Titlebook: Intelligent Data Engineering and Automated Learning – IDEAL 2024; 25th International C Vicente Julian,David Camacho,Antonio Tallón-Balles C

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书目名称Intelligent Data Engineering and Automated Learning – IDEAL 2024影响因子(影响力)




书目名称Intelligent Data Engineering and Automated Learning – IDEAL 2024影响因子(影响力)学科排名




书目名称Intelligent Data Engineering and Automated Learning – IDEAL 2024网络公开度




书目名称Intelligent Data Engineering and Automated Learning – IDEAL 2024网络公开度学科排名




书目名称Intelligent Data Engineering and Automated Learning – IDEAL 2024被引频次




书目名称Intelligent Data Engineering and Automated Learning – IDEAL 2024被引频次学科排名




书目名称Intelligent Data Engineering and Automated Learning – IDEAL 2024年度引用




书目名称Intelligent Data Engineering and Automated Learning – IDEAL 2024年度引用学科排名




书目名称Intelligent Data Engineering and Automated Learning – IDEAL 2024读者反馈




书目名称Intelligent Data Engineering and Automated Learning – IDEAL 2024读者反馈学科排名

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Association Rules Mining with Auto-encodersn auto-encoder solution to mine association rule called ARM-AE. We compare our algorithm to FP-Growth and NSGAII on three categorical datasets, and show that our algorithm discovers high support and confidence rule set and has a better execution time than classical methods while preserving the quality of the rule set produced.

Consequence

How Resilient are Language Models to Text Perturbations?, with varying degrees of vulnerability depending on both the specific task and the type of perturbation. In particular, the XLNet model consistently shows superior robustness, while tasks involving grammatical coherence are most adversely affected.

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Emotional Sequential Influence Modeling on False Informationluence based on social context, content, and emotional-based features. The contribution of this paper is to build an emotionally infused model called the Emotional-based User Sequential Influence Model+(E-USIM+) to understand users’ temporal emotional propagation patterns and predict future emotions against false information.

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Efficient Radar Scheduling Using Genetic Algorithms and Stochastic Heuristic Initializationnt-preserving recombination operator, and a customized mutation method, our approach generates high-quality solutions that respect the problem’s many constraints. Our research demonstrates the adaptability of genetic algorithms to real-world problems, showing significant improvements in scheduling efficiency and operational flexibility.

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Conference proceedings 2025Automated Learning, IDEAL 2024, held in Valencia, Spain, during November 20–22, 2024. ..The 86 full papers and 6 short papers presented in this book were carefully reviewed and selected from 130 submissions. IDEAL 2024 is focusing on Big Data Analytics and Privacy, Machine Learning & Deep Learning f

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Quantitative Estimation of Reputation Riskrom descriptive statistics of the reputation data. The derived values are validated using a “sense test” based on a Loess quantile. The results show that the Minimum Acceptable Sentiment value is given approximately by a two standard deviation lower tail of the observed data.