Fast and Accurate Deep Learning Framework for Secure Fault Diagnosis in the Industrial Internet of Things

This article introduced a new deep learning framework for fault diagnosis in electrical power systems. The framework integrates the convolution neural network and different regression models to visually identify which faults have occurred in electric power systems. The approach includes three main steps: 1) data preparation;2) object detection;and 3) hyperparameter optimization. Inspired by deep learning and evolutionary computation (EC) techniques, different strategies have been proposed in each step of the process. In addition, we propose a new hyperparameters optimization model based on EC that can be used to tune parameters of our deep learning framework. In the validation of the framework's usefulness, experimental evaluation is executed using the well known and challenging VOC 2012, the COCO data sets, and the large NESTA 162-bus system. The results show that our proposed approach significantly outperforms most of the existing solutions in terms of runtime and accuracy.

Fuzzy Contrast Set Based Deep Attention Network for Lexical Analysis and Mental Health Treatment

Internet-delivered psychological treatments (IDPT) consider mental problems based on Internet interaction. With such increased interaction because of the COVID-19 pandemic, more online tools have been widely used to provide evidence-based mental health services. This increase helps cover more population by using fewer resources for mental health treatments. Adaptivity and customization for the remedy routine can help solve mental health issues quickly. In this research, we propose a fuzzy contrast-based model that uses an attention network for positional weighted words and classifies mental patient authored text into distinct symptoms. After that, the trained embedding is used to label mental data. Then the attention network expands its lexicons to adapt to the usage of transfer learning techniques. The proposed model uses similarity and contrast sets to classify the weighted attention words. The fuzzy model then uses the sets to classify the mental health data into distinct classes. Our method is compared with non-embedding and traditional techniques to demonstrate the proposed model. From the experiments, the feature vector can achieve a high ROC curve of 0.82 with problems associated with nine symptoms.

Persistent symptoms and spirometric abnormalities after COVID-19: A prospective cohort study

Introduction: The persistence of symptoms beyond 4 weeks of SARS-CoV-2 infection is referred to as long COVID. There is lack of data about the clinical determinants and natural history of this condition. Aims & objectives: We aimed to determine the risk factors, symptomatology, spirometric abnormalities and evolution of long COVID over a 1-year period. Method(s): We enrolled adult patients at 1-4 months after diagnosis of COVID-19. The demographics, COVID-19 history, clinical symptoms and spirometric results were recorded. Follow-up assessments were done in-person or telephonically at 4-8 months and 8-12 months, respectively. Result(s): We enrolled 128 patients (69% male) with median (IQR) age of 49 (37-56) years. Among these, 99 (77%) needed hospitalization, and 47 (37%) received oxygen for COVID-19. At the 1st assessment at median (IQR) of 64.5 (39.5-90) days after COVID-19 onset, 86 (67%) patients had symptoms, most commonly dyspnea (34%), fatigue (19%) and cough (19%). Reduced FVC (<80% of predicted) was found in 61% subjects. On multivariate analysis, the predictors of symptomatic long COVID were female gender (OR, 4.1;95% CI: 1.5-11;p=0.006) and dyspnea during acute COVID-19 (OR, 3.6;95% CI:1.1-11.3;p=0.03). The predictors of reduced FVC were dyspnea (OR, 4.4;95% CI: 1.6-12;p=0.004) and oxygen therapy (OR, 5.6;95% CI: 1.5-21;p=0.01) during acute COVID-19. The proportion of symptomatic patients reduced between 1st and 2nd assessment (67% vs 37%, p<0.001) and then plateaued at 3rd assessment (42%). Conclusion(s): Persistent symptoms are common over a 1-year follow-up among survivors of COVID-19. Female gender and dyspnea during acute COVID-19 may predict development of long COVID.

