Category: Artificial Intelligence

Knowledge Sparsification

Knowledge sparsification is the process of reducing the amount of information or connections in a knowledge system while keeping its most important parts. This helps make large and complex knowledge bases easier to manage and use. By removing redundant or less useful data, knowledge sparsification improves efficiency and can make machine learning models faster and…

Autonomous Workflow Optimization

Autonomous workflow optimisation refers to the use of intelligent systems or software that can automatically analyse, adjust, and improve the steps involved in a business process without requiring constant human input. These systems monitor how work is being done, identify inefficiencies or bottlenecks, and make changes to streamline tasks. The goal is to save time,…

Process Discovery Algorithms

Process discovery algorithms are computer methods used to automatically create a process model by analysing data from event logs. These algorithms look for patterns in the recorded steps of real-life processes, such as how orders are handled in a company. The resulting model helps people understand how work actually happens, spot inefficiencies, and suggest improvements.

Cognitive Automation Frameworks

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…

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…