Predictive modelling in times of public health emergencies: patients’ non-transport decisions during the COVID-19 pandemic by Hassan Farhat (9000509)

“…</p><h3>Conclusion</h3><p dir="ltr">This study indicated that predictive modelling could accurately help identify patients who refuse transport to hospital and may not require treatment on the scene. …”

A study on Speaker Recognition System by Bakkar, Hazem Wa'il Mohammed

“…Another main goal for conducting this research is to make a scientific comparison between tools and methods that are related to speaker recognition domain, the following are the techniques that were studied : 1) Energy based tool and Long-Term Spectral Divergence (LTSD) in the preprocessing module of the system, 2) Mel Frequency Cepstral Coefficients (MFCC) and Linear Predictive Cepstral Coefficients (LPCC) in the feature extraction module, and 3) scikit-learn Gaussian Mixture Model (GMM), Universal Background Model (UBM), Continuous Restricted Boltzmann Machine (CRBM) and Joint Factor Analysis (JFA) in the recognition module. …”
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Exploring the potential of onboard energy scavenging subsystems for generating valuable data by Raziq Yaqub (16488878)

“…This gathered data is seamlessly synchronized with GPS coordinates and timestamps, meticulously organized within a system architecture, and harnessed through meticulously crafted Python code. The wealth of data that we obtain from an onboard energy scavenging subsystem holds significant potential. …”

KNNOR: An oversampling technique for imbalanced datasets by Ashhadul Islam (16869981)

“…<p>Predictive performance of Machine Learning (ML) models rely on the quality of data used for training the models. …”