11 Result(s)
within
Son T. Mai
Computer Science
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Article
Dynamic weighted ensemble for diarrhoea incidence predictions
Diarrhoea (DH) disease pose significant threats to national morbidity and mortality in Vietnam, especially on children. Being a climate sensitive disease, it has strong links to various meteorological factors ...
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Article
Learning to summarize multi-documents with local and global information
The importance estimation of sentences plays an important role in the extractive summarization of multi-documents. This paper introduces a method to estimate the importance of sentences by using feature engine...
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Chapter and Conference Paper
An Intelligent Image Processing System for Enhancing Blood Vessel Segmentation on Low-Power SoC
Machine learning offers the potential to enhance real-time image analysis in surgical operations. This paper presents results from the implementation of machine learning algorithms targeted for an intelligent ...
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Chapter and Conference Paper
Multi-spectral In-Vivo FPGA-Based Surgical Imaging
Intelligent and adaptive in-vivo, catheter-based imaging systems with enhanced processing and analytical capability have the potential to enhance surgical operations and improve patient care. The paper describ...
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Article
Open AccessEvolutionary Active Constrained Clustering for Obstructive Sleep Apnea Analysis
We introduce a novel interactive framework to handle both instance-level and temporal smoothness constraints for clustering large longitudinal data and for tracking the cluster evolutions over time. It consist...
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Article
Anytime parallel density-based clustering
The density-based clustering algorithm DBSCAN is a state-of-the-art data clustering technique with numerous applications in many fields. However, DBSCAN requires neighborhood queries for all objects and propag...
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Chapter and Conference Paper
Scalable Active Constrained Clustering for Temporal Data
In this paper, we introduce a novel interactive framework to handle both instance-level and temporal smoothness constraints for clustering large temporal data. It consists of a constrained clustering algorithm...
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Chapter
Interactive Exploration of Subspace Clusters on Multicore Processors
The PreDeCon clustering algorithm finds arbitrarily shaped clusters in high-dimensional feature spaces, which remains an active research topic with many potential applications. However, it suffers from poor ru...
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Chapter and Conference Paper
Interactive Exploration of Subspace Clusters for High Dimensional Data
PreDeCon is a fundamental clustering algorithm for finding arbitrarily shaped clusters hidden in high-dimensional feature spaces of data, which is an important research topic and has many potential application...
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Chapter and Conference Paper
Anytime OPTICS: An Efficient Approach for Hierarchical Density-Based Clustering
OPTICS is a fundamental data clustering technique that has been widely applied in many fields. However, it suffers from performance degradation when faced with large datasets and expensive distance measures be...
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Article
Anytime density-based clustering of complex data
Many clustering algorithms suffer from scalability problems on massive datasets and do not support any user interaction during runtime. To tackle these problems, anytime clustering algorithms are proposed. The...
