ABSTRACT
The COVID-19 Research Database is a public data platform. This platform is a result of private and public partnerships across industries to facilitate data sharing and promote public health research. We analyzed its linked database and examined claims of 2,850,831 unique persons to investigate the influence of demographic, socio-economic, and behavioral factors on telehealth utilization in the low-income population. Our results suggest that patients who had higher education, income, and full-time employment were more likely to use telehealth. Patients who had unhealthy behaviors such as smoking were less likely to use telehealth. Our findings suggest that interventions to bolster education, employment, and healthy behaviors should be considered to promote the use of telehealth services. © 2023 IEEE Computer Society. All rights reserved.
ABSTRACT
Considering the public safety in current COVID-19 out-break, an IOT (Internet of Things) based non-contact temperature monitoring system integrated with RFID authentication system with an interactive Android application, and a web-portal to manage users and temperature records has been proposed. Temperature screening has become essential for all the industries, educational institutions, factories and corporate sector. This system is an online real-time non-contact monitoring system with an interactive android application and user-friendly web portal that help end-users to monitor and keep a record of temperature variations of registered users on daily/weekly/monthly basis. The temperature records are saved in a real-time database which is embedded with the user's RFID card information. In case of an alert (high temperature), a notification is sent to the authorized personnel on their cellphones or their desktop systems via web portal. An alarm is also generated immediately on the device (buzzer and blinking LEDs) to indicate high temperature, alerting the nearby security staff. As per the survey and testing of the device under different temperature environments it has been found that the proposed system has an overall accuracy of 99%. © 2022 IEEE.
ABSTRACT
As the coronavirus disease (COVID-19) broke out in late 2019, the electricity sector was significantly impacted. Hence, the effects of the pandemic and restricting measures in power system operation are investigated during pandemic circumstances. The secure operation of the power system is a fundamental requirement. Appropriate procedures should be taken to mitigate these effects and ensure the power system's security. Accordingly, in this study, the authors determine that the COVID-19 pandemic can change the system's operating conditions in the first stage. Since data-driven security assessment methods require the training database to learn about Security constraints, this paper proposes an efficient database generation strategy respecting the consequences of the COVID-19 outbreak. The proposed strategy provides a training set with high information content compatible with the operating conditions. To this end, the method consists of a characteristics extraction approach and updating scheme. The characteristics should be extracted to represent the operating conditions of the system. Further, the similarity of intervals is compared using characteristics in updating scheme. The copula-based sampling approach is provided to generate the random samples. The proposed strategy generates a database for data-driven methods. Therefore, it can be utilized in various applications of security assessment. Real-world data is mapped to the IEEE 39-bus system to illustrate the framework efficiency. The outcomes indicate that a classification using the proposed strategy outperforms conventional methods in terms of evaluation metrics. © 2017 Elsevier Inc. All rights reserved. © 2023 Elsevier Ltd
ABSTRACT
The main tools for allowing customers to communicate openly and transparently with the company and other stakeholders are their electricity bills. However, last year due to pandemics, some residents petitioned the Madras high court claiming TANGEDCO's technique to measure power was arbitrary and unreasonable during the lockdown that COVID provoked. This was done amid complaints about excessive electricity bills. When compared to bills from other states, TANGEDCO's white meter card for energy bills is found to provide insufficient information on use and rates, according to a survey by the certain associations. Power bills urgently need to be redesigned to include comprehensive billing details and accurate assessments of electricity consumption from closed homes or homes in restricted zones. [4] We proposed and designed a Smart Home Energy Meter Monitoring System to solve this crisis. It consists of three systems. First system: Customized built energy meter with LCD. [6] Second system: Wi-Fi module with the Microcontroller (ATMega328P) and an alert system. Third system: Database (MySQL database). [12] The quantity of power used by the device is measured by an energy meter user and every two months, the final reading of the power consumption is taken by the micro controller where the electricity bill calculation program has been pre-programmed to give the value of power consumed during the two months and amount to be paid by the user and [4] It will be shown on the Energy Meter's LCD. The micro controller with a built-in Wi-Fi module (ESP8266) will send these displayed data to the service provider's database. [2] An alert system has been added to counteract the hefty usage and electricity bills to create awareness to the consumer about the slab-wise tariffs increase in the per-unit cost data that has been set by TANGEDCO. [10] The alert has been set in a way that the consumer receives a message for every 200 unit usage of power. The third system is a database created using MySQL database to transport the data to the service provider. © 2022 IEEE.
