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1.
The EU between Federal Union and Flexible Integration: Interdisciplinary European Studies ; : 159-184, 2023.
Article in English | Scopus | ID: covidwho-20232332

ABSTRACT

This chapter addresses developments in EU tax policy with an emphasis on processes for decision-making. It explores the change in views on tax competition and how this has led to restrictions in member states' ability to design their own tax systems. The chapter highlights the impetus towards increased federalism entailed by the EU policies to recover from COVID-19 and to promote a green and digital transition for the EU economy. The chapter discusses how the Commission has worked to change the EU decision processes in the field of taxes from unanimity to qualified majority voting. The chapter concludes that the EU must show that it can promote economic growth and efficiency in the tax systems before member states will have enough confidence to entrust more decision-making in tax matters to the EU. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

2.
2nd International Conference on Applied Intelligence and Informatics, AII 2022 ; 1724 CCIS:308-319, 2022.
Article in English | Scopus | ID: covidwho-2273530

ABSTRACT

Coronavirus Disease 2019 (COVID-19) emerged towards the end of 2019, and it is still causing havoc on the lives and businesses of millions of people in 2022. As the globe recovers from the epidemic and intends to return to normalcy, there is a spike of anxiety among those who expect to resume their everyday routines in person.The biggest difficulty is that no effective therapeutics have yet been reported. According to the World Health Organization (WHO), wearing a face mask and keeping a social distance of at least 2 m can limit viral transmission from person to person. In this paper, a deep learning-based hybrid system for face mask identification and social distance monitoring is developed. In the OpenCV environment, MobileNetV2 is utilized to identify face masks, while YoLoV3 is used for social distance monitoring. The proposed system achieved an accuracy of 0.99. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

3.
Lecture Notes in Networks and Systems ; 569 LNNS:948-957, 2023.
Article in English | Scopus | ID: covidwho-2243690

ABSTRACT

COVID-19 is tumultuous creating our life so unpredictable. There has no solution of this contagious disease rather than vaccination and prevention. The first and foremost preventative step is using face masks. Face mask can hindrance its droplet from one to another. So this paper has focused the detection of facial mask from image processing using Transfer Learning. For this purpose, total 1376 images have been collected where 690 images of with mask and 686 images of without a mask. Here transfer learning is chosen for the reason of its capability to produce best accurate regardless the limited size of the image dataset. Here, multifarious transfer learning models have been trained to find out the best fitting model. Finally, We have found the VGG16 model with the best accuracy where training accuracy is 98.25% and testing accuracy is 96.38%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

4.
5th International Conference on Intelligent Computing and Optimization, ICO 2022 ; 569 LNNS:948-957, 2023.
Article in English | Scopus | ID: covidwho-2173742

ABSTRACT

COVID-19 is tumultuous creating our life so unpredictable. There has no solution of this contagious disease rather than vaccination and prevention. The first and foremost preventative step is using face masks. Face mask can hindrance its droplet from one to another. So this paper has focused the detection of facial mask from image processing using Transfer Learning. For this purpose, total 1376 images have been collected where 690 images of with mask and 686 images of without a mask. Here transfer learning is chosen for the reason of its capability to produce best accurate regardless the limited size of the image dataset. Here, multifarious transfer learning models have been trained to find out the best fitting model. Finally, We have found the VGG16 model with the best accuracy where training accuracy is 98.25% and testing accuracy is 96.38%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

5.
5th International Conference on Intelligent Computing and Optimization, ICO 2022 ; 569 LNNS:330-340, 2023.
Article in English | Scopus | ID: covidwho-2173740

ABSTRACT

In the age of modern technology peoples are still facing a great challenges to manage and monitor the infected patients of COVID-19. Many systems have been implemented to track the location of infected person to reduce the spread of diseases. In today's world IoT with the health care system plays an important role specially in this COVID situation. In this research an IoT based monitoring system is designed to monitor and measure different signs of COVID-19 using wearable device. It also sends notification to the proper authority by monitoring the activity of infected patient. To determine the condition of patient, sensor data are analyzed which is passed from edge node, as body sensor are connected to IoT cloud via edge node. Three layered architecture is implemented in our proposed design, wearable sensor layer, Peripheral Interface (API) layer and Android web layer. Different layer have different work, at first health symptom is determined by analyzing data from IoT sensor layer. In next layer information is stored in the cloud database to take immediate actions. Finally android application layer is used to send notifications and alerts for the infected patient. To predict the health condition and alarming the situation both API and mobile application communicate with each other. The designed system has simple structure and helps the authority to find the infected person. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

