Literature Review and Theories in Graph Neural Networks (GNNs) Introduction Graph Neural Networks (GNNs) are a class of neural networks designed to handle data structured as graphs. Unlike traditional neural networks that process fixed-size vector data, GNNs can operate on graph-structured data, wh
Literature Review and Theories in Unsupervised Feature Learning Introduction Unsupervised feature learning is a subset of machine learning where the goal is to discover and learn useful features from unlabeled data. Unlike supervised learning, which relies on labeled data for training, unsupervised
Literature Review and Theories in Elasticsearch Introduction Elasticsearch is an open-source, distributed search and analytics engine built on top of Apache Lucene. It is designed for horizontal scalability, reliability, and real-time search capabilities. Elasticsearch is widely used for log and ev
Literature Review and Theories in Surrogate Modeling Introduction Surrogate modeling, also known as metamodeling, is a technique used to approximate complex and computationally expensive simulations with simpler models. These surrogate models serve as substitutes for detailed simulations, providing
Literature Review and Theories in Neuroevolutionary Learning Introduction Neuroevolutionary learning combines principles from neural networks and evolutionary algorithms to create and optimize artificial neural network architectures and weights. This approach leverages the power of evolutionary com
Literature Review and Theories in Adaptive Resonance Theory (ART) Introduction Adaptive Resonance Theory (ART) is a cognitive and neural theory developed by Stephen Grossberg in the late 1970s to explain how the brain processes information in a stable yet adaptable manner. ART addresses the challen
Literature Review and Theories in Neuromorphic Computing Introduction Neuromorphic computing is a branch of artificial intelligence and computer engineering that aims to design computer systems inspired by the human brain's architecture and functionality. This field encompasses various interdiscipl
Literature Review and Theories in Neuromorphic Computing Introduction Neuromorphic computing is a branch of artificial intelligence and computer engineering that aims to design computer systems inspired by the human brain's architecture and functionality. This field encompasses various interdiscipl
Literature Review on Capsule Networks Introduction Capsule Networks (CapsNets) represent a novel architecture in deep learning designed to address some limitations of traditional convolutional neural networks (CNNs). Proposed by Geoffrey Hinton and his colleagues, Capsule Networks aim to better c...
Literature Review on Anomaly Detection Introduction Anomaly detection is a critical area of machine learning and data analysis that involves identifying patterns in data that do not conform to expected behavior. These anomalies can indicate critical incidents, such as fraud, network intrusions, e...
Literature Review on Few-shot Learning Introduction Few-shot learning (FSL) is a subfield of machine learning that focuses on training models to recognize new classes with only a few labeled examples. This is in contrast to traditional machine learning methods, which typically require large amounts
Literature Review on Quantum-inspired Computing Introduction Quantum-inspired computing leverages principles and algorithms inspired by quantum mechanics to solve complex computational problems. While distinct from quantum computing, which relies on actual quantum bits (qubits) and quantum gates,...