Monitoring, Tracking and Fighting Pandemics using Drone-based Artificial Intelligence in IoT

The world has altered since the World Health Organization (WHO) designated (COVID-19) a worldwide epidemic. Everything in society, from professions to routines, has shifted to accommodate the new reality. The World Health Organization warns that future pandemics of infectious diseases are likely and that people should be ready for the worst. Therefore, this study presents a framework for tracking and monitoring COVID-19 using a Deep Learning (DL) perfect. The suggested framework utilises UAVs (such as a quadcopter or drone) equipped with artificial intelligence (AI) and the Internet of Things (IoT) to keep an eye on and combat the spread of COVID-19. AI/IoT for COVID-19 nursing and a drone-based IoT scheme for sterilisation make up the bulk of the infrastructure. The proposed solution is based on the use of a current camera installed in a face-shield or helmet for use in emergency situations like pandemics. The developed AI algorithm processes the thermal images that have been detected using multi-scale similar convolution blocks (MPCs) and Res blocks that are trained using residual learning. When infected cases are detected, the helmet's embedded Internet of Things system can trigger the drone system to intervene. The infected population is eradicated with the help of the drone's sterilisation process. The developed system undergoes experimental evaluation, and the findings are presented. The developed outline delivers a novel and well-organized arrangement for monitoring and combating COVID-19 and additional future epidemics, as evidenced by the results. © 2023 IEEE.

ATFormer: Advanced transformer for medical image segmentation

Combining transformers and convolutional neural networks is considered one of the most important directions for tackling medical image segmentation problems. To learn the long-range dependencies and local contexts, previous approaches embedded a convolutional layer into feedforward neural network inside the transformer block. However, a common issue is the instability during training since large differences in amplitude across layers by pre-layer normalization. Furthermore, multi-scale features were directly fused using the transformer from the encoder to decoder, which could disrupt valuable information for segmentation. To address these concerns, we propose Advanced TransFormer (ATFormer), a novel hybrid architecture that combines convolutional neural networks and transformers for medical image segmentation. First, the traditional transformer block has been refined into an Advanced Transformer Block, which adopts post-layer normalization to obtain mild activation values and employs the scaled cosine attention with shifted window for accurate spatial information. Second, the Progressive Guided Fusion module is introduced to make multi-scale features more discriminative while reducing the computational complexity. Experimental results on the ACDC, COVID-19 CT-Seg, and Tumor datasets demonstrate the significant advantage of ATFormer over existing methods that rely solely on convolutional neural networks, transformers, or their combination.

COVID-19 in Tunisia: Mapping and Documenting the Impacts on Those on the Margins

Since the 2011 uprisings, Tunisia has been going through a delicate political transition while the socio-economic context is continuously deteriorating. Our analysis focuses on the exceptional period of the lock down (from the 20th of March 2020 to mid-June 2020). With a large portion of the population deprived of their daily informal jobs, the collateral damages of the coercive measures were immediately visible in Tunisia. By critically engaging with how the coronavirus was politically managed in Tunisia, we propose to map and document plural impacts of the pandemic contextualizing this crisis for specific groups of population and territories: Tunisia's young population from hinterland regions (symptomatic of the 2011 uprising and the territorial division) and illegalized sub-Saharan migrants. By focusing on precarious, invisibilized and marginalized groups, we question processes of politization of socio-economic claims under the circumstantial constraints of the pandemic. Besides, this period (re-)activates new forms of civil society mobilization as well as cooperation through solidarity. In a nutshell, the effects of COVID-19 allow us to observe the transformations in the Tunisian post-revolutionary context through a much broader lens. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Spatial analysis of the spread of Covid-19 and its demographic consequences in the regions of the South of European Russia

The article presents results of the multi-scale analysis of the processes of coronavirus infection spread and its impact on the demographic situation in the world, Russia and regions of the South of the European part of Russia. The methodological basis of the study was the principles of geoinformation monitoring, making it possible to process and visualize large volumes of diverse materials. The information base was statistical data from the Russian and foreign sources reflecting the spread of coronavirus infection at various spatial levels from global to regional-local. The characteristic features of changes in the parameters of the disease during its active expansion are described. The article also deals with dynamics in demographic indicators and identifies trends in their widespread deterioration. The contribution of the South of European Russia macro-region to the all-Russian Covid-19 situation is determined. Development of the coronavirus pandemic at the level of municipal districts is analyzed using individual regions as an example. The study identifies main factors of the Covid-19 pandemic development and demonstrates some of its features and consequences in the largest urban agglomerations.

