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Research on Methods of Large Language Models in the Field of Sensitive data Governance 24

Research on Methods of Large Language Models in the Field of...

引用

2nd international conference on Artificial Intelligence, Systems and Network Security, AISNS 2024

作者： Zhang, Zhixian Bai, Yin Yu, Xiaoting Wang, Yu Yang, Siqi Yang, Shuo China Mobile Chengdu Institute of Research and Development Sichuan Chengdu China

ISBN: (纸本)9798400711237

With the rapid advancement of big data and artificial intelligence technologies, the governance of sensitive data has become a critical and pressing issue. Traditional data de-identification approaches exhibit inefficiencies and a lack of flexibility when processing unstructured data. This paper introduces a sensitive data governance method based on Large Language Models, leveraging the natural language processing capabilities of LLMs to construct prompts that enable the identification of fields requiring de-identification and the selection of appropriate de-identification methods. Furthermore, it harnesses the LLMs to automatically generate Python processing code, thus circumventing the limitations of singular and inflexible built-in de-identification functionalities in big data platforms. The objective of this study is to reduce the technical barriers in sensitive data governance and to enhance the flexibility and efficiency of data processing. The paper provides a detailed exposition of the technical implementation of this method, encompassing multiple stages such as data identification, instruction processing, model invocation, result adjudication, and data caching, and discusses its advantages and potential value in practical applications. © 2024 Copyright held by the owner/author(s).

关键词： data assimilation

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AI IN INFORMATION processing FOR DECISION MAKING - PERCEPTIONS AND USE INTENTION OF UK MANAGERS 18

AI IN INFORMATION PROCESSING FOR DECISION MAKING - PERCEPTIO...

引用

18th international conferences IADIS Information Systems 2025 and 23rd international conference on e-Society 2025

作者： Duan, Yanqing Cao, Guangming Xu, Mark Ong, Vincent Dietzmann, Christian University of Bedfordshire LutonLU1 3JU United Kingdom Ajman University United Arab Emirates University of Portsmouth United Kingdom Regent’s University London United Kingdom University of Leipzig Germany Business Engineering Institute St. Gallen AG Switzerland

ISBN: (纸本)9789898704658

Information processing forms an essential part of any decision making process as it enables decision makers to assess various options, evaluate risks and opportunities to make best choices. In the context of business decision making, information processing also enable managers to make decisions that align with organizational goals and objectives. However, business managers are facing unprecedented challenges in dealing with ever growing abundant data and struggling to make sense of it to make well informed decisions. As Artificial Intelligence (AI) is making profound impact on every aspect of business operations and management, it has the great potential in assisting/replacing business managers’ information processing activities Although the use of AI in decision making in general has attracted significant attention from researchers and practitioners, its specific role and impact on business information processing at the individual manager level have not been well explored and examined theoretically and empirically. To address this knowledge gap, the study reported in this short paper aims to empirically investigate the current challenges faced by business managers and their perception and intention in utilising AI for information processing in their decision making process. Three key research questions covering perceived challenges, AI roles and use intention are raised. A questionnaire survey was conducted in the UK to collect data to understand the managers perceptions and use intention. Preliminary data analysis provides very interesting findings and valuable insights. Important implications for theory development and research directions are highlighted. © 2025 proceedings of the international conferences IADIS Information Systems 2025 and e-Society 2025. All rights reserved.

关键词： Big data

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Practice Research on Campus data Center Construction Based on data Governance 24

Practice Research on Campus Data Center Construction Based o...

引用

4th international conference on Signal processing and Communication Technology, SPCT 2024

作者： Hu, Yongchao Ye, Yanming Hangzhou Dianzi University Information Engineering College Zhejiang Hangzhou China

ISBN: (纸本)9798400710636

With the rapid development of information technology, campus data centers have become an important component of educational informatization construction. It not only carries the exchange of information inside and outside the campus, but also serves as the core support for education, teaching, scientific research management, and life services. Therefore, building an efficient, stable, and secure smart campus data center is the key to improving the level of campus informatization. Starting from the construction practice of campus data center in HZIEE College, this article introduces the overall structure of the data center. Currently, HZIEE data center serves more than 10000 teachers and students throughout the college, carrying out the digital campus business of the college. The existing multi business systems have been integrated and unified into the platform, forming a cloud data center that is uniformly planned and managed by the college. The investigation and evaluation show that the data center can meet the needs of the college well, reaching E2 level. © 2024 Copyright held by the owner/author(s).

关键词： Campus data center

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Next timestamp prediction in business process monitoring using large language models 2

Next timestamp prediction in business process monitoring usi...

