Smart Auditing Rules for Rental Subsidy Using Pfhrcnns Algorithm

作     者：Chen, Jieh-Haur Su, Mu-Chun Hsu, Shu-Chien Wei, Hsi-Hsien Su, Chih-Ko 

作者机构：Department of Civil Engineering Taiwan Research Center of Smart Construction National Central University Jhongli Taoyuan320317 Taiwan APEC Society of Project Forensics Jhongli Taoyuan320317 Taiwan Safety and Health Association of Taiwan Miaoli Zhunan350007 Taiwan Department of Computer Science and Information Engineering Taiwan College of Electrical Engineering and Computer Science National Central University Jhongli Taoyuan320317 Taiwan Department of Civil and Environmental Engineering Hong Kong Polytechnic University Kowloon Hong Kong Department of Building and Real Estate The Hong Kong Polytechnic University Kowloon Hong Kong School of Civil Engineering Purdue University West LafayetteIN47907 United States 

出 版 物：《SSRN》 

年 卷 期：2023年

核心收录：

主　　题：Fuzzy neural networks 

摘      要：The aim of this research is to create an efficient and precise tool that can rapidly screen and eliminate unqualified applications, while also establishing consistent review guidelines across various cities and townships in Taiwan. The proposed approach involves developing a tool that employs PSO-based Fuzzy Hyper-rectangular Composite Neural Networks (PFHRCNNS). Utilizing a dataset of 36,086 entries from the government data bank, each application encompasses 10 distinct features that can be further scrutinized. The resultant tool achieves an impressive accuracy rate of 98.6% and generates 66 recommended rules for assessing eligibility for rental subsidies. © 2023, The Authors. All rights reserved.