ABSTRACT
In this article, we present a [Formula Omitted]-learning-enabled safe navigation system—S-Nav—that recommends routes in a road network by minimizing traveling through categorically demarcated COVID-19 hotspots. S-Nav takes the source and destination as inputs from the commuters and recommends a safe path for traveling. The S-Nav system dodges hotspots and ensures minimal passage through them in unavoidable situations. This feature of S-Nav reduces the commuter's risk of getting exposed to these contaminated zones and contracting the virus. To achieve this, we formulate the reward function for the reinforcement learning model by imposing zone-based penalties and demonstrate that S-Nav achieves convergence under all conditions. To ensure real-time results, we propose an Internet of Things (IoT)-based architecture by incorporating the cloud and fog computing paradigms. While the cloud is responsible for training on large road networks, the geographically aware fog nodes take the results from the cloud and retrain them based on smaller road networks. Through extensive implementation and experiments, we observe that S-Nav recommends reliable paths in near real time. In contrast to state-of-the-art techniques, S-Nav limits passage through red/orange zones to almost 2% and close to 100% through green zones. However, we observe 18% additional travel distances compared to precarious shortest paths.
ABSTRACT
Recently, as a consequence of the COVID-19 pandemic, dependence on telecommunication for remote learning/working and telemedicine has significantly increased. In this context, preserving high Quality of Service (QoS) and maintaining low-latency communication are of paramount importance. In cellular networks, the incorporation of unmanned aerial vehicles (UAVs) can result in enhanced connectivity for outdoor users due to the high probability of establishing Line of Sight (LoS) links. The UAV's limited battery life and its signal attenuation in indoor areas, however, make it inefficient to manage users' requests in indoor environments. Referred to as the cluster-centric and coded UAV-aided femtocaching (CCUF) framework, the network's coverage in both indoor and outdoor environments increases by considering a two-phase clustering framework for Femto access points (FAPs)' formation and UAVs' deployment. Our first objective is to increase the content diversity. In this context, we propose a coded content placement in a cluster-centric cellular network, which is integrated with the coordinated multipoint (CoMP) approach to mitigate the intercell interference in edge areas. Then, we compute, experimentally, the number of coded contents to be stored in each caching node to increase the cache-hit-ratio, signal-to-interference-plus-noise ratio (SINR), and cache diversity and decrease the users' access delay and cache redundancy for different content popularity profiles. Capitalizing on clustering, our second objective is to assign the best caching node to indoor/outdoor users for managing their requests. In this regard, we define the movement speed of ground users as the decision metric of the transmission scheme for serving outdoor users' requests to avoid frequent handovers between FAPs and increase the battery life of UAVs. Simulation results illustrate that the proposed CCUF implementation increases the cache-hit-ratio, SINR, and cache diversity and decrease the users' access delay, cache redundancy, and UAVs' energy consumption.
ABSTRACT
After release of Web 2.0 in 2004 user spawned contents on the internet eminently in abundant review sites, online forums, online blogs, and many other sites. Entire user generated contents are considerable bunches of unorganized text written in different languages that encompass user emotions about one or more entities. Mainly predictive analysis exerts the existing data to forecast future outcomes. Currently, a massive amount of researches are being engrossed in the area of opinion mining, also called sentiment analysis, opinion extraction, review analysis, subjective analysis, emotion analysis, and mood extraction. It can be an utmost choice whilst perceiving the meaning and patterns in prevailing data. Most of the time, there are various algorithms available to work with polling. There are contradictory opinions among researchers regarding the effectiveness of algorithms. We have compared different opinion mining algorithms and presented the findings in this paper.
