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Strategic Framework for the Use of Computation in Natural Language Processing (NLP)

Muhammad Rawish Siddiqui
The integration of computational techniques in language processing, often referred to as Natural Language Processing (NLP), has transformed the way humans interact with machines. This paper explores the critical role of computation in language understanding and generation. It addresses the strategic considerations, methodologies, tools, and challenges involved in NLP. Practical use cases demonstrate its significance across various industries, and dependencies highlight the need for synergistic technologies. This research outlines activation steps, risks, and tools essential for implementing computation-driven language solutions, providing a comprehensive guide for both researchers and practitioners.
Harnessing Big Data for Machine Learning (Strategies, Approaches, and Challenges)

Muhammad Rawish Siddiqui
This paper explores the dynamic relationship between big data and machine learning, highlighting the key strategies, methodologies, and challenges associated with their integration. The convergence of these technologies presents transformative opportunities across industries, but it also introduces complexities in terms of data management, infrastructure, and real-time processing. This study examines the role of big data in fueling machine learning models, discusses critical success factors, and identifies best practices for implementing machine learning at scale.
Designing Big Data Infrastructure and Modeling for Diverse Data Elements

Muhammad Rawish Siddiqui
In today's data-driven world, understanding and managing vast amounts of information is crucial for success in various fields. This research paper outlines the significance of data management systems, focusing on Big Data Infrastructure Plans and Information System Design. We will explore different data elements, the importance of designing Big Data infrastructure, and the practical application of data models. Furthermore, the paper discusses the difference between traditional Database Management Systems (DBMS) and Big Data Management Systems (BDMS), the handling of streaming data, and the rationale behind the diversity of data management systems.
Leveraging Big Data through NoSQL Databases -Architecture Methodologies, and Strategi...

Muhammad Rawish Siddiqui
The rise of Big Data has necessitated the evolution of databases beyond traditional relational models, paving the way for NoSQL databases to manage unstructured and semi-structured data at scale. This paper explores the architecture, methodologies, and strategic applications of NoSQL databases in the Big Data ecosystem. We will examine key challenges, tools, and technologies supporting NoSQL adoption and discuss real-world use cases across industries. Additionally, we will outline general activation steps, dependencies, and the risks associated with deploying NoSQL databases in enterprise settings.