Artificial Intelligence-enabled Internet of Medical Things for COVID-19 pandemic data management

The dreaded coronavirus (COVID-19) disease traceable to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2) has killed thousands of people worldwide, and the World Health Organization (WHO) has proclaimed the viral respiratory disease a human pandemic. The adverse flare of COVID-19 and its variants has triggered collaborative research interests across all disciplines, especially in medicine and healthcare delivery. Complex healthcare data collected from patients via sensors and devices are transmitted to the cloud for analysis and sharing. However, it is pretty difficult to achieve rapid and intelligent decisions on the processed information due to the heterogeneity and complexity of the data. Artificial intelligence (AI) has recently appeared as a promising paradigm to address this issue. The introduction of AI to the Internet of Medical Things (IoMT) births the era of AI of Medical Things (AIoMT). The AIoMT enables the autonomous operation of sensors and devices to provide a favourable and secure environmental landscape to healthcare personnel and patients. AIoMT finds successful applications in natural language processing (NLP), speech recognition, and computer vision. In the current emergency, medical-related records comprising blood pressure, heart rate, oxygen level, temperature, and more are collected to examine the medical conditions of patients. However, the power usage of the low-power sensor nodes employed for data transmission to the remote data centres poses significant limitations. Currently, sensitive medical information is transmitted over open wireless channels, which are highly susceptible to malicious attacks, posing a significant security risk. An insightful privacy-aware energy-efficient architecture using AIoMT for COVID-19 pandemic data handling is presented in this chapter. The goal is to secure sensitive medical records of patients and other stakeholders in the healthcare domain. Additionally, this chapter presents an elaborate discussion on improving energy efficiency and minimizing the communication cost to improve healthcare information security. Finally, the chapter highlights the open research issues and possible lines of future research in AIoMT.

The Perspectives of Individuals with Comorbidities Towards COVID-19 Booster Vaccine Shots in Twitter: A Social Media Analysis Using Natural Language Processing, Sentiment Analysis and Topic Modeling

Individuals with comorbidities (i.e., Diabetes Mellitus, hypertension, heart diseases) are more likely to develop a more severe form of coronavirus disease 2019 (COVID-19), thus, they should take necessary precautions to avoid infection with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and its emerging variants and subvariants by getting COVID-19 vaccination and booster doses. In this regard, we used text analytics techniques, specifically Natural Language Processing (NLP), to understand the perception of Twitter users having comorbidities (diabetes, hypertension, and heart diseases) towards the COVID-19 vaccine booster doses. Understanding and identifying Twitter users' perceptions and perspectives will help the members of medical fraternities, governments, and policymakers to frame and implement a suitable public health policy for promoting the uptake of booster shots by such vulnerable people. A total of 176,540 tweets were identified through the scrapping process to understand the perception of individuals with the mentioned comorbidities regarding the COVID-19 booster dose. From sentiment analysis, it was revealed that 57.6% out of 176,540 tweets expressed negative sentiments about the COVID-19 vaccine booster doses. The reasons for negative expressions have been found using the topic modeling approach (i.e., risk factors, fear of myocardial fibrosis, stroke, or death, and using vaccines as bio-weapons). Of note, enhancing the COVID-19 vaccination drive by administering its booster doses to more and more people is of paramount importance for rendering higher protective immunity under the current threats of recently emerging newer Omicron subvariants which are presently causing a rise in cases in a few countries, such as China and others, and might lead to a feasible new wave of the pandemic with the surge in cases at the global level. Copyright © The Author(s) 2023.

Robotics and the Internet of Health Things to Improve Healthcare: Especially during the COVID-19 Pandemic

At the same time, the pandemic has highlighted the need to use new IoT, AI, and robotics technologies to cope. This chapter describes the infuence of COVID-19 on the world of work, particularly in the health sector. We aim to highlight the role of new technologies in the improvement of healthcare as well as the advantages of an intelligent care platform that involves robotics coupled with the IoT to fght against the spread of the disease. © 2023 selection and editorial matter, Ben Othman Soufene, Chinmay Chakraborty, Faris. A. Almalki. All rights reserved.