Depthwise convolution based pyramid ResNet model for accurate detection of COVID-19 from chest X-Ray images

The global pandemic of coronavirus disease 2019 (COVID-19) causes severe respiratory problems in humans. The Chest X-ray (CXR) imaging technique majorly assists in detecting abnormalities in the chest and lung areas caused by COVID-19. Hence, developing an automatic system for CXR-based COVID-19 detection is vital for disease diagnosis. To accomplish this requirement, an enhanced Residual Network (ResNet) model is proposed in this paper for accurate COVID-19 detection. The proposed model combines the Depthwise Separable Convolutional ResNet and Pyramid dilated module(DSC-ResNet-PDM) for deep feature extraction. Employing the DSC layer minimizes the number of parameters to mitigate the overfitting issue. Further, the pyramid dilated module is used for extracting multi-scale features. The extracted features are finally fed into the optimized Medium Gaussian kernel Support Vector Machine classifier (MGKSVM) for COVID-19 detection. The proposed model attained an accuracy of 99.5%, which is comparatively higher than the standard ResNet50 and ResNet101 models. [ FROM AUTHOR] Copyright of Imaging Science Journal is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Multiscale Approach in Deep Convolutional Networks for Minutia Extraction from Contactless Fingerprint Images

Biometric identification by contactless fingerprinting has been a trend in recent years, reinforced by the pandemic of the new coronavirus (COVID-19). Contactless acquisition tends to be a more hygienic acquisition category with greater user acceptance because it is less invasive and does not require the use of a surface touched by other people as traditional acquisition does. However, this area presents some challenging tasks. Contact-based sensors still generally provide greater biometric effectiveness since the minutiae are more pronounced due to the high contrast between ridges and valleys. On the other hand, contactless images typically have low contrast, so the methods fail with spurious or undetectable details, demonstrating the need for further studies in this area. In this work, we propose and analyze a robust scaled deep learning model for extracting minutiae in contactless fingerprint images. The results, evaluated on three datasets, show that the proposed method is competitive against other minutia extraction algorithms and commercial software. © 2022 IEEE.

Spatial differentiation of urban economic resilience and its influencing factors: evidence from Baidu migration big data

Under the development pattern of the "double cycle”, optimizing urban economic resilience is tremendously meaningful to improving a city's affordability and the adaptability of the economy and to promoting the Chinese economy to develop with high quality. Based on Baidu migration big data perspective, exploratory spatial data analysis (ESDA) and multi-scale geographical weighted regression (MGWR) model were used to analyze the spatial characteristics and driving factors of economic resilience in 287 Chinese cities in 2019. The results show that (1) the number of low-level economically resilient cities is the largest and distributed continuously, while the number of high-level economically resilient cities is the lowest and distributed in clusters and blocks;(2) compared with the Pearl River Delta and Yangtze River Delta, the population accumulation characteristic of the Beijing- Tianjin-Hebei region is relatively slow;(3) Both net inflow of population after spring festival and daily flow scale are significantly correlated with urban economic resilience, and the former will affect urban economic resilience;and (4) the spatial heterogeneity of each factor driving is significant, and they have different impact scales. The impact intensity is as follows: net population inflow > innovation ability > public financial expenditure > financial efficiency > urban size.

Correlation of viral loads in disease transmission could affect early estimates of the reproduction number.

