Self-Sovereign Identity for Consented and Content-Based Access to Medical Records Using Blockchain

Electronic health records (EHRs) and medical data are classified as personal data in every privacy law, meaning that any related service that includes processing such data must come with full security, confidentiality, privacy, and accountability. Solutions for health data management, as in storing it, sharing and processing it, are emerging quickly and were significantly boosted by the COVID-19 pandemic that created a need to move things online. EHRs make a crucial part of digital identity data, and the same digital identity trends - as in self-sovereign identity powered by decentralized ledger technologies like blockchain, are being researched or implemented in contexts managing digital interactions between health facilities, patients, and health professionals. In this paper, we propose a blockchain-based solution enabling secure exchange of EHRs between different parties powered by a self-sovereign identity (SSI) wallet and decentralized identifiers. We also make use of a consortium IPFS network for off-chain storage and attribute-based encryption (ABE) to ensure data confidentiality and integrity. Through our solution, we grant users full control over their medical data and enable them to securely share it in total confidentiality over secure communication channels between user wallets using encryption. We also use DIDs for better user privacy and limit any possible correlations or identification by using pairwise DIDs. Overall, combining this set of technologies guarantees secure exchange of EHRs, secure storage, and management along with by-design features inherited from the technological stack. © 2023 Marie Tcholakian et al.

Digital Imaging System for the Detection of Diabetic Retinopathy

COVID-19 since its appearance caused serious problems to the health sector due to the increase in infected and deceased people by directly affecting their respiratory system, making it a primordial disease that led all countries to fight this virus, generating that other diseases go to the background such as diabetes mellitus, which is a disease caused by the neglect of people's lifestyles, that has been increasing over time and that has no cure but can be prevented by controlling your blood glucose level, this disease causes diabetic retinopathy in people that with the advance of it can cause loss of sight. In addition, to detect its stage the ophthalmologist relies on his experience, occupying a lot of time and being prone to make mistakes about the patient. In view of this problem, in this article a digital image processing system was performed for the detection of diabetic retinopathy and classified according to the characteristics obtained from the features by analyzing the fundus of the eye automatically and determining the stage in which the patient is. Through the development of this system, it was determined that it works in the best way, visualizing an efficiency of 95.78% in the detection of exudates, and an efficiency of 97.14% in the detection of hemorrhages and blood vessels, resulting in a reliable and safe system to detect diabetic retinopathy early in diabetic patients. © 2023 IEEE.

Detection and Classification of Pneumonia Using Deep Learning by the Dense Net-121 Model

A chronic respiratory disease known as pneumonia can be devastating if it is not identified and treated in a timely manner. For successful treatment and better patient outcomes, pneumonia must be identified early and properly classified. Deep learning has recently demonstrated considerable promise in the area of medical imaging and has successfully applied for a few image-based diagnosis tasks, including the identification and classification of pneumonia. Pneumonia is a respiratory illness that produces pleural effusion (a condition in which fluids flood the lungs). COVID-19 is becoming the major cause of the global rise in pneumonia cases. Early detection of this disease provides curative therapy and increases the likelihood of survival. CXR (Chest X-ray) imaging is a common method of detecting and diagnosing pneumonia. Examining chest X-rays is a difficult undertaking that often results in variances and inaccuracies. In this study, we created an automatic pneumonia diagnosis method, also known as a CAD (Computer-Aided Diagnosis), which may significantly reduce the time and cost of collecting CXR imaging data. This paper uses deep learning which has the potential to revolutionize in the area of medical imaging and has shown promising results in the detection and classification of pneumonia. Further research and development in this area is needed to improve the accuracy and reliability of these models and make them more accessible to healthcare providers. These models can provide fast and accurate results, with high sensitivity and specificity in identifying pneumonia in chest X-rays. © 2023 IEEE.