ABSTRACT
The article examines the application of e-commerce systems and technologies that have a positive impact on the development of the economy of the post-coronavirus period and the formation of appropriate technical and technological infrastructure for it, as well as promising features and directions of e-commerce. The physical and virtual opportunities created by e-commerce technologies for buyers and sellers are explained. The advantages of e-commerce in the international economic space have been identified. The functions of e-business models in accordance with the commercial stages of enterprises are explained. It was noted that the development of ICT has accelerated the process of transition from traditional commerce to e-commerce, led to the emergence of new global trends in e-commerce. These innovations have raised the issue of the application of modern ICT in the development of e-commerce on the platform of the 4.0 Industrial Revolution. Taking into account these factors, the presented article discusses the application of modern technologies in e-commerce systems, such as 3D modeling, the Internet of Things, artificial intelligence, big data. Features of application and regulation mechanisms of E-commerce systems in real economic sectors, which have a direct stimulating effect on economic growth in Azerbaijan, have been studied. Recommendations were given for the modernization and use of e-commerce systems with the application of the latest ICT technologies.The purpose of the research. The main goal of the scientific research carried out in the article was to develop the scientific-methodological basis for the regulation of the application of e-commerce systems and the study of perspective development problems in the so-called post-coronavirus period after 2020. In the article, attention was paid to the problems of regulation of the application of e-commerce systems and the development of recommendations on increasing the efficiency of prospective development directions.Taking into account the characteristics of the relevant electronic business models, applying them in accordance with the commercial stages of the enterprises' activities and obtaining effective results were among the main goals. Attempts have been made to implement e-commerce systems based on the developing technologies of the Industry 4.0 platform. An attempt was made to solve the issue of using modern ICT in the development of trade processes, which corresponds to the 4.0 Industrial revolution platform. The main stages of application of modern technologies such as 3D modeling, the Internet of Things, artificial intelligence, and Big Data in electronic commerce systems are described.The following are included among the goals of the conducted scientific research: investigation of the application features and regulation mechanisms of e-commerce systems that have a stimulating effect on the economic development of Azerbaijan in real economic sectors, development of recommendations on increasing the efficiency of electronic commerce systems using modern ICT technologies, etc.Research methods used. In the post-coronavirus period, the following research methods were used in the study of the problems of regulation of the application of e-commerce systems and prospective development directions and in the development of their scientific and methodological bases: a systematic analysis, correlation, and regression analysis, mathematical and econometric modeling methods, expert evaluation method, measurement theory, algorithmization, ICT tools, and technologies, etc.Achievements of the author. Achievements of the author. In the so-called post-coronavirus period after 2020, a special approach was taken to the application of e-commerce systems and technologies, which have a positive impact on the development of the economy as an innovative element, and to the study of its prospective development features and directions. By providing scientific support to ensure the effective formation of the digital economy and its sustainability, the researcher offered relev nt recommendations to achieve the solution to some of the goals set before the country. It should be noted that the development of e-commerce systems based on technologies relevant to the Industry 4.0 platform can give a serious impetus to the development of the sustainability of the digital economy.Due to the fact that e-commerce technologies create new additional physical and virtual opportunities for buyers and sellers, the scientific-methodological approaches proposed by the author develop them as a special tool for ensuring the stability of both e-commerce systems and the digital economy in general. The proposals presented will lead to more effective results for the economy to be more cyber resilient through the application of e-commerce systems in the so-called post-coronavirus era. The researcher showed that the effective application of electronic business models in the activities of enterprises can help to achieve effective results. In the development of e-commerce, solutions to the issues of application of 4.0 Industrial technologies such as 3D modeling, Internet of Things, artificial intelligence, and Big Data can be considered as a contribution to the investigation of solutions to existing problems in economic development. For this reason, the means and mechanisms proposed by the author for solving the problems of regulation of the application of e-commerce systems in the post-coronavirus era can be considered one of the main ways to ensure the stability and development of the digital economy.
ABSTRACT
The emergence of the COVID-19 pandemic has increased research outputs in telemedicine over the last couple of years. One solution to the COVID-19 pandemic as revealed in literature is to leverage telemedicine for accessing health care remotely. In this survey paper, we review several articles on eHealth and Telemedicine with emphasis on the articles' focus area, including wireless technologies and architectures in eHealth, communications protocols, Quality of Service, and Experience Standards, among other considerations. In addition, we provide an overview of telemedicine for new readers. This survey reviews several telecommunications technologies currently being proposed along with their standards and challenges. In general, an encompassing survey on the developments in telemedicine technology, standards, and protocols is presented while acquainting researchers with several open issues. Special mention of the state-of-the-art specialist application areas are presented. We conclude the survey paper by presenting important research challenges and potential future directions as they pertain to telemedicine technology.