Early estimates of the transmission properties of a newly emerged pathogen are critical to an effective public health response, and are often based on limited outbreak data. Here, we use simulations to investigate how correlations between the viral load of cases in transmission chains can affect estimates of these fundamental transmission properties. Our computational model simulates a disease transmission mechanism in which the viral load of the infector at the time of transmission influences the infectiousness of the infectee. These correlations in transmission pairs produce a population-level convergence process during which the distributions of initial viral loads in each subsequent generation converge to a steady state. We find that outbreaks arising from index cases with low initial viral loads give rise to early estimates of transmission properties that could be misleading. These findings demonstrate the potential for transmission mechanisms to affect estimates of the transmission properties of newly emerged viruses in ways that could be operationally significant to a public health response.

MLCA2F: Multi-Level Context Attentional Feature Fusion for COVID-19 lesion segmentation from CT scans.

In the field of diagnosis and treatment planning of Coronavirus disease 2019 (COVID-19), accurate infected area segmentation is challenging due to the significant variations in the COVID-19 lesion size, shape, and position, boundary ambiguity, as well as complex structure. To bridge these gaps, this study presents a robust deep learning model based on a novel multi-scale contextual information fusion strategy, called Multi-Level Context Attentional Feature Fusion (MLCA2F), which consists of the Multi-Scale Context-Attention Network (MSCA-Net) blocks for segmenting COVID-19 lesions from Computed Tomography (CT) images. Unlike the previous classical deep learning models, the MSCA-Net integrates Multi-Scale Contextual Feature Fusion (MC2F) and Multi-Context Attentional Feature (MCAF) to learn more lesion details and guide the model to estimate the position of the boundary of infected regions, respectively. Practically, extensive experiments are performed on the Kaggle CT dataset to explore the optimal structure of MLCA2F. In comparison with the current state-of-the-art methods, the experiments show that the proposed methodology provides efficient results. Therefore, we can conclude that the MLCA2F framework has the potential to dramatically improve the conventional segmentation methods for assisting clinical decision-making.

A Management Method of Multi-Granularity Dimensions for Spatiotemporal Data

To understand the complex phenomena in social space and monitor the dynamic changes in people's tracks, we need more cross-scale data. However, when we retrieve data, we often ignore the impact of multi-scale, resulting in incomplete results. To solve this problem, we proposed a management method of multi-granularity dimensions for spatiotemporal data. This method systematically described dimension granularity and the fuzzy caused by dimension granularity, and used multi-scale integer coding technology to organize and manage multi-granularity dimensions, and realized the integrity of the data query results according to the correlation between the different scale codes. We simulated the time and band data for the experiment. The experimental results showed that: (1) this method effectively solves the problem of incomplete query results of the intersection query method. (2) Compared with traditional string encoding, the query efficiency of multiscale integer encoding is twice as high. (3) The proportion of different dimension granularity has an impact on the query effect of multi-scale integer coding. When the proportion of fine-grained data is high, the advantage of multi-scale integer coding is greater.

MWG-Net: Multi-scale Wavelet Guidance Network for COVID-19 Lung Infection Segmentation from CT Images

Recently, accurate segmentation of COVID-19 infection from computed tomography (CT) scans is critical for the diagnosis and treatment of COVID-19. However, infection segmentation is a challenging task due to various textures, sizes and locations of infections, low contrast, and blurred boundaries. To address these problems, we propose a novel Multi-scale Wavelet Guidance Network (MWG-Net) for COVID-19 lung infection by integrating the multi-scale information of wavelet domain into the encoder and decoder of the convolutional neural network (CNN). In particular, we propose the Wavelet Guidance Module (WGM) and Wavelet &Edge Guidance Module (WEGM). Among them, the WGM guides the encoder to extract infection details through the multi-scale spatial and frequency features in the wavelet domain, while the WEGM guides the decoder to recover infection details through the multi-scale wavelet representations and multi-scale infection edge information. Besides, a Progressive Fusion Module (PFM) is further developed to aggregate and explore multi-scale features of the encoder and decoder. Notably, we establish a COVID-19 segmentation dataset (named COVID-Seg-100) containing 5800+ annotated slices for performance evaluation. Furthermore, we conduct extensive experiments to compare our method with other state-of-the-art approaches on our COVID-19-Seg-100 and two publicly available datasets, i.e., MosMedData and COVID-SemiSeg. The results show that our MWG-Net outperforms state-of-the-art methods on different datasets and can achieve more accurate and promising COVID-19 lung infection segmentation. IEEE

Fully feature fusion based neural network for COVID-19 lesion segmentation in CT images.

