Secure Data Transactions based on Hash Coded Starvation Blockchain Security using Padded Ring Signature-ECC for Network of Things

Blockchain is one of the decentralized processes in a worldview that works with parallel and distributed ledger technology, the application process, and service-oriented design. To propose a Secure data Transaction based on Hash coded Starvation Blockchain security using Padded Ring signature-ECC for Network of Things. Initially, the crypto policy is authenticated based on the user-owner shared resource policy and access rights. This creates a Public blockchain environment with a P2P Blockchain network. The owner encrypts the data using optimized ECC through Hash-coded Starvation Blockchain security (HCSBS). This makes the encrypted block's provable partition chain Link (P2CL). The encrypted blocks are transmitted into the network of nodes monitored by NoT. During the data transmission, the Network of Things monitors the transaction flow to verify the authenticity over the network of nodes. The monitored data be securely stored in transaction Block storage with the hash-indexed block with chain ring policy (HICLP). This creates controller node aggregation over the transaction environment to securely transfer the data to the peer end. The User gets the access Key to decrypt the data with policy aggregated shared resource policy to access the data. The proposed system produces high security as well compared to the previous design

DCNET-DCGAN: A Novel Deep Convolutional Neural Network for COVID-19 Classification

Coronavirus referred to as COVID-19 affects innumerable lives, causing havoc on public health and global economy. The limitation of clinical expertise, medical tools, and testing kits increases the widespread of COVID -19 across the globe. An accurate identification process is necessary for early detection of COVID-19. Recent studies state that the images obtained from the chest X- Rays are highly consistent in diagnosing COVID-19 rather from RT-PCR (Reverse-Transcription-Polymerase-Chain Reaction). Developing an automated CXR image diagnosing method for the accurate prediction is the objective of the proposed model. This objective is achieved by developing a proposed model composed of Deep Convolutional Generative Adversarial Networks (DCGANs) and a Deep Convolutional Neural Network (DCNET) using four distinct datasets (COVID -19 X-ray, COVID-chest X-ray, COVID-19 Radiography, and Corona Hack-chest X-ray). The proposed model exploits the deep learning features of DCNET with four layers of convolution, three layers of max pooling and fully connected layers, thereby achieving a classification accuracy of 98.8% which is better than the pre-existing method. It classifies the result as Normal, COVID-19 and Pneumonia. This model will be an apt solution for facilitating faster screening process for affected patients

An introduction to Pythagorean fuzzy hyperideals in hypersemigroups

As the generalization of intuitionistic fuzzy set, Pythagorean fuzzy set was introduced. It is a pair of membership and non-membership grade where the sum of the squares of membership and non-membership grade should be less than or equal to 1. Pythagorean fuzzy set get more attention to deal with uncertainity. In this paper we apply Pythagorean fuzzy set in ideal theory of hypersemigroups. We introduce Pythagorean fuzzy left(right) hyperideals in hypersemigroups. We define t−level cut of Pythagorean fuzzy hyperideal is in hypersemigroup. Also we introduce Pythagorean fuzzy interior hyperideals in hypersemigroups and explain it with detailed example. Some theorems and results are also studied. Relation between Pythagorean fuzzy right(left) hyperideal, Pythagorean fuzzy subsemihypergroup and Pythagorean fuzzy interior hyperideal is given.Publisher's Versio

Ontology Based Approach for Services Information Discovery using Hybrid Self Adaptive Semantic Focused Crawler

Focused crawling is aimed at specifically searching out pages that are relevant to a predefined set of topics. Since ontology is an all around framed information representation, ontology based focused crawling methodologies have come into exploration. Crawling is one of the essential systems for building information stockpiles. The reason for semantic focused crawler is naturally finding, commenting and ordering the administration data with the Semantic Web advances. Here, a framework of a hybrid self-adaptive semantic focused crawler – HSASF crawler, with the inspiration driving viably discovering, and sorting out administration organization information over the Internet, by considering the three essential issues has been displayed. A semi-supervised system has been planned with the inspiration driving subsequently selecting the ideal limit values for each idea, while considering the optimal performance without considering the constraint of the preparation of data set. DOI: 10.17762/ijritcc2321-8169.15072