<p>Analysing environmental events such as predicting the risk of flood is considered as a challenging task due to the dynamic behaviour of the data. One way to correctly predict the risk of such events is by gathering as much of related historical data and analyse the correlation between the features which contribute to the event occurrences. Inspired by the brain working mechanism, the spiking neural networks have proven the capability of revealing a significant association between different variables spike behaviour during an event. Personalised modelling, on the other hand, allows a personal model to be created for a specific data model and experiment. Therefore, a personalised modelling method incorporating spiking neural network is used to create a personalised model for assessing a real-world flood case study in Kuala Krai, Kelantan based on historical data of 2012-2016 provided by Malaysian Meteorological Department. The result shows that the method produces the highest accuracy among the selected compared algorithms.</p>
The evolution of Artificial Neural Network recently gives researchers an interest to explore deep learning evolved by Spiking Neural Network clustering methods. Spiking Neural Network (SNN) models captured neuronal behaviour more precisely than a traditional neural network as it contains the theory of time into their functioning model [1]. The aim of this paper is to reviewed studies that are related to clustering problems employing Spiking Neural Networks models. Even though there are many algorithms used to solve clustering problems, most of the methods are only suitable for static data and fixed windows of time series. Hence, there is a need to analyse complex data type, the potential for improvement is encouraged. Therefore, this paper summarized the significant result obtains by implying SNN models in different clustering approach. Thus, the findings of this paper could demonstrate the purpose of clustering method using SNN for the fellow researchers from various disciplines to discover and understand complex data.
Mastoiditis is a common complication of acute otitis media. It is common in younger age compared to adulthood. Mastoiditis occurs when an otitis media infection spread directly to involve the bone of mastoid air cell causing osteitis. Cholesteatoma can contribute to the development of mastoiditis. This typically leads to breakdown of some of the fine bony trabeculae of mastoid cells producing a coalescent mastoiditis with an emphyema in mastoid antrum. Cholesteatoma can contribute to the development of mastoiditis. The common treatment for mastoiditis is intravenous antibiotic. Our cases show that local antibiotic treatment is superior compared to systemic antibiotic in treating multi-drug resistant chronic. Pseudomonas mastoiditis compared to intravenous antibiotic. However, if it presents together with cholesteatoma the main treatment is still early mastoidectomy.
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