality and morbidity. This study evaluated the awareness and attitudes of undergraduate medical students concerning the 2019-novel Corona virus. Materials & Methods:235 participants were taken into account subsequent to receiving informed written consent. The questionnaire consisted of Demographic profile, Information Regarding Source of Knowledge, awareness questions and attitude-based questions. Results: Out of 235 subjects, males were 110 and females were 125. Correct response in reference to questionnaire were you diagnosed with covid-19,if yes then specify the method of diagnosis was given by 78%, how does COVID-19 transmit by 85%, how satisfied are you with the amount of health information available about COVID-19 by 86%, how do you mainly obtain health information by 90%, can COVID-19 be cured with antibiotics by 94%, what is the main test to be done for covid 19 by 88%, which organ mainly gets effected in covid-19 by 94%, how confident are you in your own hospital to diagnose or recognize COVID-19 by 90%, please rate your likelihood of contracting COVID-19 during the current outbreak by 91%, please rate your likelihood of surviving COVID-19 if infected by 78%, please rate your concerns about other family members getting COVID-19 by 82%, which type of mask did you buy during the pandemic by 86%, do you think country's health department is doing enough to prevent the outbreak from spreading by 88%, do you think your country's health department is doing enough to cure those infected by 90%, do you cover your mouth while coughing and sneezing by 85% and do you avoid sharing utensils by 89%. Conclusion: Undergraduate medical students possessed adequate awareness and attitude towards 2019-novel Corona virus.
ABSTRACT
The outburst of novel corona viruses aggregated worldwide and has undergone severe trials to manage medical sector all over the world. A radiologist uses x-rays and Computed Tomography (CT) scans to analyze images through which the existence of corona virus is found. Therefore, imaging and visualization systems contribute a dominant part in diagnosing process and thereby assist the medical experts to take necessary precautions and to overcome these rigorous conditions. In this research, a Multi-Objective Black Widow Optimization based Convolutional Neural Network (MBWO-CNN) method is proposed to diagnose and classify covid-19 data. The proposed method comprises of four stages, preprocess the covid-19 data, attribute selection, tune parameters, and classify cov-id-19 data. Initially, images are fed to preprocess and features are selected using Convolutional Neural Network (CNN). Next, Multi-objective Black Widow Optimization (MBWO) method is imparted to finely tune the hyper parameters of CNN. Lastly, Extreme Learning Machine Auto Encoder (ELM-AE) is used to check the existence of corona virus and further classification is done to classify the covid-19 data into respective classes. The suggested MBWO-CNN model was evaluated for effectiveness by undergoing experiments and the outcomes attained were matched with the outcome stationed by prevailing methods. The outcomes confirmed the astonishing results of the ELM-AE model to classify cov-id-19 data by achieving maximum accuracy of 97.53%. The efficacy of the proposed method is validated and observed that it has yielded outstanding outcomes and is best suitable to diagnose and classify covid-19 data. © 2022, Tech Science Press. All rights reserved.
ABSTRACT
COVID-19 is a novel virus that spreads in multiple chains from one person to the next. When a person is infected with this virus, they experience respiratory problems as well as rise in body temperature. Heavy breathlessness is the most severe sign of this COVID-19, which can lead to serious illness in some people. However, not everyone who has been infected with this virus will experience the same symptoms. Some people develop cold and cough, while others suffer from severe headaches and fatigue. This virus freezes the entire world as each country is fighting against COVID-19 and endures vaccination doses. Worldwide epidemic has been caused by this unusual virus. Several researchers use a variety of statistical methodologies to create models that examine the present stage of the pandemic and the losses incurred, as well as considered other factors that vary by location. The obtained statistical models depend on diverse aspects, and the studies are purely based on possible preferences, the pattern in which the virus spreads and infects people. Machine Learning classifiers such as Linear regression, Multi-Layer Perception and Vector Auto Regression are applied in this study to predict the various COVID-19 blowouts. The data comes from the COVID-19 data repository at Johns Hopkins University, and it focuses on the dissemination of different effect patterns of Covid-19 cases throughout Asian countries. © 2022, Tech Science Press. All rights reserved.
