Grace Azilken

This project explored how open-source software (OSS) is being used for academic activities in Nigerian universities, focusing on the University of Benin as a case study. The aim was to find out how aware students and ICT personnel are of OSS, how they use
it for learning, teaching, and research, the benefits they gain from it, and the challenges that limit its proper use. A descriptive survey design was used for the study, and data were gathered through a structured questionnaire created with Google Forms. Seventy (70) valid responses were collected from students and ICT staff across different faculties. The data were analyzed using simple descriptive statistics such as frequencies, percentages, and mean scores, and the results were presented in tables for clarity. Findings showed that most respondents were aware of and made use of open-source tools like Moodle, Google Workspace, DSpace, and Koha. These tools were mainly used for online learning, collaboration, and research work. The study also revealed that OSS is appreciated for being affordable, flexible, and easy to access, though some challenges—such as poor internet connection, limited training, and lack of Institutional support—still make it difficult to use effectively

This project implements a deep learning algorithm for image recognition, focusing on traffic sign classification. Traditional machine learning methods struggle with manual feature extraction and dataset diversity. To address these limitations, a robust convolutional neural network (CNN) with residual blocks, dropout layers, and global average pooling is utilized. Preprocessing techniques like normalization and data augmentation enhance accuracy and
generalization. Using TensorFlow and Keras, experiments were conducted on the German Traffic Sign Recognition Benchmark (GTSRB) and the Chinese Traffic Sign Dataset. The model achieved 99.54% validation accuracy and 94.95% test accuracy on GTSRB, but overfitting led to 60.38% accuracy on the smaller Chinese dataset. The study highlights CNN effectiveness in pattern recognition, with strengths in GPU acceleration and modular architecture. Challenges like overfitting and computational constraints persist. Future research should explore transfer learning, ensemble methods, and real-time optimization to enhance performance. This study advances deep learning-based
image recognition for applications in autonomous driving and traffic management. This project implements a deep learning algorithm for image recognition, focusing on traffic sign classification. Traditional machine learning methods struggle with manual feature extraction and dataset diversity. To address these limitations, a robust convolutional neural network (CNN) with residual blocks, dropout layers, and global average pooling is utilized. Preprocessing techniques like normalization and data augmentation enhance accuracy and generalization. Using TensorFlow and Keras, experiments were conducted on the German Traffic Sign Recognition Benchmark (GTSRB) and the Chinese Traffic Sign Dataset. The model achieved 99.54% validation accuracy and 94.95% test accuracy on GTSRB, but overfitting led to 60.38% accuracy on the smaller Chinese dataset. The study highlights CNN effectiveness in pattern recognition, with strengths in GPU acceleration and modular architecture. Challenges like overfitting and computational
constraints persist. Future research should explore transfer learning, ensemble methods, and real-time optimization to enhance performance. This study advances deep learning-based image recognition for applications in autonomous driving and traffic management.