ABSTRACT
The coronavirus pandemic has been globally impacting the health and prosperity of people. A persistent increase in the number of positive cases has boost the stress among governments across the globe. There is a need of approach which gives more accurate predictions of outbreak. This paper presents a novel approach called diffusion prediction model for prediction of number of coronavirus cases in four countries: India, France, China and Nepal. Diffusion prediction model works on the diffusion process of the human contact. Model considers two forms of spread: when the spread takes time after infecting one person and when the spread is immediate after infecting one person. It makes the proposed model different over other state-of-the art models. It is giving more accurate results than other state-of-the art models. The proposed diffusion prediction model forecasts the number of new cases expected to occur in next 4 weeks. The model has predicted the number of confirmed cases, recovered cases, deaths and active cases. The model can facilitate government to be well prepared for any abrupt rise in this pandemic. The performance is evaluated in terms of accuracy and error rate and compared with the prediction results of support vector machine, logistic regression model and convolution neural network. The results prove the efficiency of the proposed model.
ABSTRACT
Coronavirus (COVID-19) is a very contagious infection that has drawn the world's attention. Modeling such diseases can be extremely valuable in predicting their effects. Although classic statistical modeling may provide adequate models, it may also fail to understand the data's intricacy. An automatic COVID-19 detection system based on computed tomography (CT) scan or X-ray images is effective, but a robust system design is challenging. In this study, we propose an intelligent healthcare system that integrates IoT-cloud technologies. This architecture uses smart connectivity sensors and deep learning (DL) for intelligent decision-making from the perspective of the smart city. The intelligent system tracks the status of patients in real time and delivers reliable, timely, and high-quality healthcare facilities at a low cost. COVID-19 detection experiments are performed using DL to test the viability of the proposed system. We use a sensor for recording, transferring, and tracking healthcare data. CT scan images from patients are sent to the cloud by IoT sensors, where the cognitive module is stored. The system decides the patient status by examining the images of the CT scan. The DL cognitive module makes the real-time decision on the possible course of action. When information is conveyed to a cognitive module, we use a state-of-the-art classification algorithm based on DL, i.e., ResNet50, to detect and classify whether the patients are normal or infected by COVID-19. We validate the proposed system's robustness and effectiveness using two benchmark publicly available datasets (Covid-Chestxray dataset and Chex-Pert dataset). At first, a dataset of 6000 images is prepared from the above two datasets. The proposed system was trained on the collection of images from 80% of the datasets and tested with 20% of the data. Cross-validation is performed using a tenfold cross-validation technique for performance evaluation. The results indicate that the proposed system gives an accuracy of 98.6%, a sensitivity of 97.3%, a specificity of 98.2%, and an F1-score of 97.87%. Results clearly show that the accuracy, specificity, sensitivity, and F1-score of our proposed method are high. The comparison shows that the proposed system performs better than the existing state-of-the-art systems. The proposed system will be helpful in medical diagnosis research and healthcare systems. It will also support the medical experts for COVID-19 screening and lead to a precious second opinion.
ABSTRACT
Internet of Things (IoT) and smart medical devices have improved the healthcare systems by enabling remote monitoring and screening of the patients' health conditions anywhere and anytime. Due to an unexpected and huge increasing in number of patients during coronavirus (novel COVID-19) pandemic, it is considerably indispensable to monitor patients' health condition continuously before any serious disorder or infection occur. According to transferring the huge volume of produced sensitive health data of patients who do not want their private medical information to be revealed, dealing with security issues of IoT data as a major concern and a challenging problem has remained yet. Encountering this challenge, in this paper, a remote health monitoring model that applies a lightweight block encryption method for provisioning security for health and medical data in cloud-based IoT environment is presented. In this model, the patients' health statuses are determined via predicting critical situations through data mining methods for analyzing their biological data sensed by smart medical IoT devices in which a lightweight secure block encryption technique is used to ensure the patients' sensitive data become protected. Lightweight block encryption methods have a crucial effective influence on this sort of systems due to the restricted resources in IoT platforms. Experimental outcomes show that K-star classification method achieves the best results among RF, MLP, SVM, and J48 classifiers, with accuracy of 95%, precision of 94.5%, recall of 93.5%, and f-score of 93.99%. Therefore, regarding the attained outcomes, the suggested model is successful in achieving an effective remote health monitoring model assisted by secure IoT data in cloud-based IoT platforms.
