ABSTRACT
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.
ABSTRACT
During the COVID-19 pandemic, point-of-care genetic testing (POCT) devices were used for on-time and on-site detection of the virus, which helped to prevent and control the spread of the pandemic. Smartphones, which are widely used electronic devices with many functions, have the potential to be used as a molecular diagnostic platform for universal healthcare monitoring. Several integrated diagnostics platforms for the real-time and end-point detection of COVID-19 were developed using the functions of smartphones, such as the operating system, power, sound, camera, data storage, and display. These platforms use the 5V output power of smartphones, which can be amplified to power a micro-capillary electrophoresis system or a thin-film heater, and the CMOS camera of smartphones can capture the color change during a colorimetric loop-mediated isothermal amplification test and detect fluorescence signals. Smartphones can also be used with self-written web-based apps to enable automatic and remote pathogen analysis on POCT platforms. Our lab developed a handheld micro-capillary electrophoresis device for end-point detection of SARS-CoV-2, as well as an integrated smartphone-based genetic analyzer for the qualitative and quantitative colorimetric detection of foodborne pathogens with the help of a custom mobile app. © 2023 SPIE.
ABSTRACT
A horrifying number of people died because of the COVID-19 pandemic. There was an unexpected threat to food systems, public health, and the workplace. The pandemic has severely disturbed society and there was a serious impediment to the economy. The world went through an unprecedented state of chaos during this period. To avoid anything similar, we can only be cautious. The project aims to develop a web application for the preliminary detection of COVID-19 using Artificial Intelligence(AI). This project would enable faster coordination, secured data storage, and normalized statistics. First, the available chest X-ray datasets were collected and classified as Covid, Non-Covid, and Normal. Then they were trained using various state-of-the-art pre-trained Convolutional Neural Network (CNN) models with the help of Tensor-flow. Further, they were ranked based on their accuracy. The best-performing models were ensembled into a single model to improve the performance. The model with the highest accuracy was transformed into an application programming interface (API) and integrated with the Decentralized application (D-App). The user needs to upload an image of their chest X-ray, and the D-App then suggests if they should take a reverse transcription-polymerase chain reaction (RT-PCR) test for confirmation. © 2022 IEEE.
ABSTRACT
This unprecedented time of the COVID-19 outbreak challenged the status-quo whether it is on business operation, political leadership, scientific capability, engineering implementation, data analysis, and strategic thinking, in terms of resiliency, agility, and innovativeness. Due to some identified constraints, while addressing the issue of global health, human ingenuity has proven again that in times of crisis, it is our best asset. Constraints like limited testing capacity and lack of real-time information regarding the spread of the virus, are the highest priority in the mitigation process, aside from the development of vaccines and the pushing through of vaccination programs. Using the available Chest X-Ray Images dataset and an AI-Computer Vision Technique called Convolutional Neural Network, features of the images were extracted and classified as COVID-19 positive or not. This paper proposes the usage of the 18-layer Residual Neural Network (ResNet-18) as an architecture instead of other ResNet with a higher number of layers. The researcher achieves the highest validation accuracy of 99.26%. Moving forward, using this lower number of layers in training a model classifier, resolves the issue of device constraints such as storage capacity and computing resources while still assuring highly accurate outputs. © 2022 IEEE.
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Data visualizations are vital to scientific communication on critical issues such as public health, climate change, and socioeconomic policy. They are often designed not just to inform, but to persuade people to make consequential decisions (e.g., to get vaccinated). Are such visualizations persuasive, especially when audiences have beliefs and attitudes that the data contradict? In this paper we examine the impact of existing attitudes (e.g., positive or negative attitudes toward COVID-19 vaccination) on changes in beliefs about statistical correlations when viewing scatterplot visualizations with different representations of statistical uncertainty. We find that strong prior attitudes are associated with smaller belief changes when presented with data that contradicts existing views, and that visual uncertainty representations may amplify this effect. Finally, even when participants' beliefs about correlations shifted their attitudes remained unchanged, highlighting the need for further research on whether data visualizations can drive longer-term changes in views and behavior. © 2023 ACM.
