Hierarchical energy efficient secure routing protocol for optimal route selection in wireless body area networks

Growth in technology has witnessed the comfort of an individual in domestic and professional life. Although, such existence was not able to meet the medical emergencies during the pandemic COVID-19 and during other health monitoring scenarios. This demand is due to the untouched Quality of Service network parameters like throughput, reliability, security etc. Hence, remote health monitoring systems for the patients who have undergone a medical surgery, bed ridden patients, autism affected subjects etc is in need that considers postural change and then forward to the caretaker in hospitals through wireless body area networks (WBAN). Security in these data are very important as it deals with the life of a subject. In this work, a Hierarchical Energy Efficient Secure Routing protocol (HEESR) is proposed that categorizes the deployed body nodes in to direct node and relay node based on the threshold vale. Unlike other conventional protocols the cluster head selection is based on the energy levels and the traffic priority data like critical and non-critical data, followed by an optimal route to forward the acquired data is identified and the data is compressed using Huffman encoding technique and encrypted using asymmetric cryptographic algorithm for secure data transmission. This protocol mainly appends security and routing efficiency in a hierarchical pattern through data prioritization and out performs the other conventional routing protocols by yielding a better energy consumption of 6%, throughput 92% and security of 93%, which has balanced the packet drop rate considerably and deliver the data within the stipulated time period. © 2022 The Authors

Using cloud manufacturing to establish an ecosystem network for COVID-19 ventilator production

Cloud Manufacturing (CMfg) as a service-oriented manufacturing (SOM) paradigm promotes the paradigm of partnership and collaboration among the globally distributed resources. Like technology-based marketplaces, it can identify different suppliers, determine their available services, and assign them to the requested orders based on the service-demand matching mechanism. The dominant capabilities of the SOM as a service can provide a collaborative and flexible manufacturing network configuration. This paper has focused on developing a new CMfg architecture with a concentrating on collaborative concepts to elaborate the modular manufacturing through the virtual process. In this model, different parts of the customized products can be designed as modules produced by distributed suppliers. A (Formula presented.) representation model for the SOM system has been proposed by this architecture. The proposed architecture is enriched by the help of novel technologies presented in Industry 4.0 (I4.0). The model's performance can be evaluated through different approaches, like topology analysis. Furthermore, to simulate a modeling procedure of the architecture, the process of the ventilator production marketplace is discussed in Tehran, Iran. The capabilities of the model analysis to configure the CMfg network and fulfilling the demands have also been described. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Application of machine learning approaches to predict the impact of ambient air pollution on outpatient visits for acute respiratory infections

With a remarkable increase in industrialization among fast-developing countries, air pollution is rising at an alarming rate and has become a public health concern. The study aims to examine the effect of air pollution on patient's hospital visits for respiratory diseases, particularly Acute Respiratory Infections (ARI). Outpatient hospital visits, air pollution and meteorological parameters were collected from March 2018 to October 2021. Eight machine learning algorithms (Random Forest model, K-Nearest Neighbors regression model, Linear regression model, LASSO regression model, Decision Tree Regressor, Support Vector Regression, X.G. Boost and Deep Neural Network with 5-layers) were applied for the analysis of daily air pollutants and outpatient visits for ARI. The evaluation was done by using 5-cross-fold confirmations. The data was randomly divided into test and training data sets at a scale of 1:2, respectively. Results show that among the studied eight machine learning models, the Random Forest model has given the best performance with R2 = 0.606, 0.608 without lag and 1-day lag respectively on ARI patients and R2 = 0.872, 0.871 without lag and 1-day lag respectively on total patients. All eight models did not perform well with the lag effect on the ARI patient dataset but performed better on the total patient dataset. Thus, the study did not find any significant association between ARI patients and ambient air pollution due to the intermittent availability of data during the COVID-19 period. This study gives insight into developing machine learning programs for risk prediction that can be used to predict analytics for several other diseases apart from ARI, such as heart disease and other respiratory diseases. © 2022 Elsevier B.V.