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The fish industry experiences substantial illegal, unreported, and unregulated (IUU) activities within traditional supply chain systems. Blockchain technology and the Internet of Things (IoT) are expected to transform the fish supply chain (SC) by incorporating distributed ledger technology (DLT) to build trustworthy, transparent, decentralized traceability systems that promote secure data sharing and employ IUU prevention and detection methods. We have reviewed current research efforts directed toward incorporating Blockchain in fish SC systems. We have discussed traceability in both traditional and smart SC systems that make use of Blockchain and IoT technologies. We demonstrated the key design considerations in terms of traceability in addition to a quality model to consider when designing smart Blockchain-based SC systems. In addition, we proposed an Intelligent Blockchain IoT-enabled fish SC framework that uses DLT for the trackability and traceability of fish products throughout harvesting, processing, packaging, shipping, and distribution to final delivery. More precisely, the proposed framework should be able to provide valuable and timely information that can be used to track and trace the fish product and verify its authenticity throughout the chain. Unlike other work, we have investigated the benefits of integrating machine learning (ML) into Blockchain IoT-enabled SC systems, focusing the discussion on the role of ML in fish quality, freshness assessment and fraud detection.
Subject(s)
Blockchain , Internet of Things , Animals , Fish Products , Fishes , IndustryABSTRACT
Health is the centre of human enlightenment. Due to the recent Covid outbreak and several environmental pollutions, checking one's vitals regularly has become a necessity. Ours is an IoT-based device that measures a user's heart rate, blood oxygen level and body temperature. The device is compact and portable, making it easy for users to wear. The readings are measured and shown on an OLED display with the help of sensors. The data is also available on the cloud. A webpage and a mobile application were developed to view the data from the cloud. Individual graphs of the vitals with time are available on the mobile application. This can be used for progress measurement and statistical analyses. Authorized personnel can access the patient's vitals. This creates a scope for Tele-medication in rural and underdeveloped regions. Besides, one can also view his/her vitals for personal health routine. © 2022 IEEE.
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Teleconsultation is a type of medical practice similar to face-to-face consultations, and it allows a health professional to give a consultation remotely through information and communication technologies. In the context of the management of the coronavirus epidemic, the use of teleconsultation practices can facilitate healthcare access and limit the risk of avoidable propagation in medical cabinets. This paper presents the monitoring of international teleconsultation referrals in the era of Covid-19 to facilitate and prevent the suspension of access to care, the most common architecture for teleconsultation, communication technologies and protocols, vital body signals, video transmission, and the conduct of teleconsultation. The aim is to develop a teleconsultation platform to diagnose the patient in real time, transmit data from the remote location to the doctor, and provide a teleconsultation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Remote healthcare is a well-accepted telemedicine service that renders efficient and reliable healthcare to patients suffering from chronic diseases, neurological disorders, diabetes, osteoporosis, sensory organs, and other ailments. Artificial intelligence, wireless communication, sensors, organic polymers, and wearables enable affordable, non-invasive healthcare to patients in all age groups. Telehealth services and telemedicine are beneficial to people residing in remote locations or patients with limited mobility, rehabilitation treatment, and post-operative recovery. Remote healthcare applications and services proved to be significant during the COVID-19 pandemic for both patients and doctors. This study presents a detailed study of the use of artificial intelligence and the internet of things in applications of remote healthcare in many domains of health, along with recent patents. This research also presents network diagrams of documents from the Scopus database using the tool VOSViewer. The paper highlights gap which can be undertaken by future researchers. © 2023 IEEE.
