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Stefan Ma

dblp:73/7134 · DBLP profile ↗
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2ranked-venue papers
0as first author
1since 2021 · last 2026
0000-0002-2415-5045ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Databases, data management, data science and information retrieval · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Medical and health informatics · 77% Smart cities and intelligent transportation · 23%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Medical and health informatics › public health › public health informatics
infectious disease surveillance
1.012026
Mining Citywide Dengue Spread Patterns in Singapore Through Hotspot Dynamics from Open Web Data · WWW 2026

Methods — techniques the papers use, named apart from their topics

open web data mining · 1.0
YearPublicationVenuePosition
2026 Mining Citywide Dengue Spread Patterns in Singapore Through Hotspot Dynamics from Open Web Data
Gaoxi Xiao, Stefan Ma, Hechang Chen, Shisong Tang, Flora D. Salim
WWW3
2009 Key node selection for containing infectious disease spread using particle swarm optimization
abstract
In recent years, some emerging and reemerging infectious diseases have grown into global health threats due to high human mobility. It is important to have intervention plans for containing the spread of such infectious diseases. Among various intervention strategies, screening infected people is an efficient way for evaluating the infection scale and controlling the spread of infectious diseases. Considering the cost in manpower and limited screening machines available, we face to challenges for selecting the optimal nodes (sites) in order to obtain better screening and control effects. In this paper, particle swarm optimization technique is used to determine key nodes for controlling infectious disease spread, through evaluating the number of people captured at each key node. The research example is shown on evaluating the screening control over train stations in Singapore. The optimization algorithm and control concept can be easily extended to large-scale infectious disease control in other kinds of key nodes and in other geographical regions. The selection for optimal control set of the multi objective optimization problem is done using particle swarm optimization. Numerical simulation shows the effectiveness of the proposed algorithm.
Xiuju Fu, Sonja Lim, Lipo Wang 0001, Gary Geunbae Lee, Stefan Ma, Limsoon Wong, Gaoxi Xiao
SIS5