A Hybrid Deep Fused Learning Approach to Segregate Infectious Diseases

Humankind is facing another deadliest pandemic of all times in history, caused by COVID-19. Apart from this challenging pandemic, World Health Organization (WHO) considers tuberculosis (TB) as a preeminent infectious disease due to its high infection rate. Generally, both TB and COVID-19 severely affect the lungs, thus hardening the job of medical practitioners who can often misidentify these diseases in the current situation. Therefore, the time of need calls for an immediate and meticulous automatic diagnostic tool that can accurately discriminate both diseases. As one of the preliminary smart health systems that examine three clinical states (COVID-19, TB, and normal cases), this study proposes an amalgam of image filtering, data-augmentation technique, transfer learning-based approach, and advanced deep-learning classifiers to effectively segregate these diseases. It first employed a generative adversarial network (GAN) and Crimmins speckle removal filter on X-ray images to overcome the issue of limited data and noise. Each pre-processed image is then converted into red, green, and blue (RGB) and Commission Internationale de l'Elcairage (CIE) color spaces from which deep fused features are formed by extracting relevant features using DenseNet121 and ResNet50. Each feature extractor extracts 1000 most useful features which are then fused and finally fed to two variants of recurrent neural network (RNN) classifiers for precise discrimination of three-clinical states. Comparative analysis showed that the proposed Bi-directional long-short-term-memory (Bi-LSTM) model dominated the long-short-term-memory (LSTM) network by attaining an overall accuracy of 98.22% for the three-class classification task, whereas LSTM hardly achieved 94.22% accuracy on the test dataset. © 2023 Tech Science Press. All rights reserved.

Speech emotion recognition with artificial intelligence for contact tracing in the COVID-19 pandemic

If understanding sentiments is already a difficult task in human-human communication, this becomes extremely challenging when a human-computer interaction happens, as for instance in chatbot conversations. In this work, a machine learning neural network-based Speech Emotion Recognition system is presented to perform emotion detection in a chatbot virtual assistant whose task was to perform contact tracing during the COVID-19 pandemic. The system was tested on a novel dataset of audio samples, provided by the company Blu Pantheon, which developed virtual agents capable of autonomously performing contacts tracing for individuals positive to COVID-19. The dataset provided was unlabelled for the emotions associated to the conversations. Therefore, the work was structured using a sort of transfer learning strategy. First, the model is trained using the labelled and publicly available Italian-language dataset EMOVO Corpus. The accuracy achieved in testing phase reached 92%. To the best of their knowledge, thiswork represents the first example in the context of chatbot speech emotion recognition for contact tracing, shedding lights towards the importance of the use of such techniques in virtual assistants and chatbot conversational contexts for psychological human status assessment. The code of this work was publicly released at: https://github.com/fp1acm8/SER. © 2023 The Authors. Cognitive Computation and Systems published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology and Shenzhen University.

Prognostic Assessment of COVID-19 Vaccination Levels

In order to move to a stable life rhythm and a satisfactory condition of people, which would ensure the organization of the usual mode of daily activities, it is necessary to achieve a sufficiently complete vaccination of the population in a region. At the same time, significant obstacles to achieving the desired result in Ukraine are the hesitation of a large part of the population regarding the vaccination, fear of a purely medical procedure, and distrust of its effectiveness. Due to the lack of a wide range of scientifically grounded research of this problem, insufficient attention is paid to a deeper analysis of the factors influencing the intensity and effectiveness of vaccination. In view of what has been said in the proposed article, many factors related to the vaccination process have been identified based on the developed ontology. A formalized representation of the connections between factors has been made using the semantic network as an information database, which has become a prerequisite for ranking by weight factors. Using the methodology of hierarchies modelling, the levels of factors preferences are established and a multilevel model of their priority influence on the researched process is synthesized. Alternative options for the vaccination process have been designed and a prognostic assessment of the levels of COVID-19 vaccination intensity has been carried out, which allows the selection of the optimal option for the specific parameters of the initial factors. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Performance evaluation of a fast and efficient intrusion detection framework for advanced persistent threat-based cyberattacks

