Samuel Kakraba
Biographic Data
| ID | 9485697 |
|---|---|
| NAME | Samuel Kakraba |
| GIVEN NAMES | Samuel |
| FAMILY NAME | Kakraba |
| SIGNATURE | KAKRABA S |
| AFFILIATIONS | Tulane University |
| ORCID | 0000-0002-6362-5126 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2026 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Cognitive sovereignty and decolonial public health
As artificial intelligence (AI) becomes critical infrastructure for global health, it reproduces colonial patterns of extraction, mining data from the Global South to train models owned by the Global North. While international bodies like the WHO emphasize “ethical AI,” they often overlook the structural violence of this digital colonialism. This perspective argues that true health equity requires more than bias mitigation; it demands cognitive s…
Leveraging machine learning algorithms and explainable AI for predicting mental health disorder treatment at the workplace
Mental health disorders in the workplace pose a significant global public health challenge, often resulting in reduced productivity. Timely and accurate prediction is essential for facilitating early and effective interventions. In this study, we evaluated six machine learning (ML) algorithms namely logistic regression, random forest, gradient boosting (GB), categorical boosting (CB), support vector machine, and neural network for their ability t…
No prominent works on this page.
Cognitive sovereignty and decolonial public health
As artificial intelligence (AI) becomes critical infrastructure for global health, it reproduces colonial patterns of extraction, mining data from the Global South to train models owned by the Global North. While international bodies like the WHO emphasize “ethical AI,” they often overlook the structural violence of this digital colonialism. This perspective argues that true health equity requires more than bias mitigation; it demands cognitive s…
Leveraging machine learning algorithms and explainable AI for predicting mental health disorder treatment at the workplace
Mental health disorders in the workplace pose a significant global public health challenge, often resulting in reduced productivity. Timely and accurate prediction is essential for facilitating early and effective interventions. In this study, we evaluated six machine learning (ML) algorithms namely logistic regression, random forest, gradient boosting (GB), categorical boosting (CB), support vector machine, and neural network for their ability t…
Big data (1 works) · Cognition (1 works) · Corporate governance (1 works) · Digital Mental Health Interventions (1 works) · Ethics and Social Impacts of AI (1 works) · Explainable Artificial Intelligence (XAI (1 works) · Global governance (1 works) · Global Health and Surgery (1 works) · Global Security and Public Health (1 works) · Human Factors and Ergonomics (1 works)