Performance Evaluation of State-of-The-Art 2D Face Recognition Algorithms on Real and Synthetic Masked Face Datasets

Face recognition systems based on Convolutional neural networks have recorded unprecedented performance for multiple benchmark face datasets. Due to the Covid-19 outbreak, people are now compelled to wear face masks to reduce the virus's transmissibility. Recent research shows that when given the masked face recognition scenario, which imposes up to 70% occlusion of the face area, the performance of the FR algorithms degrades by a significant margin. This paper presents an experimental evaluation of a subset of the MFD-Kaggle and Masked-LFW (MLFW) datasets to explore the effects of face mask occlusion against implementing seven state-of-the-art FR models. Experiments on MFD-Kaggle show that the accuracy of the best-performing model, VGGFace degraded by almost 40%, from 82.1% (unmasked) to 40.4% (masked). On a larger-scale dataset MLFW, the impact of mask-wearing on FR models was also up to 50%. We trained and evaluated a proposed Mask Face Recognition (MFR) model whose performance is much better than the SOTA algorithms. The SOTA algorithms studied are unusable in the presence of face masks, and MFR performance is slightly degraded without face masks. This show that more robust FR models are required for real masked face applications while having a large-scale masked face dataset. © 2023, Penerbit Akademia Baru. All rights reserved.

The post-pandemic public transport crisis: a statistical analysis of travel habits in Sicily, Italy

The mobility sector accounts for about 10% of Italy's GDP, where the role of local Public Transport (PT) is crucial. Generally, PT use can limit the negative environmental impacts. However, with the advent of the recent pandemic, the local PT demand and mobility rate in Italy has decreased significantly, particularly during the lockdown period. Further restrictions were recently implemented (October - December 2021) on PT and on gathering areas to reduce potential contagion from virus variants. Such an uncertain situation has resulted in an epoch-making change in travel habits due to both the relevant restrictions for containing contagions (e.g., quotas of seats on-board, sanitization, use of masks) and the consequent psychosocial effects (e.g., anxiety, fear, and stress perceived by users when travelling). The present research assesses the PT use habits in the different pandemic phases. The data were collected from a sample of regular PT users in Sicily, Italy. The correlation analysis helped uncover significant factors responsible for influencing travel habits. The statistical analysis further revealed that the users travel habits have certainly changed during all the pandemic phases. The results lay the foundations for planning transport services and improving post-pandemic mobility in the examined context. © 2023 The Authors. Published by ELSEVIER B.V.

A New-Onset Facial Lesion in a Hospitalized COVID-19 Patient.

Pressure ulcers form when skin is compressed against a bony prominence, often in the context of prolonged supine or prone-based care. Hospitalized, bedridden patients are at the highest risk of this complication, especially when preventative measures like regular rotational bed treatment are not employed. In this case report, we present a rare case of a COVID-19-related facial pressure ulcer that occurred in the context of regular rotational bed treatment. The lesion was managed by wound care and allowed to heal by secondary intention. Ultimately, we hope that this manuscript will raise awareness for this atypical ulcer location, especially as prone-position treatment approaches take hold.

The post-pandemic public transport crisis: a statistical analysis of travel habits in Sicily, Italy

The mobility sector accounts for about 10% of Italy's GDP, where the role of local Public Transport (PT) is crucial. Generally, PT use can limit the negative environmental impacts. However, with the advent of the recent pandemic, the local PT demand and mobility rate in Italy has decreased significantly, particularly during the lockdown period. Further restrictions were recently implemented (October - December 2021) on PT and on gathering areas to reduce potential contagion from virus variants. Such an uncertain situation has resulted in an epoch-making change in travel habits due to both the relevant restrictions for containing contagions (e.g., quotas of seats on-board, sanitization, use of masks) and the consequent psychosocial effects (e.g., anxiety, fear, and stress perceived by users when travelling). The present research assesses the PT use habits in the different pandemic phases. The data were collected from a sample of regular PT users in Sicily, Italy. The correlation analysis helped uncover significant factors responsible for influencing travel habits. The statistical analysis further revealed that the users travel habits have certainly changed during all the pandemic phases. The results lay the foundations for planning transport services and improving post-pandemic mobility in the examined context. © 2023 The Authors. Published by ELSEVIER B.V.