After the COVID-19 pandemic, cyberattacks are increasing as non-face-to-face environments such as telecommuting and telemedicine proliferate. Cyberattackers exploit vulnerabilities in remote systems and endpoint devices in major enterprises and infrastructures. To counter these attacks, fast detection and response are essential because advanced persistent threat (APT) attacks intelligently infiltrate endpoint devices for long periods and spread to large-scale environments. However, because conventional security systems are signature-based, fast detection of APT attacks is challenging, and it is difficult to respond flexibly to the environment. In this study, we propose an APT fast detection and response technique using open-source tools that improves the efficiency of existing endpoint information protection systems and swiftly detects the APT attack process. Performance test results based on realistic scenarios using the open-source APT attack library and MITER ATT&CK indicated that fast detection was possible with higher accuracy for the early stages of APT attacks in scenarios where endpoint attack detectors are interworking environments. © 2022 The Authors

CSI-Based Joint Location and Activity Monitoring for COVID-19 Quarantine Environments

The recent SARS-COV-2 virus, also known as COVID-19, badly affected the world's healthcare system due to limited medical resources for a large number of infected human beings. Quarantine helps in breaking the spread of the virus for such communicable diseases. This work proposes a nonwearable/contactless system for human location and activity recognition using ubiquitous wireless signals. The proposed method utilizes the channel state information (CSI) of the wireless signals recorded through a low-cost device for estimating the location and activity of the person under quarantine. We propose to utilize a Siamese architecture with combined one-dimensional convolutional neural networks (1-D-CNNs) and bi-directional long short-term memory (Bi-LSTM) networks. The proposed method provides high accuracy for the joint task and is validated on two real-world testbeds, first, using the designed low-cost CSI recording hardware, and second, on a public dataset for joint activity and location estimation. The human activity recognition (HAR) results outperform state-of-the-art machine and deep learning methods, and localization results are comparable with the existing methods. © 2001-2012 IEEE.

Real-world Human Gender Classification from Oral Region using Convolutional Neural Network

Gender classification is an important biometric task. It has been widely studied in the literature. Face modality is the most studied aspect of human -gender classification. Moreover, the task has also been investigated in terms of different face components such as irises, ears, and the periocular region. In this paper, we aim to investigate gender classification based on the oral region. In the proposed approach, we adopt a convolutional neural network. For experimentation, we extracted the region of interest using the RetinaFace algorithm from the FFHQ faces dataset. We achieved acceptable results, surpassing those that use the mouth as a modality or facial sub-region in geometric approaches. The obtained results also proclaim the importance of the oral region as a facial part lost in the Covid-19 context when people wear facial mask. We suppose that the adaptation of existing facial data analysis solutions from the whole face is indispensable to keep-up their robustness.

Pathogen diversity in meta-population networks

The pathogen diversity means that multiple strains coexist, and widely exist in the biology systems. The new mutation of SARS-CoV-2 leading to worldwide pathogen diversity is a typical example. What are the main factors of inducing the pathogen diversity? Previous studies indicated the pathogen mutation is the most important reason for inducing the pathogen diversity. The traffic network and gene network are crucial in shaping the dynamics of pathogen contagion, while their roles for the pathogen diversity still lacking a theoretical study. To this end, we propose a reaction–diffusion process of pathogens with mutations on meta-population networks, which includes population movement and strain mutation. We extend the Microscopic Markov Chain Approach (MMCA) to describe the model. Traffic networks make pathogen diversity more likely to occur in cities with lower infection densities. The likelihood of pathogen diversity is low in cities with short effective distances in the traffic network. Star-type gene network is more likely to lead to pathogen diversity than lattice-type and chain-type gene networks. When pathogen localization is present, infection is localized to strains that are at the endpoints of the gene network. Both the increased probability of movement and mutation promote pathogen diversity. The results also show that the population tends to move to cities with short effective distances, resulting in the infection density is high. © 2022 Elsevier Ltd

Analysis of Communicable Disease Symptoms Using Bag-of-Neural Network at Edge Networks

