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检索条件"任意字段=2015 International Conference on Big Data and Smart Computing, BIGCOMP 2015"

共 2627 条记录，以下是121-130 订阅

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An Automated Scenario Generation Model for Anti-phishing using Generative AI

An Automated Scenario Generation Model for Anti-phishing usi...

引用

2025 IEEE international conference on big data and smart computing, bigcomp 2025

作者： Park, Jin Yong Kim, Tae-Sung Chungbuk National University Dept. of Big Data Cheong-ju Korea Republic of Chungbuk National University Dept of Management Information System Cheong-ju Korea Republic of

Generative AI could be used not only for classification or prediction, but also to generate images, music, etc. based on the input data. Generative AI was abused to generate deepfake and spear-phishing to cause social and economic problems. To prevent these attacks, simulation training is necessary. Simulation training was necessary because it allowed us to effectively respond to attacks and reduce damage. We proposed an ensemble model that automated scenarios generation by utilizing multiple generative AIs. This study was expected to develop an automated training scenario generation model using generative AI that could be used in cybersecurity training, anti-phishing training, and security consulting to automatically generate realistic and customized training scenarios. © 2025 IEEE.

关键词： Automation

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Personalized Customer Churn Analysis with Long Short-Term Memory

Personalized Customer Churn Analysis with Long Short-Term Me...

引用

IEEE international conference on big data and smart computing (bigcomp)

作者： Bayrak, Ahmet Tugrul Aktas, Asmin Alev Tunali, Okan Susuz, Orkun Abbak, Nese Ata Technol Platforms Res & Innovat Istanbul Turkey

ISBN: (纸本)9781728189246

In today's competitive business environment, companies aim to retain their existing customers. To achieve that, churn prediction is crucial. Predicting churning customers is not a simple task. It is even more challenging in the fast-food industry since there can be various reasons when a customer stops ordering. To overcome that situation, this study is proposed. The customer data structure is formed sequentially with customers' individual churn periods in different windowing approaches. A long short-term memory model is built with the sequential data to predict the customers' churn stages, and it is compared with the other common classification methods. The proposed model presents promising results and stands out with its personalized prediction among similar studies.

关键词： churn analysis sequential data deep learning recurrent neural network long short-term memory

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Examining the Impact of Expression Fragmentation and Number Redundancy on Financial QA task

Examining the Impact of Expression Fragmentation and Number ...

引用

international conference on big data and smart computing (bigcomp)

作者： Yoonjae Lee Kyung Seo Ki Gahgene Gweon Bugeun Kim Graduate School of Convergence Science and Technology Seoul National University South Korea Interdisciplinary Program in Artificial Intelligence Seoul National University South Korea Department of AI Chung-Ang University South Korea

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

The goal of financial QA is to generate solution equations by solving problems about financial reports. Current financial QA models can suffer from two issues: expression fragmentation and number redundancy. We conduct experiments to examine the impact of the two issues. The experimental results show that addressing the expression fragmentation issue in financial QA using EPT improves the execution accuracy by 0.09, and alleviating the number redundancy issue by removing redundant numbers in the input of the FinQANet generator improves the execution accuracy by 0.07.

关键词： Accuracy Computational modeling Redundancy big data Mathematical models Generators

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Comparison of Three Korean Sentiment/Emotion Word Dic tionaries

Comparison of Three Korean Sentiment/Emotion Word Dic tionar...

引用

international conference on big data and smart computing (bigcomp)

作者： Heeryon Cho Humanities Research Institute Chung-Ang University Seoul South Korea

We compare three publicly available Korean sentiment/emotion word dictionaries with emphasis on a newly accessible multi-label Korean emotion word dictionary developed by Humanities Research Institute, Chung-Ang University. The three dictionaries are KOSAC sentiment lexicon, KNU Korean sentiment lexicon, and a new multi-label Korean emotion word dictionary with 24 emotions.

关键词： Sentiment analysis Humanities Dictionaries Training data big data Motion pictures Task analysis

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AI-Based Color Prediction Analysis for Recycled Polyester Dyeing Processes

AI-Based Color Prediction Analysis for Recycled Polyester Dy...

引用

international conference on big data and smart computing (bigcomp)

作者： Kim Sung Hoon F M Fahmid Hossain Kwan Hee Yoo Shimaa Kotb Kim Ah Rong Kim Hyun Jong Chi Su Young Department of Computer Science Chungbuk National University Cheongju South Korea Testbed Research Center DyTech Institute Daegu South Korea Electronics and Telecommunications Research Institute Daejeon South Korea

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

This method proposes a deep learning-based approach to forecast dyeing color requirements for recycled polyester fabric to increase dyeing efficiency and decrease redundant re-dyeing. Out of the four models that were tested Random Forest Regression proved to be the best performing one because of comparatively better MSE and $\mathrm{R}^{\mathbf{2}}$ score.

关键词： Measurement Deep learning Analytical models Image color analysis High dimensional data Time series analysis Predictive models big data Fabrics Random forests

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Effective Detection Technique of Copyright Infringement Illegal Sharing Sites

Effective Detection Technique of Copyright Infringement Ille...

