Objective:To evaluate the effect of percutaneous closure of patent ductus arteriosus (PDA) on left ventricular (LV) systolic and diastolic function in children.Background:Limited studies are available on alteration in LV hemodynamics, especially diastolic function, after PDA closure.Methods:Thirty-two consecutive children with isolated PDA treated by trans-catheter closure were studied. The LV systolic and diastolic function were assessed by two-dimensional (2D) echocardiography and tissue Doppler imaging 1 day before the PDA closure, on day 1, and on follow-up.Results:At baseline, none of the patients had LV systolic dysfunction. On day 1 post-PDA closure, 8 (25%) children developed LV systolic dysfunction. The baseline LV ejection fraction (LVEF), LV end-systolic dimension (LVESD), and PDA diastolic gradient predicted the post-closure LVEF. Patients who developed post-closure LV systolic dysfunction had poorer LV diastolic function than those who did not. LV diastolic properties improved after PDA closure; however, the improvement in LV diastolic properties lagged behind the improvement in the LV systolic function. All children were asymptomatic and had normal LVEF on follow up of >3 months.Conclusions:Percutaneous closure of PDA is associated with the reversible LV systolic dysfunction. Improvement in the LV diastolic function lags behind that in the LV systolic function.

Skin cancer is a deadly disease, and its early diagnosis enhances the chances of survival. Deep learning algorithms for skin cancer detection have become popular in recent years. A novel framework based on deep learning is proposed in this study for the multiclassification of skin cancer types such as Melanoma, Melanocytic Nevi, Basal Cell Carcinoma and Benign Keratosis. The proposed model is named as SCDNet which combines Vgg16 with convolutional neural networks (CNN) for the classification of different types of skin cancer. Moreover, the accuracy of the proposed method is also compared with the four state-of-the-art pre-trained classifiers in the medical domain named Resnet 50, Inception v3, AlexNet and Vgg19. The performance of the proposed SCDNet classifier, as well as the four state-of-the-art classifiers, is evaluated using the ISIC 2019 dataset. The accuracy rate of the proposed SDCNet is 96.91% for the multiclassification of skin cancer whereas, the accuracy rates for Resnet 50, Alexnet, Vgg19 and Inception-v3 are 95.21%, 93.14%, 94.25% and 92.54%, respectively. The results showed that the proposed SCDNet performed better than the competing classifiers.

Coronavirus (COVID-19) has adversely harmed the healthcare system and economy throughout the world. COVID-19 has similar symptoms as other chest disorders such as lung cancer (LC), pneumothorax, tuberculosis (TB), and pneumonia, which might mislead the clinical professionals in detecting a new variant of flu called coronavirus. This motivates us to design a model to classify multi-chest infections. A chest x-ray is the most ubiquitous disease diagnosis process in medical practice. As a result, chest x-ray examinations are the primary diagnostic tool for all of these chest infections. For the sake of saving human lives, paramedics and researchers are working tirelessly to establish a precise and reliable method for diagnosing the disease COVID-19 at an early stage. However, COVID-19’s medical diagnosis is exceedingly idiosyncratic and varied. A multi-classification method based on the deep learning (DL) model is developed and tested in this work to automatically classify the COVID-19, LC, pneumothorax, TB, and pneumonia from chest x-ray images. COVID-19 and other chest tract disorders are diagnosed using a convolutional neural network (CNN) model called CDC Net that incorporates residual network thoughts and dilated convolution. For this study, we used this model in conjunction with publically available benchmark data to identify these diseases. For the first time, a single deep learning model has been used to diagnose five different chest ailments. In terms of classification accuracy, recall, precision, and f1-score, we compared the proposed model to three CNN-based pre-trained models, such as Vgg-19, ResNet-50, and inception v3. An AUC of 0.9953 was attained by the CDC Net when it came to identifying various chest diseases (with an accuracy of 99.39%, a recall of 98.13%, and a precision of 99.42%). Moreover, CNN-based pre-trained models Vgg-19, ResNet-50, and inception v3 achieved accuracy in classifying multi-chest diseases are 95.61%, 96.15%, and 95.16%, respectively. Using chest x-rays, the proposed model was found to be highly accurate in diagnosing chest diseases. Based on our testing data set, the proposed model shows significant performance as compared to its competitor methods. Statistical analyses of the datasets using McNemar’s, and ANOVA tests also showed the robustness of the proposed model.

