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Autore: | Cao Cungeng |
Titolo: | Knowledge Science, Engineering and Management : 17th International Conference, KSEM 2024, Birmingham, UK, August 16–18, 2024, Proceedings, Part V / / edited by Cungeng Cao, Huajun Chen, Liang Zhao, Junaid Arshad, Taufiq Asyhari, Yonghao Wang |
Pubblicazione: | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
Edizione: | 1st ed. 2024. |
Descrizione fisica: | 1 online resource (341 pages) |
Disciplina: | 006.3 |
Soggetto topico: | Artificial intelligence |
Computer engineering | |
Computer networks | |
Computers | |
Information technology - Management | |
Social sciences - Data processing | |
Application software | |
Artificial Intelligence | |
Computer Engineering and Networks | |
Computing Milieux | |
Computer Application in Administrative Data Processing | |
Computer Application in Social and Behavioral Sciences | |
Computer and Information Systems Applications | |
Altri autori: | ChenHuajun ZhaoLiang ArshadJunaid AsyhariTaufiq WangYonghao |
Nota di contenuto: | -- Special Track. -- Adversary and Attention Guided Knowledge Graph Reasoning based on Reinforcement Learning. -- Evaluating GPT’s Programming Capability through CodeWars’ Katas. -- An Online Portfolio Selection Algorithm with Dynamic Coreset Construction. -- Interval-valued Fuzzy Portfolio decision Model with Transaction Cost and Liquidity Constraint. -- Active Learning for Low-Resource Project-Specific Code Summarization. -- A Survey of Game-Theoretic Methods for Controlling COVID-19. -- ComPAT: a Compiler Principles course AssisTant. -- Tram Air Conditioning Fault Prediction Using Machine learning. -- Lexicon Graph Adapter based BERT Model For Chinese Named Entity Recognition. -- Insider Threat Defense Strategies: Survey and Knowledge Integration. -- GA-MEPS: Multiple Experts Portfolio Selection Based on Genetic Algorithm. -- Deep Learning and Machine Learning-Based Approaches to Inferring Social Media Network Users’ Interests from a Missing Data Issus. -- Customer Segmentation for Telecommunication using Machine Learning. -- DP-MFRNN: Difficulty Prediction for Examination Questions Based on Neural Network Framework. -- Causal Relationship Extraction Combined Boundary Detection and Information Interaction. -- Profit Maximization in Edge-enabled Multimedia Data Market: A Game-based Pricing Approach. -- Reinforcement learning for scientific application: A survey. -- Zunna: A New Browser Extension for Protecting Personal Data. -- HRTC:A Triple Joint Extraction Model Based on Cyber Threat Intelligence. -- Personalized Image Aesthetics Assessment based onTheme and Personality. -- A Spatio-temporal Neural Network for Medical Insurance Fraud Detection. -- Exploring Language Diversity to Improve Neural Text Generation. -- Diffusion Review-based Recommendation. -- IntellectSeeker: A Personalized Literature Management System with the Probabilistic Model and Large Language Model. -- A novel network intrusion detection method for unbalanced data in open scenarios. -- Explainable Knowledge-Based Learning For Online Medical Question Answering. -- Energy consumption prediction method for refrigeration systems based on adversarial networks and Transformer networks. -- P-Vit: A simplified Vision Transformer model based on FFN and Simple Attention. |
Sommario/riassunto: | The five-volume set LNCS 14884, 14885, 14886, 14887 & 14888 constitutes the refereed deadline proceedings of the 17th International Conference on Knowledge Science, Engineering and Management, KSEM 2024, held in Birmingham, UK, during August 16–18, 2024. The 160 full papers presented in these proceedings were carefully reviewed and selected from 495 submissions. The papers are organized in the following topical sections: Volume I: Knowledge Science with Learning and AI (KSLA) Volume II: Knowledge Engineering Research and Applications (KERA) Volume III: Knowledge Management with Optimization and Security (KMOS) Volume IV: Emerging Technology Volume V: Special Tracks. |
Titolo autorizzato: | Knowledge Science, Engineering and Management |
ISBN: | 981-9754-89-5 |
Formato: | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione: | Inglese |
Record Nr.: | 9910878046903321 |
Lo trovi qui: | Univ. Federico II |
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