Internet of Things (IoT) based Health Monitor Device

Health is the centre of human enlightenment. Due to the recent Covid outbreak and several environmental pollutions, checking one's vitals regularly has become a necessity. Ours is an IoT-based device that measures a user's heart rate, blood oxygen level and body temperature. The device is compact and portable, making it easy for users to wear. The readings are measured and shown on an OLED display with the help of sensors. The data is also available on the cloud. A webpage and a mobile application were developed to view the data from the cloud. Individual graphs of the vitals with time are available on the mobile application. This can be used for progress measurement and statistical analyses. Authorized personnel can access the patient's vitals. This creates a scope for Tele-medication in rural and underdeveloped regions. Besides, one can also view his/her vitals for personal health routine. © 2022 IEEE.

A Comparative Evaluation between Convolutional Neural Networks and Vision Transformers for COVID-19 Detection

Early illness detection enables medical professionals to deliver the best care and increases the likelihood of a full recovery. In this work, we show that computer-aided design (CAD) systems are capable of using chest X-ray (CXR) medical imaging modalities for the identification of respiratory system disorders. At present, the COVID-19 pandemic is the most well-known illness. We propose a system based on explainable artificial intelligence to detect COVID-19 from CXR images by using several cutting-edge convolutional neural network (CNN) models, as well as the Vision of Transformer (ViT) models. The proposed system also visualizes the infected areas of the CXR images. This gives doctors and other medical professionals a second option for supporting their decision. The proposed system uses some preprocessing of the images, which includes the segmentation of the region of interest using a UNet model and rotation augmentation. CNN employs pixel arrays, while ViT divides the image into visual tokens;therefore, one of the objectives is to compare their performance in COVID-19 detection. In the experiments, a publicly available dataset (COVID-QU-Ex) is used. The experimental results show that the performances of the CNN-based models and the ViT-based models are comparable. The best accuracy was 99.82%, obtained by the EfficientNetB7 (CNN-based) model, followed by the SegFormer (ViT-based). In addition, the segmentation and augmentation enhanced the performance. © 2023 by the authors.

A Hybrid Deep Learning System to Detect Face-Mask and Monitor Social Distance

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.

Application of the SOBC model to study customers' online purchase intentions in an emerging economy during COVID-19: does gender matter?

Purpose: Online shopping around the world is growing exponentially, especially during the COVID-19 pandemic. This study aims to examine how an online customer's purchasing experience influences his/her buying intention and willingness to believe in fraud news, as well as the ripple impact of satisfaction and trust, with gender as a moderator in an emerging economy during COVID-19. Design/methodology/approach: Based on the underpinning of the stimulus-organism-behavior-consequence (SOBC) theory, the research model was developed, and collected data from 259 respondents using convenience samples technique. Next, the data were analyzed using partial least squares-based structural equation modeling (PLS-SEM), SPSS (Statistical Package for the Social Sciences) and Hayes Process Macro. Findings: The study results confirmed that the online shopping experience (OSE) has positive impact on customers' satisfaction (CS), purchase intention (PI) and customer trust (CT);CS has positive effects on trust toward online shopping and their future product PI;future product PI significantly affects customers' propensity to believe and act on fraud news (PBAFN). The finding also states that gender moderates the relationships of CS to PI, OSE to PI and PI to PBAFN, but doesn't moderate the CT to PI relationship. Originality/value: The study findings will assist policymakers and online vendors to win customers' hearts and minds' through confirming satisfaction, trust and a negative attitude toward fake news, which will lead to customer loyalty and the sustainable development of the industry. Finally, the limitations and future research directions are discussed. © 2023, Md. Rabiul Awal, Md. Shakhawat Hossain, Tahmina Akter Arzin, Md. Imran Sheikh and Md. Enamul Haque.

