This paper introduces an accurate time-domain approach to model and classify the Malayalam consonantVowel (CV) speech unit waveforms. The technique is based on statistical models of Reconstructed State Space (RSS). A feature extraction method using RSS based State Space Point Distribution (SSPD) parameters are studied. The results of the simulation experiment performed on the Malayalam CV speech databases using Artificial Neural Network (ANN) and k-Nearest Neighborhood (k-NN) classifiers are also presented. The results indicate that the efficiency of the RSS approach is capable of increasing speaker independent consonant speech recognition accuracy.
In this work, we propose to leverage the advantages of both the Artificial Neural Network (ANN) based Second Order Reliability Method (SORM) and Importance sampling to yield an Adaptive Importance Sampling based ANN, with specific application towards failure probability and sensitivity estimates of Variable Stiffness Composite Laminate (VSCL) plates, in the presence of multiple independent geometric and material uncertainties. The performance function for the case studies is defined based on the fundamental frequency of the VSCL plate. The accuracy in both the reliability estimates and sensitivity studies using the proposed method were found to be in close agreement with that obtained using the ANN based brute-force MCS method, with a significant computational savings of 95%. Moreover, the importance of taking into account the randomness in ply thickness for failure probability estimates is also highlighted quantitatively under the sensitivity studies section.
The shortage of competent professionals has long plagued the health workforce globally. The increase in workload brought on by the COVID-19 outbreak has made things worse. The factors influencing turnover intention are working conditions and burnout. Social support has been taken as the mediating factor. The hypotheses are formulated among these factors. Significant healthcare system failures occurred during the epidemic's peak, leading to requests for answers to the industry's mounting problem of high employee turnover. Pre-emptive measures should be taken to retain healthcare workers because of the potential for this turnover to worsen given the tremendous strain the healthcare profession has already been under throughout the epidemic. This study looks into the factors that affect healthcare employees' decisions to depart. SMARTPLS is used to adopt structural equation modelling and analyse it. The partially mediated model was supported by the findings. The likelihood of turnover was positively correlated with both burnout and working conditions. The usage of this theoretical framework by leaders of various sorts of organisations should be further investigated in future research utilising more precise measurements for requirements and resources. The usage of this theoretical framework by leaders of various sorts of organisations should be further investigated in future research utilising more precise measurements for requirements and resources.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.