4 papers
Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methods
Alhassan Mumuni, Fuseini Mumuni
Data augmentation is arguably the most important regularization technique commonly used to improve generalization performance of machine learning models. It primarily involves the…
Explainable artificial intelligence (XAI): from inherent explainability to large language models
Fuseini Mumuni, Alhassan Mumuni
Artificial Intelligence (AI) has continued to achieve tremendous success in recent times. However, the decision logic of these frameworks is often not transparent, making it diffic…
Large language models for artificial general intelligence (AGI): A survey of foundational principles and approaches
Alhassan Mumuni, Fuseini Mumuni
Generative artificial intelligence (AI) systems based on large-scale pretrained foundation models (PFMs) such as vision-language models, large language models (LLMs), diffusion mod…
Segment Anything Model for automated image data annotation: empirical studies using text prompts from Grounding DINO
Fuseini Mumuni, Alhassan Mumuni
Grounding DINO and the Segment Anything Model (SAM) have achieved impressive performance in zero-shot object detection and image segmentation, respectively. Together, they have a g…