Personalised modelling with spiking neural networks integrating temporal and static information.

This paper proposes a new personalised prognostic/diagnostic system that supports classification, prediction and pattern recognition when both static and dynamic/spatiotemporal features are presented in a dataset. The system is based on a proposed clustering method (named d2WKNN) for optimal selection of neighbouring samples to an individual with respect to the integration of both static (vector-based) and temporal individual data. The most relevant samples to an individual are selected to train a Personalised Spiking Neural Network (PSNN) that learns from sets of streaming data to capture the space and time association patterns. The generated time-dependant patterns resulted in a higher accuracy of classification/prediction (80% to 93%) when compared with global modelling and conventional methods. In addition, the PSNN models can support interpretability by creating personalised profiling of an individual. This contributes to a better understanding of the interactions between features. Therefore, an end-user can comprehend what interactions in the model have led to a certain decision (outcome). The proposed PSNN model is an analytical tool, applicable to several real-life health applications, where different data domains describe a person's health condition. The system was applied to two case studies: (1) classification of spatiotemporal neuroimaging data for the investigation of individual response to treatment and (2) prediction of risk of stroke with respect to temporal environmental data. For both datasets, besides the temporal data, static health data were also available. The hyper-parameters of the proposed system, including the PSNN models and the d2WKNN clustering parameters, are optimised for each individual

Mathematical modeling of a solar powered humidification dehumidification desalination prototype

In recent times the issue of fresh water shortages and salinity contamination of existing water sources has become a serious problem in a number of locations around the world. Hence, developing an environmentally friendly desalination technique is essential. In this work a theoretical model is developed in order to optimize a novel humidification-dehumidification desalination system. A sensitivity analysis was carried out, in order to find the optimum values for air and water flow rates. From this analysis it was found that a maximum of production rate of 1.5 kg/hr.m2 was achievable, however it was also found that this rate was particularly influenced by the incident radiation, the inlet water temperature and the water flow rate