Human Mobility Driven Modeling of an Infectious Disease

In conventional disease models, disease properties are dominant parameters (e.g., infection rate, incubation pe-riod). As seen in the recent literature on infectious diseases, human behavior - particularly mobility - plays a crucial role in spreading diseases. This paper proposes an epidemiological model named SEIRD+m that considers human mobility instead of modeling disease properties alone. SEIRD+m relies on the core deterministic epidemic model SEIR (Susceptible, Exposed, Infected, and Recovered), adds a new compartment D - Dead, and enhances each SEIRD component by human mobility information (such as time, location, and movements) retrieved from cell-phone data collected by SafeGraph. We demonstrate a way to reduce the number of infections and deaths due to COVID-19 by restricting mobility on specific Census Block Groups (CBGs) detected as COVID-19 hotspots. A case study in this paper depicts that a reduction of mobility by 50 % could help reduce the number of infections and deaths in significant percentages in different population groups based on race, income, and age. © 2022 IEEE.

Clinical Spectrum, Geographical Variability of COVID-19, and its Implications

The coronavirus disease 2019 (COVID-19) pandemic has triggered a worldwide unprecedented public health crisis. Initially, COVID-19 was considered a disease of the respiratory system, as fever and at least one respiratory symptom was used to identify a suspected COVID-19 case. But there are now numerous reports of COVID-19 patients presenting with myriads of extra-pulmonary symptoms, however, a substantial number of patients are asymptomatic. Additionally, there are significant clinical and epidemiological variations of severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) infection across different geographical locations. The updated re-search, thus, challenges the existing surveillance system that is mainly based on fever and respiratory symptoms. As countries are coming out of lockdown to save economic fallout, a revised surveillance strategy is required to effectively identify and isolate the infected patients. Besides, since developing countries are becoming the new epicenters of pandemic and there are limited resources for RT-PCR based tests, documenting the clinical spectrum can play a vital role in the syndromic clinical diagnosis of COVID-19. A plethora of atypical symptoms also aids in guiding better treatment and remains as a source for further research. It is, therefore, crucial to understand the com-mon and uncommon clinical manifestations of SARS-COV-2 infection and its variability across different geographic regions.Copyright © 2021 Bentham Science Publishers.

Cardiovascular comorbidity and its impact on patients with COVID-19 in a tertiary care hospital, Bangladesh

Background: People with cardiovascular issues have been shown to be at an elevated risk of acquiring the 2019 new corona virus infection, according to studies (COVID-19). This study's objective was to determine if cardiovascular disease has any effect on the severity of COVID-19. Material & Methods: Between January 2020 and December 2020, 210 comorbid patients aged over 40 years old diagnosed with COVID-19 admitted in BIRDEM hospital in Bangladesh were recruited purposively for a cross sectional study as per inclusion criteria. A baseline study was created for each patient based on their medical history, physical examination, biochemical tests, and the amount of care they needed (intensive care vs. ward-based care). SPSS 26 was used to analyze the data. Results: Among the 210 comorbid individuals, 74 had cardiovascular comorbidities and the remaining 136 had other comorbidities. Among the respondents, 48% were serious cases and required ICU support within 30 days. Cases with up to 2 comorbidities did not require ICU support. The severity of COVID-19 was predicted by factors such as age above 80 years (OR 35.5, 95 percent CIs 18.7,98.5), male gender (OR 3.14, CI 1.16, 3.50), and a high troponin level in the patient's blood (OR 1.34, CI 0.84,3.54). It was shown that cardiovascular comorbidities (CI=1.8,3.0) were 2.9 times more likely to be linked to severity. The risk factors also included a history of diabetes, hypertension, and chronic obstructive pulmonary disease. Conclusions: Cardiovascular patients, who were previously grouped together as high risk due to the nature of their ailment, need more tailored counseling and treatment from public health authorities and clinicians.

A Transfer Learning Approach to Detect Face Mask in COVID-19 Pandemic

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.

A Transfer Learning Approach to Detect Face Mask in COVID-19 Pandemic

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.

An IoT Prototype for Monitoring Covid19 Patients Using Real Time Data from Wearable Sensor Through Android App

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.