Getting Ready for the New Normal Way of Working: Using Business Simulation Projects to Foster Work-from-Anywhere Skills

While entering the post-COVID-19 pandemic phase, to define a new normal way of working, some companies are transitioning toward a permanent WFX model, while others are combining WFX with colocated work (i.e., hybrid work). Therefore, fostering WFX skills (usually classified as soft skills) in early-career students becomes crucial;additionally, it can help reduce early school leaving. This work aims at understanding how business simulation projects foster the WFX skills deemed crucial by industries. To this end, we conducted two case studies involving high school students. The final questionnaire revealed that most participants evaluate their WFX as fair or higher. Moreover, they believe that business simulation projects help in developing WFX skills. Based on our results we highlight recommendations for educational practice. Copyright © 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

DenseNet201 Model for Robust Detection on Incorrect Use of Mask

In 2022, the COVID-19 pandemic is still occurring. One of the optimal prevention efforts is to wear a mask properly. Several previous studies have classified the use of masks incorrectly. However, the accuracy resulting from the classification process is not optimal. This research aims to use the transfer learning method to achieve optimal accuracy. In this research, we used three classes, namely without a mask, incorrect mask, and with a mask. The use of these three classes is expected to be more detailed in detecting violations of the use of masks on the face. The classification method used in this research uses transfer learning as feature extraction and Global Average Pooling and Dense layers as classification layers. The transfer learning models used in this research are MobileNetV2, InceptionV3, and DenseNet201. We evaluate the three models' accuracy and processing time when using video data. The experimental results show that the DenseNet201 model achieves an accuracy of 93%, but the processing time per video frame is 0.291 s. In contrast to the MobileNetV2 model, which produces an accuracy of 89% and the processing speed of each video frame is 0.106 s. This result is inversely proportional to accuracy and speed. The DenseNet201 model produces high accuracy but slow processing time, while the MobileNetV2 model is less accurate but has faster processing. This research can be applied in the crowd center to monitor health protocols in the use of masks in the hope of inhibiting the transmission of the COVID-19 virus. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Collection and Analysis of Narratives for a Values Charter of the Italian Society for Hospital Pharmacy

In recent years, the field of Narrative Pharmacy was introduced, which particularly addresses the pharmacist not only to guide a relationship of listening to and caring for the patient but also to strengthen and motivate toward the profession, improve relationships with colleagues, enhance the ability to teamwork, and understand emotions. In this paper, we report the analysis behind the construction of the Value Chart from the personal narratives of members of the Italian Society of Hospital Pharmacy. Each member's subjective professional experiences and their own view of themselves within society were collected through a semi-structured interview. Personal thinking, including experiences, feelings, opinions, desires, and regrets was classified by objective methods, from which main concepts were extracted for the Value Chart. The feedback to the survey, including activities during the Covid-19 pandemic management, is classified according to the analytical methods of Kleinman, Frank, Bury and Launer-Robinson. Regarding sentiment analysis, the emotional and subjective context of the text provides an ideal baseline to validate the result. The analysis was implemented using neural networks trained on dictionaries and natural language (i.e., Tweets). The originality of the work lies in the fact that generally value charters are built on a Society's values. In contrast, in this case, individual contributions were gathered to complement the ethical values on which the society is founded. © 2022 IEEE.

Statistical analysis of crash data and impact of COVID-19 on road crashes in Maharashtra, India

This study analyzes crash data from 2016 to 2020 on a National Highway in Maharashtra, India. The impact of the COVID-19 lockdown on the road crashes of the study area is presented, and recommendations to improve road safety are proposed. The crash data is collected from the "National Highways Authority of India, Kolhapur” from 2016 to 2020, and the information is classified into three scenarios: Before Lockdown, After Lockdown, and Strict Lockdown. The crash data is analyzed under three scenarios for seven different classifications followed by their sub-classifications. The time-wise analysis of crash data is performed in four-time slots, namely 00:00–05:59 AM, 06:00–11:59 AM, 12:00–17:59 PM, and 18:00–23:59 PM. The season-wise analysis of crash data is performed in three seasons: Summer, Monsoon, and Winter. The crashes that occurred on 2-lane-straight roads having T-junction are more than 90% in all three scenarios. The significant factors responsible for crashes are "Head-on collision,” "Vehicle out of control,” and "Overspeeding.” Most crashes (more than 36%) occurred between 12:00 and 17:59 PM and in the Summer season (more than 42%) in all three scenarios. The crashes in the COVID-19 "Strict Lockdown” scenario witnessed a fall of 254.55% compared to 2019 and 2018. Surprisingly, there was a rise of 137.5% and a fall of 127.27% in crashes of the COVID-19 2020 "Strict Lockdown” scenario, compared to 2017 and 2016, respectively. The crashes under the sub-classifications "Right angle collision” and "Fatal” increased in 2020 compared to the previous 4 years due to the impact of COVID-19. © 2023, The Author(s).

