S
Saman K. Halgamuge
Researcher at University of Melbourne
Publications - 264
Citations - 10378
Saman K. Halgamuge is an academic researcher from University of Melbourne. The author has contributed to research in topics: Artificial neural network & Cluster analysis. The author has an hindex of 42, co-authored 260 publications receiving 8882 citations. Previous affiliations of Saman K. Halgamuge include University of South Australia & Peter MacCallum Cancer Centre.
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Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
TL;DR: A novel parameter automation strategy for the particle swarm algorithm and two further extensions to improve its performance after a predefined number of generations to overcome the difficulties of selecting an appropriate mutation step size for different problems.
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Dynamic self-organizing maps with controlled growth for knowledge discovery
TL;DR: The growing self-organizing map (GSOM) is presented in detail and the effect of a spread factor, which can be used to measure and control the spread of the GSOM, is investigated.
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CONTRA: copy number analysis for targeted resequencing
Jason Li,Richard Lupat,Kaushalya C. Amarasinghe,Ella R. Thompson,Maria A. Doyle,Georgina L Ryland,Richard W. Tothill,Saman K. Halgamuge,Ian G. Campbell,Kylie L. Gorringe +9 more
TL;DR: This work presents a method for CNV detection for TR data, including whole-exome capture data, and assesses the methods using samples from seven different target enrichment assays, and evaluated the results using simulated data and real germline data with known CNV genotypes.
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A Review on efficient thermal management of air- and liquid-cooled data centers: From chip to the cooling system
TL;DR: In this article, the state-of-the-art of multi-level thermal management techniques for both air- and liquid-cooled data centers is reviewed. But the main focus is on the sources of inefficiencies and the improvement methods with their configuration features and performances at each level.
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Neural networks in designing fuzzy systems for real world applications
TL;DR: The new method employed to identify the rule relevant nodes before the rules are extracted makes FuNe I suitable for applications with large number of inputs, and optimization of the knowledge base in possible including the tuning of membership functions.