Evaluating the effectiveness of safety countermeasures at highway-railway grade crossing based on a machine learning framework.

Objective: This research aims to cluster similar highway-railway grade crossings (HRGCs) to examine the safety countermeasures at HRGCs.

Methods: The methodology integrates inventory and collision data from Federal Railroad Association (FRA) data set during years 2010 to 2022 . The XGBoost and random forest (RF) algorithms are employed to identify influential collision severity factors.

Analyzing the effects of streetscape and land use on urban accidents and predicting future accidents by using machine learning algorithms (case study: Mashhad).

In general, land use and layout of streets can have a significant impact on the behavior of drivers and pedestrians. In particular, streetscape has often been overlooked that recognizing the role of streetscape on street accident in urban areas is important. The aim of this research is to investigate the influence of streetscape and land use on urban accidents that occurred in Mashhad between the years 2017 and 2021.

Modeling and comparing data mining algorithms for prediction of recurrence of breast cancer.

Breast cancer is the most common invasive cancer and the second leading cause of cancer death in women. and regrettably, this rate is increasing every year. One of the aspects of all cancers, including breast cancer, is the recurrence of the disease, which causes painful consequences to the patients.