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BackgroundShortly after the emergence of COVID-19, researchers rapidly mobilized to study numerous aspects of the disease such as its evolution, clinical manifestations, effects, treatments, and vaccinations. This led to a rapid increase in the number of COVID-19–related publications. Identifying trends and areas of interest using traditional review methods (eg, scoping and systematic reviews) for such a large domain area is challenging. ObjectiveWe aimed to conduct an extensive bibliometric analysis to provide a comprehensive overview of the COVID-19 literature. MethodsWe used the COVID-19 Open Research Dataset (CORD-19) that consists of a large number of research articles related to all coronaviruses. We used a machine learning–based method to analyze the most relevant COVID-19–related articles and extracted the most prominent topics. Specifically, we used a clustering algorithm to group published articles based on the similarity of their abstracts to identify research hotspots and current research directions. We have made our software accessible to the community via GitHub. ResultsOf the 196,630 publications retrieved from the database, we included 28,904 in our analysis. The mean number of weekly publications was 990 (SD 789.3). The country that published the highest number of COVID-19–related articles was China (2950/17,270, 17.08%). The highest number of articles were published in bioRxiv. Lei Liu affiliated with the Southern University of Science and Technology in China published the highest number of articles (n=46). Based on titles and abstracts alone, we were able to identify 1515 surveys, 733 systematic reviews, 512 cohort studies, 480 meta-analyses, and 362 randomized control trials. We identified 19 different topics covered among the publications reviewed. The most dominant topic was public health response, followed by clinical care practices during the COVID-19 pandemic, clinical characteristics and risk factors, and epidemic models for its spread. ConclusionsWe provide an overview of the COVID-19 literature and have identified current hotspots and research directions. Our findings can be useful for the research community to help prioritize research needs and recognize leading COVID-19 researchers, institutes, countries, and publishers. Our study shows that an AI-based bibliometric analysis has the potential to rapidly explore a large corpus of academic publications during a public health crisis. We believe that this work can be used to analyze other eHealth-related literature to help clinicians, administrators, and policy makers to obtain a holistic view of the literature and be able to categorize different topics of the existing research for further analyses. It can be further scaled (for instance, in time) to clinical summary documentation. Publishers should avoid noise in the data by developing a way to trace the evolution of individual publications and unique authors.

BackgroundIn December 2019, COVID-19 broke out in Wuhan, China, leading to national and international disruptions in health care, business, education, transportation, and nearly every aspect of our daily lives. Artificial intelligence (AI) has been leveraged amid the COVID-19 pandemic; however, little is known about its use for supporting public health efforts. ObjectiveThis scoping review aims to explore how AI technology is being used during the COVID-19 pandemic, as reported in the literature. Thus, it is the first review that describes and summarizes features of the identified AI techniques and data sets used for their development and validation. MethodsA scoping review was conducted following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). We searched the most commonly used electronic databases (eg, MEDLINE, EMBASE, and PsycInfo) between April 10 and 12, 2020. These terms were selected based on the target intervention (ie, AI) and the target disease (ie, COVID-19). Two reviewers independently conducted study selection and data extraction. A narrative approach was used to synthesize the extracted data. ResultsWe considered 82 studies out of the 435 retrieved studies. The most common use of AI was diagnosing COVID-19 cases based on various indicators. AI was also employed in drug and vaccine discovery or repurposing and for assessing their safety. Further, the included studies used AI for forecasting the epidemic development of COVID-19 and predicting its potential hosts and reservoirs. Researchers used AI for patient outcome–related tasks such as assessing the severity of COVID-19, predicting mortality risk, its associated factors, and the length of hospital stay. AI was used for infodemiology to raise awareness to use water, sanitation, and hygiene. The most prominent AI technique used was convolutional neural network, followed by support vector machine. ConclusionsThe included studies showed that AI has the potential to fight against COVID-19. However, many of the proposed methods are not yet clinically accepted. Thus, the most rewarding research will be on methods promising value beyond COVID-19. More efforts are needed for developing standardized reporting protocols or guidelines for studies on AI.

