Knowledge Transfer Protocols are structured methods or systems used to pass information, skills, or procedures from one person, group, or system to another. They help make sure that important knowledge does not get lost when people change roles, teams collaborate, or technology is updated. These protocols can be written guides, training sessions, digital tools, or…
Category: Data Science
Domain Generalization Techniques
Domain generalisation techniques are methods used in machine learning to help models perform well on new, unseen data from different environments or sources. These techniques aim to make sure a model can handle differences between the data it was trained on and the data it will see in real use. This helps reduce the need…
Knowledge-Augmented Inference
Knowledge-augmented inference is a method where artificial intelligence systems use extra information from external sources to improve their understanding and decision-making. Instead of relying only on what is directly given, the system looks up facts, rules, or context from databases, documents, or knowledge graphs. This approach helps the AI make more accurate and informed conclusions,…
Uncertainty-Aware Models
Uncertainty-aware models are computer models designed to estimate not only their predictions but also how confident they are in those predictions. This means the model can communicate when it is unsure about its results. Such models are useful in situations where making a wrong decision could be costly or risky, as they help users understand…
Temporal Knowledge Graphs
Temporal Knowledge Graphs are data structures that store information about entities, their relationships, and how these relationships change over time. Unlike standard knowledge graphs, which show static connections, temporal knowledge graphs add a time element to each relationship, helping track when things happen or change. This allows for more accurate analysis of events, trends, and…
Graph-Based Knowledge Fusion
Graph-based knowledge fusion is a technique for combining information from different sources by representing data as nodes and relationships in a graph structure. This method helps identify overlaps, resolve conflicts, and create a unified view of knowledge from multiple datasets. By using graphs, it becomes easier to visualise and manage complex connections between pieces of…
Multi-Modal Data Fusion
Multi-modal data fusion is the process of combining information from different types of data sources, such as images, text, audio, or sensor readings, to gain a more complete understanding of a situation or problem. By integrating these diverse data types, systems can make better decisions and provide more accurate results than using a single source…
Knowledge Fusion Techniques
Knowledge fusion techniques are methods used to combine information from different sources to create a single, more accurate or useful result. These sources may be databases, sensors, documents, or even expert opinions. The goal is to resolve conflicts, reduce errors, and fill in gaps by leveraging the strengths of each source. By effectively merging diverse…
Simulation Modeling
Simulation modelling is a method used to create a virtual version of a real-world process or system. It allows people to study how things work and make predictions without affecting the actual system. By adjusting different variables in the model, users can see how changes might impact outcomes, helping with planning and problem-solving.
Sales Forecasting Automation
Sales forecasting automation uses technology to predict future sales by analysing past data and current market trends. This process replaces manual calculations with software or AI tools, helping businesses estimate upcoming revenue more quickly and accurately. Automating sales forecasts allows companies to make better decisions about inventory, staffing, and budgeting, reducing the risk of errors…