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The Alzheimer's disease damages neuronal and synaptic system due to the high level of amyloid beta in the brain. It is the common cause of dementia which is more common to afflict the elderly where they will gradually loss their memory and communication skills as well as deterioration of thinking and reasoning ability. Hence, it is crucial for elderly people to monitor their cognitive performance consistently and continuously to detect the Alzheimer's symptoms, such as dementia or Mild Cognitive Impairment. There are many technologies that have been established in healthcare for its detection; however, such technologies, mostly medical treatments could not be self-catered by elderly on daily basis and in fact the use of this technology incurs cost each time. Therefore, this study looks at an alternative technology called leisure technology that allows access to the elderly every day at home in an enjoyable and relaxing manner. The aim of this study is to study applications of leisure activities that could stimulate brain cognitive function to be turned to a leisure technology application. Prior to proposing the conceptual design of this application, a user acceptance study of leisure technology among elderly people has been conducted. This study involves interviews and survey through distribution of questionnaires. The survey results shows that 90% of the participants stated that there was an improvement in cognitive abilities after using leisure technology and 98.4% of the participants stated that they could adapt to leisure technology. On the other hand, the outcomes from the interview show that they agreed that different types of leisure technology provide heterogeneous benefits, which can improve their cognitive abilities. Finally, this study proposes a conceptual design for leisure technology application that elderly people can adapt to.

An end-to-end process to achieve a complete framework methodology for Harmful Algal Bloom (HAB) growth prediction is crucial for water management, especially in implementing robust predictive modelling of HAB to prevent water pollution. Previous works have separately focused on the prediction part or the implementation of the water monitoring system that involves the integration of sensors through the Internet of Things (IoT). These studies lack in terms of discussion of both IoT with the algae ecological domain and prediction method. Therefore, this paper takes the initiative to provide a wider coverage on the end-to-end process including the assembly and integration of sensors, data acquisition and predictive modelling using data-driven approaches, for example, machine learning, deep learning and deep time series forecasting algorithm for future algal bloom outbreak mitigation. This paper believes that discussion in a complete framework perspective based on the execution of each phase is important besides providing a true understanding of the algae growth factors and prediction problems to achieve a robust prediction algorithm for algal growth. In the end, this paper presents proof that selecting the right features and utilising time series with deep learning are much better for tackling the issues of highly non-linear and dynamic algae ecological data that are briefly introduced in this paper. Among all the algorithms selected, Long Short-term Memory (LSTM) is the best fit for the prediction method and has outperformed other basic machine learning methods in accurately predicting algal growth through the prediction of chlorophyll-a (Chla) as a strong indicator of algal presence for coastal studies.

Analysing and processing the EEG dataset is crucial. Countless actions have been taken to ensure that the researcher in brain studies always achieves informative data and produces notable findings. There are several standard procedures to produce an informative result in analysing the EEG data. However, the techniques used in each standard procedure might be different for the researcher or data analyst because they have their preferences to suit the purpose of their experiments to adapt with the dataset collected. Not only the current manual method is time-consuming, but the main challenges are that researchers need to analyse only a small portion of the brain signals that are the most relevant to be observed through the analysis of several bands such as Very low, Delta, Theta, Alpha-1, Alpha-2, Beta-1, Beta-2, and Gamma. Therefore, one of the best alternatives is to automate the process of classifying the eight bands and extract the most relevant features. Hence, this paper proposed an automated classification method and feature extraction method through hybridising Fast Fourier Transform (FFT) with three different machine learning methods (KNN, SVM, and ANN) that can improve the efficiency of EEG analysis. Based on the result, the FFT + SVM method gives a 100% accuracy and successfully classified the bands into different of eight EEG bands accurately.