BackgroundThe recent coronavirus disease (COVID-19) pandemic is taking a toll on the world’s health care infrastructure as well as the social, economic, and psychological well-being of humanity. Individuals, organizations, and governments are using social media to communicate with each other on a number of issues relating to the COVID-19 pandemic. Not much is known about the topics being shared on social media platforms relating to COVID-19. Analyzing such information can help policy makers and health care organizations assess the needs of their stakeholders and address them appropriately. ObjectiveThis study aims to identify the main topics posted by Twitter users related to the COVID-19 pandemic. MethodsLeveraging a set of tools (Twitter’s search application programming interface (API), Tweepy Python library, and PostgreSQL database) and using a set of predefined search terms (“corona,” “2019-nCov,” and “COVID-19”), we extracted the text and metadata (number of likes and retweets, and user profile information including the number of followers) of public English language tweets from February 2, 2020, to March 15, 2020. We analyzed the collected tweets using word frequencies of single (unigrams) and double words (bigrams). We leveraged latent Dirichlet allocation for topic modeling to identify topics discussed in the tweets. We also performed sentiment analysis and extracted the mean number of retweets, likes, and followers for each topic and calculated the interaction rate per topic. ResultsOut of approximately 2.8 million tweets included, 167,073 unique tweets from 160,829 unique users met the inclusion criteria. Our analysis identified 12 topics, which were grouped into four main themes: origin of the virus; its sources; its impact on people, countries, and the economy; and ways of mitigating the risk of infection. The mean sentiment was positive for 10 topics and negative for 2 topics (deaths caused by COVID-19 and increased racism). The mean for tweet topics of account followers ranged from 2722 (increased racism) to 13,413 (economic losses). The highest mean of likes for the tweets was 15.4 (economic loss), while the lowest was 3.94 (travel bans and warnings). ConclusionsPublic health crisis response activities on the ground and online are becoming increasingly simultaneous and intertwined. Social media provides an opportunity to directly communicate health information to the public. Health systems should work on building national and international disease detection and surveillance systems through monitoring social media. There is also a need for a more proactive and agile public health presence on social media to combat the spread of fake news.

Machine learning are vulnerable to the threats. The Intruders can utilize the malicious nature of the nodes to attack the training dataset to worsen the process and manipulate the learning and make the over all system with less efficiency and performance. The optimized poison attack procedures are already introduced to estimate the overall bad scenario, design the intrusion as bi‐level optimization and it is considered computational complexity is high and demanding, in contrary the applicability is limited such models deep neural networks. In this research papers, we have proposed, novel proposed system, poisoning attacks against the Machine learning training dataset, including the genuine data points that reduce the accuracy of the classifier in the process of training. The proposed system have 3 components of Generative Adverserial networks (GAN) generator, discriminator, and the target classifier. The proposed system allows to detect the vulnerability easy and it can be found as similar as realistic attacks to detect the area where the underlying data distribution have more possibility of poising attack which cause vulnerability to the network. Our experimentation, proves the claim our that the proposed model is effective on compromising the classifiers uses the machine learning algorithms and also deep learning networks. [ABSTRACT FROM AUTHOR]

