Machine Learning for Empowering Community Applications and Security

Date

2023

Authors

Nadim, Mohammad

Journal Title

Journal ISSN

Volume Title

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Abstract

Machine learning, a branch of artificial intelligence, has emerged as a powerful tool with the ability to analyze vast amounts of data and extract meaningful patterns. Its potential for revolutionizing various domains, including healthcare, transportation, finance, and entertainment, is undeniable. However, my research lies in its applications within our communities, where its transformative potential holds tremendous promise. This research study addresses several important research questions across various domains. Firstly, it investigates the comparative analysis of unsupervised keyword extraction tools, examining their accuracy, computational efficiency, and adaptability to different text types. The study aims to provide insights into the strengths and limitations of these tools and guide their selection for specific applications. Secondly, it explores methods and techniques for aligning public feedback with different city documents, aiming to improve decision-making processes by connecting public opinions with relevant city information. Additionally, the research investigates the adoption of machine learning applications for community research collaboration and security enhancement, exploring the potential benefits and addressing security concerns. Furthermore, the study examines the effectiveness and limitations of current detection approaches for Kernel-level rootkits, aiming to enhance understanding and identify areas for improvement. Lastly, it identifies the characteristic features of Kernel-level rootkits to train learning-based models for improved detection and mitigation strategies. Collectively, this research contributes to advancements in natural language processing, urban governance, community research collaboration, and cybersecurity.

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Keywords

Community application, Kernel-level rootkit, Keyword extraction tools, Machine learning, Text similarity matching

Citation

Department

Electrical and Computer Engineering