This paper discusses the bio-inspired algorithm of the Particle Swarm Optimisation (PSO) for a wheeled robot's displacement. PSO was selected because its flexibility and its tempting results. An omnidirectional wheeled robot was simulated on a flat environment with two tasks: 'Reach a goal' or 'collect balls'. This paper checks on the performance of PSO for the displacement studied. In the first case, we discussed the variation of execution time compared to the particles' and the neighbours' number. In the second one, we studied the change in the path's length compared to execution time depending on the particles' and balls' number.
Speech recognition is an essential ability of human beings and is crucial for communication. Consequently, automatic speech recognition (ASR) is a major area of research that is increasingly using artificial intelligence techniques to replicate this human ability. Among these techniques, deep learning (DL) models attract much attention, in particular, convolutional neural networks (CNN) which are known due to their power to model spatial relationships. In this article, three CNN architectures that performed well in recognized competitions were implemented to compare their performance in Arabic speech recognition; these are the well-known models AlexNet, ResNet, and GoogLeNet. These models were compared based on a corpus composed of Arabic spoken digits collected from various sources, including messaging and social media applications, in addition to an online corpus. The architectures of AlexNet, ResNet, and GoogLeNet achieved respectively an accuracy of 86.19%, 83.46%, and 89.61%. The results show the superiority of GoogLeNet, and underline the potential of CNN architectures to model acoustic features of low-resource languages such as Arabic.
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