One of the major announcements made on 4 December 2025, during the annual staff year-end function, was the staff’s academic progress. More than 40 staff members filed to the podium as their names were announced. Professor Alfred Msomi, Dean of the Faculty of Applied and Health Sciences, was waiting to shake their hands and congratulate them. The 43 staff members were divided into four categories – Postgraduate Diploma, Degree, Master’s, and PhD. The latter category, PhD, had 19, which is approximately 44.19%, a very pleasing piece of news indeed! The next category was the Diploma/Postgraduate Diploma, with 11 candidates, beating the Master’s category by just one. The remaining three candidates made the Degree category.
What was interesting was that 15 of these newly graduated employees are support staff members; two of them, Gloria Mkhize from the Technology Station in Chemicals (TSC) and NomaXhosa Msimango from the Department of Chemistry, obtained a Master’s degree, which is a baseline for academic staff. These MUT staff see a need to further their studies even though they are not under the same amount of pressure as their academic colleagues. Another interesting case is that of Amos Njapha, who graduated with a Master’s degree in Town and Regional Planning. Nowadays, research work is problem and solution-based. That is what all that conducted research in their studies; they had to identify a problem, conduct research, and propose some possible solutions. Kavita Behara, a Lecturer in the Department of Electrical Engineering, was hailed for her groundbreaking research. Dr Behara discovered an improved method for diagnosing skin cancer. Dr Behara’s study was motivated by the fact that current skin cancer diagnoses were inaccurate. Dr Behara explains further: “Skin cancer, especially melanoma, is one of the most dangerous cancers, but early detection can save lives. Doctors use dermatoscopic images to examine skin lesions. However, diagnosis is still difficult because images vary in quality, some cancer types are underrepresented, and diagnostic tools often lack accuracy or explainability.”
Dr Behara continued: “My research addressed these challenges by developing four deep learning models, which are: baseline DCNN: with advanced preprocessing to reduce image variability; DCGAN-based augmentation: generating synthetic images to balance underrepresented lesion types; GBSD-ECNN: an explainable AI model that produced visual heatmaps to increase clinician trust; and ACSM-CapsNet: a hybrid segmentation-classification model that improved lesion localization and classification accuracy,” Dr Behara said that together, these models improved accuracy, robustness, and interpretability, making AI-driven skin cancer detection more clinically viable.