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After the outbreak of corona virus, all counties are paying special attention to their healthcare infrastructure. During second phase of covid-19, entire world has seen health care crisis. Large number of people died globally. Entire world was affected mentally or physically. There is a great need to strengthen the healthcare infrastructure, to vaccinate the population against covid virus infection and to take proper precaution to avoid spread of the virus, so that the world will not see such deadly days again. This paper discusses how technologies like Internet of Things (IoT), Artificial Intelligence (AI), Drones etc can help in remote monitoring of patients, judicious hospital admission, conscious distribution of lifesaving drugs etc. Investment in technology with not only help in the reduction of spread of the virus but will also help in fighting with all other future pandemics. All the countries must have to invest more on latest technologies in their healthcare to make themselves ready for such future pandemics. When the things will improve, the new normal will be very much different from the life that was before pandemic. IoT, AI and other technologies will become the non-separatable part of our life. © 2023 Bharati Vidyapeeth, New Delhi.
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As intelligent driving vehicles came out of concept into people’s life, the combination of safe driving and artificial intelligence becomes the new direction of future transportation development. Autonomous driving technology is developing based on control algorithms and model recognitions. In this paper, a cloud-based interconnected multi-sensor fusion autonomous vehicle system is proposed that uses deep learning (YOLOv4) and improved ORB algorithms to identify pedestrians, vehicles, and various traffic signs. A cloud-based interactive system is built to enable vehicle owners to master the situation of their vehicles at any time. In order to meet multiple application of automatic driving vehicles, the environment perception technology of multi-sensor fusion processing has broadened the uses of automatic driving vehicles by being equipped with automatic speech recognition (ASR), vehicle following mode and road patrol mode. These functions enable automatic driving to be used in applications such as agricultural irrigation, road firefighting and contactless delivery under new coronavirus outbreaks. Finally, using the embedded system equipment, an intelligent car was built for experimental verification, and the overall recognition accuracy of the system was over 96%. Author
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Sustainable agriculture is the backbone of food security systems and a driver of human well-being in global economic development (Sustainable Development Goal SDG 3). With the increase in world population and the effects of climate change due to the industrialization of economies, food security systems are under pressure to sustain communities. This situation calls for the implementation of innovative solutions to increase and sustain efficacy from farm to table. Agricultural social networks (ASNs) are central in agriculture value chain (AVC) management and sustainability and consist of a complex network inclusive of interdependent actors such as farmers, distributors, processors, and retailers. Hence, social network structures (SNSs) and practices are a means to contextualize user scenarios in agricultural value chain digitalization and digital solutions development. Therefore, this research aimed to unearth the roles of agricultural social networks in AVC digitalization, enabling an inclusive digital economy. We conducted automated literature content analysis followed by the application of case studies to develop a conceptual framework for the digitalization of the AVC toward an inclusive digital economy. Furthermore, we propose a transdisciplinary framework that guides the digitalization systematization of the AVC, while articulating resilience principles that aim to attain sustainability. The outcomes of this study offer software developers, agricultural stakeholders, and policymakers a platform to gain an understanding of technological infrastructure capabilities toward sustaining communities through digitalized AVCs.
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Children's engagement with the internet has exploded. From education to social media, companies have offered products and services that-far from being mere distractions for children-have increasingly become necessities. These necessities are most keenly felt in the EdTech world. As companies in the United States rely on the verifiable parental consent required by the Children 's Online Privacy Protection Act (COPPA) to collect and use minors' data, reviewing boilerplate waivers of liability and consent forms for children's online activities has thus become part of parenting. This piece argues that under the common law tradition of protecting the best interests of the child, when it comes to protecting children's digital privacy, relying solely on parental consent is insufficient and ill-suited. This work compares parental consent forms for children's online activities to parental waivers for tort liability for physical injuries suffered by children. In the latter, courts have not reached a consensus on whether such contracts are enforceable or altogether void. However, most courts have struck down such waivers as against public policy in commercial settings. By relying on courts ' decisions regarding the role of parents in protecting the best interests of the child when faced with a child's physical injury, this piece argues that public policy should have to force to override parental consent as it pertains to the protection of a minor's digital privacy and their use of EdTech tools. It thus encourages lawmakers at the federal and state levels to move away from a parental consent apparatus and instead put forward new measures for the protection of children's digital privacy. It further illustrates that, despite COPPA, common law privacy torts are not fully preempted. Adopting the approach proposed in this work will also motivate companies to be more vigilant towards handling minors' data to avoid potential lawsuits. It will further encourage a market for competition between socially responsible companies that would prioritize children's privacy over an endless list of corporate interests.