ABSTRACT
The recent covid-19 pandemic created a barrier to every activity that needed physical interaction and involvement, especially in the judiciary. Careful research of some courts in Nigeria shown that case records are still been manually processed and stored and some courts operate a semi-digital and semi-manual processing pattern, which also has its own shortcoming of preprocessing manual records and converting them into digital records and physical presence is required to access court records. This research develops a secure electronic Cybercrime Cases Database System (eCCDBS), for prosecuted cybercrime in the judicial service in Nigeria. The system will provide an efficient method for collecting, retrieving, preserving, and management of court case records. The Rapid Application Development (RAD) methodology is used for the system development, because of its speed and time friendliness and can be easily restructured to meet the client's requirement at any point in time during the development life span. RAD can also present a prototype of the final system software to the client. Access control mechanism and secure password hashing were used to ensure the security of the system. The system was implemented and evaluated through deployment and found to have functioned according to the specification. The application subunits of records' creation, submission, modification, deletion, retrieval, and storage functioned effectively. Hence this system provides a secure online repository specifically for cybercrime case records that have elements of confidentiality, integrity and availability.
ABSTRACT
In order to control the first wave of COVID-19 pandemic in 2020, many models have shown effectiveness in predicting the spread of new coronary pneumonia and the different interventions. However, few models can collect large amounts of high-quality real-time data faster under the premise of protecting privacy, considering the impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant and the mass vaccination program as a new intervention. Therefore, we developed a mobile intelligent application that can collect a large amount of real-time data while protecting privacy and conducted a feasibility study by defining a new COVID-19 mathematical model SEMCVRD. By simulating different intervention measures, the prediction model of the mobile intelligent application used in this article simulates the epidemic situation in the U.K. as an example. The findings are as below: the optimal intervention strategy is to suppress the intervention at [Formula Omitted] (intervention intensity: the average number of contacts per person per day) before the end of March 2021, then gradually release the intervention intensity at a rate of [Formula Omitted], and finally release the intensity to [Formula Omitted] in June 2021. The COVID-19 pandemic will end at the end of June 2021, when the total number of deaths will reach 128772. This strategy will be able to balance the tradeoff between loss of life and economic loss. Compared with the official statistics released by the U.K. government on May 31, 2021, our model can accurately predict the relative error rate of the total number of cases is less than 6.9%, and the relative error rate of the total number of deaths is less than 1%. Furthermore, the model is also suitable for collecting data from countries/regions around the world.
ABSTRACT
The 28th Asia and South Pacific Design Automation Conference (ASP-DAC 2023) was held at Miraikan, National Museum of Emerging Science and Innovation, Tokyo, Japan, 16 char6319 January 2023. ASP-DAC, started in 1995, is a high-quality and premium conference on Electronic Design Automation (EDA) like other sister conferences such as Design Automation Conference (DAC);Design, Automation Test in Europe (DATE);International Conference on Computer Aided Design (ICCAD);and Embedded Systems Week (ESWEEK). ASP-DAC 2023 adopted an in-person conference style with online features which is the first time in ASP-DAC. Even though the last two ASP-DAC conferences were held as virtual conferences due to the COVID-19 pandemic, ASPDAC 2023 provided opportunities for face-to-face communication not only at sessions, but also at coffee breaks, banquet, and so on for in-person attendees. Online access mainly for participants who were difficult to physically attend was also provided as much as possible.
ABSTRACT
In early 2020, the world was shocked by the outbreak of COVID-19. World Health Organization (WHO) urged people to stay indoors to avoid the risk of infection. Thus, more people started to shop online, significantly increasing the number of e-commerce users. After some time, users noticed that a few irresponsible online retailers misled customers by hiking product prices before and during the sale, then applying huge discounts. Unfortunately, the "discounted” prices were found to be similar or only slightly lower than standard pricing. This problem occurs because users were unable to monitor product pricing due to time restrictions. This study proposes a Web application named PriceCop to help customers' monitor product pricing. PriceCop is a significant application because it offers price prediction features to help users analyse product pricing within the next day;thus, it can help users to plan before making purchases. The price prediction model is developed by using Linear Regression (LR) technique. LR is commonly used to determine outcomes and used as predictors. Least Squares Support Vector Machine (LSSVM) and Artificial Bee Colony (ABC) are used as a comparison to evaluate the accuracy of the LR technique. LSSVM-ABC was initially proposed for stock market price predictions. The results show the accuracy of pricing prediction using LSSVM-ABC is 84%, while it is 62% when LR is employed. ABC is integrated into SVM to optimize the solution and is responsible for the best solution in every iteration. Even though LSSVM-ABC predicts product pricing more accurately than LR, this technique is best trained using at least a year's worth of product prices, and the data is limited for this purpose. In the future, the dataset can be collected daily and trained for accuracy.
