UH Biocomputation Group - Signal processing algorithmshttp://biocomputation.herts.ac.uk/2025-03-25T12:28:32+00:00Detecting Anxiety via Machine Learning Algorithms: A Literature Review2025-03-25T12:28:32+00:002025-03-25T12:28:32+00:00Shamim Ibne Shahidtag:biocomputation.herts.ac.uk,2025-03-25:/2025/03/25/detecting-anxiety-via-machine-learning-algorithms-a-literature-review.html<p class="first last">Shamim Ibne Shahid's Journal Club session where he will talk about "Detecting Anxiety via Machine Learning Algorithms: A Literature Review".</p>
<p>On this week's Journal Club session, Shamim Ibne Shahid will talk about his review paper "Detecting Anxiety via Machine Learning Algorithms: A Literature Review".</p>
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<p>Recent machine learning (ML) advances have opened up new possibilities for addressing
various challenges. Given their ability to tackle complex problems, the use of ML
algorithms in diagnosing mental health disorders has seen substantial growth in both the
number and scope of studies. Anxiety, a major health concern in today's world, affects a
significant portion of the population. Individuals with anxiety often exhibit distinct
characteristics compared to those without the disorder. These differences can be observed
in their outward appearance—such as voice, facial expressions, gestures, and movements—and
in less visible factors like heart rate, blood test results, and brain imaging data. In
this context, numerous studies have utilized ML algorithms to extract a diverse range of
features from individuals with anxiety, aiming to build predictive models capable of
accurately identifying those affected by the disorder. This paper performs a comprehensive
literature review on the state-of-the-art studies that employ machine learning algorithms
to identify anxiety. This paper aims to cover a wide range of studies and categorize them
based on their methodologies and data types used.</p>
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<p>Papers:</p>
<ul class="simple">
<li>M. Tayarani-N., S. Shahid, <a class="reference external" href="https://doi.org/10.1109/TETCI.2025.3543307">"Detecting Anxiety via Machine Learning Algorithms: A Literature Review"</a>, 2025, IEEE Transactions on Emerging Topics in Computational Intelligence, 1--24</li>
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<p><strong>Date:</strong> 2025/03/28 <br />
<strong>Time:</strong> 14:00 <br />
<strong>Location</strong>: SP3011 & online</p>
Event-Driven Sensing for Efficient Perception: Vision and Audition Algorithms2021-05-12T13:13:07+01:002021-05-12T13:13:07+01:00Nik Dennlertag:biocomputation.herts.ac.uk,2021-05-12:/2021/05/12/event-driven-sensing-for-efficient-perception-vision-and-audition-algorithms.html<p class="first last">Nik Dennler's Journal Club session where he will talk about a paper "Event-Driven Sensing for Efficient Perception: Vision and Audition Algorithms"</p>
<p>This week on Journal Club session Nik Dennler will talk about a paper "Event-Driven Sensing for Efficient Perception: Vision and Audition Algorithms".</p>
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<p>Event sensors implement circuits that capture partial functionality of
biological sensors, such as the retina and cochlea. As with their
biological counterparts, event sensors are drivers of their own
output. That is, they produce dynamically sampled binary events to
dynamically changing stimuli. Algorithms and networks that process
this form of output representation are still in their infancy, but
they show strong promise. This article illustrates the unique form of
the data produced by the sensors and demonstrates how the properties
of these sensor outputs make them useful for power-efficient,
low-latency systems working in real time.</p>
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<p>Papers:</p>
<ul class="simple">
<li>S. Liu, B. Rueckauer, E. Ceolini, A. Huber, T. Delbruck, <a class="reference external" href="https://doi.org/10.1109/MSP.2019.2928127">"Event-Driven Sensing for Efficient Perception: Vision and Audition Algorithms"</a>, 2019, IEEE Signal Processing Magazine, 36, 29--37</li>
</ul>
<p><strong>Date:</strong> 2021/05/14 <br />
<strong>Time:</strong> 14:00 <br />
<strong>Location</strong>: online</p>