Search results

The Covid-19 prediction process is more indispensable to handle the spread and death occurred rate because of Covid-19. However early and precise prediction of Covid-19 is more difficult because of different sizes and resolutions of input image. Thus these challenges and problems experienced by traditional Covid-19 detection methods are considered as major motivation to develop JHBO-based DNFN.

The major contribution of this research is to design an effectual Covid-19 detection model using devised JHBO-based DNFN. Here, the audio signal is considered as input for detecting Covid-19. The Gaussian filter is applied to input signal for removing the noises and then feature extraction is performed. The substantial features, like spectral roll-off, spectral bandwidth, Mel-frequency cepstral coefficients (MFCC), spectral flatness, zero crossing rate, spectral centroid, mean square energy and spectral contract are extracted for further processing. Finally, DNFN is applied for detecting Covid-19 and the deep leaning model is trained by designed JHBO algorithm. Accordingly, the developed JHBO method is newly designed by incorporating Honey Badger optimization Algorithm (HBA) and Jaya algorithm.

The performance of proposed hybrid optimization-based deep learning algorithm is estimated by means of two performance metrics, namely testing accuracy, sensitivity and specificity of 0.9176, 0.9218 and 0.9219.

The JHBO-based DNFN approach is developed for Covid-19 detection. The developed approach can be extended by including other hybrid optimization algorithms as well as other features can be extracted for further improving the detection performance.

The proposed Covid-19 detection method is useful in various applications, like medical and so on.

Developed JHBO-enabled DNFN for Covid-19 detection: An effective Covid-19 detection technique is introduced based on hybrid optimization–driven deep learning model. The DNFN is used for detecting Covid-19, which classifies the feature vector as Covid-19 or non-Covid-19. Moreover, the DNFN is trained by devised JHBO approach, which is introduced by combining HBA and Jaya algorithm.

This study aims to examine performance assessment of organizational units through psychological empowerment (PE) and employee engagement (EE) approach and whether this relationship differs among efficient and inefficient organization units.

This study drew on merging the principal component analysis (PCA), data envelopment analysis (DEA) and partial least square-multigroup analysis (PLS-MGA) to benchmark the performance of organizational units affiliated with Zagazig University in Egypt using PE dimensions as inputs and EE as output. Besides investigating whether PE inputs have the same effect among efficient and inefficient units.

Performance assessment based on independent data showed that all the investigated organizational units are not at the same efficiency level. The results revealed that there are eight efficient units versus seven inefficient ones. Moreover, PLS-MGA results demonstrated that no significant differences concerning the impact of PE inputs on EE between efficient and inefficient units groups. Nevertheless, the effect of these inputs was slightly higher in the former.

Studies on EE performance in the service sector are scarce in the literature, this study is a novel contribution of exploring EE efficiency in Egypt as a developing economy. Specifically, using the PCA-DEA-structural equation modeling approach.

The purpose of this study is to identify the level of quality of work life (QWL) of employees working in the Garment industries using a validated scale.

Survey methods were used for this study. A questionnaire was designed to collect the data and information, and it is validated through exploratory factor analysis, confirmatory factor analysis.

The majority of employees are not satisfied with the present status of QWL in garment units. Followings are the predominant components, which influence the QWL of employees compensation and rewards; job security; grievance handling; work environment; training and development; job nature; satisfaction in job; facilities and relation and cooperation.

The study was conducted in 133 garment industries where sample responses were obtained from 851 workers working in Indian Garment industries. In the competitive business environment, retaining a talented workforce is one of the big challenges to the organization. An unsatisfied employee is the first enemy of the organization, it is the prime task of the employers to keep the workforce at a satisfying level, otherwise, it will lead to employee turnover, performance and productivity. This paper helps to identify and quantify the components of the quality of work-life of employees if employers address these components job satisfaction level of employees will increase; therefore, our results will help the HR managers and policymakers to take appropriate decisions to enhance QWL.