Functional effects of cortical auditory evoked potentials and hearing ADIS on speech perception in sensorineural hearing loss individuals / Mohammed Gamal Nasser Al-Zidi

Cortical Auditory Evoked Potentials (CAEPs) represent summation of neural activity in the auditory pathways in reaction to sounds. They provide an objective measure of the brain’s response to sound. For this reason, CAEPs are an ideal tool for scientists and audiologists for investigating auditory function in people both, normal and with hearing loss. The main objective of this study is to determine which CAEP components among the P1, N1, P2, N2, or P3 are most beneficial in assessing the speech detection and discrimination abilities of adults Sensorineural Hearing Loss (SNHL) population. This study also intends to investigate whether changes in the amplitudes and latencies of these CAEP components occurring with SNHL and hearing aids reflect various stages of auditory processing. CAEPs were recorded from two groups of participants. A control group that comprising of 12 right-handed Malay adults having normal hearing and a second group that consists of 10 right-handed Malay adults with sensorineural hearing loss who were recruited from the local community in the department of Otorhinolaryngology (ENT), UMMC hospital, Kuala Lumpur. The results showed that P2 and P3 components had the most benefits from the use of hearing aids in the SNHL subjects and could be used in both clinical and research applications as a predictor and objective indicator of hearing aids performance in speech perception. The study also showed that the brain processes both stimuli in a different pattern for both normal and aided SNHL subjects. These findings suggest that the aided SNHL subject, despite the benefits they get from the hearing aids, find it difficult to detect and discriminate the acoustic differences between the two speech stimuli. The present study could provide more diagnostic information for clinicians and could also offer better speech perception benefits for hearing-impaired individuals from their personal hearing aids

Classification of Reservoir Recovery Factor for Oil and Gas Reservoirs: A Multi-Objective Feature Selection Approach

The accurate classification of reservoir recovery factor is dampened by irregularities such as noisy and high-dimensional features associated with the reservoir measurements or characterization. These irregularities, especially a larger number of features, make it difficult to perform accurate classification of reservoir recovery factor, as the generated reservoir features are usually heterogeneous. Consequently, it is imperative to select relevant reservoir features while preserving or amplifying reservoir recovery accuracy. This phenomenon can be treated as a multi-objective optimization problem, since there are two conflicting objectives: minimizing the number of measurements and preserving high recovery classification accuracy. In this study, wrapper-based multi-objective feature selection approaches are proposed to estimate the set of Pareto optimal solutions that represents the optimum trade-off between these two objectives. Specifically, three multi-objective optimization algorithms—Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Grey Wolf Optimizer (MOGWO) and Multi-Objective Particle Swarm Optimization (MOPSO)—are investigated in selecting relevant features from the reservoir dataset. To the best of our knowledge, this is the first time multi-objective optimization has been used for reservoir recovery factor classification. The Artificial Neural Network (ANN) classification algorithm is used to evaluate the selected reservoir features. Findings from the experimental results show that the proposed MOGWO-ANN outperforms the other two approaches (MOPSO and NSGA-II) in terms of producing non-dominated solutions with a small subset of features and reduced classification error rate