Considering the increasing growth of communicable diseases worldwide such as COVID-19, it is recommended to stay at home for patients with fewer chronic health problems. In recent times, the high chance of COVID-19 spread and the lack of an excellent remote monitoring system make the situation challenging for hospital administrators. Inspired by these challenges, in this paper, we develop a new edge-centric healthcare framework for remote health monitoring and disease prediction using Wearable Sensors (WSs) and advanced Machine Learning (ML) model, namely Bag-of-Neural Network (BoNN), respectively. The epidemic model collects the health symptoms of the patient using various a set of WSs and preprocesses the data in distributed edge devices for preparing a useful dataset. Finally, the proposed BoNN model is applied over the refined dataset for detecting COVID-19 disease at centralized cloud servers using a set of random neural networks. To demonstrate the efficiency of the proposed BoNN model over the standard ML models, the system is fine-tuned and trained over a synthetic COVID-19 dataset before being evaluated on a benchmark Brazil COVID-19 dataset using various performance metrics. The experimental results demonstrate that the proposed BoNN model achieves 99.8% accuracy while analyzing the Brazil dataset. © 2001-2012 IEEE.

Soft computing techniques for forecasting of COVID-19 in Pakistan

Novel Pandemic COVID-19 led globally to severe health barriers and financial issues in different parts of the world. The forecast on COVID-19 infections is significant. Demeanor vital data will help in executing policies to reduce the number of cases efficiently. Filtering techniques are appropriate for dynamic model structures as it provide reasonable estimates over the recursive Bayesian updates. Kalman Filters, used for controlling epidemics, are valuable in knowing contagious infections. Artificial Neural Networks (ANN) have generally been used for classification and forecasting problems. ANN models show an essential role in several successful applications of neural networks and are commonly used in economic and business studies. Long short-term memory (LSTM) model is one of the most popular technique used in time series analysis. This paper aims to forecast COVID-19 on the basis of ANN, KF, LSTM and SVM methods. We applied ANN, KF, LSTM and SVM for the COVID-19 data in Pakistan to find the number of deaths, confirm cases, and cases of recovery. The three methods were used for prediction, and the results showed the performance of LSTM to be better than that of ANN and KF method. ANN, KF, LSTM and SVM endorsed the COVID-19 data in closely all three scenarios. LSTM, ANN and KF followed the fluctuations of the original data and made close COVID-19 predictions. The results of the three methods helped significantly in the decision-making direction for short term strategies and in the control of the COVID-19 outbreak. © 2022 Faculty of Engineering, Alexandria University

Pandemic silver lining: how the COVID-19 pandemic helped women to beat the "gender stereotypes pandemic” in entrepreneurship

Purpose: This study aims to explore how the COVID-induced exogenous shock changed the prevalent occupational gender stereotypes in entrepreneurship in urban Turkey and presented an opportunity to some Turkish women to start their own business. Furthermore, this study investigated how women entrepreneurs' social networks helped them to clear the gendered hurdles that hindered their entrepreneurial endeavors in the times of COVID-19. Design/methodology/approach: Highly personal topics like gender stereotypes are complex and nebulous, as is entrepreneurship as lived experience. Moreover, the COVID-induced crisis complicates the context further, which is why the addressal of questions about gendered stereotypes in the process of entrepreneurship, and the role of social networks in that process, warrants a qualitative research approach. Consequently, this study relied on in-depth semistructured interviews for investigating the study's research questions. Findings: The findings suggest that research participants used the COVID-induced crisis conditions as an opportunity to beat the existing occupational gender stereotyping in entrepreneurship in the context of urban Turkey that opened a window of opportunity for the women participants to enter into entrepreneurship. In addition, social networks significantly helped the women entrepreneurs to acquire resources, and provided the leverage needed to clear the gendered hurdles that hindered the women's entrepreneurial endeavors. Originality/value: To the best of the author's knowledge, this study is among the first that generates insights into occupational gender stereotyping in entrepreneurship within the context of a developing country in the times of COVID-19 crisis. Hence, this study can help to understand the broader implications of the crisis like COVID-19 for gender-related beliefs and attitudes toward women entrepreneurship within the context of developing countries. © 2022, Emerald Publishing Limited.

