A Bayesian adaptive procedure, the quick-auditory-filter (qAF) procedure, was used to estimate auditory-filter shapes that were asymmetric about their peaks. In three experiments, listeners who were naive to psychoacoustic experiments detected a fixed-level, pure-tone target presented with a spectrally notched noise masker. The qAF procedure adaptively manipulated the masker spectrum level and the position of the masker notch, which was optimized for the efficient estimation of the five parameters of an auditory-filter model. Experiment I demonstrated that the qAF procedure provided a convergent estimate of the auditory-filter shape at 2 kHz within 150 to 200 trials (approximately 15 min to complete) and, for a majority of listeners, excellent test-retest reliability. In experiment II, asymmetric auditory filters were estimated for target frequencies of 1 and 4 kHz and target levels of 30 and 50 dB sound pressure level. The estimated filter shapes were generally consistent with published norms, especially at the low target level. It is known that the auditoryfilter estimates are narrower for forward masking than simultaneous masking due to peripheral suppression, a result replicated in experiment III using fewer than 200 qAF trials.

Parkinson's disease (PD) affects over 6.2 million people around the world. Despite its prevalence, there is still no cure, and diagnostic methods are extremely subjective, relying on observation of physical motor symptoms and response to treatment protocols. Other neurodegenerative diseases can manifest similar motor symptoms and often too much neuronal damage has occurred before motor symptoms can be observed. The goal of our study is to examine diffusion tensor images (DTI) from Parkinson's and control patients through linear dynamical systems and tensor decomposition methods to generate features for training classification models. Diffusion tensor imaging emphasizes the spread and density of white matter in the brain. We will reduce the dimensionality of these images to allow us to focus on the key features that differentiate PD and control patients. We show through our experiments that these approaches can result in good classification accuracy (90%), and indicate this avenue of research has a promising future.

The Bayesian adaptive procedure proposed by Shen and Richards [J. Acoust. Soc. Am. 134(2), 1134–1145 (2013)] was extended to allow the rapid estimation of auditory filters that were asymmetric about their peak frequencies. The estimation of the auditory-filter shape (five free parameters) was achieved using single Bayesian adaptive tracks of 150–200 trials (approximately 15 min to complete). During the experimental track, listeners detected a tonal signal presented with either simultaneous or forward maskers. Both types of maskers consisted of two bands of noises, one on each side of the signal frequency. The Bayesian adaptive procedure iteratively updated the parameter estimates following each experimental trial and determined the stimulus that would maximize the gain of information on the following trial. The stimuli were adaptively manipulated along three dimensions: the masker level and the spectral location of the upper and lower masker bands. The proposed procedure allowed the reliable estimation of the auditory-filter shape for naïve normal-hearing listeners. The model predictions replicated the known effect of the masker-signal simultaneity on the auditory filter.

Feature selection plays a vital role for every data analysis application. Feature selection aims to choose prominent set of features after removing redundant and irrelevant features from original set of features. High Dimensional dataset poses a challenging task for Machine Learning algorithms. Many state-of-art solutions were developed to handle this issue. High dimensionality in addition to imbalance ratio in the dataset becomes a tedious task. To overcome the issue, this paper introduces a novel method namely Pearson’s Redundancy Based Multi Filter algorithm with improved BAT algorithm (PRBMF-iBAT) to obtain multiple feature subsets. PRBMF is implemented using multiple filters to obtain highly relevant features. iBAT algorithm uses these features to find best subset of features for classification. The results prove that PRBMF-iBAT perform better for the classifier in terms of Accuracy, Precision, Recall and F- Measure for three micro array datasets with SVM classifier. The proposed system achieves 97.99% of accuracy as highest compared to the existing rCBR-BGOA algorithm.