Cognitive automation frameworks are structured sets of tools and methods that help computers carry out tasks that usually require human thinking, such as understanding language, recognising patterns, or making decisions. These frameworks combine artificial intelligence techniques like machine learning and natural language processing to automate complex processes. By using these frameworks, organisations can automate not…
Category: Artificial Intelligence
Intelligent Task Scheduling
Intelligent task scheduling is the use of smart algorithms and automation to decide when and how tasks should be carried out. It aims to organise work in a way that makes the best use of time, resources, and priorities. By analysing factors like deadlines, task dependencies, and available resources, intelligent task scheduling helps ensure that…
Encrypted Machine Learning
Encrypted machine learning is a method where data is kept secure and private during the process of training or using machine learning models. This is done by using encryption techniques so that data can be analysed or predictions can be made without ever revealing the raw information. It helps organisations use sensitive information, like medical…
Heterogeneous Graph Attention
Heterogeneous graph attention is a method in machine learning that helps computers analyse and learn from complex networks containing different types of nodes and connections. Unlike standard graphs where all nodes and edges are the same, heterogeneous graphs have a mix, such as people, organisations, and products connected in various ways. The attention mechanism helps…
Graph Neural Network Scalability
Graph Neural Network scalability refers to the ability of graph-based machine learning models to efficiently process and learn from very large graphs, often containing millions or billions of nodes and edges. As graphs grow in size, memory and computation demands increase, making it challenging to train and apply these models without special techniques. Solutions for…
Knowledge Graph Reasoning
Knowledge graph reasoning is the process of drawing new conclusions or finding hidden connections within a knowledge graph. A knowledge graph is a network of facts, where each fact links different pieces of information. Reasoning uses rules or algorithms to connect the dots, helping computers answer complex questions or spot patterns that are not immediately…
Graph-Based Clustering
Graph-based clustering is a method of grouping items by representing them as points, called nodes, and connecting similar ones with lines, called edges, to form a network or graph. The method looks for clusters, which are groups of nodes that are more closely linked to each other than to the rest of the network. This…
Graph Autoencoders
Graph autoencoders are a type of machine learning model designed to work with data that can be represented as graphs, such as networks of people or connections between items. They learn to compress the information from a graph into a smaller, more manageable form, then reconstruct the original graph from this compressed version. This process…
Subgraph Matching Algorithms
Subgraph matching algorithms are methods used to find if a smaller graph, called a subgraph, exists within a larger graph. They compare the structure and connections of the nodes and edges to identify matches. These algorithms are important in fields where relationships and patterns need to be found within complex networks, such as social networks,…
Temporal Graph Embedding
Temporal graph embedding is a method for converting nodes and connections in a dynamic network into numerical vectors that capture how the network changes over time. These embeddings help computers understand and analyse evolving relationships, such as friendships or transactions, as they appear and disappear. By using temporal graph embedding, it becomes easier to predict…