Toward an Agent-Based Multi-scale Recommendation System for Brown Plant Hopper Control

2012 ◽  
Author(s):  
Vinh Gia Nhi Nguyen ◽  
Alexis Drogoul ◽  
Hiep Xuan Huynh
Keyword(s):  
Recommendation System ◽  
Agent Based ◽  
Multi Scale ◽  
Brown Plant Hopper

scholarly journals Improving Recommendation Accuracy Using Social Network of Owners in Social Internet of Vehicles

2020 ◽  
Vol 12 (4) ◽  
pp. 69 ◽  
Author(s):  
Kashif Zia ◽  
Muhammad Shafi ◽  
Umar Farooq
Keyword(s):  
Social Network ◽  
Recommendation System ◽  
Internet Of Vehicles ◽  
Agent Based ◽  
Smart Vehicles ◽  
Multi Scale ◽  
The Social ◽  
Simulation Results ◽  
Connected Person ◽  
The Internet Of Things

The latest manifestation of “all connected world" is the Internet of Things (IoT), and Internet of Vehicles (IoV) is one of the key examples of IoT these days. In Social IoV (SIoV), each vehicle is treated as a social object where it establishes and manages its own Social Network (SN). Incidentally, most of the SIoV research in the literature is related to proximity-based connectivity and interactions. In this paper, we bring people in the loop by incorporating their SNs. While emphasizing a recommendation scenario, in which vehicles may require recommendations from SNs of their owners (in addition to their own SIoV), we proposed an agent-based model of information sharing (for context-based recommendations) on a hypothetical population of smart vehicles. Some important hypotheses were tested using a realistic simulation setting. The simulation results reveal that a recommendation using weak ties is more valuable than a recommendation using strong ties in pure SIoV. The simulation results also demonstrate that recommendations using the most-connected person in the social network are not more valuable than recommendation using a random person in the social network. The model presented in this paper can be used to design a multi-scale recommendation system, which uses SIoV and a typical SN in combination.

Theory, Data, and Methods

2019 ◽  
pp. 156-202
Author(s):  
Eda Ustaoglu ◽  
Arif Çagdaş Aydinoglu
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Information System ◽  
Land Use Change ◽  
Model Development ◽  
Agent Based ◽  
Change Models ◽  
Multi Scale ◽  
Land Use Dynamics ◽  
Scale Characteristics

Land-use change models are tools to support analyses, assessments, and policy decisions concerning the causes and consequences of land-use dynamics, by providing a framework for the analysis of land-use change processes and making projections for the future land-use/cover patterns. There is a variety of modelling approaches that were developed from different disciplinary backgrounds. Following the reviews in the literature, this chapter focuses on various modelling tools and practices that range from pattern-based methods such as machine learning and GIS (Geographic Information System)-based approaches, to process-based methods such as structural economic or agent-based models. For each of these methods, an overview is given for the advances that have been progressed by geographers, natural and economy scientists in developing these models of spatial land-use change. It is noted that further progress is needed in terms of model development, and integration of models operating at various scales that better address the multi-scale characteristics of the land-use system.

Sign in / Sign up

Export Citation Format

Share Document