ABSTRACT
In December 2019, the Novel Coronavirus became a global epidemic. Because of COVID-19, all ongoing plans had been postponed. Lockdowns were imposed in areas where there was an excessive number of patients. Constantly locking down areas had a significant negative influence on the economy, particularly on developing and underdeveloped countries. But the majority of countries were locking down their areas without making any assumptions where some were successful and some were failures. In this situation, this paper presents a novel approach for determining which parts of a country should be immediately placed under lockdown during any pandemic situation while considering the lockdown history at the time of COVID-19. This work makes use of a self-established dataset containing data from several countries of the world and uses the successful presence of lockdown in that area as the target attribute for machine learning algorithms to determine the areas to keep under lockdown in the future. Here, the Random Forest algorithm has provided the highest accuracy of 92.387% indicating that this model can identify the areas with an impressive level of accuracy to retain under lockdown.
ABSTRACT
The deployment of mobile health (mHealth) apps can transform healthcare in rural and remote communities worldwide. Rural communities in Zimbabwe have limited access to information that affects their health, economic and social being due to structural and social barriers related to the inaccessibility of traditional media. mhealth apps are a valuable tool to monitor disease outbreaks and provide preventative information to the public. Lack of access to COVID-19 information results in high fatalities and public panic, and it is critical to publish reliable and timely information. The study's objective was to demonstrate the utility of a mHealth app prototype developed to enhance access to COVID-19 information in rural and remote communities in Zimbabwe. The prototype provides COVID-19 information such as statistics, preventative measures, self-diagnostics, social distancing information, and general hygiene to rural communities with limited access to official information channels on the pandemic. A design science research methodology was used to design, build and evaluate the COVID-19 mHealth app and fulfil the study's objectives. Thirty potential users participated in the evaluation of the prototype. The evaluation results show that potential users perceived that the prototype was useful, engaging, easy to learn, well designed, and provided relevant information. A strong correlation was observed between the design, engagement, functionality, and learnability. More widespread usability and more representative tests should be conducted to ascertain the efficacy and usability of the app. The study contributes literature on usability studies in developing countries. As more mHealth apps are being developed and deployed, more usability tests will be required to ensure that they are fit for purpose. The paper provides a baseline for developing related health information apps. Policymakers, health practitioners, technologists, and scholars can further investigate the deployment of digital technologies to improve healthcare and control the transmission and spread of COVID-19.
ABSTRACT
COVID-19 pandemic has changed the lifestyle of all aspects of life. These circumstances have created new patterns in lifestyle that people had to deal with. As such, full and direct dependence on the use of the unsafe Internet network in running all aspects of life. As example, many organizations started officially working through the Internet, students moved to e-education, online shopping increased, and more. These conditions have created a fertile environment for cybercriminals to grow their activity and exploit the pressures that affected human psychology to increase their attack success. The purpose of this paper is to analyze the data collected from global online fraud and cybersecurity service companies to demonstrate on how cybercrimes increased during the COVID-19 epidemic. The significance and value of this research is to highlight by evident on how criminals exploit crisis, and for the need to develop strategies and to enhance user awareness for better detection and prevention of future cybercrimes.
ABSTRACT
Until today, Information Technology (IT) has been felt by aviation industry showed by positive growth of operating revenue before Covid-19 pandemic. The pandemic of Covid-19 changes the world especially the aviation industry by slowing down the business transaction. This study presents statistical model on recent e-commerce revenue of aviation, the number of passengers and the IT investments then predicts future of e-commerce revenue, the number of passengers and the IT spending using Neural Networks. This method is useful to predict the future because it follows the time being. The chosen variables are intended whether IT has an impact during the pandemic for passenger generation year by year. The results show that for the next few years, the revenue, the number of passengers and the IT spending are significantly increasing, while there are problems faced in aviation industry because of Covid-19. This model also can be applied for other industry.
