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Nur Al Hasan Haldar
Researcher at University of Western Australia
Publications - 19
Citations - 326
Nur Al Hasan Haldar is an academic researcher from University of Western Australia. The author has contributed to research in topics: Computer science & Wireless network. The author has an hindex of 6, co-authored 16 publications receiving 235 citations. Previous affiliations of Nur Al Hasan Haldar include King Saud University.
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A Cloud-based Healthcare Framework for Security and Patients’ Data Privacy Using Wireless Body Area Networks
TL;DR: The research work presented here is twofold: first, it attempts to secure the inter-sensor communication by multi-biometric based key generation scheme in WBANs; and secondly, the electronic medical records are securely stored in the hospital community cloud and privacy of the patients’ data is preserved.
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Arrhythmia classification using Mahalanobis distance based improved Fuzzy C-Means clustering for mobile health monitoring systems
TL;DR: An improved electrocardiogram (ECG) beats classification system is proposed, which is based on Fuzzy C-Means (FCM) clustering algorithm, and Mahalanobis Distance (MD) is used in the proposed model in order to improve the distance measurement procedure.
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BiSAL - A bilingual sentiment analysis lexicon to analyze Dark Web forums for cyber security
TL;DR: A Bilingual Sentiment Analysis Lexicon (BiSAL) for cyber security domain, which consists of a Sentiment Lexicon for ENglish (SentiLEN and SentiLAR), that can be used to develop opinion mining and sentiment analysis systems for bilingual textual data from Dark Web forums is presented.
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A Continuous Change Detection Mechanism to Identify Anomalies in ECG Signals for WBAN-Based Healthcare Environments
Farrukh Aslam Khan,Nur Al Hasan Haldar,Aftab Ali,Mohsin Iftikhar,Tanveer A. Zia,Albert Y. Zomaya +5 more
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.
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Location prediction in large-scale social networks: an in-depth benchmarking study
TL;DR: A generalized procedure-oriented location prediction framework is formulated which allows for an in-depth empirical comparison of eight representative prediction models using five metrics on four real-world large-scale datasets, namely Twitter, Gowalla, Brightkite, and Foursquare.