引用

2nd international conference on Big data, Computational Intelligence, and Applications, BDCIA 2024

作者： Xu, Yifei Fang, Huan School of Mathematics and Big Data Anhui University of Science and Technology Anhui Huainan China Anhui Province Engineering Laboratory for Big Data Analysis and Early Warning Technology of Coal Mine Safety Anhui Huainan China

ISBN: (纸本)9781510689053

Predictive business process monitoring is a critical domain within process mining, with the primary goal of forecasting characteristics of the next event or the remaining time for process executions. Large language models (LLMs) such as Generative Pretrained Transformer (GPT) and Tongyi Qianwen (Qwen) have demonstrated exceptional performance in natural language processing but face challenges when applied to process data prediction. This paper introduces LLM4NT, which leverages the decoder component of the Qwen2.5-0.5B model as its backbone. By employing a multi-head cross-attention mechanism, it reprograms process data and textual information, aligning process and text modalities to achieve precise predictions of the next timestamp in predictive monitoring. Experimental results on multiple real-life event logs demonstrate that LLM4NT achieves state-of-the-art average performance in next-timestamp prediction tasks, showcasing its powerful capabilities in business process reasoning. © 2025 SPIE.

关键词： Deep neural networks

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Sentiment Analysis and Forecasting for Improved business Performance in E-Commerce using Machine Learning Algorithms 3

Sentiment Analysis and Forecasting for Improved Business Per...

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Tiwari, Naveen Kumar Arora, Himanshu Arya College of Engineering Department of Cse Rajasthan Jaipur India

ISBN: (纸本)9798331509675

The study explores how sentiment analysis and forecasting can improve the business performance, especially for e-commerce platforms like Amazon to analyze customer feedback for enhancing future work. This study focuses on integrating machine learning models, including SVM, Random Forest and Naive Bayes to categorize product reviews into positive, negative or neutral sentiment analysis, achieving accuracy rates of 0.85, 0.87 and 0.85 based on more than 11,000 datasets. The process of analysis includes data Acquisition, data Pre-processing, Feature Extraction, Model Training and using a GUI that allows users to write their reviews for sentiment prediction. Additionally, the study also involves ARIMA model to train the model for likes forecasting analysis on historical data, that helps many businesses sectores with valuable and meaningful insights to optimize the engagement of the customer and their decision-making. © 2025 IEEE.

关键词： Adversarial machine learning

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Weak Signal Detection Technology in Big data Based on Knowledge Base 24

Weak Signal Detection Technology in Big Data Based on Knowle...

引用

2nd international conference on Advances in Artificial Intelligence and Applications, AAIA 2024

作者： Zhou, Lei School of Cixi Zhejiang Business Technology Institute Ningbo China

ISBN: (纸本)9798400712883

With the development of science and technology and the progress of data processing technology, big data weak signal detection is playing an increasingly important role in scientific research and engineering application. However, the existing detection systems face three main challenges: high false alarm rate and redundant information problems, difficulty in identifying multi-target dispersed homologous signals, and complex signal detection problems with spatio-temporal dispersion. To solve these problems, this paper proposes a weak signal detection model based on knowledge base, by integrating the data of distributed computing framework and ESOM time series analysis methods into the system design, and adds data filtering and signal analysis and state evaluation engine to the existing detection sensors. The experimental results show that this method not only significantly reduces the systematic false alarm rate, but also effectively improves the detection ability of complex signals, providing a new technical path for weak signal detection in big data environment. © 2024 Copyright held by the owner/author(s).

关键词： Spatio-temporal data

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Measuring and Analyzing the Quality of High-Quality Development of Private Enterprises in Gansu Province Based on K-Means Clustering Algorithm 24

Measuring and Analyzing the Quality of High-Quality Developm...

引用

3rd international conference on Artificial Intelligence and Intelligent Information processing, AIIIP 2024

作者： Wei, Chunming Lanzhou Resources and Environment Vocational and Technical University Gansu Lanzhou China

ISBN: (纸本)9798400707308

In recent years, private enterprises in Gansu Province have played an increasingly important role in promoting the development of the local economy, however, subject to a variety of internal and external factors, the development status of many private enterprises still face many challenges. In order to address this problem, this paper adopts the K-Means clustering algorithm to measure and analyze the quality of the high-quality development of private enterprises in Gansu Province. By constructing the corresponding index system, this paper analyzes the private enterprise business data clustered during the period of 2015-2022 to reveal the differences in business performance of different types of enterprises. The results of the study show that the high-quality development of private enterprises in Gansu Province presents obvious stratification characteristics, with some enterprises excelling in terms of business performance, while the development level of most enterprises still needs to be improved. Further analysis shows that local financial support, market environment and enterprises’ own innovation ability are the key factors affecting the high-quality development of private enterprises. This paper not only provides a scientific basis for the high-quality development of private enterprises in Gansu Province, but also provides reference suggestions for local governments to formulate corresponding policies and measures, with a view to promoting the sustainable development of private enterprises and realizing high-quality economic growth. © 2024 Copyright held by the owner/author(s).