ABSTRACT
Since the last 5 years, digital economy is growing steadily in Indonesia. Right now, the digital economy faces some potential problems and Covid-19 pandemic. This paper presents current data of the national Gross Domestic Product (GDP) and other GDPs (billion IDR) and the number of start-up, and predicts near some categories of future GDP and numbers of available new start-up for the next few years. The forecast will use Markov chain analysis. The results indicate that, while there are problems faced by the digital economy industry, the GDP and numbers of start-up are significantly increasing.
ABSTRACT
Privacy in Electronic Health Records (EHR) has become a significant concern in today's rapidly changing world, particularly for personal and sensitive user data. The sheer volume and sensitive nature of patient records require healthcare providers to exercise an intense quantity of caution during EHR implementation. In recent years, various healthcare providers have been hit by ransomware and distributed denial of service attacks, halting many emergency services during COVID-19. Personal data breaches are becoming more common day by day, and privacy concerns are often raised when sharing data across a network, mainly due to transparency and security issues. To tackle this problem, various researchers have proposed privacy-preserving solutions for EHR. However, most solutions do not extensively use Privacy by Design (PbD) mechanisms, distributed data storage and sharing when designing their frameworks, which is the emphasis of this study. To design a framework for Privacy by Design in Electronic Health Records (PbDinEHR) that can preserve the privacy of patients during data collection, storage, access and sharing, we have analysed the fundamental principles of privacy by design and privacy design strategies, and the compatibility of our proposed healthcare principles with Privacy Impact Assessment (PIA), Australian Privacy Principles (APPs) and General Data Protection Regulation (GDPR). To demonstrate the proposed framework, ‘PbDinEHR', we have implemented a Patient Record Management System (PRMS) to create interfaces for patients and healthcare providers. In addition, to provide transparency and security for sharing patients' medical files with various healthcare providers, we have implemented a distributed file system and two permission blockchain networks using the InterPlanetary File System (IPFS) and Ethereum blockchain. This allows us to expand the proposed privacy by design mechanisms in the future to enable healthcare providers, patients, imaging labs and others to share patient-centric data in a transparent manner. The developed framework has been tested and evaluated to ensure user performance, effectiveness, and security. The complete solution is expected to provide progressive resistance in the face of continuous data breaches in the patient information domain.
ABSTRACT
In the current pandemic, global issues have caused health issues as well as economic downturns. At the beginning of every novel virus outbreak, lockdown is the best possible weapon to reduce the virus spread and save human life as the medical diagnosis followed by treatment and clinical approval takes significant time. The proposed COUNTERSAVIOR system aims at an Artificial Intelligence of Medical Things (AIoMT), and an edge line computing enabled and Big data analytics supported tracing and tracking approach that consumes global positioning system (GPS) spatiotemporal data. COUNTERSAVIOR will be a better scientific tool to handle any virus outbreak. The proposed research discovers the prospect of applying an individual's mobility to label mobility streams and forecast a virus such as COVID-19 pandemic transmission. The proposed system is the extension of the previously proposed COUNTERACT system. The proposed system can also identify the alternative saviour path concerning the confirmed subject's cross-path using GPS data to avoid the possibility of infections. In the undertaken study, dynamic meta direct and indirect transmission, meta behavior, and meta transmission saviour models are presented. In conducted experiments, the machine learning and deep learning methodologies have been used with the recorded historical location data for forecasting the behavior patterns of confirmed and suspected individuals and a robust comparative analysis is also presented. The proposed system produces a report specifying people that have been exposed to the virus and notifying users about available pandemic saviour paths. In the end, we have represented 3-D tracker movements of individuals, 3-D contact analysis of COVID-19 and suspected individuals for 24 h, forecasting and risk classification of COVID-19, suspected and safe individuals.