An Intelligent Collaborative Image-Sensing System for Disease Detection

With the growth of smart medical devices and applications in smart hospitals, home care facilities, nursing, and the Internet of Medical Things (IoMT) are becoming more ubiquitous. It uses smart medical devices and cloud computing services, and basic Internet of Things (IoT) technology, to detect key body indicators, monitor health situations, and generate multivariate data to provide just-in-time healthcare services. In this article, we present a novel collaborative disease detection system based on IoMT amalgamated with captured image data. The system can be based on intelligent agents, where every agent explores the interaction between different medical data obtained by smart sensor devices using reinforcement learning as well as targets to detect diseases. The agents then collaborate to make a reliable conclusion about the detected diseases. Intensive experiments were conducted using medical data. The results show the importance of using intelligent agents for disease detection in healthcare decision-making. Moreover, collaboration increases the detection rate, with numerical results showing the superiority of the proposed framework compared with baseline solutions for disease detection. © 2001-2012 IEEE.

Anti-Biotics And Anti-Viral Drugs In Covid Management: Systemic Review Based On Current Evidence

This review focuses on the management of Novel Corona Virus with antiviral drugs and antibiotics and therefore the worldwide dissemination of COVID-19 has been accompanied by increased use of antibiotics, according to this review, which focuses on the therapy of Novel Corona Virus with antiviral medicines and antiviral. This is linked to COVID-19 patients' priority of viral infections. In low-and middle-income countries, identifying viruses is difficult because to a lack of medical or cheap infrastructure that is easily accessible and inexpensive among diseases and pathogens. The possibility of COVID-19 spreading has increased public awareness of the need of antibiotic management systems, as well as infection control and control measures that can minimize microbial load. In underdeveloped nations, these measures are commonly employed. During the COVID-19 pandemic, studies were conducted as a test for worldwide antibiotic resistance. Respiratory problems are being blamed on the Novel Corona Virus that Include pneumonia, colds, sneezing and coughing, and other respiratory diseases. Humans are infected with the Coronavirus by airborne droplets. The World Health Organization has warned against visiting public areas and avoiding close contact with an infected individual. First, on December 31, 2019, the Coronavirus (2019-nCoV) was separated from the Wuhan market in China, resulting in the COVID-19 pandemic of extremely complicated viral illnesses. Patients with risk factors are more prone to develop secondary infections, which necessitate the use of antibiotics. Attempts to duplicate the medication, on the other hand, raised knowledge of the antibiotics' significance beyond infection management. Antiviral, immunomodulatory action, and unique pharmacokinetic profile of antibiotics play a significant part in the therapy of pneumonia;other benefits include cardiac safety, improved lung tissue access, and possible antiviral, and immunomodulation, but some adverse effects by usage. SARS-CoV-2 has generated an epidemic of the highly infectious new coronavirus 2019 (COVID-19), which poses a severe public health concern. Given the potential for a COVID-19 outbreak, a better knowledge of the virus is critical in the event of therapeutic alternatives. We offer a thorough analysis of antimicrobials and antiviral COVID-19 in this review. We also go about COVID-19's current treatments. Copyright © 2022, Anka Publishers. All rights reserved.

An Intelligent Collaborative Image-Sensing System for Disease Detection

With the growth of smart medical devices and applications in smart hospitals, home care facilities, nursing, as well as the Internet of Medical Things (IoMT) are becoming more ubiquitous. It uses smart medical devices as well as cloud computing services, as well as basic Internet of Things (IoT) technology, to detect key body indicators, monitor health situations, as well as generate multivariate data to provide just-in-time healthcare services. In this paper, we present a novel collaborative disease detection system based on IoMT as well as captured image data. The system can be based on intelligent agents, where each and every agent explores the interaction between different medical data obtained by smart sensor devices using reinforcement learning as well as targets to detect diseases. The agents then collaborate to make a reliable conclusion about the detected diseases. Intensive experiments were conducted using medical data. The results show the importance of using intelligent agents for disease detection in healthcare decision-making. Moreover, collaboration increases the detection rate, with numerical results showing the superiority of the proposed framework compared to baseline solutions for disease detection. IEEE

