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Linked data for interoperable knowledge – Complete Phd and Masters Thesis

[ad_1] Introduction In the era of big data, one of the major challenges faced by researchers and organizations is the interoperability of knowledge across different domains and systems. Linked data has emerged as a powerful…

Ontology learning for domain modeling – Complete Phd and Masters Thesis

[ad_1] Introduction Ontology learning is a crucial aspect of knowledge engineering that involves the automatic extraction of domain-specific knowledge from unstructured text or data. It plays a vital role in the development of domain models,…

Graph convolutional networks for node classification – Complete Phd and Masters Thesis

[ad_1] Introduction Graph Convolutional Networks (GCNs) have gained significant attention in recent years due to their effectiveness in node classification tasks on graph-structured data. GCNs are a type of neural network designed to operate on…

Neuroevolution for learning and optimization – Complete Phd and Masters Thesis

[ad_1] Introduction Neuroevolution is a computational method that combines the principles of artificial neural networks and evolutionary algorithms to enable learning and optimization in complex systems. By leveraging the adaptability of neural networks and the…

Neural architecture search for optimal design – Complete Phd and Masters Thesis

[ad_1] Introduction Neural architecture search (NAS) has emerged as a powerful technique for automatically designing neural network architectures to achieve optimal performance on a given task. The main goal of NAS is to replace the…

Continual learning for lifelong adaptation – Complete Phd and Masters Thesis

[ad_1] Introduction Continual learning is a key concept in the field of artificial intelligence and machine learning, emphasizing the ability of a system to adapt and learn continuously from new data and experiences in order…

Quantum neural networks for exponential speedup – Complete Phd and Masters Thesis

[ad_1] Introduction Quantum computing has emerged as a revolutionary technology with the potential to solve complex problems that are intractable for classical computers. In recent years, there has been significant interest in leveraging the power…

Spiking neural networks for energy efficiency – Complete Phd and Masters Thesis

[ad_1] Introduction: In the field of artificial neural networks, spiking neural networks (SNNs) have gained significant attention for their potential in achieving high energy efficiency. SNNs are biologically inspired neural networks that operate based on…

Bayesian neural networks for uncertainty estimation – Complete Phd and Masters Thesis

[ad_1] Introduction Bayesian neural networks (BNNs) have gained significant attention in recent years due to their ability to provide uncertainty estimates in neural network models. This is crucial in various applications such as medical diagnosis,…

Mixture of experts for specialized sub-networks – Complete Phd and Masters Thesis

[ad_1] Introduction Mixture of experts for specialized sub-networks is a cutting-edge approach in the field of machine learning and artificial intelligence that aims to improve the performance of neural networks by combining multiple specialized sub-networks…

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