Pedrycz / Wang / Tseng Advances in Intelligent Data and Information Processing

Proceedings of the International Conference on Intelligent Data and Information Processing (IDIP2025), Volume 2
Erscheinungsjahr 2026
ISBN: 978-3-032-16702-6
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 304 Seiten

Reihe: Lecture Notes in Networks and Systems

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 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.

Zielgruppe

Research

Autoren/Hrsg.

Pedrycz, Witold

Wang, John

Tseng, Kuo-Kun

Qu, Xilong

Inhaltsverzeichnis

.- 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.