关键词： data quality

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Leveraging Unstructured Teaching data and AI for Enhanced Pedagogical Practices: A data-Driven Approach 24

Leveraging Unstructured Teaching Data and AI for Enhanced Pe...

引用

2024 international conference on Artificial Intelligence and Future Education, AIFE 2024

作者： Liu, Fangfang Xiao, Jing Li, Guang Deng, Chen Zhu, Linkai Wang, Yiyun Capital Normal University High School Beijing China School of Management Science and Information Engineering Hebei University of Economics and Business Shijiazhuang Hebei China School of History Capital Normal University Beijing China National Institute of Education Nanyang Technological University Singapore Singapore Hebei University of Economics and Business Shijiazhuang Hebei China

ISBN: (纸本)9798400710650

As data science and artificial intelligence (AI) gain traction in education, their effectiveness remains uneven, particularly in capturing student comprehension. While tools like facial expression analysis offer insights, they cannot replace the nuanced judgment of experienced teachers. This study leverages unstructured teaching data from nationally recognized teachers-combining audio and textual content-to enhance instructional practices. We utilize Hidden Markov Models (HMM) and the Jieba segmentation algorithm for speech and text processing, supported by bitmap, hash, BTREE, and inverted indexes for efficient data retrieval. Our approach highlights how AI can align with educational needs by uncovering implicit teaching patterns, promoting classroom reforms, and improving pedagogical strategies. This work underscores the importance of discipline-specific data applications to effectively integrate AI into teaching practices. © 2024 Copyright held by the owner/author(s).

关键词： data assimilation

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Research on Economic Growth Forecasting Models Based on Big data 24

Research on Economic Growth Forecasting Models Based on Big ...

引用

3rd international conference on Algorithms, data Mining, and Information Technology, ADMIT 2024

作者： Sun, Yihan Zhang, Yijian Zhejiang University Zhejiang Hangzhou China Wuhan Business University Hubei Wuhan China

ISBN: (纸本)9798400718120

Traditional economic growth forecasting methods, though somewhat successful, have limitations such as assuming a linear data generation process and ignoring the nonlinear relationships between economic variables. This study aims to integrate big data technology and machine learning methods to construct an efficient and accurate economic growth forecasting model. By systematically collecting and processing multi-source data, we apply machine learning algorithms such as linear regression, support vector machines (SVM), random forests, and gradient boosting decision trees (GBDT) for modeling and evaluation. The experimental results show that GBDT performs the best across various evaluation metrics, with the lowest mean squared error (MSE) and mean absolute error (MAE), as well as the highest R2 value. Further validation through 5-fold cross-validation and time series split methods demonstrates the robustness and generalization ability of the model, showing that the GBDT model outperforms other models in different data partitions and time series predictions. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Multimodal Learning in Natural Language processing: Fact-Checking, Ethics and Misinformation Detection

Multimodal Learning in Natural Language Processing: Fact-Che...

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2025 international conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Joshi, Himanshu Sanjay Taherdoost, Hamed College of Business Westcliff University CA United States College of Technology & Engineering Westcliff University CA United States University Canada West Vancouver Canada Gus Institute Global University Systems London United Kingdom Q Minded Quark Minded Technology Inc. Vancouver Canada

ISBN: (纸本)9798331509828

Multimodal learning (MML) is a subtype of deep learning that improves the capabilities of machine learning models by integrating modalities from multiple data sources such as text, images, audio, and video. This integration of modalities produces a more comprehensive understanding of complex data and nuanced outputs. Applications of this approach in real-time decision-making, information retrieval, and knowledge representation can improve the insights derived from the natural language processing (NLP) system. Moreover, in a time of information overload, there are concerns regarding the credibility of the content. The role of NLP systems is central to processing textual data in MML systems. In this environment, the ethical development of multimodal systems for fact-checking is crucial. This paper addresses an existing gap in use of multimodal learning in the context of fact-checking by investigating issues related to integration of NLP, Question-Answering (QA) systems, and knowledge graphs to combat misinformation. This research presents multimodal data based frameworks based on knowledge graph integration. The exploration of the frameworks enables to enhance the accuracy, reliability, and ethical soundness of fact-checking mechanisms. This study also highlights new directions for overcoming challenges related to modality alignment, computational efficiency, and ethical transparency. © 2025 IEEE.

关键词： Knowledge graph

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