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Methods for monitoring the human physical activity are recently investigated in order to assess the health status of the individuals and thus promote a healthier lifestyle. This paper proposes ‘dist-colorimetrics’, a methodology that aims to represent and classify ambulatory activities based on the spectral distances measures. A data collection platform including four accelerometer sensors mounted...
This paper attempts to predict the survival of patients using supervised machine learning techniques. To predict this task, the variables were identified and retrieved from the StatLib database. Both the artificial neural networks and linear regression models were used to perform the task. Experimental results, based on the classification accuracy were analysed from training and testing datasets....
Particle Swarm Optimisation (PSO) algorithm is known to be better than Genetic Algorithm (GA) as fewer operators are needed in its algorithm. However, it still has some weaknesses such as immature convergence; a condition whereby PSO tends to get trapped in a local optimum. This condition prevents them from being converged towards a better position. Various techniques have been proposed to tackle...
This paper proposes a new feature-selection strategy by integrating the Rough Set Theory (RST) and Particle Swarm Optimisation (PSO) algorithms to generate a set of discriminatory features for the classification problem. The proposed method is seen as a marriage between filter and wrapper approaches in which the RST is used to pre-reduce the feature set before optimisation by PSO, a meta-heuristic...
Determining the functions of uncharacterized proteins from sequences remains a challenge despite the growth of the number of prediction methods. This is due to the nature of the inherent limitations of current tools and databases and the ambiguity of the function definition. Additionally, standard methods of functional assignment involve sequence alignment to a gene function often fail to find the...
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