ABSTRACT
The Data Logger (DL) is a unique tool created to carry out the typical duty of gathering data in a specific area. This common task can include measuring humidity, temperature, pressure or any other physical quantities. Due to the current pandemic situation, its use in temperature monitoring of Covid vaccine will be crucial. According to World Health Organization (WHO) guidelines, COVID vaccine can be stored and transported at -80 °C, -20°C and +2-8°C and shelf life is reduced as vaccine is transferred from one storage temperature to another. So cost effective, efficient and standalone Data Logger (DL) is the need of the hour. The Data logger is proposed to be developed with the use of ESP8266 Node MCU microcontroller. It takes power from a 5V Battery. DS18B20 sensor will be used for temperature sensing. Here we will use Wi-Fi module of ESP8266 Node MCU to send the temperature data from sensor to the Google Sheet over the internet. This real time data will be stored in the format of time and month/date/year. Data logged in Google Sheet will be displayed to the user with the help of graphical user interface (GUI) which is developed using PYTHON scripting language. GUI will allow user to interact with Data Logger through visual graphs. The Data Logger components are mounted on a double layered PCB. © 2022 IEEE.
ABSTRACT
2020-2022 provided nearly ideal circumstances for cybercriminals, with confusion and uncertainty dominating the planet due to COVID-19. Our way of life was altered by the COVID-19 pandemic, which also sparked a widespread shift to digital media. However, this change also increased people's susceptibility to cybercrime. As a result, taking advantage of the COVID-19 events' exceedingly unusual circumstances, cybercriminals launched widespread Phishing, Identity theft, Spyware, Trojan-horse, and Ransomware attacks. Attackers choose their victims with the intention of stealing their information, money, or both. Therefore, if we wish to safeguard people from these frauds at a time when millions have already fallen into poverty and the remaining are trying to survive, it is imperative that we put an end to these attacks and assailants. This manuscript proposes an intelligence system for identifying ransomware attacks using nature-inspired and machine-learning algorithms. To classify the network traffic in less time and with enhanced accuracy, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), two widely used algorithms are coupled in the proposed approach for Feature Selection (FS). Random Forest (RF) approach is used for classification. The system's effectiveness is assessed using the latest ransomware-oriented dataset of CIC-MalMem-2022. The performance is evaluated in terms of accuracy, model building, and testing time and it is found that the proposed method is a suitable solution to detect ransomware attacks. © 2022 IEEE.
ABSTRACT
An increase in interest in research projects which involves the design of robotic systems that minimizes interactions between humans has been caused by the COVID-19 outbreak, as such technology can greatly benefit healthcare industries in preventing the spread of highly infectious diseases. The utilization of remote-controlled robots in many different fields, especially in the medical field is becoming more and more necessary. However, mobile robots are susceptible to both systematic and nonsystematic errors that cause deviations in its trajectory. In view thereof, the researchers explored the possibility of minimizing the trajectory errors through speed calibration. The differential drive robot was navigated to finish a five-meter linear path of forward and backward motion. The test was conducted with a default linear speed of 0.5 m/s in which a high trajectory error was observed. Upon changing the speed of the robot, the same trajectory test was conducted at four additional different speeds, namely;0.25 m/s, 0.35 m/s, 0.65m/s and 0.75 m/s. With the gathered data, the researchers conducted a linear least-squares regression model using MATLAB wherein there is only one predictor variable (speed of the robot) and one response variable (deviation). Based on the results, the researchers concluded that the speed of 0.35 m/s is the optimal speed in which the trajectory error of the robot is minimal. The researchers recommend improving the design of the caster wheels to minimize the effects of nonsystematic errors. © 2022 IEEE.
ABSTRACT
From the beginning of 2020, we saw a rise of a new virus called the Coronavirus and ultimately a pandemic that anyone reading this paper must have been through. With the rise of COVID,many vaccines were found, the global vaccination drive as a result of this naturally fueled a possibility of Pro-Vaxxers and Anti-Vaxxers strongly expressing their support and concerns regarding the vaccines on social media platforms and along with this came up the need of quick identification of people who are experiencing COVID-19 symptoms. So in this paper, an effort has been made to facilitate the understanding of all these complications and help the concerned authorities. With the help of data in the form of Covid-19 tweets, a (machine-learning) classifier has been built which can classify users as per their vaccine related stance and also classify users who have reported their symptoms through tweets. © FIRE 2022: Forum for Information Retrieval Evaluation.
