International Journal of Cancer Research & Therapy(IJCRT)

Department of Computer Science, Bayero University Kano, Nigeria

Publications

• Research Article   
  Leveraging Machine Learning for Resilient Urban Transport Planning Under Deep Uncertainty: A Case Study of Abuja, Nigeria
  Author(s): Ahmad Muhammad Salisu*
  
  Urban transportation systems in developing cities like Abuja, Nigeria, face growing uncertainties driven by rapid urbanization, fluctuating economic conditions, climate variability, and evolving technologies. Conventional planning models often rely on static assumptions that fail to capture these unpredictable dynamics. This study introduces a machine learning (ML)–based framework designed to enhance resilience in urban transport planning by identifying, modeling, and forecasting uncertain factors that affect mobility and accessibility in Abuja. The research integrates multiple data sources—traffic flow, satellite imagery, weather data, and socioeconomic indicators—to train predictive models using supervised and unsupervised ML techniques such as Random Forests and Gradient Boosting. These models are employed to detect complex, non-linear relationships between transp.. Read More»
  

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