With the increasing occurrence of disasters, how to respond to disasters has attracted a lot of
interest. However, a systematic study of emergency response in disasters (ER) has been …

Recent advances in automatic machine learning (aML) allow solving problems without any
human intervention. However, sometimes a human-in-the-loop can be beneficial in solving …

Suppliers can use different strategies to distribute their products, Among the most common
complex optimization problems related to the transportation of products is the traveling …

The goal of Machine Learning to automatically learn from data, extract knowledge and to
make decisions without any human intervention. Such automatic (aML) approaches show …

Abstract Most Machine Learning (ML) researchers focus on automatic Machine Learning
(aML) where great advances have been made, for example, in speech recognition …

This paper explores the utilisation of knowledge graphs and an agent-based implementation
to enhance urban resilience and accessibility in city planning. We expand The World Avatar …

With the development of mobile terminal technology, GIS has begun to be applied in all
walks of life. With its powerful spatial indexing, positioning, query, analysis, and graphics …

In the process of solving the Traveling Salesman Problem (TSP), both Ant Colony
Optimization and simulated annealing exhibit different limitations depending on the dataset …

Complex networks have been widely applied in many complex systems existed in nature
and society because of its rapid development. Many methods have been proposed to …

We propose a novel metric called MetrIntMeas (Metric for the Intelligence Measuring) for an
accurate and robust measurement of the difficult problem-solving intelligence of a swarm …