The Ubiquitous Role of Mobile Technology Application in the Australian Open

Australia is one of the largest global sport tourist destinations. This industry employs a significant number of Australia’s total employment with contributions to the national economy. There is no shortage of exciting sporting events in Australia. Sporting activities in Australia are as much about having a good time and getting some fresh air as they are about fierce competition and amazing athleticism. It is no surprise that Australia hosts world-renowned tournaments like the Australian Open, attracting sports fans from all over the world to enjoy the adrenaline-pumping atmosphere and world-class competitors. Considering the increasing rate of emerging technologies, tour operators can use them to remain competitive in the market. Adopting the content analysis method, this study explores how the technology applications are impacting events in Australia. The study identified that the use of sharing of content via social media, use of sharing platforms like Airbnb and Uber, use of payment platforms like PayPal and Apple Pay, use of apps to provide visitor information on nearby offerings and the use of virtual reality are providing virtual tours of hotels and hotel rooms are increasing the attractiveness of this popular sports event. Though currently, the country is struggling to overcome the challenges of COVID-19 to regain the losses of revenue in the last year, it is expected that this sport event will demonstrate resilience and continue to succeed in the coming years using technology applications. Further research can be conducted to measure the impact of technology application in other sports events in Australia. © 2022 selection and editorial matter, Azizul Hassan;individual chapters, the contributors.

Has COVID-19 made the vulnerable groups even more vulnerable? Exploring the conditions of diabetic patients of Dhaka city amidst COVID-19 pandemic

SARS-CoV-2, commonly referred as COVID-19, has emerged as the most severe public health concern of the twenty - first century. Coronavirus usually is not very lethal to the persons who do not have any medical conditions, but it is fatal to people who have had past medical conditions that have often resulted in death. The objectives of this paper is to look at the effects of coronavirus on older diabetes patients, who are thought to be the ones who were affected the most by COVID-19. This research used a qualitative approach and was descriptive in nature. The researcher has purposefully chosen three areas in Dhaka city as the studies fixate: Shahbag, Khilgaon, and Rampura. Data was gathered using qualitative methods such as focus group discussion and key informant interviews. According to the study's findings, COVID-19 had a serious effect on older adults with diabetes. The number of patients at the hospital had significantly decreased. Despite the fact that hospitals were equipped to provide treatment and care, patients' mobility was limited. Many people preferred virtual consultation or telemedicine to face-to-face consultation and care. Doctors encouraged individuals to connect digitally, which is both safe and feasible in the face of the global pandemic. The expense of a diabetic patient rose, according to the majority of respondents. According to the findings, the pandemic is spurring new diabetes-care delivery methods. Many structural flaws were exposed as a result of Covid-19, paving the door for additional improvements in healthcare delivery in the study area.

MO8-4 Knowledge, attitudes and participants risk of lung cancer in Bangladeshi population: A nationwide cross-sectional study

Introduction: Lung cancer is the leading cause of cancer death worldwide and Covid-19 pandemic has exacerbated the problem much more. There is a high risk of being infected with SARS-CoV-2 among patients having lung cancer. This study aims to assess the knowledge, attitudes, and help-seeking for early symptoms of lung cancer in Bangladeshi people. Method: A cross-sectional study was conducted with 744 randomly selected respondents from eight different administrative regions of Bangladesh between June and August 2021. A structured questionnaire was used covering socio-demographic characteristics of the participants including their knowledge, attitudes, and participant's risk about lung cancer to accomplish our aim and objectives. Multivariable logistic regression models were used to identify factors associated with the knowledge and awareness of lung cancer. Result: Of the 744 participants, 90.3% (672/744) reported to have heard about lung cancer. A total of 17 participants were identified as lung cancer patients. Being a smoker (96.7%) and unexplained shortness of breath (92.6%) were identified as the most common risk factor and symptoms of lung cancer respectively. Among the socio-demographic variables, the level of education of the respondents was identified as an independent predictor for both knowledge (p<0.001) and awareness (p<0.001) about lung cancer. Smoking status was significantly associated with the participant's awareness of lung cancer (p<0.001). Conclusion: Although most participants were knowledgeable about smoking as a major risk factor, it was not proportional to their actions to stop smoking. This study highlights the importance of raising awareness and enhancing positive steps to avoid modified risk factors or even encourage early testing for lung cancer.

