Category: Artificial Intelligence

Behavioral Biometrics

Behavioural biometrics is a technology that identifies or verifies people based on how they interact with devices or systems. It analyses patterns such as typing speed, mouse movements, touchscreen gestures, or how someone walks. These patterns are unique to individuals and can be used to strengthen security or personalise user experiences. Unlike passwords or fingerprints,…

Stochastic Gradient Descent Variants

Stochastic Gradient Descent (SGD) variants are different methods built on the basic SGD algorithm, which is used to train machine learning models by updating their parameters step by step. These variants aim to improve performance by making the updates faster, more stable, or more accurate. Some common variants include Momentum, Adam, RMSprop, and Adagrad, each…

Model Interpretability

Model interpretability refers to how easily a human can understand the decisions or predictions made by a machine learning model. It is about making the inner workings of a model transparent, so people can see why it made a certain choice. This is important for trust, accountability, and identifying mistakes or biases in automated systems.

Synthetic Feature Generation

Synthetic feature generation is the process of creating new data features from existing ones to help improve the performance of machine learning models. These new features are not collected directly but are derived by combining, transforming, or otherwise manipulating the original data. This helps models find patterns that may not be obvious in the raw…

Label Noise Robustness

Label noise robustness refers to the ability of a machine learning model to perform well even when some of its training data labels are incorrect or misleading. In real-world datasets, mistakes can occur when humans or automated systems assign the wrong category or value to an example. Robust models can tolerate these errors and still…

Neural Symbolic Integration

Neural Symbolic Integration is an approach in artificial intelligence that combines neural networks, which learn from data, with symbolic reasoning systems, which follow logical rules. This integration aims to create systems that can both recognise patterns and reason about them, making decisions based on both learned experience and clear, structured logic. The goal is to…

Robust Optimization

Robust optimisation is a method in decision-making and mathematical modelling that aims to find solutions that perform well even when there is uncertainty or variability in the input data. Instead of assuming that all information is precise, it prepares for worst-case scenarios by building in a margin of safety. This approach helps ensure that the…

Fairness-Aware Machine Learning

Fairness-Aware Machine Learning refers to developing and using machine learning models that aim to make decisions without favouring or discriminating against individuals or groups based on sensitive characteristics such as gender, race, or age. It involves identifying and reducing biases that can exist in data or algorithms to ensure fair outcomes for everyone affected by…