ABSTRACT
Polysemy is a constant issue in biomedical terms which also affects its QA system. In our work, we consider polysemous words as weak aspect in biomedical question classification and propose two vector model-based solutions that determine the class-specific features of biomedical terms. In first approach, label independent class vector and general word vector are combined using linear compositionality property of vector to generate multiple class-specific embeddings of words. Second is the feature fusion approach, which combines the class-specific sense vector of a word with vectors generated in the first approach. Besides this, a survey dataset COVID-S is introduced in this paper, which is a collection of public queries, myths, and doubts about novel COVID-19 diseases. The series of experiments are performed on two biomedical questions datasets, BioASQ8b and COVID-S, and the results of comparisons with other state-of-arts prove its integrity using SVM and naïve Bayes.
ABSTRACT
Recently, as a consequence of the coronavirus disease (COVID-19) pandemic, dependence on contact tracing (CT) models has significantly increased to prevent the spread of this highly contagious virus and be prepared for the potential future ones. Since the spreading probability of the novel coronavirus in indoor environments is much higher than that of the outdoors, there is an urgent and unmet quest to develop/design efficient, autonomous, trustworthy, and secure indoor CT solutions. Despite such an urgency, this field is still in its infancy. This article addresses this gap and proposes the trustworthy blockchain-enabled system for an indoor CT (TB-ICT) framework. The TB-ICT framework is proposed to protect privacy and integrity of the underlying CT data from unauthorized access. More specifically, it is a fully distributed and innovative blockchain platform exploiting the proposed dynamic Proof-of-Work (dPoW) credit-based consensus algorithm coupled with randomized hash window (W-Hash) and dynamic Proof-of-Credit (dPoC) mechanisms to differentiate between honest and dishonest nodes. The TB-ICT not only provides a decentralization in data replication but also quantifies the node's behavior based on its underlying credit-based mechanism. For achieving a high localization performance, we capitalize on the availability of Internet of Things (IoT) indoor localization infrastructures, and develop a data-driven localization model based on bluetooth low-energy (BLE) sensor measurements. The simulation results show that the proposed TB-ICT prevents the COVID-19 from spreading by the implementation of a highly accurate CT model while improving the users' privacy and security.
ABSTRACT
The demand for contactless biometric authentication has significantly increased during the COVID-19 pandemic and beyond to prevent the spread of Coronavirus. The global pandemic unexpectedly affords a greater opportunity for contactless authentication, but iris and facial recognition biometrics have many usability, security, and privacy challenges, including mask-wearing and presentation attacks (PAs). Mainly, liveness detection against spoofing is notably a challenging task as various biometric authentication methods cannot efficiently assess the real user's physical presence in unsupervised environments. Although several face anti-spoofing methods have been proposed using add-on sensors, dynamic facial texture features, and 3-D mapping, most of them require expensive sensors and substantial computational resources, or fail to detect sophisticated 3-D face spoofing. This article presents a software-based facial liveness detection method named Apple in My Eyes (AIME). AIME is intended to detect the liveness against spoofing for mobile device security using challenge-response testing. AIME generates various screen patterns as authentication challenges, then passively detects corneal-specular reflection responses from human eyes using a frontal camera and analyzes the detected reflections using lightweight machine learning techniques. AIME system components include challenge and pattern detection, feature extraction and classification, and data augmentation and training. We have implemented AIME as a cross-platform application compatible with Android, iOS, and the Web. Our comprehensive experimental results reveal that AIME detects liveness with high accuracy at around 200-ms against different types of sophisticated PAs. AIME can also efficiently detect liveness in multiple contactless biometric authentications without any costly extra sensors nor involving users' active responses.
ABSTRACT
Federated learning is an emerging privacy-preserving AI technique where clients (i.e., organizations or devices) train models locally and formulate a global model based on the local model updates without transferring local data externally. However, federated learning systems struggle to achieve trustworthiness and embody responsible AI principles. In particular, federated learning systems face accountability and fairness challenges due to multistakeholder involvement and heterogeneity in client data distribution. To enhance the accountability and fairness of federated learning systems, we present a blockchain-based trustworthy federated learning architecture. We first design a smart contract-based data-model provenance registry to enable accountability. Additionally, we propose a weighted fair data sampler algorithm to enhance fairness in training data. We evaluate the proposed approach using a COVID-19 X-ray detection use case. The evaluation results show that the approach is feasible to enable accountability and improve fairness. The proposed algorithm can achieve better performance than the default federated learning setting in terms of the model's generalization and accuracy.