Introduction to Islamic Fintech: A Challenge or an Opportunity?

Islamic finance offers an alternative financial system that prohibits the use of interest and other economic exploitations and intends to establish a just and fair economic system. The industry has seen some extraordinary growth, primarily in systemically important Islamic finance countries in the Middle East, North Africa, Southeast Asia, South Asia, and Central Asia. Fintech has evolved fast as a massive change-maker in the financial sector globally, with a focus to deregulate/personalize financial transactions at a lower cost for the customers and stable income for the financial institutions. While COVID-19 has pushed the Fintech agenda quickly forward, the use of Fintech has received momentum since the introduction of mobile payments. Islamic as well as traditional financial institutions are making an increasing amount of investment to offer services that are embedded into mobile applications. While Fintech adoption is a major barrier in countries with larger share of Islamic finance users, there are other challenges, and opportunities, awaiting Islamic financial institutions. This chapter provides a brief introduction to principles of Islamic finance and Fintech, and offers a description of potential benefits and drawbacks that influence Fintech engagement among Islamic financial institutions. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Effect of COVID-19 Pandemic on the Diagnosis of Breast Lesions Compared with Breast Lesions in the Previous Five Years

Background: Covid-19 pandemic caused havoc in both people, the health care system and more so in patients with malignancies. Breast malignancies being one of the most common and relatively curable malignancies got hit a lot due to the impact of the Covid-19 pandemic. The multistep impact of pandemic delayed the diagnosis as also the treatment of this multidisciplinary approach disease. Objective(s): To determine the effect of the Covid-19 pandemic on the diagnosis of breast lesions. Method(s): This is a descriptive type of histopathological study in which we have collected and studied the data of Breast lesions over the Five years (Jan 2015-Dec 2019) and compared it with the data of Breast lesions in the Covid-19 year (the year 2020). Result(s): The number of cases of breast specimens received for histopathology per year declined from an average of 224/year to 124/year in the Covid-19 year of 2020. Conclusion(s): The decrease in the number of cases alludes to the downfall in the number of patients coming for diagnosis and in turn getting treatment. This study also highlights the importance of anticipation of various cases which will cluster shortly.

COVID-19 Challenges and the Role of Islamic Fintech

COVID-19 has created massive havoc to the global operations and business processes and the uncertain economic conditions have made the world think about the abrupt solutions to tackle the problem efficiently. Islamic fintech has provided the world with innovative solutions to overcome the devastating impact of this pandemic. Against this backdrop, this study aims to investigate the effective solutions provided by Islamic fintech in the post-COVID period. The study mainly opted for the qualitative framework to carry out its research and provide workable solutions to the world offered by Islamic fintech in the post-COVID era. Various technological innovations compatible with Islamic finance have initiated a great deal of competition with its long-lasting and sustainable impact on the growth of the economies. The COVID period, which is still going on, is marked by substantial growth and development followed by the fintech innovations to address the demands of the customers. This study is expected to play a key role in promoting the Islamic fintech solutions to overcome the economic hazards created by the coronavirus pandemic. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Islamic Fintech, Blockchain and Crowdfunding: Current Landscape and Path Forward

The new age Fintech-driven innovative financial services started with the fourth industrial revolution and COVID-19 has stimulated supreme innovation in the global financial services industry. It is the young and millennial population driving these innovations and startups are responding, as there are more than 15, 000 startups and global Fintech-based transaction crossed $6.308 Billion by the year 2020. Islamic finance industry has experienced an unprecedented growth, partly due to incremental investment in Fintech-based financial intermediation. Two most commonly cited high growth areas for Islamic finance lie within blockchain and crowdfunding. This study provides an advanced overview of the Islamic Fintech, blockchain and crowdfunding;their current landscape;and path forward. We discuss opportunities for Islamic financial institutions and a clear roadmap to capitalize on those opportunities. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

