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Record Nr. |
UNINA9911066010503321 |
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Autore |
Pedrycz Witold |
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Titolo |
Advances in Intelligent Data and Information Processing : Proceedings of the International Conference on Intelligent Data and Information Processing (IDIP2025), Volume 2 / / edited by Witold Pedrycz, John Wang, Kuo-Kun Tseng, Xilong Qu |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2026 |
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ISBN |
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Edizione |
[1st ed. 2026.] |
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Descrizione fisica |
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1 online resource (418 pages) |
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Collana |
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Lecture Notes in Networks and Systems, , 2367-3389 ; ; 1808 |
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Disciplina |
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Soggetti |
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Engineering - Data processing |
Computational intelligence |
Artificial intelligence |
Data Engineering |
Computational Intelligence |
Artificial Intelligence |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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-- Application of Fused Neural Network Model in English Sentiment Analysis -- Research on Prediction of Housing Security Demand Based on Big Data and its Impact on Policy Making -- Deep Learning Model Optimization for Natural Language Processing -- Early Warning Model Construction of Enterprise Financial Crisis Based on Random Forest Algorithm, etc. |
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Sommario/riassunto |
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This book integrates practical engineering insights with cutting-edge AI/ML methodologies to address real-world intelligent data processing challenges, prioritizing actionable solutions over theoretical abstraction. By bridging algorithmic foundations with industry-specific use cases, it equips readers to translate technical concepts into deployable systems efficiently. Unlike traditional texts that silo theory and practice, this approach embeds hands-on implementation frameworks, including data preprocessing pipelines, model optimization techniques, and scalability strategies, directly within contextualized problem-solving scenarios. Covering core topics from |
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edge AI deployment to large-scale data analytics, it spans both foundational principles and emerging trends like federated learning and real-time processing. Tailored for IT professionals, computer science practitioners, and engineering researchers, it also serves as a valuable resource for graduate students specializing in data science or intelligent systems. Ideal for upskilling, project reference, or curriculum supplementation, it empowers readers to tackle complex data-intensive tasks with confidence in academic, corporate, or R&D settings. |
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