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Albert Y. Zomaya

Researcher at University of Sydney

Publications -  1020
Citations -  30827

Albert Y. Zomaya is an academic researcher from University of Sydney. The author has contributed to research in topics: Cloud computing & Scheduling (computing). The author has an hindex of 75, co-authored 946 publications receiving 24637 citations. Previous affiliations of Albert Y. Zomaya include University of Alabama & University of Sheffield.

Papers
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Modeling and Analysis of the Thermal Properties Exhibited by Cyberphysical Data Centers

TL;DR: This paper model a DC as a cyberphysical system (CPS) to capture the thermal properties exhibited by the DC and proposes a thermal-aware control strategy that uses a high-level centralized controller and a low- level centralized controller to manage and control the thermal status of the cyber components at different levels.
Journal ArticleDOI

Reinforcement learning for the adaptive control of nonlinear systems

TL;DR: An attempt is made to present a method for the adaptive control of nonlinear systems based on a feedfoward neural network that incorporates a neuro-controller used within a reinforcement learning framework, which reduces the problem to one of learning a stochastic approximation of an unknown average error surface.
Proceedings ArticleDOI

Parallel ant colony optimization for 3D protein structure prediction using the HP lattice model

TL;DR: A novel method of solving the HP protein folding problem in both two and three dimensions is introduced using ant colony optimizations and a distributed programming paradigm.
Book

Neuro-Adaptive Process Control: A Practical Approach

TL;DR: Using a series of rigorous authentic examples, the authors demonstrate several simple yet practical techniques for utilizing adaptive neural networks to produce more efficient process control.
Journal ArticleDOI

A Combined Genetic-neural Algorithm for Mobility Management

TL;DR: This work presented a new approach to solve the location management problem by using the location areas approach, and a combination of Genetic Algorithm and Hopfield Neural Network is used to find the optimal configuration of location areas in a mobile network.