An Efficient Deep Learning based Approach for the Detection of Brain Tumors

Deep learning has stretched out its roots even more in our daily lives. As a society, we are witnessing small changes in lifestyle such as self-driving cars, Google Assistant, Netflix recommendations, and spam email detection. Similarly, deep learning is also evolving in healthcare, and today many doctors often use it more comfortably. Using deep learning models we can detect severe brain tumors with the help of MRI scans, in fact in the Covid era, deep learning evolved majorly to detect the disease with the help of Lung X-Rays. Magnetic Resonance Imaging (MRI) is used when a person has a brain tumor to detect it. Brain tumors can fall into any category, and MRI scans of these millions of people are needed to determine if they have the disease and if so, which category they belong to. Determining the type of brain tumor can be a rigid task and deep learning models play an important role here. For the proposed deep learning model, we have implemented convolution neural networks (CNN) through which our model has achieved a testing accuracy of 96.5%. Also, along with this, the libraries of Keras and Tensorflow have been explored by the authors in this research. © 2022 IEEE.

Acceptance and Perceptions of COVID-19 Booster Dose;A Survey among Residents of Rawalpindi

Objective: To determine public acceptance of COVID-19 booster dose, to know about perceptions and possible barriers regarding the vaccine. Study Design: Analytical cross-sectional study. Place and Duration of Study: Conducted regarding the acceptance and perception of the vaccine booster dose among the residents of Rawalpindi Pakistan, from Jun till Aug 2022 Methodology: The sample size was 320 and collected using convenient sampling technique. The study included residents of Rawalpindi between ages 18 and 65. Google forms based on Health Belief Model were used for data collection. Data analysis was done using SPSS version 28 and summary statistics were produced using frequencies, percentages and mean. Chi-square test was also used to determine associations between categorical variables. Results: The results revealed that 74% of the participants' primary reason for getting booster dose was their own safety while major barrier to booster dose vaccination was side effects related to vaccine-62%. 81.3% people were willing to get the booster dose and 87.9% people agreed to receive booster dose for free. Chi-square test indicated a significant association between acceptance and perceived susceptibility, benefits and severity. Conclusion: In conclusion, our findings reveal that majority of people were willing to get booster dose primarily for own safety. However, counselling is required to decrease the perceived barriers to vaccination regarding side effects of the vaccine. © 2022, Army Medical College. All rights reserved.

Praxis of the undercommons: rupturing university conviviality and coded formations of diversity

The concept of the undercommons offers a lens for complicating the discourse of conviviality in education. In this paper, the authors draw on learning experiences opened through black feminisms, decoloniality and anti-coloniality, and new materialisms in an experimental graduate course. Presenting stories drawn from a shared educational space, they theorise fugitive convivial praxis and elucidate insurgent opportunities for intellectual study that nurture decolonial undercurrents of the university apparatus. Such engagements allowed for radical vulnerability in coming together through processes of ethical care, place-making, and counter-expressive thinking especially when coded formations of diversity insist on colonial logics and modes of operation in higher education. With curiosities and questions emerging in the undercommons, the authors also consider remote learning during the coronavirus pandemic toward future possibilities. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Role of nanotechnology in diagnosis and disease control with a focus on COVID-19 and future perspectives