Contribution: Recently, real-time data warehousing (DWH) and big data streaming have become ubiquitous due to the fact that a number of business organizations are gearing up to gain competitive advantage. The capability of organizing big data in efficient manner to reach a business decision empowers data warehousing in terms of real-time stream processing. A systematic literature review for real-time stream processing systems is presented in this paper which rigorously look at the recent developments and challenges of real-time stream processing systems and can serve as a guide for the implementation of real-time stream processing framework for all shapes of data streams. Background: Published surveys and reviews either cover papers focusing on stream analysis in applications other than real-time DWH or focusing on extraction, transformation, loading (ETL) challenges for traditional DWH. This systematic review attempts to answer four specific research questions. Research Questions: 1)Which are the relevant publication channels for real-time stream processing research? 2) Which challenges have been faced during implementation of real-time stream processing? 3) Which approaches/tools have been reported to address challenges introduced at ETL stage while processing real-time stream for real-time DWH? 4) What evidence have been reported while addressing different challenges for processing real-time stream? Methodology: A systematic literature was conducted to compile studies related to publication channels targeting real-time stream processing/joins challenges and developments. Following a formal protocol, semi-automatic and manual searches were performed for work from 2011 to 2020 excluding research in traditional data warehousing. Of 679,547 papers selected for data extraction, 74 were retained after quality assessment. Findings: This systematic literature highlights implementation challenges along with developed approaches for real-time DWH and big data stream processing systems and provides their comparisons. This study found that there exists various algorithms for implementing real-time join processing at ETL stage for structured data whereas less work for un-structured data is found in this subject matter. INDEX TERMS Real-time stream processing, big data streaming, structured/un-structured data, ETL, systematic literature review Publication source Channel References No. %age IEEE Transactions on Knowledge and Data Engineering

Globally, coronavirus disease (COVID-19) has badly affected the medical system and economy. Sometimes, the deadly COVID-19 has the same symptoms as other chest diseases such as pneumonia and lungs cancer and can mislead the doctors in diagnosing coronavirus. Frontline doctors and researchers are working assiduously in finding the rapid and automatic process for the detection of COVID-19 at the initial stage, to save human lives. However, the clinical diagnosis of COVID-19 is highly subjective and variable. The objective of this study is to implement a multi-classification algorithm based on deep learning (DL) model for identifying the COVID-19, pneumonia, and lung cancer diseases from chest radiographs. In the present study, we have proposed a model with the combination of Vgg-19 and convolutional neural networks (CNN) named BDCNet and applied it on different publically available benchmark databases to diagnose the COVID-19 and other chest tract diseases. To the best of our knowledge, this is the first study to diagnose the three chest diseases in a single deep learning model. We also computed and compared the classification accuracy of our proposed model with four well-known pre-trained models such as ResNet-50, Vgg-16, Vgg-19, and inception v3. Our proposed model achieved an AUC of 0.9833 (with an accuracy of 99.10%, a recall of 98.31%, a precision of 99.9%, and an f1-score of 99.09%) in classifying the different chest diseases. Moreover, CNN-based pre-trained models VGG-16, VGG-19, ResNet-50, and Inception-v3 achieved an accuracy of classifying multi-diseases are 97.35%, 97.14%, 97.15%, and 95.10%, respectively. The results revealed that our proposed model produced a remarkable performance as compared to its competitor approaches, thus providing significant assistance to diagnostic radiographers and health experts.

Brain tumors are a deadly disease with a high mortality rate. Early diagnosis of brain tumors improves treatment, which results in a better survival rate for patients. Artificial intelligence (AI) has recently emerged as an assistive technology for the early diagnosis of tumors, and AI is the primary focus of researchers in the diagnosis of brain tumors. This study provides an overview of recent research on the diagnosis of brain tumors using federated and deep learning methods. The primary objective is to explore the performance of deep and federated learning methods and evaluate their accuracy in the diagnosis process. A systematic literature review is provided, discussing the open issues and challenges, which are likely to guide future researchers working in the field of brain tumor diagnosis.

Due to an increase in the number of devices, the Web of Things (WoT) has attracted a great deal of attention and focus from researchers in the past few years. The ultimate goal of Web of Things is to build an ideal search engine where the user or even devices can find other devices anywhere and at any time for using the resources of other devices. The purpose of the paper is to identify and to present the current research on Web of Things. Additionally, the paper focuses on the research gap that currently exists and on future needs in the domain of WoT. In Author's opinion, the literature review presented in the paper will effectively help the researchers in finding resources in WoT as it highlights the research gap in the domain of Web of Things and searching resources in WoT. The results of the review indicate that the current challenges for the Web of Things are dynamic searching, scalability, data integration, intent-based searching, etc. The focus of this paper is on dynamic searching.

Web of things (WoT) is an improved and most promising infrastructure of the internet of things (IoT) which permits the smart things to not only integrate to the internet but also to the web. It allows the users to share and create content as well as provide capabilities for data aggregation and analysis through a network to become part of the World Wide Web (W3). Despite these advances, it has shown several security challenges that need to be addressed for the successful deployment of WoT on a commercially variable and large scale. In this paper, authors have analyzed the most noticeable security challenges related to WoT such as unauthorized access, eavesdropping, denial of service attack, tempering, and impersonating, through an analysis of already published empirical studies. Further, we have discussed some of the available mechanisms to overcome security related issues while taking into account the network size and mobility. Authors have used Threat analysis and attack modeling methods to inform the users about defensive measures and to prevent security threats from taking advantage of system flaws Authors have provided the necessary insight into how security can be improved by using certain existing mechanisms and algorithms. The findings of the study revealed that security mechanisms to secure WoT are still immature and future research is required to resolve these challenges. INDEX TERMS Web of things, internet of things, security challenges, security mechanisms, World Wide Web, security analysis, attack modelling I.