ABSTRACT
This paper presents the baseline approach for the organized 2nd Covid-19 Competition, occurring in the framework of the AIMIA Workshop in the European Conference on Computer Vision (ECCV 2022). It presents the COV19-CT-DB database which is annotated for COVID-19 detection, consisting of about 7,700 3-D CT scans. Part of the database consisting of Covid-19 cases is further annotated in terms of four Covid-19 severity conditions. We have split the database and the latter part of it in training, validation and test datasets. The former two datasets are used for training and validation of machine learning models, while the latter is used for evaluation of the developed models. The baseline approach consists of a deep learning approach, based on a CNN-RNN network and report its performance on the COVID19-CT-DB database. The paper presents the results of both Challenges organised in the framework of the Competition, also compared to the performance of the baseline scheme. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Most people infected with the virus will have mild to moderate respiratory diseases, however, the elderly population is the most vulnerable, becoming seriously ill, requiring continuous medical follow-up. In this sense, technologies were developed that allow continuous and individual monitoring of patients, in a home environment, namely through wearable devices, thus avoiding continuous hospitalization. Thus, these devices allow great improvements in data analysis methods since they can continuously acquire the physiological signals of an individual and process them in real-Time through artificial intelligence (AI) methods. However, training of AI methods is not straightforward, requiring a large amount of data. In this study, we review the most common biosignal databases available in the literature. A total of thirteen databases were selected. Most of the databases (9 databases) were related to ECG signal, as well as 4 databases containing signals from SPO2, Heart Rate, Blood Pressure, etc. Characteristics were described, namely: The population of the databases, data resolution, sampling rates, sample time, number of signal samples, annotated classes, data acquisition conditions, among other aspects. Overall, this study summarizes and described the public biosignals databases available in the literature, which may be important in the implementation of intelligent classification methods. © 2022 ACM.
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Completely contactless and at-a-distance personal identification provides enhanced user convenience, and improved hygiene and is highly sought under the COVID-19 pandemic. This paper proposes an accurate and generalizable deep neural network-based framework for the ‘completely’contactless finger knuckle identification. We design and introduce a new loss function to enable a fully convolutional network to more effectively learn knuckle features that are imaged under at-a-distance imaging. A ‘completely’contactless system also requires efficient online finger knuckle detection capabilities. This paper, for the first time in our knowledge, develops and introduces accurate capabilities to efficiently detect and segment finger knuckle patterns from images with complex backgrounds as widely observed in real-world applications. We introduce angular loss to accurately predict oriented knuckle patterns and incorporate into our framework. Experimental results presented in this paper on five different public databases, using challenging protocols and cross-database performance evaluation, illustrate outperforming results and validate the effectiveness of the proposed framework for completely contactless applications. IEEE
ABSTRACT
Cloud technology usage in nowadays companies constantly grows every year. Moreover, the COVID-19 situation caused even a higher acceleration of cloud adoption. A higher portion of deployed cloud services, however, means also a higher number of exploitable attack vectors. For that reason, risk assessment of the cloud environment plays a significant role for the companies. The target of this paper is to present a risk assessment method specialized in the cloud environment that supports companies with the identification and assessments of the cloud risks. The method itself is based on ISO/IEC 27005 standard and addresses a list of predefined cloud risks. Besides, the paper also presents the risk score calculation definition. The risk assessment method is then applied to an accounting company in a form of a case study. As a result, 24 risks are identified and assessed within the case study where each risk included also exemplary countermeasures. Further, this paper includes a description of the selected cloud risks. © 2022 Slovak Academy of Sciences. All rights reserved.
ABSTRACT
The widespread adoption of deep learning (DL) solutions in the healthcare organizations is obstructed by their compute intensive nature and dependability on massive datasets. In this regard, cloud-services such as cloud storage and computational resources are emerging as an effective solution. However, when the image data are outsourced to avail such services, there is a privacy concern that the data should be kept protected not only during transmission but during computations as well. To meet these requirements, this study proposed a privacy-preserving DL (PPDL) scheme that enable computations without the need of decryption. The encryption is based on perceptual encryption (PE) that only hides the perceivable information in an image while preserves other characteristics that are necessary for DL computations. Precisely, we have implemented a binary classifier based on EfficientNetV2 for the COVID-19 screening in the chest X-ray (CXR) images. For the PE algorithm, the suitability of two pixel-based and two block-based PE methods was analyzed. The analysis have shown that when global contents are left unmodified (pixel-based PE), then the DL-based model achieved the classification accuracy same as that of the plain images. On the other hand, for block-based PE algorithms, there is up to 3% drop in the model's accuracy and sensitivity scores. © 2023 IEEE.