Implementation of Smart Healthcare Systems using AI, IoT, and Blockchain

Implementation of Smart Healthcare Systems using AI, IoT, and Blockchain provides imperative research on the development of data fusion and analytics for healthcare and their implementation into current issues in a real-time environment. While highlighting IoT, bio-inspired computing, big data, and evolutionary programming, the book explores various concepts and theories of data fusion, IoT, and Big Data Analytics. It also investigates the challenges and methodologies required to integrate data from multiple heterogeneous sources, analytical platforms in healthcare sectors. This book is unique in the way that it provides useful insights into the implementation of a smart and intelligent healthcare system in a post-Covid-19 world using enabling technologies like Artificial Intelligence, Internet of Things, and blockchain in providing transparent, faster, secure and privacy preserved healthcare ecosystem for the masses. © 2023 Elsevier Inc. All rights reserved.

Artifcial Intelligence at the Service of the Detection of COVID-19

Toward the end of December 2019, the COVID-19 epidemic appeared in the Chinese city of Wuhan, and due to its rapid spread, the World Health Organization designated it a global pandemic on March 11, 2020. Recently, many researchers from around the world have proposed new ways to detect COVID-19 making use of medical pictures like chest X-rays and CT scans combined with artifcial intelligence. In this chapter, we study, analyze, discuss, and compare some of these proposed solutions. © 2023 selection and editorial matter, Ben Othman Soufene, Chinmay Chakraborty, Faris. A. Almalki. All rights reserved.

Light Deep CNN Approach for Multi-Label Pathology Classifcation Using Frontal Chest X-Ray

This chapter propounds custom deep convolutional neural network architecture for classifying COVID-19 CXR images. The method makes use of conglomeration of state-of-the-art CNNs by applying transfer learning. To solve the problem of the data imbalance, we used oversampling. The proposed methods with the MobileNet V3 implementation achieved 95.91% of the test accuracy of four class predictions and 96% recall. According to the obtained results, the model also achieved 97% precision and a 98% F1 score in coronavirus detection task;the other implementations also show good results, with an F1 score of 93-96%. These obtained fndings reveal that the suggested techniques exceed the comparison models in classifcation accuracy, recall, precision, and F1 score, which illustrates their promise in computer-aided diagnosis and smart healthcare. © 2023 selection and editorial matter, Ben Othman Soufene, Chinmay Chakraborty, Faris. A. Almalki. All rights reserved.

Application of Deep Learning in Healthcare

Automated Patient monitoring is rising to importance in the mobile healthcare services as it makes day-to-day activities risk-free, by continuously monitoring their vital signs. Clinical solutions are being provided to patients in no time, which is made possible due to the latest improvements in the "Internet of Things (IoT), cloud computing, and fog computing”. "Machine learning and Deep learning” are now being extensively used for various applications in healthcare such as extracting relations from vast amounts of patient data, analyzing patterns to predict the propagation of diseases, classify reports and X-rays to detect diseases, to name a few. In this paper, a deep learning-based model is proposed to monitor Covid-affected patients within hospitals. Our model can provide an online link between a patient and medical facility while also collecting patient data. This will enhance the care taken for patients. At the hospital end, we present a deep learning model using ResNet-50 that could classify chest X-rays as Covid positive or No Covid. Through this model we expect to quicken the process of COVID-19 detection while lowering the healthcare expenses. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Smart Visual Sensing for Overcrowding in COVID-19 Infected Cities Using Modified Deep Transfer Learning

Currently, COVID-19 is circulating in crowded places as an infectious disease. COVID-19 can be prevented from spreading rapidly in crowded areas by implementing multiple strategies. The use of unmanned aerial vehicles (UAVs) as sensing devices can be useful in detecting overcrowding events. Accordingly, in this article, we introduce a real-time system for identifying overcrowding due to events such as congestion and abnormal behavior. For the first time, a monitoring approach is proposed to detect overcrowding through the UAV and social monitoring system (SMS). We have significantly improved identification by selecting the best features from the water cycle algorithm (WCA) and making decisions based on deep transfer learning. According to the analysis of the UAV videos, the average accuracy is estimated at 96.55%. Experimental results demonstrate that the proposed approach is capable of detecting overcrowding based on UAV videos' frames and SMS's communication even in challenging conditions. © 2005-2012 IEEE.