Recently the Coronavirus disease-2019 (COVID-19) has affected the public throughout the world and has become a severe risk to human being. COVID-19 is a growing communicable disease and has caused major mortality and morbidity beside socioeconomic damage. No successful treatment has been accepted for this type of pandemic disease. In this situation nanotechnology has been very much helpful in preclinical studies for different types of pathogens of diseases like respiratory viruses, herpes virus, human papillomavirus, and HIV. Therefore nanoparticles have been of interest for the management of COVID-19 disease due to their small and distinctive properties, like simple preparation, low cost, easy modification, and appropriate size. For COVID-19 disease, nanotechnology-based management comprises the development of tools for exact, speedy, and responsive diagnosis, the manufacture of efficient disinfectants, and the delivery of vaccines and antiviral drugs into the human body. In this review the main object is to recommend some approaches to design and produce successful diagnostic and beneficial drugs against COVID-19 with the help of nanotechnology. This review mainly focuses on COVID-19 prevention, diagnosis, and therapy. Molecular nanotechnology is a provisional field of nanotechnology investigating the opportunity of industrial molecular assembly machinery that can rearrange substances at a molecular or atomic level. Nanomedicine is the application of these nanorobots, introduced into the body, to identify viruses. Molecular nanotechnology is extremely hypothetical, anticipating what innovations nanotechnology may yield and the programs offered for the investigation of COVID-19. © 2023 Elsevier Inc. All rights reserved.

THE IMPLICATION OF MOVEMENT CONTROL ORDER (MCO) TOWARD THE SMALL-SCALE FOOD AND BEVERAGE ACTIVITIES IN JOHOR, MALAYSIA

The implementation of a movement control order (MCO) in Malaysia due to the COVID-19 pandemic has widely affected the majority of small-scale economic activities, including the food and beverage (F&B) sector. This paper aims to analyse the impact of MCO implementation on small-scale F&B activities in Majlis Bandaraya Iskandar Puteri (MBIP), Johor, Malaysia. Descriptive and inferential analyses were employed to analyse the impact of MCO implementation via a questionnaire sampling of 60 small-scale F&B operators across the case study. The research found that the movement restrictions had a massive influence on the economy of most of the businesses, particularly on their income generations. The MCO also obstructed a smooth economic recovery of any business, including the small-scale F&B activities, despite the various initiatives being addressed to minimise the economic impact of pandemic and sustain the business activities and income generations. Hence, a planning approach should be taken to manage the chain impact of a health crisis, such as movement restrictions, in reducing the economic impacts of small-scale F&B activities. © 2022 by MIP.

Acute Kidney Injury after Covid 19 Infection in Chronic Kidney Disease Patients of Bangladesh: Retrospective Study

Introduction: COVID 19 pandemic has caused unprecedented devastation worldwide. Spectrum of Covid 19 illness is wide and variable. Risk of mortality is increased in chronic kidney disease patients, during coronavirus disease. CKD is an independent risk factor for poor outcome. AKI is also common in COVID-19 patients who are hospitalized. This study was undertaken to see the outcome of Covid-19 infection in CKD patients. Method(s): This retrospective observational study was carried out in the Kidney Foundation Hospital and Research Institute, Bangladesh from January 2021 to July 2022. One hundred CKD patients who were on regular follow up in the outpatient department and developed COVID-19 as confirmed by reverse transcription polymerase chain reaction (RT-PCR) test underwent chart review after they consented to be part of the study. Their clinical parameters, treatment regiments and laboratory investigations were noted in a data collection sheet. Data was analyzed by Statistical Analysis Software. Result(s): The mean age of the patients was 55.2 years. Of them 43% were female. Diabetes mellitus was the most common comorbidity, seen in 65% of the patients. 24% were CKD stage 4 or 5 prior to the onset of COVID-19, rest were of earlier stage. Hospitalization was required in 65.3% patients;41.1% required oxygen, steroid given in 19.8% patients,8.4% required ICU transfer. 7 patients died, all of respiratory failure. Treatment with antiviral, biologics like Tocilizumab and plasma exchange was not commonly done. AKI developed in 28% of the patients during the course of the illness. Males were more prone to develop AKI (p = 0.23). People with longer duration of symptoms had higher incidence of AKI (p < 0.0001). AKI incidence did not vary according to baseline eGFR (p = 0.16). Among those who developed AKI, 17.9% required temporary dialysis and 7.1% went on to develop end stage kidney disease. Interim outcomes such as hospitalization, oxygen requirement, ICU transfer and death did not vary according to development of AKI. Conclusion(s): People with chronic kidney disease and other comorbid conditions are at higher risk for more serious COVID-19 illness. In our study it has been shown that a significant proportion of CKD patients developed AKI after COVID 19 infection of which a number of patients develop end stage kidney disease and required renal replacement therapy. No conflict of interestCopyright © 2023