Breast cancer is the most menacing cancer among all types of cancer in women around the globe. Early diagnosis is the only way to increase the treatment options which then decreases the death rate and increases the chance of survival in patients. However, it is a challenging task to differentiate abnormal breast tissues from normal tissues because of their structure and unclear boundaries. Therefore, early and accurate diagnosis and classification of breast lesions into malignant or benign lesions is an active domain of research. Over the decade, numerous artificial neural network (ANN)-based techniques were adopted in order to diagnose and classify breast cancer due to the unique characteristics of learning key features from complex data via a training process. However, these schemes have limitations like slow convergence and longer training time. To address the above mentioned issues, this paper employs a meta-heuristic algorithm for tuning the parameters of the neural network. The main novelty of this work is the computer-aided diagnosis scheme for detecting abnormalities in breast ultrasound images by integrating a wavelet neural network (WNN) and the grey wolf optimization (GWO) algorithm. Here, breast ultrasound (US) images are preprocessed with a sigmoid filter followed by interference-based despeckling and then by anisotropic diffusion. The automatic segmentation algorithm is adopted to extract the region of interest, and subsequently morphological and texture features are computed. Finally, the GWO-tuned WNN is exploited to accomplish the classification task. The classification performance of the proposed scheme is validated on 346 ultrasound images. Efficiency of the proposed methodology is evaluated by computing the confusion matrix and receiver operating characteristic (ROC) curve. Numerical analysis revealed that the proposed work can yield higher classification accuracy when compared to the prevailing methods and thereby proves its potential in effective breast tumor detection and classification. The proposed GWO-WNN method (98%) gives better accuracy than other methods like SOM-SVM (87.5), LOFA-SVM (93.62%), MBA-RF (96.85%), and BAS-BPNN (96.3%) [ABSTRACT FROM AUTHOR]

Block chain provides an innovative solution to information storage, transaction execution, security, and trust building in an open environment. The block chain is technological progress for cyber security and cryptography, with efficiency-related cases varying in smart grids, smart contracts, over the IoT, etc. The movement to exchange data on a server has massively increased with the introduction of the Internet of Things. Hence, in this research, Splitting of proxy re-encryption method (Split-PRE) has been suggested based on the IoT to improve security and privacy in a private block chain. This study proposes a block chain-based proxy re-encryption program to resolve both the trust and scalability problems and to simplify the transactions. After encryption, the system saves the Internet of Things data in a distributed cloud. The framework offers dynamic, smart contracts between the sensor and the device user without the intervention of a trustworthy third party to exchange the captured IoT data. It uses an efficient proxy re-encryption system, which provides the owner and the person existing in the smart contract to see the data. The experimental outcomes show that the proposed approach enhances the efficiency, security, privacy, and feasibility of the system when compared to other existing methods. [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.comnet.2005.09.001 Byline: Lotfi Mhamdi (a), Mounir Hamdi (b), Christopher Kachris (a), Stephan Wong (a), Stamatis Vassiliadis (a) Keywords: Buffered crossbar fabric; Matched scheduling; Priority Abstract: As buffer-less crossbar scheduling algorithms reach their practical limitations due to higher port numbers and data rates, internally buffered crossbar (IBC) switches have gained a lot of interest recently due to their great potential in solving the complexity and scalability issues faced by their buffer-less predecessors. The IBC switching architecture combined with the virtual output queueing (VOQ) architecture was shown, through distributed scheduling algorithms, to be able to sustain the current and expected increases in Internet throughput rates. Due to the architectural similarity between the input queued (IQ) and IBC switches, all the algorithms proposed for the latter were just a simple mapping of earlier algorithms proposed for the former. In this paper, we propose a set of scheduling schemes that are purely advocated for the VOQ/IBC switch architecture. We first address the issue of the internal buffers importance in the arbitration process. We propose a weighted scheduling algorithm, named Critical internal Buffer First (CBF), which takes full advantage of the internal buffer elements and makes its decision exclusively on the internal buffer information. Second, in order to simplify the scheduling scheme and make it practical, we propose a class of scheduling algorithms, named Current Arrival First-Priority Removal (CAF-PRMV) that use priority levels instead of weights. We argue that the interaction, through the internal buffer element, between the input and output schedulers is very important in designing such practical and highly scalable schemes for the IBC switching architecture. Our hardware implementation, in reconfigurable logic, shows that our CAF-PRMV class of algorithms can sustain a 10Gbps line speed for a 32x32 VOQ/IBC switch. Author Affiliation: (a) Computer Engineering Laboratory, Delft University of Technology, The Netherlands (b) Department of Computer Science, The Hong Kong University of Science and Technology, Clear Water Bay, Sai Kung, Kowloon, Hong Kong Article History: Received 28 July 2004; Revised 29 April 2005; Accepted 1 September 2005 Article Note: (miscellaneous) Responsible Editor: G. Parr

