A Comparative Study of Parameters Measuring in Data Mining Function Using SVM

Abstract

Due to the vast amount of data available on the internet nowadays, it is necessary to categorise the data, and fast, accurate, and resilient algorithms for data analysis are required. Support vector machines (SVMs) are a form of machine learning technique that is commonly used to solve a variety of statistical learning issues. It's been designed as a reliable categorization tool, and it's especially useful when there's a lot of data. Machine learning is an area of artificial intelligence (AI) and computer science that focuses on using data and algorithms to mimic the way humans learn, with the goal of steadily improving accuracy. Algorithms are trained to create classifications by using statistical approaches. These should ideally have an impact on important growth measures. In this study, we found that employing the Support Vector Machine technique provides the best accuracy and efficiency for our dataset. Our work is based on the evaluation of parameters like accuracy, recall and precision.

Efficient Multi-signature and QR Code Integration for Document Authentication Using EdDSA-based Algorithm

“…Hash functions are one of the most important aspects of authentication systems, especially digital signatures. Hash is used to produce a fixed-length unique string using a one-way function that ensures authenticity [16]. The string commonly referred to as "digest" is utilized to compare messages obtained from another source with those obtained during the decryption procedure [17].…”