LEADER 03516nam 22005655 450 001 9911066010503321 005 20260222120352.0 010 $a3-032-16702-7 024 7 $a10.1007/978-3-032-16702-6 035 $a(MiAaPQ)EBC32556478 035 $a(Au-PeEL)EBL32556478 035 $a(CKB)45408932200041 035 $a(DE-He213)978-3-032-16702-6 035 $a(EXLCZ)9945408932200041 100 $a20260222d2026 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvances in Intelligent Data and Information Processing $eProceedings of the International Conference on Intelligent Data and Information Processing (IDIP2025), Volume 2 /$fedited by Witold Pedrycz, John Wang, Kuo-Kun Tseng, Xilong Qu 205 $a1st ed. 2026. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2026. 215 $a1 online resource (418 pages) 225 1 $aLecture Notes in Networks and Systems,$x2367-3389 ;$v1808 311 08$a3-032-16701-9 327 $a -- 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. 330 $aThis book integrates practical engineering insights with cutting-edge AI/ML methodologies to address real-world intelligent data processing challenges, prioritizing actionable solutions over theoretical abstraction. By bridging algorithmic foundations with industry-specific use cases, it equips readers to translate technical concepts into deployable systems efficiently. Unlike traditional texts that silo theory and practice, this approach embeds hands-on implementation frameworks, including data preprocessing pipelines, model optimization techniques, and scalability strategies, directly within contextualized problem-solving scenarios. Covering core topics from edge AI deployment to large-scale data analytics, it spans both foundational principles and emerging trends like federated learning and real-time processing. Tailored for IT professionals, computer science practitioners, and engineering researchers, it also serves as a valuable resource for graduate students specializing in data science or intelligent systems. Ideal for upskilling, project reference, or curriculum supplementation, it empowers readers to tackle complex data-intensive tasks with confidence in academic, corporate, or R&D settings. 410 0$aLecture Notes in Networks and Systems,$x2367-3389 ;$v1808 606 $aEngineering$xData processing 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aData Engineering 606 $aComputational Intelligence 606 $aArtificial Intelligence 615 0$aEngineering$xData processing. 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 14$aData Engineering. 615 24$aComputational Intelligence. 615 24$aArtificial Intelligence. 676 $a620.00285 700 $aPedrycz$b Witold$021029 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911066010503321 996 $aAdvances in Intelligent Data and Information Processing$94547686 997 $aUNINA