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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.

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Handbook of Large-Scale Distributed Computing in Smart Healthcare

TL;DR: The contemporary research efforts mostly focus on health information delivery methods to ensure the information exchange within a single BAN, and the efforts have been very limited in interconnecting several BANs remotely through the servers.
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Safeguard Network Slicing in 5G: A Learning Augmented Optimization Approach

TL;DR: A learning augmented optimization approach with deep learning and Lyapunov stability theories enables the system to learn a safe slicing solution from both historical records and run-time observations, and proves that the proposed solution is always feasible and nearly optimal, up to a constant additive factor.
Journal ArticleDOI

DomNet: Protein Domain Boundary Prediction Using Enhanced General Regression Network and New Profiles

TL;DR: In this article, the authors proposed a new machine learning based domain predictor named DomNet that can show a more accurate and stable predictive performance than the existing state-of-the-art models.
Journal ArticleDOI

A control and decision system for smart buildings using wireless sensor and actuator networks

TL;DR: CONDE is presented, a decentralised CONtrol and DEcision-making system for smart building applications using WSANs and shows gains in terms of the following: response time; system efficiency; and energy savings from the network and the building.
Journal ArticleDOI

A Continuous Change Detection Mechanism to Identify Anomalies in ECG Signals for WBAN-Based Healthcare Environments

TL;DR: A centralized approach for the detection of abnormalities, as well as intrusions, such as forgery, insertions, and modifications in the ECG data is presented.