Vignan University

About: Vignan University is a education organization based out in Guntur, Andhra Pradesh, India. It is known for research contribution in the topics: Control theory & CMOS. The organization has 1138 authors who have published 1381 publications receiving 7798 citations.

Exploration of mechanical and durability characteristics of fly ash-GGBFS based green geopolymer concrete

Abstract: In this paper, the use and effect of Ground Granulated Blast Furnace Slag (GGBFS) addition to fly ash (FA) on the performance of Geopolymer Concrete was presented. A reference of Ordinary Portland cement concrete (OPC) mix was used to compare with geopolymer concrete. The effect of different proportions of GGBFS addition, ambient curing, and curing age on the properties of geopolymer concrete was reported. The concentration of sodium hydroxide solution with 8 M and solution to binder ratio as 0.4 were taken for all the mixes of geopolymer concrete. This paper reported an investigation data on the mechanical and durability characteristics of fly ash-GGBFS based geopolymer concrete and that data was compared with the control mix (OPC). SEM analysis was done on selected samples to estimate the microstructural characteristics. The results concluded that a geopolymer concrete mix containing 60% GGBFS and 40% fly ash at 28 days of ambient temperature achieved maximum compressive strength (55.63 MPa) and further performed durable under severe environmental conditions.

Expression Levels of Candidate Circulating microRNAs in Early-Onset Neonatal Sepsis Compared With Healthy Newborns.

Abstract: The high mortality rate of neonatal sepsis is directly connected with time-consuming diagnostic methods that have low sensitivity and specificity. The need of the hour is to develop novel diagnostic techniques that are rapid and more specific. In this study, we estimated the expression levels of circulating microRNAs (miRNAs) that are involved in regulating immune response genes and underlying inflammatory responses, which may be used for sepsis diagnosis. The total circulating miRNA was isolated and the candidate miRNAs (miR-132, miR-146a, miR-155, and miR-223) were quantified by real-time polymerase chain reaction technique. Statistical analysis revealed that miR-132 (P < .01) and miR-223 (P < .05) were downregulated in septic newborns compared with healthy babies. The decrease in expression of miR-132 and miR-223 may be associated with increased expression of immune-related genes involved in TLR (Toll-like receptor) signaling pathway. Further case-control studies with large sample size are required to identify the potential of miRNAs in neonatal sepsis diagnosis.

Smart Phishing Detection in Web Pages using Supervised Deep Learning Classification and Optimization Technique ADAM

Abstract: Phishing is the process of portraying malignant web pages in the place of genuine web pages to obtain important and delicate information from the end-user. Nowadays phishing is considered as one of the most serious threats to web security. Most of the existing techniques for phishing detection use Bayesian classification for differentiating malignant web pages from genuine web pages. These methods work well if a dataset contains less no of web pages and they provide accuracy up to 90 percent. In recent years the size of the web is increasing tremendously and the existing methods have not provideda good enough accuracy for large datasets. So this paper proffers an innovative approach to identify phishing websites using hyperlinks available in the source code of the HTML page in the corresponding website. The proposed method uses a feature vector with 30 parameters to detect malignant web pages. These features are used in training the supervised Deep Neural Network model with Adam optimizer for differentiating fraudulent websites from genuine websites. The proposed deep learning model with Adam Optimizer uses a Listwise approach to classify phishing websites and genuine websites. The performance of the proposed approach is decent when compared to other traditional machine learning approaches like SVM, Adaboost, AdaRank. The results show that the proposed approach provides more accurate results in detecting phishing websites.