Performance of the Wavelet Transform-Neural Network Based Receiver for DPIM in Diffuse Indoor Optical Wireless Links in Presence of Artificial Light Interference by Sujan Rajbhandari, Zabih Ghassemlooy, Maia Angelova

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Patterns of sedentary behaviour in adults with acute insomnia derived from actigraphy data by Sunita Rani, Sergiy Shelyag, Maia Angelova

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Patterns of sedentary behaviour in adults with acute insomnia derived from actigraphy data. by Sunita Rani, Sergiy Shelyag, Maia Angelova

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Detecting Sensor Faults, Anomalies and Outliers in the Internet of Things: A Survey on the Challenges and Solutions by Anuroop Gaddam, Tim Wilkin, Maia Angelova, Jyotheesh Gaddam

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`Friend or foe' and decision making initiative in complex conflict environments. by Mathew Zuparic, Sergiy Shelyag, Maia Angelova, Ye Zhu, Alexander Kalloniatis

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Time‐domain heart rate variability features for automatic congestive heart failure prediction by Jeban Chandir Moses, Sasan Adibi, Maia Angelova, Sheikh Mohammed Shariful Islam

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Smart Home Technology Solutions for Cardiovascular Diseases: A Systematic Review by Jeban Chandir Moses, Sasan Adibi, Maia Angelova, Sheikh Mohammed Shariful Islam

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Machine Learning Enabled Team Performance Analysis in the Dynamical Environment of Soccer by Shitanshu Kusmakar, Sergiy Shelyag, Ye Zhu, Dan Dwyer, Paul Gastin, Maia Angelova

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Automated Method for Detecting Acute Insomnia Using Multi-Night Actigraphy Data by Maia Angelova, Chandan Karmakar, Ye Zhu, Sean P. A. Drummond, Jason Ellis

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Multiscale adaptive analysis of circadian rhythms and intradaily variability: Application to actigraphy time series in acute insomnia subjects. by Ruben Fossion, Ana Leonor Rivera, Juan C Toledo-Roy, Jason Ellis, Maia Angelova

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Technical determinants of success in professional women's soccer: A wider range of variables reveals new insights. by Laura M S de Jong, Paul B Gastin, Maia Angelova, Lyndell Bruce, Dan B Dwyer

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Smartphone as a Disease Screening Tool: A Systematic Review by Jeban Chandir Moses, Sasan Adibi, Nilmini Wickramasinghe, Lemai Nguyen, Maia Angelova, Sheikh Mohammed Shariful Islam

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Artificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews by Scott D. Tagliaferri, Maia Angelova, Xiaohui Zhao, Patrick J. Owen, Clint T. Miller, Tim Wilkin, Daniel L. Belavy

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Chronic back pain sub-grouped via psychosocial, brain and physical factors using machine learning by Scott D. Tagliaferri, Tim Wilkin, Maia Angelova, Bernadette M. Fitzgibbon, Patrick J. Owen, Clint T. Miller, Daniel L. Belavy

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Towards data-driven biopsychosocial classification of non-specific chronic low back pain: a pilot study by Scott D. Tagliaferri, Patrick J. Owen, Clint T. Miller, Maia Angelova, Bernadette M. Fitzgibbon, Tim Wilkin, Hugo Masse-Alarie, Jessica Van Oosterwijck, Guy Trudel, David Connell, Anna Taylor, Daniel L. Belavy

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Evidence- and data-driven classification of low back pain via artificial intelligence: Protocol of the PREDICT-LBP study. by Daniel L Belavy, Scott D Tagliaferri, Martin Tegenthoff, Elena Enax-Krumova, Lara Schlaffke, Björn Bühring, Tobias L Schulte, Sein Schmidt, Hans-Joachim Wilke, Maia Angelova, Guy Trudel, Katja Ehrenbrusthoff, Bernadette Fitzgibbon, Jessica Van Oosterwijck, Clint T Miller, Patrick J Owen, Steven Bowe, Rebekka Döding, Svenja Kaczorowski

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Evidence- and data-driven classification of low back pain via artificial intelligence: Protocol of the PREDICT-LBP study by Daniel L. Belavy, Scott D. Tagliaferri, Martin Tegenthoff, Elena Enax-Krumova, Lara Schlaffke, Björn Bühring, Tobias L. Schulte, Sein Schmidt, Hans-Joachim Wilke, Maia Angelova, Guy Trudel, Katja Ehrenbrusthoff, Bernadette Fitzgibbon, Jessica Van Oosterwijck, Clint T. Miller, Patrick J. Owen, Steven Bowe, Rebekka Döding, Svenja Kaczorowski

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