Coronavirus Disease 2019 (COVID-19) spreads around the world, seriously affecting people's health. Computed tomography (CT) images contain rich semantic information as an auxiliary diagnosis method. However, the automatic segmentation of COVID-19 lesions in CT images faces several challenges, including inconsistency in size and shape of the lesion, the high variability of the lesion, and the low contrast of pixel values between the lesion and normal tissue surrounding the lesion. Therefore, this paper proposes a Fully Feature Fusion Based Neural Network for COVID-19 Lesion Segmentation in CT Images (F3-Net). F3-Net uses an encoder-decoder architecture. In F3-Net, the Multiple Scale Module (MSM) can sense features of different scales, and Dense Path Module (DPM) is used to eliminate the semantic gap between features. The Attention Fusion Module (AFM) is the attention module, which can better fuse the multiple features. Furthermore, we proposed an improved loss function L o s s C o v i d - B C E that pays more attention to the lesions based on the prior knowledge of the distribution of COVID-19 lesions in the lungs. Finally, we verified the superior performance of F3-Net on a COVID-19 segmentation dataset, experiments demonstrate that the proposed model can segment COVID-19 lesions more accurately in CT images than benchmarks of state of the art.

MSCAF-Net: A General Framework for Camouflaged Object Detection via Learning Multi-Scale Context-Aware Features

The aim of camouflaged object detection (COD) is to find objects that are hidden in their surrounding environment. Due to the factors like low illumination, occlusion, small size and high similarity to the background, COD is recognized to be a very challenging task. In this paper, we propose a general COD framework, termed as MSCAF-Net, focusing on learning multi-scale context-aware features. To achieve this target, we first adopt the improved Pyramid Vision Transformer (PVTv2) model as the backbone to extract global contextual information at multiple scales. An enhanced receptive field (ERF) module is then designed to refine the features at each scale. Further, a cross-scale feature fusion (CSFF) module is introduced to achieve sufficient interaction of multi-scale information, aiming to enrich the scale diversity of extracted features. In addition, inspired the mechanism of the human visual system, a dense interactive decoder (DID) module is devised to output a rough localization map, which is used to modulate the fused features obtained in the CSFF module for more accurate detection. The effectiveness of our MSCAF-Net is validated on four benchmark datasets. The results show that the proposed method significantly outperforms state-of-the-art (SOTA) COD models by a large margin. Besides, we also investigate the potential of our MSCAF-Net on some other vision tasks that are highly related to COD, such as polyp segmentation, COVID-19 lung infection segmentation, transparent object detection and defect detection. Experimental results demonstrate the high versatility of the proposed MSCAF-Net. The source code and results of our method are available at https://github.com/yuliu316316/MSCAF-COD. IEEE

Intelligent Pneumonia Detection Model of Focal Transformer and Multi-scale-input SPD

The clinical diagnosis results based on lung X-rays provide important evidence in the COVID-19 pneumonia diagnosis process and for some other disease. However, due to the similarity of the lesions among many types of pneumonia displayed by X-rays, and due to the huge amount of X-ray readings of a doctor's daily work, traditional reading and identification method purely by human have problems of diagnosis mistakes, missed diagnosis and huge time consumption. Therefore, an intelligent detection model of pneumonia with multi-scale-input Focal Transformer integrated with SPD module is proposed to automatically identify various types of pneumonia including COVID-19 pneumonia. The method can pay attention to the multi-scale characteristic features of pneumonia lesions, and then make improved classification among COVID-19 pneumonia, cases with lung opacity, viral pneumonia and normal cases, providing stronger support for radiologists in medical diagnosis. The experiment results show that the proposed model has advantages in comparison to the traditional network models ResNet-50 and Swin Transformer in aspects of accuracy, recall, F1-Measure and other indicators. © 2022 IEEE.