Transnational lived citizenship turns local: Covid-19 and Eritrean and Ethiopian diaspora in Nairobi

This paper analyses how migrant community practices of transnational lived citizenship were altered by both, COVID-19 and the policy response from the Kenyan government. It is based on interviews with members of the Eritrean and Ethiopian diaspora residing in Nairobi. The paper demonstrates how policies introduced because of the pandemic caused migrant communities to lose local and remittance income. More than the loss of material resources, however, they were impacted by the elimination of social spaces that enable diaspora lives. These two dynamics have intensified a trend that may have been present before the pandemic, a local turn of transnational lived citizenship. By focusing on lived experiences and how they have been re-assessed during the pandemic, the paper argues that transnational lived citizenship is always in flux and can easily become reconfigured as more localized practices. The concept of transnational lived citizenship is demonstrated to be a useful lens for analysing shifting migrant livelihoods and belonging. © 2022 The Authors. Global Networks published by Global Networks Partnership and John Wiley & Sons Ltd.

Face Mask Detector Using Convolutional Neural Networks

A convolutional neural network (CNN) has one or more layers and is mainly used for image processing, classification, segmentation. CNN is commonly used for satellite image capturing or classifying hand written letters and digits. In this particular project, a convolutional neural network is trained to predict whether a person is wearing a mask or not. The training is done by using a set of masked and unmasked images which constitutes the training data. The performance of the trained model is evaluated on the test dataset, and the accuracy of the prediction is observed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

OTN-over-WDM optimization in 5G networks: key challenges and innovation opportunities

The continued growth of both mobile broadband and fixed broadband subscriptions as well as the added deployment of Internet of Things devices has led to making 5G networks a reality. More specifically, 5G networks are expected to support a diverse set of new applications/services in addition to existing applications/services from previous generations (2G/3G/4G). The COVID-19 pandemic has further increased the demand for such services which has resulted in a further surge in the Internet usage. Thus, 5G networks are expected to have a highly flexible architecture at all levels including at the radio, core, and transport levels. Optical Transport Networks (OTN) have been proposed as one potential and promising supporting technology for 5G networks at the transport level, particularly for next generation transport networks featuring large-granule broadband service transmissions. This is because it allows for more flexible, efficient, and dynamic networks. However, adopting and deploying OTNs in 5G networks comes with its own set of challenges including control, management, and orchestration of such networks as well as their security. Accordingly, this paper overviews 5G networks along with their requirements and provides a brief summary of OTNs and the corresponding optimization mechanisms. Additionally, this work discusses the challenges facing OTNs and their optimization within the context of 5G. Moreover, it outlines some of the key research areas and opportunities for innovation stemming from the data-driven intelligent networking paradigm using Machine Learning techniques.

Model-agnostic meta-learning-based region-adaptive parameter adjustment scheme for influenza forecasting

Deep learning models perform well when there is enough data available for training, but otherwise the performance deteriorates rapidly owing to the so-called data shortage problem. Recently, model-agnostic meta-learning (MAML) was proposed to alleviate this problem by embedding common prior knowledge from different tasks into the initial parameters of the target model. Data shortages are very common in regional influenza predictions, and MAML also often struggles with regional influenza forecasting, especially when region-specific knowledge, such as peak timing or intensity, varies. In this paper, we propose a novel MAML-based parameter adjustment scheme for influenza forecasting, called MARAPAS. The fundamental idea of our scheme is to adjust the initial parameters obtained from common knowledge to a target region by using adjustment variables. We experimentally show that MARAPAS outperforms other MAML-based methods, in terms of root mean square error and Pearson correlation coefficient. Particularly, this scheme improves the forecasting performance by up to 34 % compared with that of the state-of-the-art schemes. We also show the robust forecasting accuracy of our scheme and demonstrate its applicability by performing zero-shot COVID-19 forecasting. © 2022 The Author(s)

Misinformation about fake news: A systematic critical review of empirical studies on the phenomenon and its status as a ‘threat'

