Browsing by Author "Hussein, Eslam"

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    Geo-physical parameter forecasting on imagery{based data sets using machine learning techniques
    (University of Western Cape, 2021) Hussein, Eslam; Ghaziasgar, Mehrdad
    This research objectively investigates the e ectiveness of machine learning (ML) tools towards predicting several geo-physical parameters. This is based on a large number of studies that have reported high levels of prediction success using ML in the eld. Therefore, several widely used ML tools coupled with a number of di erent feature sets are used to predict six geophysical parameters namely rainfall, groundwater, evapora- tion, humidity, temperature, and wind. The results of the research indicate that: a) a large number of related studies in the eld are prone to speci c pitfalls that lead to over-estimated results in favour of ML tools; b) the use of gaussian mixture models as global features can provide a higher accuracy compared to other local feature sets; c) ML never outperform simple statistically-based estimators on highly-seasonal parame- ters, and providing error bars is key to objectively evaluating the relative performance of the ML tools used; and d) ML tools can be e ective for parameters that are slow- changing such as groundwater.
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    Machine-learning approaches for classifying star-forming galaxies and active galactic nuclei from MIGHTEE-detected radio sources in the COSMOS field
    (Oxford University Press, 2025) Silima, Walter; An, Fangxia; Vaccari, Mattia; Hussein, Eslam
    Radio synchrotron emission originates from both massive star formation and black hole accretion, two processes that drive galaxy evolution. Efficient classification of sources dominated by either process is therefore essential for fully exploiting deep, wide-field extragalactic radio continuum surveys. In this study, we implement, optimize, and compare five widely used supervised machine-learning (ML) algorithms to classify radio sources detected in the MeerKAT International GHz Tiered Extragalactic Exploration (MIGHTEE)-COSMOS survey as star-forming galaxies (SFGs) and active galactic nuclei (AGNs). Training and test sets are constructed from conventionally classified MIGHTEE-COSMOS sources, and 18 physical parameters of the MIGHTEE-detected sources are evaluated as input features. As anticipated, our feature analyses rank the five parameters used in conventional classification as the most effective: the infrared–radio correlation parameter ($q_\mathrm{IR}$), the optical compactness morphology parameter (class_star), stellar mass, and two combined mid-infrared colours. By optimizing the ML models with these selected features and testing classifiers across various feature combinations, we find that model performance generally improves as additional features are incorporated. Overall, all five algorithms yield an F1-score (the harmonic mean of precision and recall) >90 per cent even when trained on only 20 per cent of the data set. Among them, the distance-based k-nearest neighbours classifier demonstrates the highest accuracy and stability, establishing it as a robust and effective method for classifying SFGs and AGNs in upcoming large radio continuum surveys.