Wu / Yang / Amin Knowledge Management and Acquisition for Intelligent Systems

19th Principle and Practice of Data and Knowledge Acquisition Workshop, PKAW 2023, Jakarta, Indonesia, November 15-16, 2023, Proceedings
1. Auflage 2023
ISBN: 978-981-99-7855-7
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 146 Seiten, eBook

Reihe: Lecture Notes in Artificial Intelligence

This book constitutes the refereed proceedings of the 19th Principle and Practice of Data and Knowledge Acquisition Workshop, PKAW 2023, held in conjunction with the 20th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2023), in November 2023, in Jakarta, Indonesia.The 9 full papers and 2 short papers included in this volume were carefully reviewed and selected from 28 initial submissions. They are organized in the topical section such as machine learning, natural language processing, and intelligent systems.

Zielgruppe

Research

Autoren/Hrsg.

Wu, Shiqing

Yang, Wenli

Amin, Muhammad Bilal

Kang, Byeong-Ho

Xu, Guandong

Inhaltsverzeichnis

Predicting Peak Demand Days for Asthma-Related Emergency Hospitalisations: A Machine Learning Approach.- Discovering Maximal High Utility Co-location Patterns from Spatial Data.-  Exploring the Potential of Image Overlay in Self-Supervised Learning: A Study on SimSiam Networks and Strategies for Preventing Model Collapse.- BoCB: Performance Benchmarking by Analysing Impacts of Cloud Platforms on Consortium Blockchain.- Automated Cattle Behavior Classification Using Wearable Sensors and Machine Learning Approach.- LexiFusedNet: A Unified Approach for Imbalanced Short-text Classification using Lexicon-based Feature Extraction, Transfer Learning and One Class Classifiers.- Information Gerrymandering in Elections.- An Assessment of the Influence of Interaction and Recommendation Approaches on the Formation of Information Filter Bubbles.- Blockchain as a Collaborative Technology - Case Studies in the Real EstateSector in Vietnam.- Indonesian Forest Fire Data Clustering using Spatiotemporal Data Using Grid Density-Based Clustering Algorithm.