Unraveling of inhibitory potential of phytochemicals against SARS-CoV-2 using in-silico approach

A new mutated coronavirus named Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) was detected first time in Wuhan, China, in 2019. The novel SARS-CoV-2 causes a respiratory disease in humans called COVID-19. Currently, the COVID-19 spread globally with over 127 million confirmed cases, of which 110 million recovered with 27 million deaths. Several broad-spectrum antivirals, remdesivir, lopinavir/ritonavir, and arbidol and antimalarial drug hydroxychloroquine, have been suggested to treat COVID-19. However, now COVID vaccines are available for SARS-CoV-2. Alternatively, researchers have been searching for novel anti-SARS-CoV-2 phytochemicals from plants, to be used as a framework for the development of new therapeutic agents for COVID-19. For this, researchers have been performing large-scale screening of anti-SARS-CoV-2 phytochemicals using in-silico approaches, against the SARS-CoV-2 targets such as spike glycoprotein (S), chymotrypsin-like cysteine protease (3CLpro), papain-like cysteine protease (PLpro), and RNA-dependent RNA polymerase (RdRp). In this chapter, we have discussed in-silico approaches and their contribution to the robust screening of phytochemicals with anti-SARS-CoV-2 potential. © 2022 Elsevier Inc. All rights reserved.

Toward a Cognitive-Inspired Hashtag Recommendation for Twitter Data Analysis

This research investigates hashtag suggestions in a heterogeneous and huge social network, as well as a cognitive-based deep learning solution based on distributed knowledge graphs. Community detection is first performed to find the connected communities in a vast and heterogeneous social network. The knowledge graph is subsequently generated for each discovered community, with an emphasis on expressing the semantic relationships among the Twitter platform's user communities. Each community is trained with the embedded deep learning model. To recommend hashtags for the new user in the social network, the correlation between the tweets of such user and the knowledge graph of each community is explored to set the relevant communities of such user. The models of the relevant communities are used to infer the hashtags of the tweets of such users. We conducted extensive testing to demonstrate the usefulness of our methods on a variety of tweet collections. Experimental results show that the proposed approach is more efficient than the baseline approaches in terms of both runtime and accuracy.

Scars of a long battle - A rare case of post covid vanishing lung

Introduction: Vanishing lung syndrome , a primary bullous disease of the lung is defined as a large bulla occupying at least one third of a hemithorax. Usually it associated with riskfactors of smoking, marijuana abuse, alpha 1 antitrypsin deficiency. Here we present a rare case of vanishing lung syndrome developed in a post covid patient without any comorbidities making it a rare presentation. History: A 35year,male with no significant cigaratte smoking presented with acute onset dyspnoea along with dry cough and right sided chest pain for 1 week duration .no history of any recent trauma Past history of COVID 19 one month back, he was hospitilized was put on NIV and HFNC and was discharged on domicillary oxygen and other medications. Clinical Findings: On examination there was hyperresont note in right side along with diminished air entry in all areas in right side along with left side mammary, infraaxillary, infrascapular areas. Diagnosis and Management: Diagnosis was made with the help of contrast enchanced computed tomography aided by other serological and microbilogical workup. Patient was managed conservatively antibiotics ,analgesics and other supportive measures. Learning Points: We are well aware of lung fibrosis post covid , our intention was to throw light into the new entity of bullous lung disease Bullous lung disease (with or without pneumothorax) should be part of differential diagnosis in a patient returning with chest pain and dyspnoea after SARS-Cov-2 infection.CT imaging essential to differentiate radiographically presumed complex pneumothoraces from large bullae to prevent erroneous chest drain insertion into a bulla.