The design of the medium-access control (MAC) protocol is the most crucial aspect for high-speed and high-performance local and metropolitan area networks, since the decisions made at this level determine the major functional characteristics of these networks. Most of the MAC protocols proposed in the literature are not suitable for multimedia applications, since they have been designed with one generic traffic type in mind. As a result, they perform quite well for the traffic types they have been designed for, but poorly for other traffic streams with different characteristics. In this paper, we propose an integrated MAC protocol called the Multimedia-MAC (M-MAC), which integrates different MAC protocols into a hybrid protocol in a shared-medium network to efficiently accommodate various types of multimedia traffic streams with different characteristics and quality-of-service demands, namely, a constant-bit-rate traffic, bursty traffic (say, variable-bit-rate traffic), and emergency messages (say, control messages). We have developed a mathematical framework for the analysis and performance evaluation of our M-MAC protocol, which involves a queueing system with vacation. We have applied our M-MAC design approach to a wavelength-division multiplexing network, and evaluated its performance under various traffic conditions. Index Terms--Performance evaluation, quality-of-service (QoS) guarantees, medium-access control (MAC) protocols, wavelength-division multiplexing (WDM) networks.

We address the problem of the performance analysis of the stochastic fair sharing (SFS) algorithm for fair link sharing. The SFS scheme has been proposed to carry out a fair link sharing and fair sharing among virtual private networks. Depending upon the current utilization and provisioned capacities of the classes, the SFS admission control algorithm decides which sessions to accept and which to reject. In this letter, we undertake the performance evaluation of the SFS scheme analytically. We explore the tradeoff between fairness and the blocking probability by varying the trunk reservation parameter. The results show that the analytical performance measure agrees well with the simulation results. Index Terms--Link sharing, routing and resource allocation, stochastic fair sharing (SFS), virtual private networks (VPNs).

In this paper, we investigate the problem of routing and wavelength assignment as well as the problem of the placement of wavelength converters in all-optical networks. In particular, we present a general framework, based on the blocking island (BI) paradigm, to illustrate how it can be used to solve these problems in a unified way. We first give a brief introduction about the BI graph network model, and then use this model to derive simple and general algorithms that can be used in various applications in optical networks. We discuss the implementation issues of our algorithms and present simulation results to evaluate and compare our solutions with other heuristic algorithms under both static and dynamic traffic assumptions. Index Terms--Blocking island (BI) paradigm, routing, wavelength assignment, wavelength converters.

A Video-on-Demand (VOD) system delivers videos on demand over an installed network. Due to the large size of digitized videos, expensive video servers with high I/O capability are needed in order to provide VoD services in metropolitan areas. In addition, there is a great need for efficient networking distribution/interaction schemes so that the video servers can serve as many clients as possible. In particular, because of scalability problems, the classical unicast VoD system is not suitable for large-scale deployments. In this paper, a highly scalable VoD system with a low per-user cost is described and evaluated. We first analyze the performance degradation problems using recently proposed VoD systems, namely batched and centralized-buffer VoD systems that occur during the handling of interactions. Then a new system called the Multi-Batch Buffer (MBB) system, which attempts to solve these problems, is proposed. The proposed system handles a majority of interaction requests by scalable buffering techniques employed in the buffer of the local servers and the set-top boxes (STBs). We have performed extensive simulation for the analysis and performance evaluation of our proposed VoD system. The simulation results will demonstrate that our VoD system is very scalable and outperforms related state-of-the-art VoD systems. Index Terms--Multicast, performance evaluation, slip-and-merge, Video-on-Demand.