Development of Personalized Sleep Induction System based on Mental States

Sleep is an essential behavior to prevent the decrement of cognitive, motor, and emotional performance and various diseases. However, it is not easy to fall asleep when people want to sleep. There are various sleep-disturbing factors such as the COVID-19 situation, noise from outside, and light during the night. We aim to develop a personalized sleep induction system based on mental states using electroencephalogram and auditory stimulation. Our system analyzes users' mental states using an electroencephalogram and results of the Pittsburgh sleep quality index and Brunel mood scale. According to mental states, the system plays sleep induction sound among five auditory stimulation: white noise, repetitive beep sounds, rainy sound, binaural beat, and sham sound. Finally, the sleep-inducing system classified the sleep stage of participants with 94.7% and stop auditory stimulation if participants showed non-rapid eye movement sleep. Our system makes 18 participants fall asleep among 20 participants. © 2023 IEEE.

Diachronic Analysis of Users' Stances on COVID-19 Vaccination in Japan using Twitter

To prevent and curb viral outbreaks, such as COVID-19, it is important to increase vaccination coverage while resolving vaccine hesitancy and refusal. To understand why COVID-19 vaccination coverage had rapidly increased in Japan, we analyzed Twitter posts (tweets) to track the evolution of people's stance on vaccination and clarify the factors of why people decide to vaccinate. We collected all Japanese tweets related to vaccines over a five-month period and classified the vaccination stances of users who posted those tweets by using a deep neural network we designed. Examining diachronic changes in the users' stances on this large-scale vaccine dataset, we found that a certain number of neutral users changed to a pro-vaccine stance while very few changed to an anti-vaccine stance in Japan. Investigation of their information-sharing behaviors revealed what types of users and external sites were referred to when they changed their stances. These findings will help increase coverage of booster doses and future vaccinations. © 2022 IEEE.

Evaluation of Various Machine Learning Based Existing Stress Prediction Support Systems (SPSSs) for COVID-19 Pandemic

COVID-19 profoundly impacts human beings in various ways, i.e., psychological, socioeconomic, fear, social isolation, etc., augmenting the prevailing inequalities in mental health. The role of machine learning (ML) can be understood through its various potential applications in Stress Prediction in mental health. This literature survey uncovered various related articles, which were utilized to determine the essential structure for analysis. The gathered information helped in providing the new ideas and the concepts, which were incorporated with the support of literature and classified under broad themes based on mental health during the pandemic COVID-19. This study emphasized assessing various existing "Stress Prediction Support Systems” based on machine learning. This article also addresses the mental health issues that were emerged due to COVID-19 pandemic, further;also analysed the previously available stress prediction Machine Learning based models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Classification of Covid19 Vaccine-Related Tweets Using Deep Learning

In recent years, the majority of the world's population has been impacted by the COVID-19 pandemic, but owing to the invention of vaccinations, the epidemic has been brought under control. Most people are hesitant to share their experiences on official platforms after being vaccinated. As a result, information about vaccine-related adverse effects other than clinical trial results is challenging to identify. However, most people have shared their opinions about vaccines on social media since the COVID-19 immunization program began worldwide. This study aims to assess, using social media, the adverse effects of the COVID-19 vaccination as perceived by the general population. The authors of the previous studies did not categorize tweets on the COVID-19 vaccine adverse effects as personal experience, informative, or advice-seeking. The authors of this study aim to classify tweets in the manner described above to fill a research gap and increase public awareness of the COVID-19 vaccine's side effects. The Kaggle repository collected tweets pertaining to COVID-19 vaccinations for this investigation. The authors manually classified collected tweets into two categories: those connected to COVID-19 vaccinations' adverse effects and those unrelated to COVID-19 vaccines' adverse effects. Then, valid tweets were further classified into three categories: personal experience, informative, and seeking advice. The authors then used the data to train four ML models. There are also SVM, Logistic Regression, LSTM, and ANN. The LSTM algorithm generated the most outstanding results, with an accuracy of 97.64&. In addition, the researchers conclude that the SVM may not be suitable for planned research since it gave the lowest degree of accuracy, 80%. © 2022 IEEE.

