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Titlebook: Neural Information Processing; 11th International C Nikhil Ranjan Pal,Nik Kasabov,Swapan Kumar Parui Conference proceedings 2004 Springer-V

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發(fā)表于 2025-3-28 16:56:28 | 只看該作者
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發(fā)表于 2025-3-29 00:42:13 | 只看該作者
Dynamics of Complex-Valued Neural Networks and Its Relation to a Phase Oscillator System of the phases of neurons is shown to be described as a system of the phase oscillators with a pair-wise sinusoidal coupling. The amplitudes affect the phase dynamics in a way to modulate the coupling strength. As the system of phase oscillators is known to be effective for an area-wide signal contr
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發(fā)表于 2025-3-29 07:01:27 | 只看該作者
Two Models for Theta Precession Generation Using the Complex Version of the Nagumo-Sato Neuron Modelpotential with certain phase lags which depend on the instantaneous position of the animal during running in a given field. In this paper, the precession is modeled by two models, one is the complex version of the Nagumo-Sato single neuron and the other the well-known Hodgkin-Huxley equations.
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發(fā)表于 2025-3-29 11:03:24 | 只看該作者
nna, Austria, during January 19–21, 2021. Due to the COVID-19 pandemic, the conference was cancelled and will be merged with SWQD 2022...The Software Quality Days (SWQD) conference started in 2009 and has grown to the biggest conference?on software quality in Europe with a strong community. The prog
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發(fā)表于 2025-3-29 14:48:05 | 只看該作者
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發(fā)表于 2025-3-29 16:33:33 | 只看該作者
Rolf Eckmiller,Oliver Baruth,Dirk Neumannthe quality of User Stories and its evolution over time. Firstly, we develop a method to automatically monitor the quality of User Stories. Secondly, we investigate the relationship between User Story quality and project performance measures such as the number of reported bugs and the occurrence of
48#
發(fā)表于 2025-3-29 23:43:32 | 只看該作者
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發(fā)表于 2025-3-30 03:11:48 | 只看該作者
50#
發(fā)表于 2025-3-30 06:50:42 | 只看該作者
Toshikazu Samura,Motonobu Hattorisoftware projects. Most of these approaches rely on supervised learning algorithms, which require labeled data for adjusting their parameters during the learning phase. Usually, such labeled training data is not available. Unsupervised algorithms do not require training data and can therefore help t
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