ABSTRACT
Globalization, technological innovations, and the coronavirus disease (COVID-19) pandemic have promoted disruptive changes in buying and selling negotiation models through e-communication. As a result, Small and Medium Enterprises (SMEs) have been forced to adapt to online channels. Considering market relevance, this article describes the survey results with 11 SMEs regarding their adherence to digital media. Moreover, a case study of a selected company demonstrated barriers and propulsions to digital adequacy. The aim was to promote SMEs' competitiveness through technology transfer, focusing on e-communication and strategic digital planning. The results show that the insertion of technology through digital media depends on the knowledge of the tools used in this medium. Therefore, despite being ready to use, SMEs have not yet fully leveraged digital media. Organizational barriers, such as lack of time for those responsible, lack of training and knowledge, and strategic planning, were observed. However, environmental factors such as competitive pressure and innovation-related policies are positive for insertion. Thus, there is room for companies to invest in digital strategic planning focused on improving sales, customer relations, and competitiveness. © 2022 IEEE.
ABSTRACT
In today's world, digital technologies are advancing at a rapid pace. Almost every industry has benefited from this ongoing change. In the health sector, the digitization of medical records was proposed decades ago. Whereas some developed countries have successfully adopted and implemented Electronic Health Record (EHR) systems. Developing countries like India still heavily rely on paper-based medical records. Although there are a number of systems for electronic medical record management, they have issues related to interoperability, timely access, and storage. Due to poor infrastructure and design, the current systems are not robust for communicating and tracking medical records. The need for a better EHR system was highly emphasized during the COVID-19 pandemic. The two major shortcomings of the existing system are a lack of interoperability, which causes delays in sharing the information, and a lack of standardization, due to which the data quality of the data that is shared suffers. To mitigate these issues, we need a nationwide EHR system. Another issue is the lack of a ubiquitous UPI (Unique Patient Identifier). In a country like India, the second most populated country in the world, Aadhar is the best option for UPI, which can be used for creating a national EHR system. In this paper, we have presented a framework for a standardized, interoperable, and unified EHR system based on blockchain technology with Aadhar as the UPI. Using blockchain as the base of this model provides numerous advantages over a cloud-based system, like decentralization, better security, immutability, and traceability. © 2023 IEEE.
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Recent research trends in the field image processing have focussed on challenges and few techniques for processing and classification tasks related to it. Image classification aims at classifying images based on several predefined categories. Several research works have been carried out to overcome shortcomings in image classification, nevertheless the output was restricted to the elementary low-level picture. Several deep neural network techniques are employed for image classification such as Convolutional Neural Network, Machine Learning Algorithms like Random Forest, SVM, etc. In this paper, we aim at designing a COVID-19 detection using the CNN model with support of Open-Source software such as Keras, Python, Google Colab, Google Drive, Kaggle, and Visual Studio for aggregate, design, create, train, visualize, and analyze bulk load of data on the cloud after programing a Deep neural network without a need for high-end processing hardware. We have made use of weights to test and analyse the accuracy, visualize and predict the condition of a lung using chest X-Rays at certain accuracy. This will help in identifying the problems of the patients at a faster rate, thus giving an appropriate treatment at an early stage itself to saving one life. © 2023 IEEE.
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The pandemic is seriously affecting individuals' wellbeing, occupations, economies, and practices. This pandemic has shaken the world dramatically and framed a moment to think about the future, incorporating our relationship with nature. Since the COVID-19 pandemic started, it's been relied upon to drive remarkable development in telehealth, especially for demonstrative patients, to stay at home and talk with specialists through virtual stations, helping with diminishing the spread of the disease to mass and the clinical staff on the ground zero. The novel coronavirus epidemic has changed our way of living, society, and human services framework. This study proposed the application of artificial intelligence to make its classification. The outcomes of the proposed systems are equated with pre-existing algorithms to highlight the benefits of test time minimization and classification error. Furthermore, this study tries to analyse corona time series data on the level of classification and found that the decision tree algorithm gives the best accuracy of approx. 100% with zero error and zero standard deviation with 7098 milliseconds. © 2022 IEEE.
