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The evolution of currency: A comparative study of the barter system and cryptocurrency
The barter system, the oldest form of exchange dating back to human civilization, involves directly exchanging goods and services without using money. However, it comes with limitations, such as the requirement for a double coincidence of wants, difficulties in valuing goods and services, and the absence of a store of value. Over time, various forms of money emerged to overcome these limitations. Commodity money, like gold and silver, gained value due to their rarity and intrinsic worth. Later, fiat currencies were introduced, backed by trust rather than physical commodities. In contrast, cryptocurrency, a new digital currency not issued by any central authority, relies on blockchain technology for secure and anonymous transactions. This paper traces the evolution of currency from medieval times to the present digital era and explores the differences between the barter system, fiat currency, and cryptocurrency. It also delves into the potential of cryptocurrency to revolutionize our perception of money. 2024, IGI Global. All rights reserved. -
Support Vector Machine Performance Improvements by Using Sine Cosine Algorithm
The optimization of parameters has a crucial influence on the solution efficacy and the accuracy of the support vector machine (SVM) in the machine learning domain. Some of the typical approaches for determining the parameters of the SVM consider the grid search approach (GS) and some of the representative swarm intelligence metaheuristics. On the other side, most of those SVM implementations take into the consideration only the margin, while ignoring the radius. In this paper, a novel radiusmargin SVM approach is implemented that incorporates the enhanced sine cosine algorithm (eSCA). The proposed eSCA-SVM method takes into the account both maximizing the margin and minimizing the radius. The eSCA has been used to optimize the penalty and RBF parameter in SVM. The proposed eSCA-SVM method has been evaluated against four binary UCI datasets and compared to seven other algorithms. The experimental results suggest that the proposed eSCA-SVM approach has superior performances in terms of the average classification accuracy than other methods included in the comparative analysis. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.