Short-term Memory Neural Model Research Articles

Objective Evaluation of the Pathological Voice Based on Deep Learning Neural Networks in an Algerian hospital environment

In this study, we propose a method based on Recurrent Neural Networks, to objectively evaluate the process of rehabilitation of the pathological voice, in an Algerian clinical environment. We choose Unilateral Laryngeal Paralysis as the pathology of the voice. In this paper, we used a Deep Learning system of pathological voice detection by Long Short Term Memory neural model (LSTM). As the dysphony studied in our work concerns essentially the laryngeal vibration, we choose the acoustic parameters based on the instability of the frequency and the amplitude of the laryngeal vibration: Jitter and Shimmer, Noise parameters and Cepstraux MFCC coefficients (Mel Frequency Cepstral Coefficients). A pathological voice detection rate of 88.65% shows important results brought by the rehabilitation technique adopted in Algerian clinical setting. The exclusive and abusive use of hearing to evaluate the effect of speech rehabilitation in the Algerian hospital environment remains insufficient. It is important to correlate perceptual data with objective methods based on detection and classification methods by introducing relevant acoustic parameters, for an effective and objective management of vocal pathology assessment.

Quaternion convolutional long short-term memory neural model with an adaptive decomposition method for wind speed forecasting: North aegean islands case studies

An accurate prediction of short-term and long-term wind speed is necessary in order to integrate wind energy into large-scale grid power. However, wind speed presents diverse and complex seasonal and stochastic characteristics that impose challenges on wind speed forecasting models. This study proposes a Quaternion Convolutional Neural Network combined with a Bi-directional Long Short-Term Memory recurrent network to forecast wind speed. Quaternion Convolutional Neural Network is used to elicit more effective features from the stochastic sub-signals of wind speed. A new decomposition method is also proposed, comprising variational mode decomposition to decompose the wind speed data into optimal signal components, and an improved arithmetic optimisation algorithm to optimise the parameters of the variational mode decomposition. Furthermore, a fast and effective hyper-parameters tuner is introduced in order to adjust the hyper-parameters and architecture of the proposed hybrid forecasting model. The proposed forecasting model is developed based on data collected from Lesvos and Samothraki Greek islands located in the North Aegean Sea with the forecasting range in one-day ahead (long-term) and achieved considerable accuracy improvements in these case studies compared with the bi-directional long short-term memory model at 13% and 20%, respectively. The experimental outcomes confirm that, first, the proposed hybrid forecasting model considerably outperforms the five existing machine learning and two hybrid models in terms of precision and stability.