ABSTRACT
There has been an increase of interest and demand in the usage of logistic indoor service robots that are designed to minimize interactions between humans due to the occurrence of the COVID-19 outbreak. The application of the rising technology in the medical sector has great benefits in the industry, such as the prevention of the spread of highly infectious diseases using distance as a factor. Rooting from the purpose of the said robot, the main focus should be the ease of navigation through achieving the desired trajectory, in order to maximize the functionality, prevent collision, reduce user maneuvering difficulties, and such. Hence, this paper is focused on improving the trajectory errors on the robot navigation performance based on different control system designs specifically, a physical joystick controller and a mobile-based Bluetooth application controller. The design of the joystick is based on a pivot as its base which is directed to all angles and the design of the Bluetooth app is based on fourdirectional buttons that will operate upon clicking, and switching to other buttons to change commands. With this, the researchers conducted linear path and rotational tests using both remote control modes that are based on five varying speed values of 0.75 m/s, 0.5m/s, 0.35m/s, 0.25m/s, and 0.15 m/s. Based on the data analysis, the yielded results showed that using the Bluetooth app lowers the robot's trajectory error by 50% to 60% compared to using ajoystick to navigate to the desired point. Thus, the researchers concluded that the design of a control system greatly affects the robot navigation in achieving the desired trajectory. Considering the nonsystematic errors, a calibration based on the hardware structure design specifically on the caster wheel is recommended. © 2023 IEEE.
ABSTRACT
A multi-phase investigation was conducted to question potentials within music therapy of a new electrorganic frame drum musical instrument from Japan titled the ‘aFrame'. Two professional music therapists collaborated in this third phase of testing under the work in progress. One of the two music therapists tested the aFrame within numerous sessions with two profoundly disabled clients across generations i.e., an adolescent male and an adult woman. Observations including video recordings as baseline analysis. A goal of the study was to identify strengths and weaknesses of the new instrument in the field of (re)habilitation, especially across spectrums of those with profound dysfunction, special needs situations, and across ages. A goal of the overall work of some four decades, titled SoundScapes, is to achieve an ultimate compendium of tools for human performance to create specific interactive environments to support therapists, caregivers, and for own self-training through engaged and motivated creativity, self-expression, and play. Such environments as created by the first author have been used in his stage performances and installations (e.g., at Museums of Modern Art). The tools are thus considered transdisciplinarity forming a new holistic approach aligned to his six patents. Results from the investigation question the contextual potential of the aFrame due to a typical lack of motoric control aligned to the fragility and expense of the instrument – challenges were evident for those with diminished or lack of physical limb control. To optimize use, add-on footswitches and pedals are recommended with the aFrame instrument. These give added options including remote switching and an audio streaming interface mixer for optimal Online streaming of instrument (and voice) that would have been especially useful during the Coronavirus pandemic so that the music therapists could have continued their interactions with clients remotely (i.e., beyond video conferencing quality). Alternatives to the aFrame are posit and selected from the new generation of instruments and pedals controlling digital media as presented at the end of the text. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
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Our collaboration seeks to demonstrate shared interrogation by exploring the ethics of machine learning benchmarks from a socio-technical management perspective with insight from public health and ethnic studies. Benchmarks, such as ImageNet, are annotated open data sets for training algorithms. The COVID-19 pandemic reinforced the practical need for ethical information infrastructures to analyze digital and social media, especially related to medicine and race. Social media analysis that obscures Black teen mental health and ignores anti-Asian hate fails as information infrastructure. Despite inadequately handling non-dominant voices, machine learning benchmarks are the basis for analysis in operational systems. Turning to the management literature, we interrogate cross-cutting problems of benchmarks through the lens of coupling, or mutual interdependence between people, technologies, and environments. Uncoupling inequality from machine learning benchmarks may require conceptualizing the social dependencies that build structural barriers to inclusion. © 2022 IEEE Computer Society. All rights reserved.
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Social networks have had a significant impact on people's personal and professional life all around the world. Since the COVID-19 pandemic has boosted the use of digital media among people, fake news and reviews have had a stronger impact on society in recent years. This study demonstrates how the stiffness index may be used to model the spread of fake news in Indian states. We demonstrate that the speed at which fake news circulates through online social networks increases with a stiffness index. We conducted a stiffness analysis for all Indian states to assess the spread of fake information in each Indian state. The stiffness analysis of the conventional SIR model, one of the widely used approaches to describe the propagation of rumors in social networks, serves as an explanation and illustration of our proposition. The rise in fake news in our society is also justified by a comparison of the stiffness index for India before and after the COVID-19 outbreak. The study provides governments and policymakers with a more comprehensive understanding of the value of early intervention to combat the spread of false information via digital media. © 2023 IEEE.
