Titlebook: Knowledge Science, Engineering and Management; 17th International C Cungeng Cao,Huajun Chen,Yonghao Wang Conference proceedings 2024 The Ed

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Insider Threat Defense Strategies: Survey and?Knowledge Integration is paid to the external knowledge bases for assisting the model in determining insider threat behaviors. Our survey aims to augment the practitioners in the insider threat domain by offering (1) a workflow-centric structural taxonomy that contributes to the orthogonal classification of incidents an

hieroglyphic

Customer Segmentation for?Telecommunication Using Machine Learningas Precision, Recall, F1 score, and accuracy. This novel approach aims to improve customer churn prediction and enable more targeted strategies to retain valuable customers. KNN has shown 96% accuracy for usage and behaviour detection, while ANN managed to get 79% accuracy for the Loyalty of custome

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不妥協

Reinforcement Learning for?Scientific Application: A Surveyfic problems, efficient large-scale training techniques are often needed. The development of efficient training techniques and scientific applications permeate and integrate with each other, providing a solid foundation for reinforcement learning in exploring scientific problems. It is foreseeable t

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Shubin Cai,Honglong Chen,Youyi Huang,Zhong Mingsgorin’s Theorem, will be of interest to many. This book is a?ectionately dedicated to my mentors, Olga Taussky-Todd and John Todd. There are two main recurring themes which the reader will see978-3-642-05928-5978-3-642-17798-9Series ISSN 0179-3632 Series E-ISSN 2198-3712

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Evaluating GPT’s Programming Capability Through CodeWars’ Katas.5 and GPT-4 using Codewars coding problems of varying difficulty. The experiments reveal a distinct boundary at the 3kyu level, beyond which these models struggle. This led to proposing a complexity measure that includes problem difficulty and solution time. The research emphasizes the need for val

ineffectual

An Online Portfolio Selection Algorithm with?Dynamic Coreset Construction signals. Existing methods often struggle to dynamically select asset subsets in order to optimize capital efficiency. To address these challenges, we propose the Portfolio Selection with Dynamic Coreset Construction (PSCS) algorithm. PSCS incorporates a novel clustering process that groups assets b

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Interval-Valued Fuzzy Portfolio Decision Model with Transaction Cost and Liquidityc return and minimizing semi-absolute deviation risk of portfolio. Then, by introducing investor’s risk preference and satisfaction degree of liquidity interval, the constructed portfolio model is converted into a single-objective optimization model to solve the optimal portfolio strategy. Finally,