In the hospital, a limited number of COVID-19 test kits are available due to the spike in cases every day. For this reason, a rapid alternative diagnostic option should be introduced as an automated detection method to prevent COVID-19 spreading among individuals. This article proposes multi-objective optimization and a deep-learning methodology for the detection of infected coronavirus patients with X-rays. J48 decision tree method classifies the deep characteristics of affected X-ray corona images to detect the contaminated patients effectively. Eleven different convolutional neuronal network-based (CNN) models were developed in this study to detect infected patients with coronavirus pneumonia using X-ray images (AlexNet, VGG16, VGG19, GoogleNet, ResNet18, ResNet500, ResNet101, InceptionV3, InceptionResNetV2, DenseNet201 and XceptionNet). In addition, the parameters of the CNN profound learning model are described using an emperor penguin optimizer with several objectives (MOEPO). A broad review reveals that the proposed model can categorise the X-ray images at the correct rates of precision, accuracy, recall, specificity and F1-score. Extensive test results show that the proposed model outperforms competitive models with well-known efficiency metrics. The proposed model is, therefore, useful for the real-time classification of X-ray chest images of COVID-19 disease.

Wireless Sensor Network is collection of wireless sensor nodes in which routing protocol is most challenging issue. Sensors have limited battery power. Battery extends the lifetime of sensor nodes. Energy utilization is one of the most important considerations. There are various routing techniques which increase lifetime of battery. One of the most energy efficient clustering routing protocols is LEACH. In this survey paper we represent LEACH & Extended version of LEACH routing protocol. Some issues are faced by LEACH. In this paper we represent how Extended LEACH version tackled these issues. We compare Extended LEACH Protocols with original LEACH. KeywordsCluster Head (CH), Time division multiple access (TDMA), Cluster member (CM), Base Station (BS), Wireless Sensor Network (WSN).

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Introduction:The main objective of a root canal sealer is to provide a fluid tight seal. The purpose of this systematic meta-analysis was to determine the relative toxicity of commonly used root canal sealers like zinc oxide eugenol, calcium hydroxide, and resin-based sealers.Materials and Methods:An online search was conducted in peer-reviewed journals listed in PubMed, Cochrane, EBSCO, and IndMed databases between 2000 and 2012). Statistical analysis was carried out by using analysis of variance (ANOVA) followed by post-hoc comparison by Bonferroni method. The comparison between toxicity at 24 h and between 3 and 7 days was done by using paired t-test for each sealer.Results:At 24 h, the relative biotoxicity of the three sealers reported was insignificant (P- value 0.29), but the difference in toxicity was found significant (P < 0.001) after 3 days.Conclusion:Calcium hydroxide sealer and zinc oxide eugenol were found to be significantly biotoxic as compared to resin-based sealers after 3 days.

WSN is a network comprising of various sensor nodes that are highly energy efficient used in various applications now a days. It helps in transmitting data from sensor nodes to the base stations in a highly reliable and secured manner. It plays a very important role in real world and integrates with many other technologies like Cloud Computing, Internet of Things (IoT). Different researchers continuously attempted to work on different constraints under Wireless Sensor Network which further becomes research motivation. In this paper, we highlighted various issues facing by different researchers while data transmission among sensor nodes. WSNs are used in various areas in all over the world, and significant progress has been made to expand the use of sensor nodes over the past two decades.