The IMU Augment SLAM on Unmanned Vehicle for Detection of Protective Measures in COVID-19

Detecting protective measures (e.g., masks, goggles and protective clothing) is a momentous step in the fight against COVID-19. The detection mode of unmanned devices based on Simultaneous localization and mapping (SLAM) and fusion technology is more efficient, economical and safe than the traditional manual detection. In this paper, a tightly-coupled nonlinear optimization approach is used to augment the visual feature extraction of SLAM by the gyroscope of the IMU to obtain a high-precision visual inertial system for joint position and pose estimation. Based on the VINS-Mono frame, first, an LSD algorithm based on a conditional selection strategy is proposed to extract line features efficiently. Then, we propose recovering missing point features from line features. Moreover, we propose a strategy to recover vanishing point features from line features, and add residuals to the SLAM cost function based on optimization, which optimizes point-line features in real time to promote the tracking and matching accuracy. Second, the wavelet threshold denoising method based on the 3σ criterion is used to carry out real-time online denoising for gyroscope to improve the output precision. Our WD-PL-VINS was measured on publicly available EuRoC datasets, TUM VI datasets and evaluated and validated in lab testing with a unmanned vehicle (UV) based on the NVIDIA Jetson-TX2 development board. The results show that our method's APE and RPE on MH-03-easy sequences are improved by 69.28% and 97.66%, respectively, compared with VINS-Mono. © 2001-2012 IEEE.

Application of Deep Learning in Healthcare

Automated Patient monitoring is rising to importance in the mobile healthcare services as it makes day-to-day activities risk-free, by continuously monitoring their vital signs. Clinical solutions are being provided to patients in no time, which is made possible due to the latest improvements in the “Internet of Things (IoT), cloud computing, and fog computing”. “Machine learning and Deep learning” are now being extensively used for various applications in healthcare such as extracting relations from vast amounts of patient data, analyzing patterns to predict the propagation of diseases, classify reports and X-rays to detect diseases, to name a few. In this paper, a deep learning-based model is proposed to monitor Covid-affected patients within hospitals. Our model can provide an online link between a patient and medical facility while also collecting patient data. This will enhance the care taken for patients. At the hospital end, we present a deep learning model using ResNet-50 that could classify chest X-rays as Covid positive or No Covid. Through this model we expect to quicken the process of COVID-19 detection while lowering the healthcare expenses. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Mitigating Information Interruptions by COVID-19 Face Masks: A Three-Stage Speech Enhancement Scheme

The coronavirus disease 2019 (COVID-19) preventive measures have resulted in significant lifestyle changes. One of the COVID-19 new normal is the usage of face masks for protection against airborne aerosol which creates distractions and interruptions in voice communication. It has a different influence on speech than the standard concept of noise affecting speech communication. Furthermore, it has varied effects on speech in different frequency bands. To provide a solution to this problem, a three-stage adaptive speech enhancement (SE) scheme is developed in this article. In the first stage, the tunable <inline-formula> <tex-math notation="LaTeX">$Q$</tex-math> </inline-formula>-factor wavelet transform (TQWT) features are extracted by properly setting the quality factor values and the number of levels from the input speech signal. In the second stage, the adjustable parameters of the preemphasis filter and modified multiband spectral subtraction (MBSS) are determined using bio-inspired techniques for different masking and signal-to-noise ratio (SNR) conditions. In the third stage, the weights, center values, standard deviation of the Gaussian radial basis functions, and input patterns of the radial basis function neural networks (RBFNNs) are updated to predict the optimized parameters from the input TQWT-based cepstral features (TQCFs). In the end, the performance of the proposed algorithm is compared with the standard SE algorithms using two speech datasets. IEEE

MEASUREMENT OF EFFICIENCY LOSS IN INDIAN AGRICULTURAL SECTOR DUE TO COVID-19: Evidence from rice production

