Data augmentation: A comprehensive survey of modern approaches von Mumuni, Alhassan, Mumuni, Fuseini

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A Survey of Synthetic Data Augmentation Methods in Machine Vision von Mumuni, Alhassan, Mumuni, Fuseini, Gerrar, Nana Kobina

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Adaptive Kalman filter for MEMS IMU data fusion using enhanced covariance scaling von Mumuni, Fuseini, Mumuni, Alhassan

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Robust appearance modeling for object detection and tracking: a survey of deep learning approaches von Mumuni, Alhassan, Mumuni, Fuseini

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Deep learning of monocular depth, optical flow and ego-motion with geometric guidance for UAV navigation in dynamic environments von Mumuni, Fuseini, Mumuni, Alhassan, Amuzuvi, Christian Kwaku

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CNN Architectures for Geometric Transformation-Invariant Feature Representation in Computer Vision: A Review von Mumuni, Alhassan, Mumuni, Fuseini

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Improving deep learning with prior knowledge and cognitive models: A survey on enhancing explainability, adversarial robustness and zero-shot learning von Mumuni, Fuseini, Mumuni, Alhassan

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Bayesian cue integration of structure from motion and CNN-based monocular depth estimation for autonomous robot navigation von Mumuni, Fuseini, Mumuni, Alhassan

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Automated data processing and feature engineering for deep learning and big data applications: a survey von Mumuni, Alhassan, Mumuni, Fuseini

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A survey of synthetic data augmentation methods in computer vision von Alhassan Mumuni, Fuseini Mumuni, Gerrar, Nana Kobina

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Kinetics and Modeling of the Heat and Mass Transfer During the Solar Drying of Cassava Slices Under Natural Convection Mode von Komolafe, Clement A., Mumuni, Fuseini, Jimoh, Kabiru A., Akendola, Frederick A.

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Segment Anything Model for automated image data annotation: empirical studies using text prompts from Grounding DINO von Mumuni, Fuseini, Mumuni, Alhassan

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Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methods von Mumuni, Alhassan, Mumuni, Fuseini

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Segment Anything Model for automated image data annotation: empirical studies using text prompts from Grounding DINO von Fuseini Mumuni, Alhassan Mumuni

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Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methods von Alhassan Mumuni, Fuseini Mumuni

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