Purpose:
The main objective of this research was to examine the importance of training and development in the workplace.
Methodology:
Several dimensions of employee performance were analyzed, including productivity, job satisfaction, employee satisfaction, employee commitment, and decision-making. An adopted five Likert scale questionnaire was adopted for the online data collection from 100 respondents from the telecommunication industry. Convenience sampling was used for sampling and the
PLS-SEM was the main technique for data analysis using smart PLS software.
Findings:
The results suggest that organizational performance and employee performance in the telecommunication sector in Pakistan increase if there is a significant relationship between employees and decision-making. Similarly, employees with a high level of job satisfaction and affective commitment will ultimately have a higher potential for productivity and career satisfaction.
Conclusions:
The study concluded that employee performance improves as teamwork increases. Teamwork within the company is very valuable; it directly affects the performance of employees. When an employee gets enough teamwork possibilities, his performance will automatically develop.
This study empirically identifies the impact of financial development, particularly exports, on trade in services. Three proxies used for measuring financial development are financial systems deposits, liquid liabilities, and private credit. After conducting statistical tests and robustness checks, we show that financial development has a positive impact on trade in services in Central and South America, but does not have a significant impact in Asia and Africa. All three proxies used for assessing financial development are found to be insignificant in Asia and Africa.
The secondary structure of a protein is critical for establishing a link between the protein primary and tertiary structures. For this reason, it is important to design methods for accurate protein secondary structure prediction. Most of the existing computational techniques for protein structural and functional prediction are based on machine learning with shallow frameworks. Different deep learning architectures have already been applied to tackle protein secondary structure prediction problem. In this study, deep learning based models, i.e., convolutional neural network and long short-term memory for protein secondary structure prediction were proposed. The input to proposed models is amino acid sequences which were derived from CulledPDB dataset. Hyperparameter tuning with cross validation was employed to attain best parameters for the proposed models. The proposed models enables effective processing of amino acids and attain approximately 87.05% and 87.47% Q 3 accuracy of protein secondary structure prediction for convolutional neural network and long short-term memory models, respectively.
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