6.
Scandinavian Journal of Immunology ; 95(6), 2022.
Article in English | EMBASE | ID: covidwho-1968183

ABSTRACT

During the first period of the Covid-19 pandemic, most of the immunological studies on SARS-CoV-2 were based on hospitalized-and intensive care unit patients. In this study, a healthy population continuously exposed to the virus, Swedish primary health care workers (n = 156), were monitored for 6 months and the development of antibody patterns and T-cell responses to SARS-CoV-2 were evaluated. In addition to blood sampling, demographic-and clinical information such as PCR-tests, self-reported symptoms, underlying medical conditions, and medications were collected. Multivariate statistical analysis using OPLS-DA showed that Covid-19 infection was associated with SARS-CoV-2 specific IgG antibodies, T-cell responses, male sex, hypertension, and higher BMI and contrary, that not contracting the infection, was associated with female sex, no-or only SARS-CoV-2 specific IgA antibodies, smoking and airborne allergy. Analysis with Cytometry by Time-of-flight (CyTOF) revealed a unique cytotoxic CD4+ T cell population in participants with IgG-dominated antibody responses which expressed CD25, CD38, CD69, CD194, CD279, CTLA-4 and granzyme B. 10% of the study participants had only SARS-CoV-2 specific IgA antibodies with no detectable SARS-CoV-2 specific IgG antibodies. These IgA antibodies could partially neutralize the virus in vitro and none of the participants with this antibody pattern contracted Covid-19 during the study period. These results have the potential to further help us understand the immunological responses to SARS-CoV-2 infection.

7.
INTERNATIONAL JOURNAL OF THE COMMONS ; 16(1):189-208, 2022.
Article in English | Web of Science | ID: covidwho-1939314

ABSTRACT

India has been hard hit by the COVID-19 pandemic. In the context of a larger quasi -experimental impact assessment, we assess the pandemic???s effects on household coping behavior in 80 villages spread across four districts and three states (n = 772). Half of these villages were targeted by a largescale common land restoration program spearheaded by an NGO, the Foundation for Ecological Security (FES). The other half are yet to be targeted but are statistically similar vis-??-vis FES???s village targeting criteria. Analyzing the results of a phone survey administered eight to ten months into the pandemic and its associated lockdowns, we find that the livelihood activities of households in both sets of villages were adversely impacted by COVID-19. Consequently, most households had to resort to various negative coping behaviors, e.g., distressed asset sales and reduced farm input expenditure. From the same mobile survey data, we construct a Livelihoods Coping Strategies Index (LCSI) and find that households in villages targeted by FES???s common land restoration initiative score 11.3% lower on this index on average, equating to a 4.5 percentage point difference. While modest, this statistically significant effect estimate (p < 0.05) is consistent across the four districts and robust to alterative model and outcome specifications. We find no empirical support that our observed effect was due to improved access to common pool resources or government social programs. Instead, we speculate that this effect may be driven by institutional factors, rather than economic, a proposition we will test in future work.

8.
Journal of Internet Services and Information Security ; 12(2):51-69, 2022.
Article in English | Scopus | ID: covidwho-1924880

ABSTRACT

Artificial intelligence has achieved notable advances across many applications, and the field is recently concerned with developing novel methods to explain machine learning models. Deep neural networks deliver the best performance accuracy in different domains, such as text categorization, image classification, and speech recognition. Since the neural network models are black-box types, they lack transparency and explainability in predicting results. During the COVID-19 pandemic, Fake News Detection is a challenging research problem as it endangers the lives of many online users by providing misinformation. Therefore, the transparency and explainability of COVID-19 fake news classification are necessary for building the trustworthiness of model prediction. We proposed an integrated LIME-BiLSTM model where BiLSTM assures classification accuracy, and LIME ensures transparency and explainability. In this integrated model, since LIME behaves similarly to the original model and explains the prediction, the proposed model becomes comprehensible. The performance of this model in terms of explainability is measured by using Kendall’s tau correlation coefficient. We also employ several machine learning models and provide a comparison of their performances. Therefore, we analyzed and compared the computation overhead of our proposed model with the other methods because the model takes the integrated strategy. © 2022, Innovative Information Science and Technology Research Group. All rights reserved.

