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Ibrahim Khalil

Researcher at RMIT University

Publications -  227
Citations -  6127

Ibrahim Khalil is an academic researcher from RMIT University. The author has contributed to research in topics: Cloud computing & Encryption. The author has an hindex of 34, co-authored 217 publications receiving 4571 citations. Previous affiliations of Ibrahim Khalil include University of Bern & NICTA.

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A Survey of Clustering Algorithms for Big Data: Taxonomy and Empirical Analysis

TL;DR: Concepts and algorithms related to clustering, a concise survey of existing (clustering) algorithms as well as a comparison, both from a theoretical and an empirical perspective are introduced.
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A context-aware approach for long-term behavioural change detection and abnormality prediction in ambient assisted living

TL;DR: A Hidden Markov Model based approach for detecting abnormalities in daily activities, a process of identifying irregularity in routine behaviours from statistical histories and an exponential smoothing technique to predict future changes in various vital signs are described.
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Wavelet-Based ECG Steganography for Protecting Patient Confidential Information in Point-of-Care Systems

TL;DR: A wavelet-based steganography technique has been introduced which combines encryption and scrambling technique to protect patient confidential data and it is found that the proposed technique provides high-security protection for patients data with low distortion and ECG data remain diagnosable after watermarking.
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CoCaMAAL: A cloud-oriented context-aware middleware in ambient assisted living

TL;DR: The proposed CoCaMAAL model seeks to address issues and implement a service-oriented architecture (SOA) for unified context generation by efficiently aggregating raw sensor data and the timely selection of appropriate services using a context management system (CMS).
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A Trustworthy Privacy Preserving Framework for Machine Learning in Industrial IoT Systems

TL;DR: This article introduces a framework named PriModChain that enforces privacy and trustworthiness on IIoT data by amalgamating differential privacy, federated ML, Ethereum blockchain, and smart contracts.