Convolutional Neural Networks (CNNs) have significantly changed image classification over the years by allowing computers to learn features directly from raw pixel data. However, deciding between building a customised model and using a pre-trained one can be a difficult task, especially when working with small datasets. In this study, we compare a custom CNN with three pre-trained models—VGG16, ResNet50, and MobileNetV2—on the CIFAR-10 dataset, which comprises 60,000 colour images (32×32 pixels) across 10 categories. We measured model performance using accuracy, precision, recall, F1-score, and training time. The results show that pre-trained models performed much better than the customised model. ResNet50 had the highest accuracy at 92.4%. However, MobileNetV2 gave the best mix of speed (1,800 seconds to train) and accuracy (90.2%). The custom CNN reached 82.3% accuracy, used less memory, and did not need image resizing. These results offer clear benchmarks for choosing models in the face of limited resources. They also demonstrate that transfer learning can achieve strong performance, while showing that custom CNNs remain useful for learning and simple tasks.
Oluwadamilare (Asabia) Joseph Omoniyi, Omotosho Olawale Jacob, Ajaegbu Chigozirim et al.· International journal of res...· 0 citations
The densification of wireless networks and growing real-time service demands have intensified the need for intelligent, energy-efficient resource allocation. Traditional static and centralized methods fall short in adapting to the dynamic and interference-prone nature of 5G and emerging 6G environments. This study proposes a decentralized reinforcement learning (RL)-based framework for joint power and spectrum allocation in ultra-dense wireless systems. Each base station acts as an autonomous agent, making real-time decisions based on local traffic and interference conditions. Simulated using a custom Python-based environment with 50 base stations and 500 users, the RL approach is benchmarked against static and optimization-based methods. Results show the RL model achieves up to 91% energy efficiency, 94% spectrum utilization, and only 5% QoS degradation, outperforming baseline models. This work demonstrates the viability of RL for distributed resource management and provides a reproducible simulation toolkit to support further research in AI-driven wireless communication systems.
Mugerwa Joseph, Ajaegbu Chigozirim· International Journal Of Eng...· 0 citations