A large amount of migration of labour from the informal industrial or unorganized sector to the agricultural sector has accelerated the problem of downward pressure in Indian economy during the Covid-19 pandemic situation. If these migrant labourers are bound to be absorbed in the agricultural sector, then not only the amount of surplus labour is aggravated, but the extent of efficiency of agricultural sector may be reduced as well, since these migrant labour would be forced to choose agricultural activities and may not be as efficient as workers who are normally employed in the agricultural sector. This chapter measures the extent of the decline in efficiency in the Indian agricultural sector and the consequent loss of output, arising out of the employment of inefficient labour due to Covid-19 problem and also the extent of the casual labour force that can be dispensed with in order to keep the output level unchanged, taking rice production as a case study and considering the data from 16 major rice-producing Indian states for the period 2004-05 to 2020. © 2022 selection and editorial matter, Rajib Bhattacharyya, Ananya Ghosh Dastidar and Soumyen Sikdar;individual chapters, the contributors.

Relooking the monkeypox virus during this present outbreak: epidemiology to therapeutics and vaccines.

OBJECTIVE: The recent monkeypox disease outbreak is another significant threat during the ongoing COVID-19 pandemic. This viral disease is zoonotic and contagious. The viral disease outbreak is considered the substantial infection possessed by the Orthopoxvirus family species after the smallpox virus' obliteration, a representative of the same family. It has potentially threatened the Republic of Congo's regions and certain African subcontinent zones. Although repeated outbreaks have been reported in several parts of the world, as conferred from the epidemiological data, very little is explored about the disease landscape. Thus, here we have reviewed the current status of the monkeypox virus along with therapeutic options available to humanity. MATERIALS AND METHODS: We have accessed and reviewed the available literature on the monkeypox virus to highlight its epidemiology, pathogenicity, virulence, and therapeutic options available. For the review, we have searched different literature and database such as PubMed, PubMed Central, Google Scholar, Web of Science, Scopus, etc., using different keywords such as "monkeypox", "Orthopox", "smallpox", "recent monkeypox outbreak", "therapeutic strategies", "monkeypox vaccines", etc. This review has included most of the significant references from 1983 to 2022. RESULTS: It has been reported that the monkeypox virus shows a remarkable similarity with smallpox during the ongoing outbreak. Sometimes, it creates considerable confusion due to misdiagnosis and similarity with smallpox. The misdiagnosis of the disease should be immediately corrected by rendering some cutting-edge techniques especially intended to isolate the monkeypox virus. The pathophysiology and the histopathological data imply the immediate need to design effective therapeutics to confer resistance against the monkeypox virus. Most importantly, the potential implications of the disease are not given importance due to the lack of awareness programs. Moreover, specific evolutionary evidence is crucial for designing effective therapeutic strategies that confer high resistance, particularly against this species. CONCLUSIONS: The review focuses on a brief overview of the recent monkeypox virus outbreak, infection biology, epidemiology, transmission, clinical symptoms, and therapeutic aspects. Such an attempt will support researchers, policymakers, and healthcare professionals for better treatment and containment of the infection caused by the monkeypox virus.

The IMU augment SLAM on Unmanned Vehicle for detection of protective measures in COVID-19

Detecting protective measures (e.g., masks, goggles and protective clothing) is a momentous step in the fight against COVID-19. The detection mode of unmanned devices based on Simultaneous localization and mapping (SLAM) and fusion technology is more efficient, economical and safe than the traditional manual detection. In this paper, a tightly-coupled nonlinear optimization approach is used to augment the visual feature extraction of SLAM by the gyroscope of the IMU to obtain a high-precision visual inertial system for joint position and pose estimation. Based on the VINS-Mono frame, first, an LSD algorithm based on a conditional selection strategy is proposed to extract line features efficiently. Then, we propose recovering missing point features from line features. Moreover, we propose a strategy to recover vanishing point features from line features, and add residuals to the SLAM cost function based on optimization, which optimizes point-line features in real time to promote the tracking and matching accuracy. Second, the wavelet threshold denoising method based on the 3σcriterion is used to carry out real-time online denoising for gyroscope to improve the output precision. Our WD-PL-VINS was measured on publicly available EuRoC datasets, TUM VI datasets and evaluated and validated in lab testing with a unmanned vehicle (UV) based on the NVIDIA Jetson-TX2 development board. The results show that our method’s APE and RPE on MH 03 easy sequences are improved by 69.28% and 97.66%, respectively, compared with VINS-Mono. IEEE