A case series on bullous lung disease in post COVID patients

Introduction: After the aftermath of covid 19 we are left to learn and understand the multiple respiratory manifestations of post covid 19 sequele The presence of bullous lung disease in post covid patients is one such a rare entity ,has been infrequently reported, studied Eventhough the exact mechanism of formation of bullae in post covid 19 are unknown, an emerging association has been observed.A bulla is an air containing space within the lung parenchyma that arises from destruction dilatation and confluence of airspaces distal to terminal bronchioles and is larger than 1 cm in diameter .Its wall are composed of attenuated and compressed parenchyma. Here in this case series we describe this unique presentation of bullous lung diseases in post covid 19 patients. Case Series: Here wepresent a case series of 7 patients without any known comorbidities who were diagnosed with post covid bullous lung disease Diagnosis was made with the help of contrast enchanced computed tomography aided by other serological and microbilogical workup. Patient was managed conservatively antibiotics, analgesics and other supportive measures. Learning Points: We are well aware of lung fibrosis post covid, our intention was to throw light into the new entity of bullous lung disease in post covid period. Bullous lung disease (with or without pneumothorax) should be part of differential diagnosis in a patient returning with chest pain and dyspnoea after SARSCov- 2 infection. CT imaging essential to differentiate radiographically presumed complex pneumothoraces from large bullae to prevent erroneous chest drain insertion into a bulla.

Mortality Rate Extrapolation Based on Symptomatic Symptoms of Novel Corona Virus

Covid-19 is a medical pandemic originated in China due to the virus, eventually affecting all parts of the world. The study deals with the mortality rate in India using various machine learning techniques. The secondary data collection is done using various websites like Kaggle. The dataset collected contains lot of noise which is pre-processed and the inputs are converted into vectors using One-Hot encoding. The results are compared using various regression techniques and various output parameters like Accuracy, Variance, Max Square etc. The results indicated that lasso regression gives the best result. © 2021 IEEE.

An Introduction to Wearable Sensor Technology

With the changes caused by the constant evolution of the Internet of Things, the healthcare area could not be left behind. With the introduction of 5G technology and modern devices, a better, real-time remote healthcare monitoring is shaping up to become the next step toward the future of medical treatment, and this is only becoming more present in a world, where at any time things might change and a presential form of monitoring can be difficult to achieve, as was the case with COVID-19. In this chapter, we take a look at some of the work that has been done in this area, as well as some projects and technologies planned for the future, in the hope to better understand how this technology works and what can be expected from it. In this chapter, the most recent papers on this field are reviewed, offering the reader a summary, providing an accessible entry point for those interested in delving into it. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Blockchain Integrated Crowdfunding Platform for Enhanced Secure Transactions

Building a project starts with innovation and idea but funding is a part that decides the evolution of the project from idea to product. During the last decade funding campaign and project via crowdfunding has become a common theme. With the COVID situation, it has become a necessity for NGOs, campaigns, projects, start-ups to consider the concept of crowdfunding and build seed funds through it, therefore organizations are trying to create a safe, secure, and fraud-proof gateway for people to get funds and as well as provide funds, which is hard in the current time of pandemic and technical advancements. It has become quite easy to fall into a trap and lose your hard-earned funds with cybercrimes as identity fraud, theft of financial data, and internet fraud. This work is an attempt to create a secure, efficient, and viable tool for crowdfunding. The solution proposed has Blockchain integrated to build trust among the funders and those raising these funds, with its characteristics as decentralized, irrefutable, distributed ledgers, consensus, and faster settlement. The proposed model has been built on the smart contract protocol, created for crowdfunding transactions, campaigns for the proposed model was implemented on remix ide, this will create a campaign for those in need of funds and for donors to donate funds to these campaigns. The campaign master has the right to reject or accept requests thus creating fraud and a tamper-proof environment. The model has been subjected to positive negative unit and integration tests on mocha, the efficiency of the model obtained is at par with existing solutions with an added edge on security via smart contract protocols. © 2021 IEEE.