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Corona virus (COVID-19) is once in a life time calamity that has resulted in thousands of deaths and security concerns. People are using face masks on a regular basis to protect themselves and to help reduce corona virus transmission. During the on-going coronavirus outbreak, one of the major priorities for researchers is to discover effective solution. As important parts of the face are obscured, face identification and verification becomes exceedingly difficult. The suggested method is a transfer learning using MobileNet V2 based technology that uses deep feature such as feature extraction and deep learning model, to identify the problem of face masked identification. In the first stage, we are applying face mask detector to identify the face mask. Then, the proposed approach is applying to the datasets from Canadian Institute for Advanced Research10 (CIFAR10), Modified National Institute of Standards and Technology Database (MNIST), Real World Masked Face Recognition Database (RMFRD), and Stimulated Masked Face Recognition Database (SMFRD). The proposed model is achieving recognition accuracy 99.82% with proposed dataset. This article employs the four pre-programmed models VGG16, VGG19, ResNet50 and ResNet101. To extract the deep features of faces with VGG16 is achieving 99.30% accuracy, VGG19 is achieving 99.54% accuracy, ResNet50 is achieving 78.70% accuracy and ResNet101 is achieving 98.64% accuracy with own dataset. The comparative analysis shows, that our proposed model performs better result in all four previous existing models. The fundamental contribution of this study is to monitor with face mask and without face mask to decreases the pace of corona virus and to detect persons using wearing face masks. © 2023 CRL Publishing. All rights reserved.
ABSTRACT
Xuebijing (XBJ) Injection is a reputable patent Chinese medicine widely used to cure sepsis, among the Chinese ″Three Medicines and Three Prescriptions″ solution to fight against COVID-19. We were aimed to achieve the comprehensive multicomponent characterization from the single drugs to traditional Chinese medicine (TCM) formula, by integrating powerful data acquisition and the in-house MS2 spectral database searching. By ultra-high performance liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry (UHPLC/IM-QTOF-MS), a hybrid scan approach (HDMSE-HDDDA) was developed, while the HDMSE data for five component drugs and 56 reference compounds were acquired and processed to establish an in-house MS2 spectral database of XBJ. Good resolution of the XBJ components was accomplished on a Zorbax Eclipse Plus C18 column within 24 min, while a fit-for-purpose HDMSE-HDDDA approach was elaborated in two ionization modes for enhanced MS2 data acquisition. XBJ MS2 spectral library was thus established on the UNIFITM platform involving rich structure-related information for the chemicals from five component drugs. We could identify or tentatively characterize 294 components from XBJ, involving 81 flavonoids, 51 terpenoids, 42 phthalides, 40 organic acids, 13 phenylpropanoids, seven phenanthrenequinones, six alkaloids, and 54 others. In contrast to the application of conventional MS1 library, this newly established strategy could demonstrate superiority in the accuracy of identification results and the characterization of isomers, due to the more restricted filtering/matching criteria. Conclusively, the integration of the HDMSE-HDDDA hybrid scan approach and the in-house MS2 spectral database can favor the efficient and more reliable multicomponent characterization from single drugs to the TCM formula. © 2022 The Author(s)
ABSTRACT
The COVID-19 virus has been spreading at an alarming rate causing life-threatening conditions in many human beings. Since vaccines to prevent this disease have been allowed for public usage, it has become extremely important to quickly immunize people to prevent fatalities, which subsequently implies the necessity of an efficient vaccine supply system. In any supply system, technology can enable the transfer and processing of large amounts of data in a quick and secure manner, for all entities involved in the process. It is useful for planning, execution, and analysis. It is helpful for tracking and real-time updates so that the journey of a commodity to be supplied is known to all entities at any given time, and this can be useful to catch any faults or for improving the process. The vaccines often need to be supplied over long distances and thus, there is an evident need to have a database system to model the supply of these vaccines effectively. For this, relational databases have been used for a long time to create a structured and well-defined model. However, when it comes to efficiency and flexibility, modern technology like graph databases can be a better fit while still keeping the structure of data in mind. In this paper, we propose a graph database system for the supply of COVID-19 vaccines and describe its advantages when compared to a traditional relational database system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Due to the global epidemic situation of the Coronavirus Disease 2019 (Covid-19), in addition to serving patients with suspected symptoms and sickness from COVID-19, the hospital also provides services to patients outside requiring a lot of treatment causing a large number of queues in patients. It takes a long time to wait to see the doctor. The researcher therefore developed a teleconsultation platform. Hence, that patients can talk or seek advice from a doctor without the need to go to the hospital, allow patients to schedule appointments to see a doctor. Also, the patient can talk to the doctor via video calling developed in the system. Moreover, doctors can dispense medicines to patients by mail. To increase the efficiency of the system more and to support a wide range of applications, any devices, real-Time data updates, appointment notification via chatbot using Cloud Firestore and Realtime Databases, a NoSQL database, and study the performance gained. The results obtained from the test were satisfactory, with an average tracing server response of 107 ms + 0.14%, and an average handling latency in Thailand at 108 ms. © 2022 IEEE.
