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[null,"1.","Record Nr.",null,"UNINA9911061845503321"]
[null,null,"Autore",null,"Nakamatsu Kazumi"]
[null,null,"Titolo",null,"New Paradigm in AI and Concurrent Engineering : Proceedings of International Conference on AI and Concurrent Engineering (AICE 2025) / / edited by Kazumi Nakamatsu, Rupinder Singh, Kalipada Maity"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2026"]
[null,null,"ISBN",null,"3-032-13817-5"]
[null,null,"Edizione",null,"[1st ed. 2026.]"]
[null,null,"Descrizione fisica",null,"1 online resource (480 pages)"]
[null,null,"Collana",null,"Smart Innovation, Systems and Technologies, , 2190-3026 ; ; 469"]
[null,null,"Disciplina",null,"670"]
[null,null,"Soggetti",null,"Industrial engineering"]
[{"$nulls":4},"Production engineering"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Engineering design"]
[{"$nulls":4},"Industrial and Production Engineering"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Engineering Design"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Bridging Theory and Practice: Developing Virtual Classroom for Interactive Lighting Education -- Mitigating Overfitting in Tikog Grass Prediction: An Enhanced LSTM-XGBoost Model for Sustainable Handicraft Production -- Livia: An Emotion-Aware AR Companion Powered by Modular AI Agents and Progressive Memory Compression -- DATA IMPUTATION STRATEGIES FOR GRU-BASED CORN YIELD FORECASTING: A COMPARATIVE ANALYSIS OF KNN, MICE, AND EM -- Seeing is Feeling: Hyper-Real VR and Emotional Engagement - A Theory-Driven Framework for Visual Factor Design."]
[null,null,null,"Sommario/riassunto","This book includes research papers presented at the International Conference on AI and Concurrent Engineering (AICE 2025) to be held at IIMT, Bhubaneswar, India, during August 23 – 24, 2025. The proceedings documents how AI systems analyse vast datasets, predict outcomes, and offer strategic insights by leveraging both historical and real-time information. The proceedings also explores how AI-driven predictive maintenance and quality control are transforming post-production processes."]