Road damage, such as potholes and cracks, represent a constant nuisance to drivers as they could potentially cause accidents and damages. Current pothole detection in Peru, is mostly manually operated and hardly ever use image processing technology. To combat this we propose a mobile application capable of real-time road damage detection and spatial mapping across a city. Three models are going to be trained and evaluated (Yolov5, Yolov8 and MobileNet v2) on a novel dataset which contains images from Lima, Peru. Meanwhile, the viability of crack detection through bounding box method will be put to the test, each model will be trained once with cracks annotations and without. The YOLOv5 model was the one with the best results, as it showed the best mAP50 across all of out experiments. It got 99.0% and 98.3% mAP50 with the dataset without crack and with crack annotations, correspondingly..

In recent years, artificial intelligence has played an important role in education, wherein one of the most commonly used applications is forecasting students’ academic performance based on personal information such as social status, income, address, etc. This study proposes and develops an artificial neural network model capable of determining whether a student will pass a certain class without using personal or sensitive information that may compromise student privacy. For model training, we used information regarding 32,000 students collected from The Open University of the United Kingdom, such as number of times they took the course, average number of evaluations, course pass rate, average use of virtual materials per date and number of clicks in virtual classrooms. Attributes selected for the model are as follows: 93.81% accuracy, 94.15% precision, 95.13% recall, and 94.64% F1-sco...

n the published article, the Acknowledgment Statement was mistakenly not included in the publication. The missing statement appears below: Acknowledgments “The authors would like to thank the Research Directorate of the Universidad Peruana de Ciencias Aplicadas for the support provided to carry out this research work through the UPC-EXPOST-2023-1 incentive.” The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.