ABSTRACT
Chronic heart failure, pulmonary hypertension, acute respiratory distress syndrome (ARDS), coronavirus disease (COVID), and kidney failure are leading causes of death in the U.S. and across the globe. The cornerstone for managing these diseases is assessing patients’volume fluid status in lungs. Available methods for measuring fluid accumulation in lungs are either expensive and invasive, thus unsuitable for continuous monitoring, or inaccurate and unreliable. With the recent COVID-19 epidemic, the development of a non-invasive, affordable, and accurate method for assessing lung water content in patients became utmost priority for controlling these widespread respiratory related diseases. In this paper, we propose a novel approach for non-invasive assessment of lung water content in patients. The assessment includes quantitative baseline assessment of fluid accumulation in lungs (normal, moderate edema, edema), as well as continuous monitoring of changes in lung water content. The proposed method is based on using a pair of chest patch radio frequency (RF) sensors and measuring the scattering parameters (S-parameters) of a 915-MHz signal transmitted into the body. To conduct an extensive computational study and validate our results, we utilize a National Institute of Health (NIH) database of computerized tomography (CT) scans of lungs in a diverse population of patients. An automatic workflow is proposed to convert CT scan images to three-dimensional lung objects in High-Frequency Simulation Software and obtain the S-parameters of the lungs at different water levels. Then a personalized machine learning model is developed to assess lung water status based on patient attributes and S-parameter measurements. Decision trees are chosen as our models for the superior accuracy and interpretability. Important patient attributes are identified for lung water assessment. A “cluster-then-predict”approach is adopted, where we cluster the patients based on their ages and fat thickness and train a decision tree for each cluster, resulting in simpler and more interpretable decision trees with improved accuracy. The developed machine learning models achieve areas under the receiver operating characteristic curve of 0.719 and 0.756 for 115 male and 119 female patients, respectively. These results suggest that the proposed “Chest Patch”RF sensors and machine learning models present a promising approach for non-invasive monitoring of patients with respiratory diseases. Author
ABSTRACT
Background:The emergence of novel coronavirus pneumonia has seriously affected people's normal life and health. Cold-dampness epidemic prescription has a good effect in the prevention and treatment of novel coronavirus pneumonia. Methods:TCMSP, PubChem, Swiss Target Prediction, PharmMapper database and related literatures were used to retrieve and predict the main chemical components and corresponding targets of Traditional Chinese Medicine (TCM)TCM. GeneCard, OMIM, NCBI and TTD databases were used to collect disease targets. Uniprot disease database was used to standardize target names. Cytoscape3.8.2 software was used to establish the 'active components-action target' network. Protein interaction (PPI) network was established by using protein interaction database (STRING), and core genes were screened by CytoNCA plug-in of Cytoscape3.8.2 software.GO enrichment analysis and KEGG pathway enrichment analysis were carried out through DAVID network database, and Hiplot network platform was used for visualization. Molecular docking technology was used to verify the docking between core components and targets. Results:After preliminary screening, 102 effective components, 255 potential targets and 2230 COVID-19 disease targets were obtained, and it was speculated that the mechanism might be related to 177 pathways such as TNF signaling pathway, IL-17 signaling pathway and AGE-RAGE signaling pathway in diabetic complications. The absolute values of docking binding energy between active components such as quercetin, luteolin and wogonin and targets such as PTGS2, AR, TP53 and CASP3 were greater than 5.0 Kcal/mol, and the docking results were good. Conclusion:Cold-dampness epidemic prescription has the characteristics of multiple components, multiple targets and multiple pathways in the prevention and treatment of COVID-19, and may play a therapeutic role through anti-inflammatory, antiviral and immune regulation. © 2022 IEEE.
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At the end of 2019,a new coronavirus suddenly broke out all over the world.To date, there is still no targeted medicine available for the treatment of this disease. Vaccineis essential for controlling the epidemicofSARS-CoV-2. But the effective ofvaccine was reduced because of the SARS-CoV-2constant mutation. It is gratifying that scientistuncover theinfection mechanisms of the SARS-CoV-2. The main protease of SARS-CoV-2 is highly conserved and plays an important role of the life cycle of virus. Therefore, we executed virtual screening on the FDA-approved database and hoped to find a potential candidate against the main protease. As a result, we obtained eight available active compounds derived from the database through molecular dynamics simulations. As antiviral treatment candidates, the drugs can also be used to clinical emergencies. © 2022 SPIE. All rights reserved.
