Employing Energy and Statistical Features for Automatic Diagnosis of Voice Disorders.

The presence of laryngeal disease affects vocal fold(s) dynamics and thus causes changes in pitch, loudness, and other characteristics of the human voice. Many frameworks based on the acoustic analysis of speech signals have been created in recent years; however, they are evaluated on just one or two corpora and are not independent to voice illnesses and human bias. In this article, a unified wavelet-based paradigm for evaluating voice diseases is presented. This approach is independent of voice diseases, human bias, or dialect. The vocal folds' dynamics are impacted by the voice disorder, and this further modifies the sound source. Therefore, inverse filtering is used to capture the modified voice source. Furthermore, the fundamental frequency independent statistical and energy metrics are derived from each spectral sub-band to characterize the retrieved voice source. Speech recordings of the sustained vowel /a/ were collected from four different datasets in German, Spanish, English, and Arabic to run the several intra and inter-dataset experiments. The classifiers' achieved performance indicators show that energy and statistical features uncover vital information on a variety of clinical voices, and therefore the suggested approach can be used as a complementary means for the automatic medical assessment of voice diseases.

Optimum Feature Selection with Particle Swarm Optimization to Face Recognition System Using Gabor Wavelet Transform and Deep Learning.

In this study, Gabor wavelet transform on the strength of deep learning which is a new approach for the symmetry face database is presented. A proposed face recognition system was developed to be used for different purposes. We used Gabor wavelet transform for feature extraction of symmetry face training data, and then, we used the deep learning method for recognition. We implemented and evaluated the proposed method on ORL and YALE databases with MATLAB 2020a. Moreover, the same experiments were conducted applying particle swarm optimization (PSO) for the feature selection approach. The implementation of Gabor wavelet feature extraction with a high number of training image samples has proved to be more effective than other methods in our study. The recognition rate when implementing the PSO methods on the ORL database is 85.42% while it is 92% with the three methods on the YALE database. However, the use of the PSO algorithm has increased the accuracy rate to 96.22% for the ORL database and 94.66% for the YALE database.

[Results of coronary angioplasty in coronary bifurcation. Report of 61 cases]. / Resultats des angioplasties des stenoses de bifurcation coronaire. A propos de 61 cas.

These are the results of 61 bifurcations treated by percutaneous coronary angioplasty in 50 patients (41 males, 9 females) between 1998 and 2003. Bifurcation stenosis, dominated by type I of bifurcation classification. Global restenosis rate was 20% and didn't concern any case of kissing balloon. Restenosis rate in bifunction angioplasty is similar to that of the other sites; besides, it's twice more important when we stent both of the principal and collateral arteries unless we did kissing balloon. Te interventional treatment of bifurcation stenosis is feasible, with restenosis rate similar to the other types of lesions if we proceed systematicaly to kissing balloon.