F
Friso De Boer
Researcher at Charles Darwin University
Publications - 50
Citations - 703
Friso De Boer is an academic researcher from Charles Darwin University. The author has contributed to research in topics: Torque & Cogging torque. The author has an hindex of 11, co-authored 42 publications receiving 374 citations. Previous affiliations of Friso De Boer include University College of Engineering.
Papers
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Journal ArticleDOI
Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques
Pronab Ghosh,Sami Azam,Mirjam Jonkman,Asif Karim,F. M. Javed Mehedi Shamrat,Eva Ignatious,Shahana Shultana,Abhijith Reddy Beeravolu,Friso De Boer +8 more
TL;DR: In this article, the authors proposed a model that incorporates different methods to achieve effective prediction of heart disease, which used efficient Data Collection, Data Pre-processing and Data Transformation methods to create accurate information for the training model.
Proceedings Article
Frequency bands effects on qrs detection
TL;DR: Investigation of the QRS frequency bands in ECG signals shows that the recommended bandpass frequency range for detecting QRS complexes is 8-20Hz which the best signal-to-noise ratio.
Proceedings ArticleDOI
R wave detection using Coiflets wavelets
TL;DR: A generic algorithm using Coiflet wavelets is introduced to improve the detection of QRS complexes in Arrhythmia ECG Signals that suffer from: 1) non-stationary effects, 2) low Signal-to-Noise Ratio, 3) negative QRS polarities, 4) low QRS amplitudes, and 5) ventricular ectopics.
Proceedings ArticleDOI
Intrusion Detection System for Internet of Things based on a Machine Learning approach
Chao Liang,Bharanidharan Shanmugam,Sami Azam,Mirjam Jonkman,Friso De Boer,Ganthan Narayansamy +5 more
TL;DR: Researchers have found that the combination of machine learning technologies with an intrusion detection system is an effective way to resolve the drawbacks traditional IDSs have when they are used for IoT.
Journal Article
Improved QRS Detection Algorithm using Dynamic Thresholds
TL;DR: An improved version of a QRS detector based on an adaptive quantized threshold that achieves high detection rates by using automatic thresholds instead of predetermined static thresholds is presented.