Medical Image Classification based on an Adaptive Size Deep Learning Model

With the rapid development of Artificial Intelligence (AI), deep learning has increasingly become a research hotspot in various fields, such as medical image classification. Traditional deep learning models use Bilinear Interpolation when processing classification tasks of multi-size medical image dataset, which will cause the loss of information of the image, and then affect the classification effect. In response to this problem, this work proposes a solution for an adaptive size deep learning model. First, according to the characteristics of the multi-size medical image dataset, the optimal size set module is proposed in combination with the unpooling process. Next, an adaptive deep learning model module is proposed based on the existing deep learning model. Then, the model is fused with the size fine-tuning module used to process multi-size medical images to obtain a solution of the adaptive size deep learning model. Finally, the proposed solution model is applied to the pneumonia CT medical image dataset. Through experiments, it can be seen that the model has strong robustness, and the classification effect is improved by about 4% compared with traditional algorithms.

Cybersecurity Education in Rural Indigenous Canada

Canada is one of the largest countries by area in the world. Rural populations of Canada have for decades suffered from lacking the infrastructure necessary to offer technological advancements to its citizens. Finally, in recent years, we have seen our Canadian government take the initiative to ensure our remote citizens have the Internet capabilities needed to operate in our digital world. With advancement also comes challenges, which in the context of this paper involves the additional risk to Canadian citizens to cyber-threats. Through an educational initiative funded Nationally, this paper discusses the efforts to offer cybersecurity education to rural Indigenous citizens in the province of Manitoba, Canada. We discuss in detail our initial progress, recent workshops with results, and the future of this program post-COVID-19. © 2021 IEEE.

Journey of customers in this digital era: Understanding the role of artificial intelligence technologies in user engagement and conversion

Purpose: The first research objective is to understand the role of digital [artificial intelligence (AI)] technologies on user engagement and conversion that has resulted in high online activities and increased online sales in current times in India. In addition, combined with changes such as social distancing and lockdown due to the COVID-19 pandemic, digital disruption has largely impacted the old ways of communication both at the individual and organizational levels, ultimately resulting in prominent social change. While interacting in the virtual world, this change is more noticeable. Therefore, the second research objective is to examine if a satisfying experience during online shopping leads to repurchase intention. Design/methodology/approach: Using primary data collected from consumers in a developing economy (India), we tested the theoretical model to further extend the theoretical debate in consumer research. Findings: This study empirically tests and further establishes that deploying AI technologies have a positive relationship with user engagement and conversion. Further, conversion leads to satisfying user experience. Finally, the relationship between satisfying user experience and repurchase intention is also found to be significant. Originality/value: The uniqueness of this study is that it tests few key relationships related to user engagement during this uncertain period (COVID-19 pandemic) and examines the underlying mechanism which leads to increase in online sales. © 2021, Emerald Publishing Limited.

Organizational Level Responses to the COVID-19 Outbreak: Challenges, Strategies and Framework for Academic Institutions

The outbreak of the novel coronavirus, severe acute respiratory syndrome (SARS)-CoV-2, has gained unprecedented global attention. SARS-CoV-2, which causes the newly described coronavirus disease 2019 (COVID-19), has affected millions of people and led to over 1.9 million deaths worldwide by the beginning of January 2021. Several governments have opted for lockdown as one of the measures to combat the rapidly increasing number of COVID-19 cases. Academic institutions (i.e., universities, colleges, research centers and national laboratories), which are home to thousands of students, researchers, technicians, and administrative staff, have strictly followed government regulations. Due to the lockdown, the majority of academics have been facing various challenges, especially in transitioning from classroom to remote teaching and conducting research activities from a home office. This article from an early-career researchers' perspective addresses the common challenges that academic institutions have encountered and possible strategies they have adopted to mitigate those challenges at the individual organizational level. Furthermore, we propose a framework to facilitate the handling of such crisis in any near future at the organizational level. We hope academics, policymakers and (non) government organizations across the globe will find this perspective a call to better improve the overall infrastructure of academic institutions.

