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Topological and geometrical joint learning for 3D graph data
Traditional convolutional neural networks (CNNs) are limited to be directly applied to 3D graph data due to their inherent grid structure. And most...
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Geometrical Feature Transformation Methods
In this chapter, we introduce the geometrical feature transformation methods for transfer learning, which is different from statistical feature...
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Boosting deep neural networks with geometrical prior knowledge: a survey
Deep neural networks achieve state-of-the-art results in many different problem settings by exploiting vast amounts of training data. However,...
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Technology and geometry: Fostering young children's geometrical concepts through a research-based robotic coding program
Geometric concepts are fundamental to early geometry education, and developmentally appropriate practices are crucial for teaching them to young...
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Low-light image enhancement with geometrical sparse representation
Low-light image enhancement (LLIE) can improve the visibility of low-light images. Low-light images exhibit a series of visual degradation, such as...
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Multi-view multi-manifold learning with local and global structure preservation
Most existing multi-view learning methods adopt a single geometrical model to describe multi-class and heterogeneous data on the original feature...
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A multi-fidelity active learning method for global design optimization problems with noisy evaluations
A multi-fidelity (MF) active learning method is presented for design optimization problems characterized by noisy evaluations of the performance...
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Enforcing Geometrical Priors in Deep Networks for Semantic Segmentation Applied to Radiotherapy Planning
Incorporating prior knowledge into a segmentation process, whether it is geometrical constraints such as volume penalisation, (partial) convexity...
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Using GPT and authentic contextual recognition to generate math word problems with difficulty levels
Automatic generation of math word problems (MWPs) is a challenging task in Natural Language Processing (NLP), particularly connecting it to real-life...
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Copy-paste forgery detection using deep learning with error level analysis
Image manipulation has become a common problem due to the tremendous growth of digital tools and applications. Several image forgeries can...
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Automated machine learning: past, present and future
Automated machine learning (AutoML) is a young research area aiming at making high-performance machine learning techniques accessible to a broad set...
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A comparative analysis of deep learning and deep transfer learning approaches for identification of rice varieties
Rice is an essential staple food for human nutrition. Rice varieties worldwide have been planted, imported, and exported. During production and...
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Learning with cone-based geometric models and orthologics
Recent approaches for knowledge-graph embeddings aim at connecting quantitative data structures used in machine learning to the qualitative...
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Facial Expression Recognition Using Machine Learning and Deep Learning Techniques: A Systematic Review
In the contemporary era, Facial Expression Recognition (FER) plays a pivotal role in numerous fields due to its vast application areas, such as...
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Dynamic parameterized learning for unsupervised domain adaptation
Unsupervised domain adaptation enables neural networks to transfer from a labeled source domain to an unlabeled target domain by learning...
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Transformer in reinforcement learning for decision-making: a survey
Reinforcement learning (RL) has become a dominant decision-making paradigm and has achieved notable success in many real-world applications. Notably,...
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A deep learning framework for copy-move forgery detection in digital images
Digital images have become widespread in modern life, and they can be modified and produced using an inclusive range of software and hardware tools....
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Enhancing cluster analysis via topological manifold learning
We discuss topological aspects of cluster analysis and show that inferring the topological structure of a dataset before clustering it can...
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Efficient three-way SVM for three-class classification problems
Many classification problems in the real world are inherently multi-class. However, most of the classifiers are binary. Solving K -class...
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Road geometrical design out of standards: a preliminary study in a simulated context
When a road design solution is quite out of standards for the presence of insurmountable constraints, there is the need for an objective procedure...
