Developing and validating a scale to identify the employability skill set vital for the frontline workers: A case of hospitals in Delhi/NCR

The purpose of this study is to identify those imperative skills required by an oligopolistic industry (healthcare) to provide significant services so as to ensure maximum customer satisfaction. Employability skills are those essential skills which are required in the workplace. Researchers are consistently analyzing it on multiple fronts and domains to help the industry identify the right person for the right job. Unfortunately, there is still a dearth of studies which have precisely recognized the employability skill set for frontline workers in hospitals. In this paper, researchers have successfully developed and validated a scale to measure the employability skill set required by frontline workers in the healthcare industry. Researchers have developed a research scale consisting of nine essential dimensions of skills. For validating the scale, a sample has been collected from 400 respondents, having at least 10 years of experience in the Medical Service Vertical using stratified random sampling. The medical service vertical comprises both front-end and back-end jobs. The skill set varies at each level and as per the key responsibility areas. An employability skill set scale has been developed, encompassing the required soft and technical skills. The study intends a significant positive correlation, which means that any positive variance in one leads to a corresponding affirmative variability in the other. This study will further assist researchers who want to develop tailored training programmes for frontline workers

An enhanced self-learning-based clustering scheme for real-time traffic data distribution in wireless networks

The process of examining the data flow over the internet to identify abnormalities in wireless network performance is known as network traffic analysis. When analyzing network traffic data, traffic classification becomes an important task. The traffic data classification is used to determine whether data in network traffic is in real-time or not. This analysis controls network traffic data in a network and allows for efficient network performance improvement. Real-time and non-real-time data are effectively classified from the given input data set using data mining clustering and classification algorithms. The proposed work focuses on the performance of traffic data classification with high clustering accuracy and low Classification Time (CT). This research work is carried out to fill the gap in the existing network traffic classification algorithms. However, the traffic data classification remained unaddressed for performing the network traffic analysis effectively. Then, we proposed an Enhanced Self-Learning-based Clustering Scheme (ESLCS) using an enhanced unsupervised algorithm and adaptive seeding approach to improve the classification accuracy while performing the real-time traffic data distribution in wireless networks. Test-bed results demonstrate that the proposed model enhances the clustering accuracy and True Positive Rate (TPR) effectively as well as reduces the CT time and Communication Overhead (CO) substantially to compare with the peer-existing routing techniques