ABSTRACT
Patients commonly visit to hospitals for monitoring and treatment of their chronic diseases especially in COVID-19 epidemic ultimately increases the patients and hospitals' burden. The foremost advancement with respect to examining a patient's critical condition in such pandemic is remote patient monitoring and providing treatment via telemedicine. The main objective of this paper is to provide a novel Internet of Things (IoT) based system for continuous remote monitoring of patients' location, health statistics related to COVID-19 infection, telemedication and maintaining E-health record database. The system monitors oxygen levels and heart-rate signals using MAX30100 (Heart Rate and Pulse Oximeter sensor) and temperature via LM-35 module interfaced with the ESP8266 WI-FI module for web-monitoring. The healthcare sector involving a web server database controlled by cPanel will be used by consultants to have patients' data remotely for telemedicine. Besides, the database is also used as an electronic health record for hospital management system to maintain E-files and history of patients' complications. Moreover, the device monitors the real time location of infected patients using GPS and alerts the medical officials if the patients breach the quarantine norms. The real time location of infected patients also enables the medical authorities to investigate about the total number of COVID-19 cases in any particular area. However, the android application is developed for patients' family/relatives so that they can also monitor the patient condition to take the necessary actions before the worst condition arises. The developed system is efficient in providing integrated services to assist healthcare officials, minimize cost, maintains security and upgrade disease diagnosis speed in less time. © 2022 IEEE.
ABSTRACT
The world immediately studied Coronavirus Disease 2019 (COVID-19) and raced towards finding the cure and developing an effective treatment. An automated approach is needed to discover drug candidates and provide those data to facilitate clinical trials in saving time and only focusing on the candidates which potentially become the cure for COVID-19. We propose the Drug Candidates for the Prevention of COVID-19 (DCPC) Database. DCPC Database provides a list of candidates of potential drugs for the prevention of COVID-19 based on disease-drug associations which are automatically discovered from biomedical literature. DCPC database is an integrative structural database, which involves a chemical database repository, such as PubChem and DrugBank to ensure that drug compound candidates have a standard representation of compounds. The database provides keyword-chosen categories and a determination of minimum supported articles for search, a list of drug candidates in the sorted table followed by the detail for each candidate, and a download feature. The keyword category consists of three keywords, they are Chinese herbal compounds, Indian medicinal plants/and Indian medicinal plants & diabetic treatment herbs. Each candidate links to an article in the biomedical literature and to a page of the compound structure visualization. DCPC is freely available at https://dcpc.brin.go.id/dcpc/. © 2022 IEEE.
ABSTRACT
The COVID-19 virus has been spreading at an alarming rate causing life-threatening conditions in many human beings. Since vaccines to prevent this disease have been allowed for public usage, it has become extremely important to quickly immunize people to prevent fatalities, which subsequently implies the necessity of an efficient vaccine supply system. In any supply system, technology can enable the transfer and processing of large amounts of data in a quick and secure manner, for all entities involved in the process. It is useful for planning, execution, and analysis. It is helpful for tracking and real-time updates so that the journey of a commodity to be supplied is known to all entities at any given time, and this can be useful to catch any faults or for improving the process. The vaccines often need to be supplied over long distances and thus, there is an evident need to have a database system to model the supply of these vaccines effectively. For this, relational databases have been used for a long time to create a structured and well-defined model. However, when it comes to efficiency and flexibility, modern technology like graph databases can be a better fit while still keeping the structure of data in mind. In this paper, we propose a graph database system for the supply of COVID-19 vaccines and describe its advantages when compared to a traditional relational database system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
As more and more people begin to wear masks due to current COVID-19 pandemic, existing face recognition systems may encounter severe performance degradation when recognizing masked faces. To figure out the impact of masks on face recognition model, we build a simple but effective tool to generate masked faces from unmasked faces automatically, and construct a new database called Masked LFW (MLFW) based on Cross-Age LFW (CALFW) database. The mask on the masked face generated by our method has good visual consistency with the original face. Moreover, we collect various mask templates, covering most of the common styles appeared in the daily life, to achieve diverse generation effects. Considering realistic scenarios, we design three kinds of combinations of face pairs. The recognition accuracy of SOTA models declines 5%–16% on MLFW database compared with the accuracy on the original images. MLFW database can be viewed and downloaded at http://whdeng.cn/mlfw. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.