After the 2016 US presidential elections, the term ‘fake news' became synonymous with disinformation and a catch-all term for the problems that social networks were bringing to communication. Four years later, there are dozens of empirical studies that have attempted to describe and analyse an issue that, despite still being in the process of definition, has been identified as one of the key COVID-19 cyberthreats by Interpol, is considered a threat to democracy by many states and supranational institutions and, as a consequence, is subject to regulation or even criminalization. These legislative and criminal policy interventions form part of the first stage in the construction of a moral panic that may lead to the restriction of freedom of expression and information. By analysing empirical research that attempts to measure the extent of the issue and its impact, the present article aims to provide critical reflection on the process of constructing fake news as a threat. Via a systematic review of the literature, we observe, firstly, that the concept of fake news used in empirical research is limited and should be refocused because it has not been constructed according to scientific criteria and can fail to include relevant elements and actors, such as governments and traditional media. Secondly, the article analyses what is known scientifically about the extent, consumption and impact of fake news and argues that it is problematic to establish causal relationships between the issue and the effects it has been said to produce. This conclusion requires us to conduct further research and to reconsider the position of fake news as a threat as well as the resulting regulation and criminalization. © The Author(s) 2021.

Lung disease detection using Self-Attention Generative Adversarial Capsule network optimized with sun flower Optimization Algorithm

Lung cancer is the uncontrolled growth of abnormal cells in one or both lungs. This is one of the dangerous diseases. A lot of feature extraction with classification methods were discussed previously regarding this disease, but none of the methods give sufficient results, not only that, those methods have high over fitting problem, as a result, the detection accuracy was minimizing. Therefore, to overcome these issues, a Lung Disease Detection using Self-Attention Generative Adversarial Capsule Network optimized with Sun flower Optimization Algorithm (SA-Caps GAN-SFOA-LDC) is proposed in this manuscript. Initially, NIH chest X-ray image dataset is gathered through Kaggle repository to diagnose the lung disease. Then, the chests X-ray images are pre-processed by using the contrast limited adaptive histogram equalization (CLAHE) filtering method to eliminate the noise and to enhance the image quality. These pre-processed outputs are fed to feature extraction process. In the feature extraction process, the empirical wavelet transform method is used. These extracted features are given into Self-Attention based Generative Adversarial Capsule classifier for detecting the lung disease. The hyper parameters of SA-Caps GAN classifier is optimized using Sun flower Optimization Algorithm. The simulation is implemented in MATLAB. The proposed SA-Caps GAN-SFOA-LDC method attains higher accuracy 21.05%, 33.28%, 30.27%, 29.68%, 32.57% and 44.28%, Higher Precision 30.24%, 35.68%, 32.08%, 41.27%, 28.57% and 34.20%, Higher F-Score 32.05%, 31.05%, 36.24%, 30.27%, 37.59% and 22.05% analyzed with the existing methods, SVM-SMO-LDC, CNN-MOSHO-LDC, XGboost-PSO-LDC respectively. © 2022 Elsevier Ltd

Through Running and Integration of Federal Railroad Administration and Federal Transit Administration Regulated Passenger Trains: A Path Toward Mixing Intercity, Commuter, Metro, and Light Rail on the Same Tracks

U.S. rail transit (subways, metros, and light rail) and Federal Railroad Administration (FRA) regulated heavy rail (commu-ter, intercity and regional rail) operate completely separately in revenue service. This necessitates transfers between the modes at terminals. While not unique to the U.S.A., its version of this practice is extreme and prevents the development of robust seamless rail networks. Especially in the post-Covid environment, this leaves commuter rail in search of a mission and rail transit isolated from suburbs. This paper discusses the statutory regulatory scheme that divides the two modes in the U.S.A. It will analyze the justification for the segregation and its history. Such issues include potential collisions, weight, crashworthiness, electrification, signaling, loading gauge, platform height, and operating practices. This paper concludes that the regulatory barrier preventing an FRA-regulated train from going onto a non-FRA railroad are surmountable. Running through trains between the FRA-regulated system and the rail transit network would enhance regional networks. The ‘‘Karlsruhe model'' in Germany and the through running of regional trains onto the Tokyo subway network are two prime examples. Recent technological advances—such as dual mode battery multiple units, robust signaling systems such as Communications Based Train Control and Positive Train Control, and advanced car body designs able to deal with different loading gauges—make through running more practical. With little or no new right-of-way, it is possible to create far more useful rail networks. Potential shared networks at the conceptual level are discussed for Los Angeles, Seattle, Washington, D.C., Dallas, and Sacramento. © National Academy of Sciences: Transportation Research Board 2022.