Zhai, S., Li, G., Wu, G., Hou, M. and Jia, Q. (2023) Cooperative Task Allocation for Multi Heterogeneous Aerial Vehicles Using Particle Swarm Optimization Algorithm and Entropy Weight Method. Applied Soft Computing, 148, Article ID: 110918.
https://doi.org/10.1016/j.asoc.2023.110918

has been cited by the following article:

• TITLE: An Effective Prediction Method for Supporting Decision Making in Real Estate Area Selection

  AUTHORS: Haoying Jin, Song Yang, Mingzhi Zhao

  KEYWORDS: Real Estate, Natural Disaster, Decision Making, Prediction Model

  JOURNAL NAME: Journal of Computer and Communications, Vol.12 No.7, July 29, 2024

  ABSTRACT: Real estate has been a dominant industry in many countries. One problem for real estate companies is determining the most valuable area before starting a new project. Previous studies on this issue mainly focused on market needs and economic prospects, ignoring the impact of natural disasters. We observe that natural disasters are important for real estate area selection because they will introduce considerable losses to real estate enterprises. Following this observation, we first develop a self-defined new indicator named Average Loss Ratio to predict the losses caused by natural disasters in an area. Then, we adopt the existing ARIMA model to predict the Average Loss Ratio of an area. After that, we propose to integrate the TOPSIS model and the Grey Prediction Model to rank the recommendation levels for candidate areas, thereby assisting real estate companies in their decision-making process. We conduct experiments on real datasets to validate our proposal, and the results suggest the effectiveness of the proposed method.