9.
3rd International Conference on Trends in Computational and Cognitive Engineering, TCCE 2021 ; 348:3-15, 2022.
Article in English | Scopus | ID: covidwho-1750622

ABSTRACT

The global epidemic of the coronavirus COVID-19 is wreaking havoc on the world’s health and according to the World Health Organization (WHO), using a face mask in crowded locations is among the most common security practices. An artificial neural network for face mask classification utilizing deep learning will be introduced in this research. As the outbreak of the COVID-19 pandemic, a remarkable development in the fields of object recognition and computer vision has been made in the identification of face masks. Many architectures and methods have been used to construct a variety of face recognition models. Face masks can be distinguished using the method proposed in this work, which makes use of deep learning, TensorFlow, Keras, and OpenCV. This approach may be evaluated for use in protection jobs due to the fact that it is quite inexpensive to execute. In fact, the GAN-generated face-masked datasets have been selected for evaluation purposes. Compared to other standard Convolutional Neural Network models, the proposed framework outscored them all, attaining a 99.73% accuracy rating. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

10.
Lecture Notes on Data Engineering and Communications Technologies ; 95:483-496, 2022.
Article in English | Scopus | ID: covidwho-1575566

ABSTRACT

In the prevailing COVID-19 pandemic, accurate diagnosis plays a vital role in preventing the mass transmission of the SARS-CoV-2 virus. Especially patients with pneumonia need correct diagnosis for proper treatment of their respiratory distress. However, the current standard diagnosis method, RT-PCR testing has a significant false negative and false positive rate. As alternatives, diagnosis methods based on artificial intelligence can be applied for faster and more accurate diagnosis. Currently, various machine learning and deep learning techniques are being researched on to develop better COVID-19 diagnosis system. However, these approaches do not consider the uncertainty in data. Deep learning approaches use backpropagation. It is an unexplainable black box approach and is prone to problems like catastrophic forgetting. This article applies a belief rule-based expert system (BRBES) for diagnosis of COVID-19 on hematological data and CT scan data of lung tissue infection of adult pneumonia patients. The system is optimized with nature-inspired optimization algorithm—BRBES-based adaptive differential evolution (BRBaDE). This model has been evaluated on a real-world dataset of COVID-19 patients published in a previous work. Also, performance of the BRBaDE has been compared with BRBES optimized with genetic algorithm and MATLAB’s fmincon function where BRBaDE outperformed genetic algorithm and fmincon and showed best accuracy of 73.91%. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

11.
Proceedings of 2020 6th Ieee International Women in Engineering ; : 189-194, 2020.
Article in English | Web of Science | ID: covidwho-1349920

ABSTRACT

The Covid-19 disease which was caused by novel coronavirus (SARS-CoV-2) has already become a great threat for humans beings. The virus is spreading rapidly around the world. Therefore, we crucially need quick diagnostic tests to identify affected patients and to minimize the spread of the virus. With the advancements of Machine Learning, the detection of Covid-19 in the early stage would facilitate taking precautions as early as possible. However, because of the lack of data-sets, especially chest X-ray images of Covid-19 affected patients, it has become challenging to detect this disease. In this paper, a deep transfer learning-based pre-trained model is named VGG16 along with adapt histogram equalization has been developed to diagnose Covid-19 by using X-ray images. An image processing technique named adaptive histogram equalization has been used to generate more images by using the existing data set. It can be observed that VGG-16 provides the highest accuracy which is 98.75% in comparison to two other pre-trained models such as VGG-19 and Mobilnenet-V2(97% accuracy for VGG-19, 92.65% accuracy for Mobilenet-V2).

12.
Current Opinion in Environmental Sustainability ; 49:66-78, 2021.
Article in English | Scopus | ID: covidwho-1231983

ABSTRACT

From state-based developmentalism to community-based initiatives to market-based conservation, the Brazilian Amazon has been a laboratory of development interventions for over 50 years. The region is now confronting a devastating COVID-19 pandemic amid renewed environmental pressures and increasing social inequities. While these forces are shaping the present and future of the region, the Amazon has also become an incubator of local innovations and efforts confronting these pressures. Often overlooked, place-based initiatives involving individual and collective-action have growing roles in promoting regional sustainability. We review the history of development interventions influencing the emergence of place-based initiatives and their potential to promoting changes in productive systems, value-aggregation and market-access, and governance arrangements improving living-standards and environmental sustainability. We provide examples of initiatives documented by the AGENTS project, contextualizing them within the literature. We reflect on challenges and opportunities affecting their trajectories at this critical juncture for the future of the region. © 2021 The Authors

13.
Algorithms ; 14(3), 2021.
Article in English | Scopus | ID: covidwho-1172993
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