國立虎尾科技大學 |

A Robust Data-Driven Framework for Artificial Intelligent Systems /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	A Robust Data-Driven Framework for Artificial Intelligent Systems // Quoc H Nguyen.
Author:	Nguyen, Quoc H.,
Description:	1 electronic resource (189 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-01, Section: B.
Contained By:	Dissertations Abstracts International86-01B.
Subject:	Engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31333722
ISBN:	9798383530917

A Robust Data-Driven Framework for Artificial Intelligent Systems /
Nguyen, Quoc H.,

A Robust Data-Driven Framework for Artificial Intelligent Systems /Quoc H Nguyen. - 1 electronic resource (189 pages)

Source: Dissertations Abstracts International, Volume: 86-01, Section: B.

Artificial Intelligence (AI) systems have demonstrated remarkable performance across various domains. However, their robustness remains a critical concern, particularly in terms of data and model reliability. This dissertation aims to address the challenges associated with building robust AI systems by focusing on two key aspects: data robustness and model robustness. Data robustness poses significant challenges, including data shift, concept shifting, limited and imbalanced datasets, and interoperability issues in IoT systems for data collection. Existing methods fall short in handling dynamic business objectives and evolving data landscapes effectively. To bridge these gaps, we propose an IoT framework that ensures interoperability, seamless communication, and scalability for efficient data collection. Furthermore, we employ data-centric approaches and generative AI techniques to enrich data quantity and quality, enabling the AI system to adapt to data shift and concept shifting. Model robustness is hindered by noisy data, which impedes the model's ability to generalize well to unseen examples. Additionally, AI models are susceptible to adversarial attacks designed to deceive the system. Prior research has not adequately explored the potential of data-centric approaches to enhance model robustness and lacks methods that simultaneously address data security and model generalization. To overcome these challenges, we propose a hybrid approach that combines data-centric and model-centric techniques to improve model generalization and resilience to noisy data. Moreover, we leverage federated learning to decentralize model training, enhancing data security and privacy while harnessing diverse data sources. By addressing the identified research gaps and implementing the proposed solutions, this dissertation contributes to the development of robust AI systems that can effectively handle data and model challenges.

English

ISBN: 9798383530917Subjects--Topical Terms:

561152
Engineering.
Subjects--Index Terms:

Data-centric AI

A Robust Data-Driven Framework for Artificial Intelligent Systems /
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520 $a Artificial Intelligence (AI) systems have demonstrated remarkable performance across various domains. However, their robustness remains a critical concern, particularly in terms of data and model reliability. This dissertation aims to address the challenges associated with building robust AI systems by focusing on two key aspects: data robustness and model robustness. Data robustness poses significant challenges, including data shift, concept shifting, limited and imbalanced datasets, and interoperability issues in IoT systems for data collection. Existing methods fall short in handling dynamic business objectives and evolving data landscapes effectively. To bridge these gaps, we propose an IoT framework that ensures interoperability, seamless communication, and scalability for efficient data collection. Furthermore, we employ data-centric approaches and generative AI techniques to enrich data quantity and quality, enabling the AI system to adapt to data shift and concept shifting. Model robustness is hindered by noisy data, which impedes the model's ability to generalize well to unseen examples. Additionally, AI models are susceptible to adversarial attacks designed to deceive the system. Prior research has not adequately explored the potential of data-centric approaches to enhance model robustness and lacks methods that simultaneously address data security and model generalization. To overcome these challenges, we propose a hybrid approach that combines data-centric and model-centric techniques to improve model generalization and resilience to noisy data. Moreover, we leverage federated learning to decentralize model training, enhancing data security and privacy while harnessing diverse data sources. By addressing the identified research gaps and implementing the proposed solutions, this dissertation contributes to the development of robust AI systems that can effectively handle data and model challenges.
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