Knowledge, attitude and practice regarding covid-19 pandemic among dental practitioners of india: A questionnaire-based survey

Background-Owing to high viral load in saliva, dental practitioners are not only susceptible for exposure during Covid-19 outbreak but also post pandemic era. During the period of evolving facts and recommendations of WHO for maintaining precautions this study is an effort to understand the preparedness of dental practitioners to resume their practice. Objectives-The objective of this study is to evaluate the knowledge, attitude and practice of dental practitioners regarding the Covid-19 pandemic. Methods-This cross-sectional study was conducted among dental practitioners of India through an online questionnaire-based survey to collect data. The questionnaire was divided into sections containing structured multiple-choice questions about the knowledge, attitude and practice of dental practitioners. Result-Upon analyzing 311 responses it has been observed that majority of dental practitioners were aware of common symptoms of Covid-19 and about the modes of transmission of the disease. They possess adequate knowledge about use of Personal Protective Equipment (PPE) with 88.4 % suggesting to use it while performing aerosol generating procedure. Their awareness about a Covid-19 patient becoming noninfectious is inadequate with only 10% reporting 30 days. Attitude and practice of dental practitioners regarding Covid-19 were stratified on the basis of years of practice. Conclusions-Dental practitioners possess adequate knowledge about standard precaution protocol although they are less aware about transmission-based precaution specific to Covid-19 situation. This study attempts to highlight some facts about Covid-19 which will enlighten the dental practitioners before resuming practice.

Clinicodemographic profile and outcomes of severe COVID-19 infection in young adults

Introduction: COVID-19 is a respiratory and systemic disorder caused by the SARS-CoV-2 virus with a range of severity from mild respiratory symptoms to severe lung injury, multiorgan failure, and death. The main risk factors of the disease are increased age and underlying comorbidity. Newer reports show that younger patients can also suffer from severe COVID pneumonia of which the data are limited. This study intends to uncover the factors that resulted in severe COVID-19 infection in young adults. Objectives: To study the clinicodemographic profile and outcomes of severe COVID-19 infection in young adults. Materials and methods: This single-center retrospective study included 163 hospitalized patients in the age group 18 to 35 years diagnosed with severe COVID-19 infection at a tertiary care hospital in Uttar Pradesh from July 2020 to November 2020. Details about patient's demographics, clinical features, previous comorbidities, laboratory and radiological investigations, and hospital outcomes were obtained from patient records and analyzed. Results: Out of 163 patients, 60.1% (98) were males and 39.8% (65) were females. The most common comorbidity was diabetes 68.7%, hyperlipidemia 33.1% and obesity 32%. 30.6.% (51) of patients were smokers. Patients presented with shortness of breath (66.9%), cough (65.6%) and fever (60.7%) respectively. Multilobe infiltrates were found in chest xray of (75.4%) patients,. Mean length for ICU stay was 15.5 days (range 3-46). Mechanical ventilation was required in 26.9% of patients .In patients requiring mechanical ventilation, 17 (38%) were discharged and 27(62.8%) died. Of the mechanically ventilated patients 44 had abnormal BMI. Overall mortality was 27% (37patients). Discussions: There was a male sex predominance with diabetes. Obesity, smoking, and hyperlipidemia were the major risk factors. The major presenting symptoms in these patients were shortness of breath, cough, and fever. Only a quarter of patients required mechanical ventilation, and in those obesity was found to be a major risk factor. Conclusion: Our study provides insight into presenting characteristics, demographics, and overall outcomes of severe COVID-19 infection in young adults. The preconceived notion of COVID-19 being a disease of the elderly should be changed. In medical emergencies like the COVID pandemic, it is very important to analyze patient demographics to identify the population at risk. Such knowledge not only allows us to produce strategies to help control the spread of disease but also helps us to risk stratify to prevent mortality. It is crucial to learn from an epidemic like this so we can be better prepared for the future.