Did national holidays accelerate COVID-19 diffusion "during the first phase of the” of pandemic in Bangladesh?

Bangladesh registered 20,117,32 confirmed cases of COVID-19 and the death toll crossed the grim milestone of 29,323 across the country as of August 31st, 2022. Despite the enforcement of stringent COVID-19 measures, the country witnessed an accelerated diffusion of coronavirus cases during the national events, inclusive of short festivals, in 2020. The present study aims to examine the association between these national holidays and the COVID-19 trasmission rate in Bangladesh. We employed a mathematical model and calculated the instantaneous reproduction number, Rt, of the 64 districts in Bangladesh to check the dynamics of COVID-19 diffusion. The comprehensive analysis shows a notable escalation of Rt value and thus the enhanced transmission rate in Dhaka and in all industrialized cities during the major events such as, garments reopening and religious holidays in Bangladesh. We further showcase the COVID-19 diffusion explicitly in Dhaka Division at the first phase of the pandemic in Bangladesh. Based on our analysis, a set of measures, including restricted public mobility and the celebration of festivals, alongside improving the public's awareness of the situation, has been recommended to evade the future pandemic risks while running the national festival activities in Bangladesh. © 2023, Intelektual Pustaka Media Utama. All rights reserved.

Searching hedging instruments against diverse global risks and uncertainties

We evaluate the influence of five major risk and uncertainty factors on four asset classes. Our time-varying findings suggest that each asset hedges only a particular uncertainty factor, whereas gold does more than one factor, especially during COVID-19. Our frequency-based quantile regression (QR) results show that in the raw frequency, gold and Islamic stock can better hedge various uncertainty factors than Bitcoin and crude oil, depending on the market conditions. Additionally, using the frequency bands (e.g., short, medium, and long term) data, we further notice that, depending on the market circumstances and investment horizons, gold and Islamic stock returns are still better hedges for the various risks and uncertainties than Bitcoin and crude oil returns. Our findings have crucial risk and portfolio management implications for investors, portfolio managers, and policymakers. © 2023 Elsevier Inc.

COVID-19 IMPACT ON THE WORKERS OF THE INFORMAL SECTOR: A LONGITUDINAL STUDY OF BANGLADESH

The rapid spread of COVID-19 and subsequent restriction measures become a growing concern for its economic impacts as well. To address it, a study was undertaken to investigate the impacts upon the low-income people employed in the informal sectors in Bangladesh. The data of 372 respondents was collected through a structured questionnaire from the informal sectors in the cities of Dhaka and Chattogram, the most predominant hubs of the country's informal workers. This study covered the period of the first wave of the pandemic in Bangladesh from its first detection (8 March 2020) to the onset of the second wave (February 2021). It was a little over the one year period that had been divided into four-time segments considering as before pandemic (January – March 2020), and during pandemic (1st quartile: April – July 2020;2nd quartile: August – November 2020 and 3rd quartile: December 2020 - February 2021). In the 1st quartile during the pandemic, 65% of respondents' income revealed a sharp decline. This scenario continued in the 2nd and 3rd quartiles with the figure of 35% and 24% respectively. Thus, in each of the three quartiles during pandemic time slots, the majority of respondents' consumption, living standard, schooling, and access to health care facilities were found negatively impacted. By the continuity of time some of the respondents were able to settle them in the new socio-economic condition. Overall, these results indicated several recommendations, including extending basic assistance to these vulnerable groups. © 2021 AESS Publications. All Rights Reserved.