Explainable Text Classification Model for COVID-19 Fake News Detection

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.

Study of UWB Near Field Microwave Imaging System for the Diagnosis of Lungs Damage Due to Fluid Accumulations

Lung damages, which is the leading cause of cancer and Covid-19 related death worldwide, can be better treated, and patients' chances of survival increased with early detection and diagnosis. PET (positron emission tomography), cone beam CT, Low dose helical CT, are advanced lung imaging techniques that allow for early diagnosis of smaller pulmonary nodules than normal chest radiography, but with ionizing radiation effect and being costly. In the field of imaging technology, microwave imaging has long been researched in the field of breast and brain. This study presents a review, conducts a feasibility study, and validates the concept of imaging the lungs in a similar manner to the breast and brain. The analysis includes designing a 3D human lung model, microwaves' various elements and factors inspection through the human body using holographic near field imaging, and image processing to estimate the percentage of lung damage. The safety and ionization exposure were also taken into consideration during the overall experiment. The use of microwave energy in various lung diseases is examined, and the basis for fluid detection utilizing microwave water content accumulation is also addressed compared to normal tissues. © 2022 IEEE.

IoT-based COVID-19 Suspect Smart Entrance Monitoring System

The concept of the Internet of Things (IoT) encompasses the connection and monitoring of various remote objects in the real world through the Internet. The COVID-19 is very infectious, and it spreads super-fast by contacting other COVID infected people. There have been few works on COVID detection and prevention in resent time. However, none of the existing works proposed any automatic IoT-based remote blocking system so our work is an autonomous system to stop entering in the campus the COVID suspected. The aim of this work is to detect COVID-19 suspect people and stop them from contacting other people. The proposed system is implemented by using IoT devices. The proposed system first measures the body temperature and Second measures blood oxygen level checks whether it exceeds the World Health Organization (WHO) recommended body temperature and blood oxygen level. In case of high body temperature or low blood oxygen level, the system blocks the person from entering the campus and sends an email to the suspected people notifying their illness. The most advantage of our system is working autonomously, cost-effective, and easy to implement on any campus or office.

Association between COVID-19 and psychological disorders with possible mechanisms. (COVID-19 dedicated issue.)

The current coronavirus pandemic is one of the most wrecking occasions in ongoing history, and it has an impact on mental health, especially in sleep disorder and anxiety. This review aimed to find an association between COVID-19 and psychological disorders like sleep disorder and anxiety by exploring its influential factors. COVID-19 patient has greater susceptibility to having anxiety and sleep disorder-related complications including post-traumatic stress disorder, obsessive-compulsive disorder (OCD), obstructive sleep apnea by infecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to the central nervous system through the olfactory lobe. Although the mechanism of COVID-19 induced sleep disorder and anxiety-related complications have not been reported yet, the investigated data suggested that sleep disorder and anxiety-related complications are arising due to increasing cortisol, norepinephrine levels in the blood and decreasing glucocorticoid receptor signaling. Further examination and clinical studies are critically required to investigate the influential factors of COVID-19 patients' susceptibility to sleep disorder, anxiety for affirming speculation, and better treatment.

Application of Machine Learning to Traditional System for Analyzing the Conditional Results of School and College Students

Machine learning algorithms are used for various purposes to predict, classify, or forecast by training the algorithms with the specific dataset. SVR and multiple linear regression can take numerous features to forecast or predict scores through the train-test-split. The education sector has been changed rapidly due to the pandemic of COVID-19 where online classes are being a module worldwide. However, junior schools or colleges stubbed into a position where student performance measurement is a hindrance due to the lack of taking physical examinations. During the COVID-19, student performance can be acquired using the previous achievement of individual students where multiple conditions can be applied. The aim of this paper is to train and test the conditional dataset of student's results through SVR and Multiple Linear Regression to predict and justify the results in accordance with using the proposed model in the future. As conditions have been applied to the individual subjects when calculating new results based on the previous achievement of student’s performance so that each subject’s score has been trained and tested individually through the machine learning algorithms. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Face Mask Detection in the Era of COVID-19: A CNN-Based Approach

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.