PVT-COV19D: COVID-19 Detection Through Medical Image Classification Based on Pyramid Vision Transformer

With the outbreak of COVID-19, a large number of relevant studies have emerged in recent years. We propose an automatic COVID-19 diagnosis model based on PVTv2 and the multiple voting mechanism. To accommodate the different dimensions of the image input, we classified the images using the Transformer model, sampled the images in the dataset according to the normal distribution, and fed the sampling results into the PVTv2 model for training. A large number of experiments on the COV19-CT-DB dataset demonstrate the effectiveness of the proposed method. Our method won the sixth place in the (2nd) COVID19 Detection Challenge of ECCV 2022 Workshop: AI-enabled Medical Image Analysis - Digital Pathology & Radiology/COVID19. Our code is publicly available at https://github.com/MenSan233/Team-Dslab-Solution. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Image Processing and Deep Neural Networks for Face Mask Detection

The proper use of a mask is crucial for lowering COVID 19 and transmission. According to the research, transmission is completely decreased when the mask is used appropriately. Factors like sunlight and several items can affect how appropriatel y applied face masks are classified and detected. Cotton masks, sponge masks, scarves, and other options greatly lessen the effect of personal protection in such circumstances. The research suggests a novel modified formula for classifying masks into three categories—a proper mask, a no mask, and an erroneous mask—using deep learning and machine learning. First, we provide a brand-new face mask classification and detection algorithm that combines deep learning, the viola Jones method, and Efficient-Yolov3 Wearing a mask, not wearing a mask, or wearing the wrong mask are the three options. On the dataset with or without mask pictures, the suggested system outperforms and is more accurate when compared to existing techniques. The results of experiments and analysis are also based on the classification knowledge set. In comparison to the present methodology's categorization accuracy of 84%, the anticipated formula boosted it to 97%. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

An analysis of Deep Learning Models for Detection of COVID-19 Diseases

The World Health Organization has classified COVID-19 as a pandemic virus at this time. The conditions posed significant challenges for every nation on the planet, notably with the preparations made for health care and the lengthy reactions required. Because of the sudden rise in the number of infections due to COVID-19 disease and the limited resources for detecting it has become requisite to develop an artificial intelligence-based system for determining the COVID-19 disease. An increasing number of people throughout the world are testing positive for COVID-19 every day. A rapid and accurate identification of COVID-19 is a time-sensitive prerequisite for preventing and controlling the pandemic by means of appropriate isolation and medical treatment. The significance of the current work lies in its discussion of the overview of the deep learning approaches with diagnostic imaging. This includes topics such as various deep learning models and its impact in efficiently detecting the virus transmitted indications. © 2022 IEEE.

Geographic Reason by Hourly Population of 500-Meter-Grid Statistics over Japan Why Tokyo Seemed to Fail Eradicating COVID-19 in Mid 2020

Since about May 2020 COVID-19 has been rampant in Japan nonetheless it is not so expanded as in the other severe countries. Two waves have been observed in the daily numbers of the reported patients in Japan of which peaks are on April and August. Looking into the patients report numbers classified by week, prefecture and age-group, the rampant prefectures were only a few overpopulated ones, and probably Tokyo is the only prefecture which failed to terminate the spread of the viruses during the state of emergency from April to May. We can look into the more detail geographically using the 500-meter grid population statistics all over Japan that is obtained by Mobile Spatial Statistics provided by DOCOMO Insight Marketing, INC. (Mobile Spatial Statistics is a registered trademark of NTT DOCOMO Inc.) It reveals that the infection largely occurred probably only a few of very narrow geographic spaces in three cities (Tokyo, Osaka, Sapporo) because the mingling of people happened effectively almost only there concerning the people influx. © 2022 IEEE.

Automated Detection of COVID-19 Infection in Lungs from CXR and CT Scan Images using Two-Stage Convolution Neural Network

COVID-19 is a virus that is highly infectious and is contractable to others. In this study, we demonstrate a CNN, which quickly detects COVID-19 from X-ray and CT scans within minutes. The performance measure of this model is classified based on an accuracy of 82%. © 2022 IEEE.