引用

international conference on big data and smart computing (bigcomp)

作者： Gyu Hyeon Choi Kyeong Seok Lee Ho Mook Cho Korea Advanced Institute of Science and Technology Information&Electronics Research Institute Daejeon Korea Cyber Security Research Center Korea Advanced Institute of Science and Technology Daejeon Korea

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

The rapid growth of the internet and digital technologies has expanded access to digital content while increasing copyright infringement through illegal content-sharing websites. These platforms cause significant financial losses to copyright holders and persist despite government monitoring efforts. This study proposes an automated method to analyze piracy site characteristics and classify sites involved in copyright infringement. By addressing the evolving nature of such platforms, the approach enhances detection and contributes to combating online piracy effectively.

关键词： Uniform resource locators Computational modeling Government Machine learning Copyright protection Nearest neighbor methods big data data models Monitoring Overfitting

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smartphone Sensor data Augmentation for Automatic Road Surface Assessment Using a Small Training dataset

Smartphone Sensor Data Augmentation for Automatic Road Surfa...

引用

IEEE international conference on big data and smart computing (bigcomp)

作者： Setiawan, Budi Darma Serdult, Uwe Imre Kryssanov, Victor Ritsumeikan Univ Grad Sch Informat Sci & Engn Kyoto Japan Ritsumeikan Univ Coll Informat Sci & Engn Kyoto Japan

ISBN: (纸本)9781728189246

smartphones equipped with motion sensors are widely used for data collection in research aimed at the establishment of smart transportation and at, more specifically, automatic road condition assessment. To perform the assessment task, machine learning classifier systems are developed to analyze patterns of vibration signals recorded from a driver's smartphone. Obtaining a balanced training dataset required for the classifier system to work properly is, however, a difficult task. The presented study develops an approach based on an Unrolled Generative Adversarial Network (Unrolled GAN) to produce synthetic data for balancing the training dataset. Experiments conducted in the study demonstrated that the approach allows for generating high-quality synthetic data as long as the unrolled GAN are kept controlled to balance the discriminator and generator modules of the networks.

关键词： unrolled GAN road surface assessment smartphone data augmentation

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TNIM: A Unified Text-to-Noise Influence Mapping Approach for Diffusion Models

TNIM: A Unified Text-to-Noise Influence Mapping Approach for...

引用

international conference on big data and smart computing (bigcomp)

作者： Seung-Ho Han Ho-Jin Choi School of Computing KAIST Daejoen Republic of Korea

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

This paper introduces Text-to-Noise Influence Mapping (TNIM), a novel explainability framework for diffusion-based text-to-image models. TNIM combines cross-attention mechanisms with interpretability methods, such as advanced Layer-wise Relevance Propagation (LRP) and CAM, to generate heatmaps that reveal how individual text tokens influence the image generation process over multiple timesteps. By mapping the relationship between text conditions and noise removal, TNIM provides fine-grained insights into the diffusion process, enhancing model interpretability. As a position paper, we present the TNIM framework along with an experimental design, including setup and evaluation plans.

关键词： Heating systems Image synthesis Scalability Noise Text to image Diffusion processes Diffusion models Control systems data models Heat maps

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Estimation of compensation scheme functions according to the level of image data de-identification

Estimation of compensation scheme functions according to the...

引用

2025 IEEE international conference on big data and smart computing, bigcomp 2025

作者： Choi, You-Jeong Kim, Tae-Sung Chungbuk National University Dept. of Big Data Cheongju Korea Republic of Chungbuk National University Dept. of Management Information System Cheongju Korea Republic of

With the rapid expansion of data markets, accurately assessing the value of data has become critical to fostering trust and fairness in data transactions. However, the lack of standardized methods for evaluating the worth of personal information, particularly personal image data, poses a significant challenge. This study examines how different levels of de-identification impact the perceived value of image data. Using the Structural Similarity Index Measure (SSIM), we assess changes in image quality, and through regression analysis, we observe a marked decrease in data value as de-identification levels increase. Our findings emphasize the importance of developing compensation models that align privacy preservation with data valuation, contributing to the creation of a transparent and secure data trading environment. © 2025 IEEE.

关键词： compensation scheme data trading de-identification image

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Experimental Analysis Based on Binary Classification to Distinguish the Authenticity of Text with Social Network data

Experimental Analysis Based on Binary Classification to Dist...

引用

IEEE international conference on big data and smart computing (bigcomp)

作者： Do, Seungwon Bang, Junseong Elect & Telecommun Res Inst Def ICT Convers Res Sect Daejeon South Korea Elect & Telecommun Res Inst Publ Safety Intelligence Res Sect Daejeon South Korea

ISBN: (纸本)9781728189246

Social network platforms have made it possible to collect and store large amounts of text data. In the fields of defense, public safety, and security, social text data is used for data analysis research and network model learning. However, the authenticity of the text data causes distortion of the information or degrades the performance of the trained model. To solve this problem, we presented a binary classification model that distinguishes authenticity using a pre-trained language model and conducted an experiment using tweet data. As a result, we showed 81% accuracy and 0.61 Matthews correlation coefficient value.

关键词： natural language processing text analysis text processing

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