A Multi-Modal Deep Learning Approach for Emotion Recognition

In recent years, research on facial expression recognition (FER) under mask is trending. Wearing a mask for protection from Covid 19 has become a compulsion and it hides the facial expressions that is why FER under the mask is a difficult task. The prevailing unimodal techniques for facial recognition are not up to the mark in terms of good results for the masked face, however, a multimodal technique can be employed to generate better results. We proposed a multimodal methodology based on deep learning for facial recognition under a masked face using facial and vocal expressions. The multimodal has been trained on a facial and vocal dataset. We have used two standard datasets, M-LFW for the masked dataset and CREMA-D and TESS dataset for vocal expressions. The vocal expressions are in the form of audio while the faces data is in image form that is why the data is heterogenous. In order to make the data homogeneous, the voice data is converted into images by taking spectrogram. A spectrogram embeds important features of the voice and it converts the audio format into the images. Later, the dataset is passed to the multimodal for training. neural network and the experimental results demonstrate that the proposed multimodal algorithm outsets unimodal methods and other state-of-the-art deep neural network models. © Tech Science Press.

IoMT with Deep CNN: AI-Based Intelligent Support System for Pandemic Diseases

The Internet of Medical Things (IoMT) is an extended version of the Internet of Things (IoT). It mainly concentrates on the integration of medical things for servicing needy people who cannot get medical services easily, especially rural area people and aged peoples living alone. The main objective of this work is to design a real time interactive system for providing medical services to the needy who do not have a sufficient medical infrastructure. With the help of this system, people will get medical services at their end with minimal medical infrastructure and less treatment cost. However, the designed system could be upgraded to address the family of SARs viruses, and for experimentation, we have taken COVID-19 as a test case. The proposed system comprises of many modules, such as the user interface, analytics, cloud, etc. The proposed user interface is designed for interactive data collection. At the initial stage, it collects preliminary medical information, such as the pulse oxygen rate and RT-PCR results. With the help of a pulse oximeter, they could get the pulse oxygen level. With the help of swap test kit, they could find COVID-19 positivity. That information is uploaded as preliminary information to the designed proposed system via the designed UI. If the system identifies the COVID positivity, it requests that the person upload X-ray/CT images for ranking the severity of the disease. The system is designed for multi-model data. Hence, it can deal with X-ray, CT images, and textual data (RT-PCR results). Once X-ray/CT images are collected via the designed UI, those images are forwarded to the designed AI module for analytics. The proposed AI system is designed for multi-disease classification. It classifies the patients affected with COVID-19 or pneumonia or any other viral infection. It also measures the intensity level of lung infection for providing suitable treatment to the patients. Numerous deep convolution neural network (DCNN) architectures are available for medical image classification. We used ResNet-50, ResNet-100, ResNet-101, VGG 16, and VGG 19 for better classification. From the experimentation, it observed that ResNet101 and VGG 19 outperform, with an accuracy of 97% for CT images. ResNet101 outperforms with an accuracy of 98% for X-ray images. For obtaining enhanced accuracy, we used a major voting classifier. It combines all the classifiers result and presents the majority voted one. It results in reduced classifier bias. Finally, the proposed system presents an automatic test summary report textually. It can be accessed via user-friendly graphical user interface (GUI). It results in a reduced report generation time and individual bias.

CovidSense: A Heterogeneous Robot System for Indoor Area for COVID-19 Mitigation using Deep Learning Techniques

The countermeasure for preventing COVID-19 should be further studied in order to make sure countries are prepared for the endemic phase. The biggest challenge of COVID-19 is its high infection rate and infection mortality rate. Robots offer a very good solution to this, hence, we developed a robot that can autonomously navigate a closed indoor room, sanitize it, and monitor social proximity practices. The quality of the hardware design, electronic system and software developments are conducted and experimental works to test the performance of the robot are performed. © 2022 IEEE.