Foreign Bodies Detector Based on DETR for High-Resolution X-Ray Images of Textiles

Foreign bodies (FBs) detection for X-ray images of textiles is a novel and challenging task. To solve the problem of poor performance of anchor-based detectors for FBs detection, we propose a feature-enhanced object detection framework with transformer (FE-DETR). Based on the split-attention of residual split-attention network (ResNeSt), we add convolutional block attention module (CBAM) between residual blocks and replace the $3\times $ 3 convolutional layer of the last residual block with deformable convolution network (DCN) to adapt FBs with different scales. Then, we propose a multiscale feature encoding (MSFE) module to solve the feature dispersion caused by deep convolution. Meanwhile, the transformer module is selected as the prediction head of the detector. During training, several heuristic strategies are used to further optimize the performance of FE-DETR. In addition, we construct a benchmark dataset for the textile FBs detection task. With end-to-end training, FE-DETR achieves higher performance than the baseline and mainstream state-of-the-art methods, with mean average precision (mAP) = 0.74, average precision (AP) = 0.992, average recall (AR) = 0.971, and $F1$ -score = 0.987. This article has been applied to the production line of medical protective clothing during the Corona Virus Disease 2019 (COVID-19) period and has yielded impressive results in actual production. © 1963-2012 IEEE.

A Robust Automated Framework for Classification of CT Covid-19 Images Using MSI-ResNet

Nowadays, the COVID-19 virus disease is spreading rampantly. There are some testing tools and kits available for diagnosing the virus, but it is in a limited count. To diagnose the presence of disease from radiological images, automated COVID-19 diagnosis techniques are needed. The enhancement of AI (Artificial Intelligence) has been focused in previous research, which uses X-ray images for detecting COVID-19. The most common symptoms of COVID-19 are fever, dry cough and sore throat. These symptoms may lead to an increase in the rigorous type of pneumonia with a severe barrier. Since medical imaging is not suggested recently in Canada for critical COVID-19 diagnosis, computer-aided systems are implemented for the early identification of COVID-19, which aids in noticing the disease progression and thus decreases the death rate. Here, a deep learning-based automated method for the extraction of features and classification is enhanced for the detection of COVID-19 from the images of computer tomography (CT). The suggested method functions on the basis of three main processes: data preprocessing, the extraction of features and classification. This approach integrates the union of deep features with the help of Inception 14 and VGG-16 models. At last, a classifier of Multi-scale Improved ResNet (MSI-ResNet) is developed to detect and classify the CT images into unique labels of class. With the support of available open-source COVID-CT datasets that consists of 760 CT pictures, the investigational validation of the suggested method is estimated. The experimental results reveal that the proposed approach offers greater performance with high specificity, accuracy and sensitivity. © 2023 CRL Publishing. All rights reserved.

Spatial distributions in disaster risk vulnerability for people with disabilities in the U.S.

Background: The spread of Coronavirus Disease 2019 (COVID-19) in the United States has centered the role of natural hazards such as pandemics into the public health sphere. The impacts of these hazards disproportionately affect people with disabilities, who are frequently in situations of social, political, or economic disadvantage. Because of these disadvantages, people with disabilities may have less access to necessary resources and services, putting them at risk due to unmet health needs. These disparities in access also highlight important regional, state, and county-level differences with regards to vulnerability and preparedness for natural hazards. Objective: The objective of this paper is to examine the relationship between disability and disaster risk in the United States. We examine the geographic variation in the relationship between risk from natural disasters and the percentage of people with disabilities living in a community. Because emergency management functions in the U.S. are directed and enacted at the county level, we also explore how these relationships change across U.S. counties. In addition to the overall prevalence of people with disabilities, we disaggregate the population of people with disabilities by gender, race, ethnicity, age, and disability impairment type. Methods: To measure risk of natural hazards, we use Expected Annual Loss index, a component of the 2020 National Risk Index, developed by Federal Emergency Management Agency, which identifies communities most at risk to18 natural hazards. We measure the percent of people with disabilities per county using the American Community Survey. We estimate the nationwide relationship between the proportion of people with disabilities and risk of natural hazards using ordinary least squares regression. To explore geographic differences in these relationships across the United States, we use a geographically weighted regression model to estimate local relationships for each county in the contiguous United States. We use mapping techniques to display regional differences across different disability demographic groups. Results: Counties with higher percentages of people with disabilities have a lower risk of natural disasters. Across the United States, a one percent increase in prevalence of people with disabilities in a county is associated with two percent decrease in the natural hazard risk score. Small but statistically significant regional differences exist as well. County-specific estimates range from a five percent decrease to a one percent increase. Stronger associations between risk and the prevalence of people with disabilities are observed in the Midwest and parts of the Southwest and West, whereas the relationship across racial groups is more scattered across the United States. Conclusion: In this study, nationwide results suggest that people with disabilities are more likely to live in communities with lower risk of natural hazards, but this relationship differs across U.S. counties and by demographic subgroups. These findings represent a contribution in further understanding the health and well-being of people with disabilities in the United States and the geographic variation therein.

