Category: Data Science

Knowledge Propagation Models

Knowledge propagation models describe how information, ideas, or skills spread within a group, network, or community. These models help researchers and organisations predict how quickly and widely knowledge will transfer between people. They are often used to improve learning, communication, and innovation by understanding the flow of knowledge.

Sparse Feature Extraction

Sparse feature extraction is a technique in data analysis and machine learning that focuses on identifying and using only the most important or relevant pieces of information from a larger set of features. Rather than working with every possible detail, it selects a smaller number of features that best represent the data. This approach helps…

Knowledge-Driven Inference

Knowledge-driven inference is a method where computers or systems use existing knowledge, such as rules or facts, to draw conclusions or make decisions. Instead of relying only on patterns in data, these systems apply logic and structured information to infer new insights. This approach is common in expert systems, artificial intelligence, and data analysis where…

Causal Effect Modeling

Causal effect modelling is a way to figure out if one thing actually causes another, rather than just being associated with it. It uses statistical tools and careful study design to separate true cause-and-effect relationships from mere coincidences. This helps researchers and decision-makers understand what will happen if they change something, like introducing a new…

Dynamic Knowledge Tracing

Dynamic Knowledge Tracing is a method used to monitor and predict a learner’s understanding of specific topics over time. It uses data from each learning activity, such as quiz answers or homework, to estimate how well a student has mastered different skills. Unlike traditional testing, it updates its predictions as new information about the learner’s…

Multi-Domain Knowledge Fusion

Multi-domain knowledge fusion is the process of combining information and expertise from different areas or fields to create a more complete understanding of a topic or to solve complex problems. By bringing together knowledge from various domains, people and systems can overcome the limitations of working in isolation and make better decisions. This approach is…

Knowledge Encoding Strategies

Knowledge encoding strategies are methods used to organise and store information so it can be remembered and retrieved later. These strategies help people and machines make sense of new knowledge by turning it into formats that are easier to understand and recall. Good encoding strategies can improve learning, memory, and problem-solving by making information more…

Model Robustness Metrics

Model robustness metrics are measurements used to check how well a machine learning model performs when faced with unexpected or challenging situations. These situations might include noisy data, small changes in input, or attempts to trick the model. Robustness metrics help developers understand if their models can be trusted outside of perfect test conditions. They…

AI-Driven Business Insights

AI-driven business insights are conclusions and recommendations generated by artificial intelligence systems that analyse company data. These insights help organisations understand trends, customer behaviour, and operational performance more effectively than manual analysis. By using AI, businesses can quickly identify opportunities and risks, making it easier to make informed decisions and stay competitive.