Fine-Grained Food Image Recognition: A Study on Optimising Convolutional Neural Networks for Improved Performance.

Addressing the pressing issue of food waste is vital for environmental sustainability and resource conservation. While computer vision has been widely used in food waste reduction research, existing food image datasets are typically aggregated into broad categories (e.g.

A behaviour biometrics dataset for user identification and authentication.

As e-Commerce continues to shift our shopping preference from the physical to online marketplace, we leave behind digital traces of our personally identifiable details. For example, the merchant keeps record of your name and address; the payment processor stores your transaction details including account or card information, and every website you visit stores other information such as your device address and type. Cybercriminals constantly steal and use some of this information to commit identity fraud, ultimately leading to devastating consequences to the victims; but also, to the card issuers and payment processors with whom the financial liability most often lies.

Personalised Accelerometer Cut-point Prediction for Older Adults' Movement Behaviours using a Machine Learning approach.

Background And Objectives: Body-worn accelerometers are the most popular method for objectively assessing physical activity in older adults. Many studies have developed generic accelerometer cut-points for defining activity intensity in metabolic equivalents for older adults. However, methodological diversity in current studies has led to a great deal of variation in the resulting cut-points, even when using data from the same accelerometer.

Efficient treatment of outliers and class imbalance for diabetes prediction.

Learning from outliers and imbalanced data remains one of the major difficulties for machine learning classifiers. Among the numerous techniques dedicated to tackle this problem, data preprocessing solutions are known to be efficient and easy to implement. In this paper, we propose a selective data preprocessing approach that embeds knowledge of the outlier instances into artificially generated subset to achieve an even distribution.