ABSTRACT
The present study aims at understanding and analyzing the COVID-19-induced behavioral change spurting artificial intelligence (AI) adoption in Indian banking industry. The study has further identified and analyzed the usage pattern of Indian customers for mobile banking/online banking services in the pre-pandemic phase and progression of Indian customers for mobile banking/online banking services during the pandemic. Secondary data has been used for deep understanding of the AI adoption in Indian banking industry, with reports from McKinsey, PWC, RBI, NPCI, BIS, etc., to form the base. The period of study was taken from 2016 to 20, and this was taken keeping in mind the timing of another unprecedented event of demonetization. Behavioral change of Indian banking industry customer was assessed on three broad parameters change in value and volume of mobile banking transactions on year on year basis. COVID-19-induced behavioral change translating in massive jump of 178% in volume of mobile transactions between March 2019 and 2021. The increase in number of smart phone users and access to connectivity and desired technology has helped the cause. With 2020–21 punctuated by several nationwide as well as localized lockdowns adoption of AI for customer engagement has been crucial for Indian banking industry, which has further translated in to designing and customizing products and risk profiling of customers further resulting in increased operational efficiency and intuitive decision making. The behavioral change induced by COVID-19 in the Indian baking industry achieves competitive advantage by truly responding to huge customer data base which has been utilized by other financial industries as now it can have systems which understand and are responsive to behavior of varied customers. From responses feeded chatbots to intuitively responsive AI bots, the customer engagement is going to be a whole new experience which will help in customer acquisition and retention. Further, with falling data storage costs, increasing processing speeds and capabilities and improved connectivity and access for all has helped the rapid automation and AI adoption. Enterprise level adoption of AI has led to revenue generation and optimization of functional resources this reducing the cost at functional level. The AI adoption has been continuous from the banks over the years though banks have started to harness its potential in the recent years with customers adoption of smart hand-held devices. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.
ABSTRACT
The health emergency derived from the spread of COVID-19 led to the declaration of confinement for the protection of the population. During this temporary period, the educational centers of the Spanish state were forced to suspend face-to-face classes at all educational levels. To safeguard the teaching processes, educational centers and teachers relied on social media to continue their work. The objective of this study is to understand the possibilities and limitations of social media as the only means of communication in the educational and socialization processes of students from the perspective of teachers. The methodology used is based on the collection of data through a questionnaire distributed in the secondary education centers of the autonomous regions of the Basque Country and Navarra. The questionnaire was distributed electronically, respecting the anonymity of the teaching staff and the center in which they practice. The results reveal that the digital media the possibilities and limitations of these media in the teaching processes, showing that some of these are surmountable and others are not. © 2023 Copyright for this paper by its authors.
ABSTRACT
Recently, innovations in the Internet-of-Medical- Things (IoMT), information and communication technologies, and Machine Learning (ML) have enabled smart healthcare. Pooling medical data into a centralised storage system to train a robust ML model, on the other hand, poses privacy, ownership, and regulatory challenges. Federated Learning (FL) overcomes the prior problems with a centralised aggregator server and a shared global model. However, there are two technical challenges: FL members need to be motivated to contribute their time and effort, and the centralised FL server may not accurately aggregate the global model. Therefore, combining the blockchain and FL can overcome these issues and provide high-level security and privacy for smart healthcare in a decentralised fashion. This study integrates two emerging technologies, blockchain and FL, for healthcare. We describe how blockchain-based FL plays a fundamental role in improving competent healthcare, where edge nodes manage the blockchain to avoid a single point of failure, while IoMT devices employ FL to use dispersed clinical data fully. We discuss the benefits and limitations of combining both technologies based on a content analysis approach. We emphasise three main research streams based on a systematic analysis of blockchain-empowered (i) IoMT, (ii) Electronic Health Records (EHR) and Electronic Medical Records (EMR) management, and (iii) digital healthcare systems (internal consortium/secure alerting). In addition, we present a novel conceptual framework of blockchain-enabled FL for the digital healthcare environment. Finally, we highlight the challenges and future directions of combining blockchain and FL for healthcare applications. IEEE