ஜர்னல் ஆஃப் கம்ப்யூட்டர் சயின்ஸ் & சிஸ்டம்ஸ் பயாலஜி

With the increasing dependence on the internet for various activities, ensuring robust security measures has become paramount. Blockchain technology has emerged as a promising solution for enhancing internet security due to its decentralized and immutable nature. This systematic review aims to provide a comprehensive understanding of the role of blockchain technology in strengthening internet security. Through a thorough analysis of existing literature, this review explores the key concepts, applications, and challenges associated with the integration of blockchain in internet security. The findings highlight the potential of blockchain technology in enhancing data integrity, authentication, access control, and privacy protection. Furthermore, this review discusses the limitations and future research directions in this domain.

Deep Neural Networks (DNNs) have achieved remarkable success in various domains, ranging from computer vision to natural language processing. However, their increasing complexity poses challenges in terms of model size, memory requirements, and computational costs. To address these issues, researchers have turned their attention to sparsity, a technique that introduces structural zeros into the network, thereby reducing redundancy and improving efficiency. This research article explores the role of sparsity in DNNs and its impact on performance improvement. We review existing literature, discuss sparsity-inducing methods, and analyze the benefits and trade-offs associated with sparse networks. Furthermore, we present experimental results that demonstrate the effectiveness of sparsity in improving performance metrics such as accuracy, memory footprint, and computational efficiency. Our findings highlight the potential of sparsity as a powerful tool for optimizing DNNs and provide insights into future research directions in this field.