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Ubiquitous environments are not fixed in time. Entities are constantly evolving;they are dynamic. Ubiquitous applications therefore have a strong need to adapt during their execution and react to the context changes, and developing ubiquitous applications is still complex. The use of the separation of needs and model-driven engineering present the promising solutions adopted in this approach to resolve this complexity. The authors thought that the best way to improve efficiency was to make these models intelligent. That's why they decided to propose an architecture combining machine learning with the domain of modeling. In this article, a novel tool is proposed for the design of ubiquitous applications, associated with a graphical modeling editor with a drag-drop palette, which will allow to instantiate in a graphical way in order to obtain platform independent model, which will be transformed into platform specific model using Acceleo language. The validity of the proposed framework has been demonstrated via a case study of COVID-19. © 2023 IGI Global. All rights reserved.
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The adoption of the Internet of Things (IoT) has revolutionized the way the health care industry works. IoT en-abled smart and connected solutions like smart sensors, wearable devices, and smart health monitoring systems are used to unleash the potential growth of the health care industry. IoT based health care solutions are on greater priority among IoT service providers since the disruptions caused by the COVID-19. According to experts, there still exist white spots in research studies on the Internet of Things (IoT) and health care Systems. The study conducted in this paper aims to explore emerging global research trends and topical focus in the field of IoT in health care System. Bibliometric analysis is used to analyze the research articles on 'Internet of Things' and 'Health care Systems' extracted from SCOPUS and WoS database using VoS Viewer tool;the analysis used to assess the growth and research trends of different research fields over a period of time. The parameters considered during analysis include year-wise citations, year-wise publications, keyword clustering analysis, author-wise analysis, country-wise research trends and publication trend over the years. The results showcased that there has been significant change in utilization of IoT in healthcare systems continuously during the period under study conducted. © 2022 IEEE.
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Agricultural tractor drivers experience a high amplitude of vibration, especially during soil tillage operations. In the past, most research studied vibration exposure with more focus on the vertical (z) axis than on the fore-and-aft (x) and lateral (y) axes. This study examines how rotary soil tillage affects the vibration acceleration and frequency, and the power spectral densities (PSDs) at the seat pan and head along three translational axes in a real-field multiaxis vibration context. Moreover, this study aimed to identify the characteristics of the seat-to-head transmissibility (STHT) response to identifying the most salient resonant frequencies along the x-, y-, and z-axes. Nine (9) male tractor drivers operated the tractor with a mounted rotary tiller throughout the soil tillage process. In the event of a COVID-19 pandemic, and to respect social distancing, this study developed an Internet of Things (IoT) module with the potential to integrate with existing data loggers for online data transmission and to make the experimentation process more effective by removing potential sources of experimenter errors. The raw acceleration data retrieved at the seat pan and the head were utilized to obtain daily exposure (A(8)), PSDs, and STHT along the x-, y-, and z-axes. The vibration energy was found to be dominant along the z-axis than the x- and y-axes. A(8) response among tractor drivers exceeds the exposure action value explicitly stated by Directive 2002/44/EU. PSDs along the x-, y-, and z-axes depicted the low-frequency vibration induced by rotary soil tillage operation. The STHT response exhibited a higher degree of transmissibility along the y- and z-axes when compared with that along the x-axis. The frequency range of 4-7 Hz may plausibly be associated with cognitive impairment in tractor drivers during rotary soil tillage.
