Jesu Narkarunai Arasu Malaiyappan

As the volume and variety of data continue to expand, the need for scalable machine learning solutions becomes increasingly vital, especially in distributed data platforms handling heterogeneous data sources. This research explores methods and techniques for developing scalable machine learning solutions tailored to the challenges posed by heteroge...

Purpose: A key component of large-scale distributed computing is the allocation of resources, as computer networks cooperate to address complex optimization problems. To get the most out of computers in general, or throughput, is the goal of resource allocation in this case. When it comes to distributed computing, there are two main varieties: grid...

This research paper explores the integration of Artificial Intelligence (AI) technologies in project management, analysing current trends, challenges, and practical examples. The paper investigates the benefits of AI in streamlining project workflows, enhancing decision-making processes, and mitigating risks. Through case studies and emerging trend...

Reinforcement learning, often known as RL, has developed as a strong paradigm to teach autonomous software agents to make choices in contexts that are both complicated and dynamic. This abstract investigates recent developments and uses of RL in a variety of fields, showing both its transformational potential and the constraints that it faces at pr...

Scalable distributed storage systems play a crucial role in modern computing environments, providing efficient and reliable storage solutions for handling vast amounts of data. This paper presents a comprehensive comparative study of key technologies used in scalable distributed storage systems, aiming to provide insights into their strengths, weak...

The use of predictive maintenance Machine learning techniques aid systems or machines in lowering the occurrence of certain types of machine failures via prediction and the use of specific methods. An essential tactic for improving the efficiency and reliability of industrial equipment and optimizing maintenance operations is predictive maintenance...

AI-driven analytics represents a transformative paradigm shift in data platforms, enabling real-time decision-making capabilities across various domains. This paper explores the integration of artificial intelligence (AI) technologies into data platforms, elucidating their role in accelerating insights generation and facilitating agile decision-mak...

Purpose: Complex artificial intelligence algorithms may make it hard to understand how they reach certain conclusions or decisions. Lack of transparency raises concerns about bias, discrimination, and opacity, all of which may detract from trust in AI systems. Businesses and developers should prioritize creating AI systems that are easy to understa...

In today's digital era, the significance of data cannot be overstated. It embodies the factual and numerical essence of our everyday transactions, arriving not just statically but dynamically, in the form of data streams. These streams constitute an influx of limitless, continuous, and swift information, particularly prominent in sectors like healt...

Intrusion poses a significant challenge in Cloud networks, necessitating the development of efficient mechanisms to mitigate intrusions and enhance system security. To address this, we propose a novel Artificial Bee-based Elman Neural Security Framework (ABENSF). This framework involves rescaling the raw dataset using preprocessing functions and in...

Cloud storage security is a paramount concern in today's digital landscape, as organizations increasingly rely on cloud services to store and manage their data. This research paper examines the threats, solutions, and future directions of cloud storage security, providing insights into the challenges faced by organizations and the strategies employ...

The rapid evolution of semiconductor packaging technologies has led to the emergence of 3D multi-die stacking as a promising approach for achieving higher levels of integration and performance in electronic devices. This research paper comprehensively examines the advancements, applications, challenges, economic implications, regulatory considerati...

sAbstract Next-generation distributed storage technologies have emerged as critical components in modern data management systems, offering scalability, performance, and security for handling vast amounts of data. This research paper explores various aspects of these technologies, including architectural innovations, performance analysis, real-world...

The rapid evolution of digital infrastructure demands innovative solutions to streamline management processes. This survey explores the emerging paradigm of autonomous infrastructure management, focusing on AI-driven approaches within platform engineering. By synthesizing current research and industry practices, we delineate the landscape of autono...

Optimizing resource allocation in cloud infrastructure is paramount for ensuring efficient utilization of computing resources and minimizing operational costs. With the proliferation of diverse workloads and dynamic user demands, manual resource management becomes increasingly challenging. In this context, artificial intelligence (AI) automation em...

This paper explores the paradigm of AI-powered self-healing systems within the context of fault-tolerant platform engineering. As systems become increasingly complex, the ability to autonomously detect and address faults is paramount for ensuring continuous operation and reliability. Through a series of case studies, this research examines the appl...

Optimizing resource allocation in cloud infrastructure is paramount for ensuring efficient utilization of computing resources and minimizing operational costs. With the proliferation of diverse workloads and dynamic user demands, manual resource management becomes increasingly challenging. In this context, artificial intelligence (AI) automation em...