A central authority, in a conventional centralized energy trading market, superintends energy and financial transactions. The central authority manages and controls transparent energy trading between producer and consumer, imposes a penalty in case of contract violation, and disburses numerous rewards. However, the management and control through the third party pose a significant threat to the security and privacy of consumers’/producers’ (participants) profiles. The energy transactions between participants involving central authority utilize users’ time, money, and impose a computational burden over the central controlling authority. The Blockchain-based decentralized energy transaction concept, bypassing the central authority, is proposed in Smart Grid (SG) by researchers. Blockchain technology braces the concept of Peer-to-Peer (P2P) energy transactions. This work encompasses the SolarCoin-based digital currency blockchain model for SG incorporating RE. Energy transactions from Prosumer (P) to Prosumer, Energy District to Energy District, and Energy District to SG are thoroughly investigated and analyzed in this work. A robust demand-side optimized model is proposed using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to maximize Prosumer Energy Surplus (PES), Grid revenue (GR), percentage energy transactions accomplished, and decreased Prosumer Energy Cost (PEC). Real-time averaged energy data of Australia are employed, and a piece-wise energy price mechanism is implemented in this work. The graphical analysis and tabular statistics manifest the efficacy of the proposed model.
Computer-Aided Detection (CAD) systems are one of
the most effected tools nowadays in aiding physicians in the
detection of liver tumors at early stage. In this paper, the CADe
system will be built which has the ability to detect the abnormal
tumor inside the liver. In order to create that system, different
types of classifiers must be implemented. In our CADe system, a
support vector machine (SVM) and K-Nearest Neighbor (KNN)
will be used as classifiers. A total number of 120 images including
the normal and abnormal cases were collected. Initially, the
features will be extracted from database images in order to
distinguish between the classes of those liver tumors. Then, by
using SVM and KNN the images will be classified into two classes
normal and abnormal cases. The paper reveals that SVM and
KNN, which demonstrated 100 percent precision, 100 percent
sensitivity, and 100 percent specificity, were the best classifiers.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.