All Outputs (12)

Who consumes anthocyanins and anthocyanidins? Mining national retail data to reveal the influence of socioeconomic deprivation and seasonality on polyphenol dietary intake (2023)
Conference Proceeding

Anthocyanins are a class of polyphenols that have received widespread recent attention due to their potential health benefits. However, estimating the dietary intake of anthocyanins at a population level is a challenging task, due to the difficulty o... Read More about Who consumes anthocyanins and anthocyanidins? Mining national retail data to reveal the influence of socioeconomic deprivation and seasonality on polyphenol dietary intake.

Assessing relative contribution of Environmental, Behavioural and Social factors on Life Satisfaction via mobile app data (2023)
Conference Proceeding

Life satisfaction significantly contributes to wellbe-ing and is linked to positive outcomes for individual people and society more broadly. However, previous research demonstrates that many factors contribute to the life satisfaction of an individua... Read More about Assessing relative contribution of Environmental, Behavioural and Social factors on Life Satisfaction via mobile app data.

Bundle entropy as an optimized measure of consumers' systematic product choice combinations in mass transactional data (2022)
Conference Proceeding

Understanding and measuring the predictability of consumer purchasing (basket) behaviour is of significant value. While predictability measures such as entropy have been well studied and leveraged in other sectors, their development and application t... Read More about Bundle entropy as an optimized measure of consumers' systematic product choice combinations in mass transactional data.

Towards Idea Mining: Problem-Solution Phrase Extraction fromText (2022)
Conference Proceeding

This paper investigates the feasibility of problem-solution phrases extraction from scientific publications using neural network approaches. Bidirectional Long Short-Term Memory with Conditional Random Fields (Bi-LSTM-CRFs) and Bidirectional Encoder... Read More about Towards Idea Mining: Problem-Solution Phrase Extraction fromText.

FIMS: Identifying, Predicting and Visualising Food Insecurity (2020)
Conference Proceeding

Food insecurity is a persistent and pernicious problem in the UK. Due to logistical challenges, national food insecurity statistics are unmeasured by government bodies - and this lack of data leads to any local estimates that do exist being routinely... Read More about FIMS: Identifying, Predicting and Visualising Food Insecurity.

The unbanked and poverty: predicting area-level socio-economic vulnerability from M-Money transactions (2018)
Conference Proceeding

Emerging economies around the world are often characterized by governments and institutions struggling to keep key demographic data streams up to date. A demographic of interest particularly linked to social vulnerability is that of poverty and socio... Read More about The unbanked and poverty: predicting area-level socio-economic vulnerability from M-Money transactions.

Seasonal Variation in Collective Mood via Twitter Content and Medical Purchases (2017)
Conference Proceeding

The analysis of sentiment contained in vast amounts of Twitter messages has reliably shown seasonal patterns of variation in multiple studies, a finding that can have great importance in the understanding of seasonal affective disorders, particularly... Read More about Seasonal Variation in Collective Mood via Twitter Content and Medical Purchases.

Exploring the capabilities of Projection Augmented Relief Models (PARM) (2017)
Conference Proceeding

This paper explores the broad capabilities of physical landscape models when augmented by projection, termed Projection Augmented Relief Models (PARM). This includes experiences of developing PARM displays in public settings such as museums and visit... Read More about Exploring the capabilities of Projection Augmented Relief Models (PARM).

A novel symbolization technique for time-series outlier detection (2015)
Conference Proceeding

The detection of outliers in time series data is a core component of many data-mining applications and broadly applied in industrial applications. In large data sets algorithms that are efficient in both time and space are required. One area where sp... Read More about A novel symbolization technique for time-series outlier detection.

AMP: a new time-frequency feature extraction method for intermittent time-series data (2015)
Conference Proceeding

The characterisation of time-series data via their most salient features is extremely important in a range of machine learning task, not least of all with regards to classification and clustering. While there exist many feature extraction techniques... Read More about AMP: a new time-frequency feature extraction method for intermittent time-series data.

A refined limit on the predictability of human mobility (2014)
Conference Proceeding

It has been recently claimed that human movement is highly predictable. While an upper bound of 93% predictability was shown, this was based upon human movement trajectories of very high spatiotemporal granularity. Recent studies reduced this spatiot... Read More about A refined limit on the predictability of human mobility.

Towards optimal symbolization for time series comparisons (2013)
Conference Proceeding

The abundance and value of mining large time series data sets has long been acknowledged. Ubiquitous in fields ranging from astronomy, biology and web science the size and number of these datasets continues to increase, a situation exacerbated by the... Read More about Towards optimal symbolization for time series comparisons.