Deep Features for COVID-19 Detection: Performance Evaluation on Multiple Classifiers

Humanity has faced the greatest difficulties in recent years in COVID-19. These diseases are caused by significant alveolar damage and progressive respiratory failure. To address this issue, healthcare facilities needed rapid testing methods to identify COVID-19 patients and treat them immediately. In this paper, we developed a rapid testing strategy using machine and deep learning architecture with three different categories of chest x-ray images, such as COVID-19, normal, and pneumonia, were considered to identify the COVID-19 affected images. It is very difficult to diagnose COVID-19 from the pool of chest x-ray images, as pneumonia and COVID-19 affected x-ray images closely resemble each other. For this issue, feature extraction plays an important role. Here we considered deep features which were extracted from deep learning models such as VGG19 and InceptionResnetV2. These deep features were classified using different machine learning algorithms. It was observed that 96.81% accuracy was obtained after classification using MLP. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Smart Visual Sensing for Overcrowding in COVID-19 Infected Cities Using Modified Deep Transfer Learning

Currently, COVID-19 is circulating in crowded places as an infectious disease. COVID-19 can be prevented from spreading rapidly in crowded areas by implementing multiple strategies. The use of unmanned aerial vehicles (UAVs) as a sensing devices can be useful in detecting overcrowding events. Accordingly, in this paper, we introduce a real-time system for identifying overcrowding due to events such as congestion and abnormal behavior. For the first time, a monitoring approach is proposed to detect overcrowding through the UAV and social monitoring system (SMS). We have significantly improved identification by selecting the best features from the water cycle algorithm (WCA) and making decisions based on Deep Transfer Learning (DTL). According to the analysis of the UAV videos, the average accuracy is estimated at 96.55%. Experimental results demonstrate that the proposed approach is capable of detecting overcrowding based on UAV videos' frames and SMS's communication even in challenging conditions. IEEE

Application of Artificial Intelligence and Big Data for Fighting COVID-19 Pandemic

The coronavirus (COVID-19) pandemic is playing sensitive havoc in socio-communal systems, humanity and creates economic crises worldwide. Many strategies have been used to managed and curtailed the COVID-19 outbreak, but many countries are still helpless in fighting and containing the outbreak. In an increasingly knowledge-driven, healthcare innovation, and linked society, fighting COVID-19 becomes easier. The Big Data drives the digital revolution by providing solutions focused on big data analytics empowered by Artificial Intelligence (AI) to reduce the difficulty and cognitive burden of accessing and processing large quantities of data. Hence, big data and AI can have been applied in fighting COVID-19 pandemic since the use of both technologies empowered Big Data Analytics (BDA) and yielded imaginable results in combating infectious diseases globally. Therefore, this paper reviews the applicability and importance of AI and Big Data methods to data produced from the countless ubiquitously connected healthcare devices that produced entrenched and distributed information handling capabilities in fighting COVID-19 outbreak. In the area of managing big data for real-time diagnosing, monitoring, and treating COVID-19 patients, AI enabled with big data analytics has shown tremendous potential. The technologies can also be used in the development of drugs and vaccines within the shortest of time, more than ever before. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

COVID-19 vaccines and vaccination program for aging adults.

OBJECTIVE: COVID-19 vaccines have developed quickly, and vaccination programs have started in most countries to fight the pandemic. The aging population is vulnerable to different diseases, also including the COVID-19. A high death rate of COVID-19 was noted from the vulnerable aging population. A present scenario regarding COVID-19 vaccines and vaccination program foraging adults had been discussed. MATERIALS AND METHODS: This paper reviews the current status and future projections till 2050 of the aging population worldwide. It also discusses the immunosenescence and inflammaging issues facing elderly adults and how it affects the vaccinations such as influenza, pneumococcal, and herpes zoster. RESULTS: This paper recommends clinical trials for all approved COVID-19 vaccines targeting the elderly adult population and to project a plan to develop a next-generation COVID-19 vaccine. CONCLUSIONS: The review has mapped the COVID-19 vaccination status from the developed and developing countries for the elderly population. Finally, strategies to vaccinate all elderly adults globally against COVID-19 to enhance longevity has been suggested.