Driving Factors of Polarization on Twitter During Protests Against COVID-19 Mitigation Measures in Vienna

We conduct the analysis of the Twitter discourse related to the anti-lockdown and anti-vaccination protests during the so-called 4th wave of COVID-19 infections in Austria (particularly in Vienna). We focus on predicting users' protest activity by leveraging machine learning methods and individual driving factors such as language features of users supporting/opposing Corona protests. For evaluation of our methods we utilize novel datasets, collected from discussions about a series of protests on Twitter (40488 tweets related to 20.11.2021;7639 from 15.01.2022 – the two biggest protests as well as 192 from 22.01.2022;8412 from 11.12.2021;3945 from 11.02.2022). We clustered users via the Louvain community detection algorithm on a retweet network into pro- and anti-protest classes. We show that the number of users engaged in the discourse and the share of users classified as pro-protest are decreasing with time. We have created language-based classifiers for single tweets of the two protest sides – random forest, neural networks and a regression-based approach. To gain insights into language-related differences between clusters we also investigated variable importance for a word-list-based modeling approach. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A review on indoor airborne transmission of COVID-19– modelling and mitigation approaches

In the past few years, significant efforts have been made to investigate the transmission of COVID-19. This paper provides a review of the COVID-19 airborne transmission modeling and mitigation strategies. The simulation models here are classified into airborne transmission infectious risk models and numerical approaches for spatiotemporal airborne transmissions. Mathematical descriptions and assumptions on which these models have been based are discussed. Input data used in previous simulation studies to assess the dispersion of COVID-19 are extracted and reported. Moreover, measurements performed to study the COVID-19 airborne transmission within indoor environments are introduced to support validations for anticipated future modeling studies. Transmission mitigation strategies recommended in recent studies have been classified to include modifying occupancy and ventilation operations, using filters and air purifiers, installing ultraviolet (UV) air disinfection systems, and personal protection compliance, such as wearing masks and social distancing. The application of mitigation strategies to various building types, such as educational, office, public, residential, and hospital, is reviewed. Recommendations for future works are also discussed based on the current apparent knowledge gaps covering both modeling and mitigation approaches. Our findings show that different transmission mitigation measures were recommended for various indoor environments;however, there is no conclusive work reporting their combined effects on the level of mitigation that may be achieved. Moreover, further studies should be conducted to understand better the balance between approaches to mitigating the viral transmissions in buildings and building energy consumption. © 2022

Using Machine Learning to Predict Business Survival in COVID-19

The global recession due to the pandemic has knocked the business landscape and brought the world to its knees. There were a number of renowned companies that made the headlines for being the top industry hard hits. Nonetheless, there were businesses that survived this pandemic and navigated the COVID complexities so effectively that it tipped the scales in their favor. We attempt to study the factors that helped these businesses masterfully work their way through the conundrums of coronavirus pandemic. We first build a dataset that entailed information pertinent to businesses and relevant COVID-related information that was sourced from Yelp and other platforms. We used a variety of classifiers to make predictions about the survival of these businesses followed by that after assessing their performance through varied methods. The model efficiency was classified based on several rating techniques to evaluate both underperforming and profitable businesses. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Identifying the Impacts of COVID-19 on Chilean Construction Projects

The construction industry has been highly disrupted by the pandemic as the development of construction projects must be adapted due to policies to minimize the spread of COVID-19, such as social distancing. As the construction industry contributes approximately 7% of Chilean GDP, it is important to identify and understand the impacts the construction industry has suffered due to the pandemic context. This study aims to identify the impacts of COVID-19 on Chilean construction projects. This study is enabled by data from 40 semi-structured interviews collected between May and November 2020 with multiple stakeholders working on projects during the pandemic, namely construction managers, construction engineers, and laborers of construction work. This study's results are obtained by categorizing the impacts of COVID-19 on Chilean construction projects, performing content analysis to the data collected. We found that the impacts of COVID-19 on construction projects can be classified in nine categories, being the categories with the most coded responses the following: economic impacts, productivity, and the stop and delay of construction projects. Additionally, the impacts from COVID-19 were identified to reach multiple levels, namely at the company, project, workers, and suppliers and subcontractors' levels. The most coded excerpts regarding the impacts of COVID-19 were found at the project and workers' levels. This study is a first step that identifies the impacts suffered by the construction industry due to pandemic conditions;understanding these impacts may guide the most appropriate plans and policies of decision-makers in the fight against COVID-19 in the construction industry. © 2023, Canadian Society for Civil Engineering.