Cascaded parallel crowd counting network with multi-resolution collaborative representation.

Accurately estimating the size and density distribution of a crowd from images is of great importance to public safety and crowd management during the COVID-19 pandemic, but it is very challenging as it is affected by many complex factors, including perspective distortion and background noise information. In this paper, we propose a novel multi-resolution collaborative representation framework called the cascaded parallel network (CP-Net), consisting of three parallel scale-specific branches connected in a cascading mode. In the framework, the three cascaded multi-resolution branches efficiently capture multi-scale features through their specific receptive fields. Additionally, multi-level feature fusion and information filtering are performed continuously on each branch to resist noise interference and perspective distortion. Moreover, we design an information exchange module across independent branches to refine the features extracted by each specific branch and deal with perspective distortion by using complementary information of multiple resolutions. To further improve the robustness of the network to scale variance and generate high-quality density maps, we construct a multi-receptive field fusion module to aggregate multi-scale features more comprehensively. The performance of our proposed CP-Net is verified on the challenging counting datasets (UCF_CC_50, UCF-QNRF, Shanghai Tech A&B, and WorldExpo'10), and the experimental results demonstrate the superiority of the proposed method.

LISNET: A COVID-19 LUNG INFECTION SEGMENTATION NETWORK BASED ON EDGE SUPERVISION AND MULTI-SCALE CONTEXT AGGREGATION

Corona Virus Disease 2019 (COVID-19) spread globally in early 2020, leading to a new health crisis. Automatic segmentation of lung infections from computed tomography (CT) images provides an important basis for early diagnosis of COVID-19 quickly. In this paper, we propose an effective COVID-19 Lung Infection Segmentation Network (LISNet) based on edge supervision and multi-scale context aggregation. More specifically, an Edge Supervision module is introduced to the feature extraction part to enhance the low contrast between lesions and normal tissues. In addition, the Multi-scale Feature Fusion module is added to enhance the segmentation ability of different scales Lesions. Finally, the Context Aggregation module is used to aggregate high- and low-level features and generate global information. Experiments demonstrate that our method outperforms other state-of-the-art methods on the public COVID-19 CT segmentation dataset. © 2022 IEEE.

MCA-UNet: multi-scale cross co-attentional U-Net for automatic medical image segmentation.

Medical image segmentation is a challenging task due to the high variation in shape, size and position of infections or lesions in medical images. It is necessary to construct multi-scale representations to capture image contents from different scales. However, it is still challenging for U-Net with a simple skip connection to model the global multi-scale context. To overcome it, we proposed a dense skip-connection with cross co-attention in U-Net to solve the semantic gaps for an accurate automatic medical image segmentation. We name our method MCA-UNet, which enjoys two benefits: (1) it has a strong ability to model the multi-scale features, and (2) it jointly explores the spatial and channel attentions. The experimental results on the COVID-19 and IDRiD datasets suggest that our MCA-UNet produces more precise segmentation performance for the consolidation, ground-glass opacity (GGO), microaneurysms (MA) and hard exudates (EX). The source code of this work will be released via https://github.com/McGregorWwww/MCA-UNet/.