ABSTRACT
We are observing in this pandemic situation of COVID-19 the world in very challenging and to solve this complex problem in quick time. Today, we are facing a difficult complex problem such as Coronavirus. This Coronavirus affects human life. Quantum computing is the only support that can give us quick results by processing the Coronavirus compound at high computation speed. Whatever present circuits in VLSI domain, we cannot perform the high-speed computation and not tackle the complex case as present COVID-19. In this article, we have been discussed about quantum computing era during the pandemic situation of COVID-19. Further, this paper presents fundamental about quantum properties such as superposition, entanglement, and quantum programming tools such as Qiskit (IBM), pyQuil (Google), ProjectQ (ETH), Revkit, and RCvewier +. We have presented quantum circuit and its decomposed circuit of such gates as Toffoli, Fredkin, Peres, and new fault tolerance. In addition, we proposed algorithm as transforming cascade to the quantum circuit which is extended for verification based. All these concepts presented here will be very useful to researcher, academician, and industry person to tackle this pandemic situation of COVID-19. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
The atmospheric buoyancy and intangible nature of fatal communicable viruses lead to rapid transmissions among individuals, resulting in global pandemics. Strategic lockdowns and mandatory social distancing are immediate solutions in such scenarios. However, this leads to operational disruptions in education, manufacturing, economy, transportation, governance, and community. Although technological assistance is beneficial in overcoming such issues, the current Internet of Things (IoT) infrastructure has limitations. In this paper, we provide a comprehensive review of the possible IoT-based solutions that have the capacity of combating the COVID-19-like viruses. We highlight the societal impacts due to pandemics and identify the specific lacunae in current IoT solutions. We also provide comprehensive detail on how to overcome the challenges along with directions towards the possible technological trends for future research. Compared to existing reviews, our work offers a holistic view of the cause, effects, and the possible solutions that are existing, along with already existing solutions that can be customized to serve the special needs during the pandemic. IEEE
ABSTRACT
A novel threat to mankind by novel coronavirus infection occurred in December 2019. According to the World Health Organization (WHO) Situation Report-141, 7,039,918 confirmed cases and 404,396 death cases were observed till 9 June 2020 in the different regions of world. Therefore, this article aims to summarize and share the update on the present status of the outbreak and possible treatment options. The present review focuses on latest statistics, diagnostic and preventive measures under study and the future planning of the researchers to discover an effective cure for this threat to the mankind. For carrying out this review, literature searches were performed on Clinicaltrials.gov, official website of WHO,Centers for Disease Control and Prevention, PubMed, Google scholars, etc. Data from these searches was collected and evaluated for getting the available literature on COVID-19 outbreak and drugs under study. The details of history, virology, epidemiology, possible therapeutic options, associated risk factors and preventive measures related to COVID-19 are compiled here in this review. Along with this, some ongoing clinical trials have also been included in this review in order to conclude the efforts of researchers towards controlling this outbreak. The trajectory and severity of this outbreak can't be predicted at present, but immediate actions are required to be taken in order to develop and implement an effective treatment against the global threat. © 2020 This is an Open Access article licensed under a Creative Commons license: Attribution 4.0 International (CC-BY). Published by Oriental Scientific Publishing Company.
ABSTRACT
The paper studies the policy changes brought in by the banking regulator i.e, Reserve Bank of India in response to the challenges faced by MSME sector in particular and financial sector in general on account of halt in economic activity due to lockdown imposed on account of COVID-19 pandemic. This paper also focuses on the movement of funds inflows and outflows by foreign institutional investors in Indian capital markets in equity, debt and derivatives segments. © 2020. All Rights Reserved.
ABSTRACT
In this paper, the volatile movements in YBL stock prices are being compared with volatility in Nifty50 index for two phases i.e, 2019 and 2020 (till 20 March 2020). The paper attempts to take into stock the developments in connection with YBL stock. These developments include meetings of capital raising committee in YBL, resignations by top officials, media news of fund diversion, RBI's direction for restricting the extension in the term of the bank's MD & CEO beyond 31 January 2019. All these developments are being explained in detail including the restructuring plans for the bank. Interestingly, NIFTY 50 of which YBL has also been the part has not witnessed the same nature of volatility in Phase-I but in Phase-II i.e, in the year 2020, NIFTY 50 has crashed badly and primarily due to the emergence of epidemic COVID 19. However, the free fall in the YBL stocks can be attributed to the failure of corporate governance and lack of a proactive role by the regulator. © 2020 Innovare Academics Sciences Pvt. Ltd. All rights reserved.
ABSTRACT
The paper studies the impact of fluctuations in the capital markets in India on the share prices of HUL and ITC since March 2019. The paper attempts to study the reasons that led to the declining growth in FMCG sector in the April-June quarter of the year 2019, important government announcements related to the sector, the fall out of the panic due to the breakout of COVID-19 pandemic on the economic in general and FMCG sector in specific. The study has specifically covered two leading FMCG players HUL and ITC. In this study, the closing prices of the shares of HUL, ITC and NIFTY50 index are considered on fortnightly basis since March 2019. © 2020 Innovare Academics Sciences Pvt. Ltd. All rights reserved.