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Currently, the world is witnessing a second wave of the Covid-19 pandemic, and the situation is getting worse day by day. Simple protocols like minimising human contact and wearing a mask outdoors are proving to be good measures to control the spread of the virus. We saw a huge rise in the demand for daily items and due to a lack of availability, large numbers of people gather without taking any precautions to stock essentials. This has led to the spread of the virus to a great extent. In self-checkout stores, the shopping experience is completely automated and there is no physical presence of the shop owner. The automation enables the customers to pick their goods, scan and make payments by themselves without the intervention of the owner or a cashier. In such stores there is a high chance of people not following Covid protocols. So, there is a need for a system that maintains an allowed threshold of people inside the store at any one time, thus minimizing the potential dangerous human contact at all possible cases. We propose an IoT-Based Self-Checkout Store Using Mask Detection. The primary goal of this project is to create a safe environment for the consumers who visit the shop, by keeping a check on the number of customers present at the store and ensuring that each and every customer is following the protocol of wearing a mask. The system consists of two parts, the face mask detection and the customer count. For the mask detection part, deep learning algorithms like CNN are used to generate a model that helps detect a mask, and for the customer count part, a threshold value is set, which gives us the maximum number of people allowed inside the store at a time. The PIR sensors detect the entry and exit of customers and help regulate the count below the threshold. So once the face mask detection of the customer is complete and the number of people present inside the store is checked, the system takes the decision of either allowing the customer inside or asking him or her to wait. This project is designed to provide a solution to the current real-world problem using minimally efficient technology with high accuracy. © 2023 Scrivener Publishing LLC. All rights reserved.
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COVID-19 vaccines, approved under emergency use authorization, were not required to meet serialization requirements, but they have been properly labelled to meet US Food and Drug Administration (FDA) requirements, he says, complete with 2D barcodes with GTIN, lot, and expiry date. The company decided to serialize its Diprovan anesthetic, a workhorse generic product, using radio frequency identification tags containing the four identifiers (2). "If an agent is handling your product on your behalf, they need to leverage GS1 Standards including GTINs for products [and] global location numbers (GLNs) for physical locations, and share data electronically using electronic product code information services (EPCIS) to capture events from manufacturing to serialization [and] capping to shipping.
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The pandemic of the coronavirus disease 2019 has shown weakness and threats in various fields of human activity. In turn, the World Health Organization has recommended different preventive measures to decrease the spreading of coronavirus. Nonetheless, the world community ought to be ready for worldwide pandemics in the closest future. One of the most productive approaches to prevent spreading the virus is still using a face mask. This case has required staff who would verify visitors in public areas to wear masks. The aim of this paper was to identify persons remotely who wore masks or not, and also inform the personnel about the status through the message queuing telemetry transport as soon as possible using the edge computing paradigm. To solve this problem, we proposed to use the Raspberry Pi with a camera as an edge device, as well as the TensorFlow framework for pre-processing data at the edge. The offered system is developed as a system that could be introduced into the entrance of public areas. Experimental results have shown that the proposed approach was able to optimize network traffic and detect persons without masks. This study can be applied to various closed and public areas for monitoring situations. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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Public restrooms can be a breeding ground for germs and viruses, especially in light of the current COVID-19 pandemic. Touching surfaces like door handles can have a lot of harmful bacteria and microorganisms that increase the risk of transmission of infectious diseases. Additionally, ensuring the cleanliness of public restrooms can be a challenge as its being used by a lot of people on a day-to-day basis. To overcome this, we propose a model that provides a touchless door-locking mechanism with self-sanitization capabilities, thereby reducing the risk of transmission and ensuring a safer and cleaner environment for users. As the Internet of Things is an evolving technology and is providing modern solutions for various problems, the proposed system uses touchless doors that are incorporated with Node Microcontroller Unit and automatic Ultraviolet C sanitization. UVC light radiation is used for disinfecting purposes. The overall invention combines various features to provide a hygienic, secure, and safe restroom experience, ensuring that the restroom is always clean, secure, and accessible to those who need it. © 2023 IEEE.
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Internet of things and Automation are two eyes that change the view of industries perspective. Automation happens in every part of the stage in day to day life. The problem statement chosen in this paper was identified during COVID pandemic situation. The problem statement was to feed the fish food into fish aquarium at work place during COVID pandemic. In order to maintain the fish tank properly it should be monitored and maintained at regular interval is necessary. During pandemic situation felt difficult in proper maintenance and feeding the fish. To overcome the difficulties, we have proposed a model to feed the food for fish. In this paper we have solved the problem by using Internet of Things, servo motor, Arduino and interfaced through Massachusetts institute of Technology (MIT) App inventor to control the device at any part of the world. © 2023 IEEE.