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Affiliation

University of Science and Technology, 217, Gajeong-ro, Yuseong-gu, Daejeon, Gyeonggi-do, Republic of Korea

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2 Papers

Clustering-Based Pattern Prediction Framework for Air Pollution Prediction

Topic: Machine Learning and Deep Learning Approaches Data Analytics

Authors: Athiruj Poositaporn ^{1

;

2} and Hanmin Jung ^{1

;

2}

Affiliations: ¹ University of Science and Technology, 217, Gajeong-ro, Yuseong-gu, Daejeon, Gyeonggi-do, Republic of Korea , ² Korea Institute of Science and Technology Information, 245, Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea

Digital Object Identifier: 10.5220/0013474300003944

Proceedings of IoTBDS 2025, ISBN: 978-989-758-750-4

Quick Abstract

Accurately predicting patterns from large and complex datasets remains a significant challenge, particularly in environments where real-time predictions are crucial. Despite advancements in predictive modeling, there remains a gap in effectively integrating clustering techniques with advanced similarity metrics to enhance prediction accuracy. This research introduces a clustering-based pattern prediction framework integrating Kmeans with our Overall Difference with Crossover Penalty (OD with CP) similarity metric to predict data patterns. In the experiment, we demonstrated its application in air pollution pattern prediction by comparing 15 different model-cluster combinations. We employed five predictive models: Euclidean Distance, Markov Chain, XGBoost, Random Forest, and LSTM to predict the next day's pollution pattern across three cluster sizes (K = 10, 20, and 30). Our aim was to address the limitation of traditional clustering methods in pattern prediction by evaluating the performance of each model-cluster combination to determine the most accurate predictions. The results showed that our framework identified the most accurate model-cluster combination. Therefore, the study highlighted the generalizability of our framework and indicated its adaptability in pattern prediction. In the future, we aim to apply our framework to a Large Language Model (LLM) combined with Retrieval Augmented Generation (RAG) to enhance in-depth result interpretation. Furthermore, we intend to expand the study to include client engagement strategy to further validate the effectiveness of our study in real-world applications.

Towards Client Engagement Using RAG System with Pattern Prediction Framework

Topic: Recommender Systems, Smart Devices and Location-Awareness

Authors: Hanmin Jung ^{1

;

2} and Athiruj Poositaporn ^{2

;

1}

Digital Object Identifier: 10.5220/0013474400003944

Proceedings of IoTBDS 2025, ISBN: 978-989-758-750-4

Quick Abstract

Client engagement refers to the process of companies and customers building and maintaining relationships through communication, personalized marketing, and value-added services. This often results in analysis reports, consulting services, and strategic planning documents. Tools like GPT-4o have significant potential to support these interactions in sectors such as meteorological organizations. However, standalone generative models like GPT-4o face challenges in accessing external datasets and often produce generic outputs. To overcome these limitations, this study introduces a chat-based Retrieval-Augmented Generation (RAG) system integrated with a pattern prediction framework. We demonstrate our RAG system in analyzing air pollution pattern prediction results from our prior study and compare its generated answers with a standalone GPT-4o model. Experimental results show that the RAG system delivers actionable recommendations and contextually enriched outputs grounded in domain-specific data. In future work, we aim to explore the potential of RAG in real-world applications, such as improving client engagement by generating client-focused reports.