150 citations · 318 across the 6 of their papers we have counts for
7 papers
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…
Automated data processing and feature engineering for deep learning and big data applications: a survey
Alhassan Mumuni, Fuseini Mumuni
Modern approach to artificial intelligence (AI) aims to design algorithms that learn directly from data. This approach has achieved impressive results and has contributed significa…
A survey of synthetic data augmentation methods in computer vision
Alhassan Mumuni, Fuseini Mumuni, Nana Kobina Gerrar
The standard approach to tackling computer vision problems is to train deep convolutional neural network (CNN) models using large-scale image datasets which are representative of t…
Improving deep learning with prior knowledge and cognitive models: A survey on enhancing explainability, adversarial robustness and zero-shot learning
Fuseinin Mumuni, Alhassan Mumuni
We review current and emerging knowledge-informed and brain-inspired cognitive systems for realizing adversarial defenses, eXplainable Artificial Intelligence (XAI), and zero-shot…
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…