Smart Computing and Systems Engineering - 2021 (SCSE 2021)

Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/25343

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Author: search.filters.author.Munasinghe, Lankeshwara

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    What makes job satisfaction in the information technology industry?
    (Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Arambepola, Nimasha; Munasinghe, Lankeshwara
    Having a rich human resource is critical for an organization to move towards success. Especially, for business organizations such as technology companies, the human resource is the driving factor of the company's growth which depends on employees' motivation, skills and quality of work. Employees often change their jobs when they are not satisfied with it. Different factors may cause a change in the level of job satisfaction of an employee. For example, the dynamic nature of the Information Technology (IT) industry is an impactful factor that determines the job satisfaction of IT professionals. Foreseeing the employees' job satisfaction makes it easy for a company to take swift actions to improve the job satisfaction of its employees. In this research, we analyzed the effectiveness of machine learning (ML) methods for predicting job satisfaction using employee job profiles. There are job-specific factors in each job domain, and those factors may influence job satisfaction levels. Therefore, this research focused on the following fundamental questions: 1) How do existing ML models perform when predicting job satisfaction of software developers? 2) Can the job satisfaction prediction models be generalized to the other job roles in the IT industry? This study compared the performance of classification models: Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Neural Network (NN) in predicting the level of job satisfaction. Our experiments used two benchmark datasets: Stack Overflow developer survey and IBM HR analytics dataset. The experimental analysis shows that both employee-related factors and company-related factors contribute similarly to predicting job satisfaction. On average, the above ML models predict the job satisfaction of software developers with an accuracy of around 79%.
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    Temporal preferential attachment: Predicting new links in temporal social networks
    (Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Wickramarachchi, Panchani; Munasinghe, Lankeshwara
    Social networks have shown an exponential growth in the recent past. It has estimated that nearly 4 billion people are currently using social networks. The growth of social networks can be explained using different models. Preferential Attachment (PA) is a widely used model, which is often used to link prediction in social networks. PA tells that the social network users prefer to get linked with popular users in the network. However, the popularity of a node depends not only on the node’s degree but also on the node's activeness which is reflected by the amount of active links the node has at present. Activeness of a link can be quantified using the timestamp of the link. The present work introduces a novel method called Temporal Preferential Attachment (TPA) which is defined on the activeness and strength of a node. Strength of a node is the sum of weights of links attached to the node. Here, the weights of the links are assigned according to their activeness. Thus, TPA captures the temporal behaviors of nodes, which is a vital factor for new link formation. The novel method uses min - max scaling to scale the time differences between current time and the timestamps of the links. Here, the min value is the earliest timestamp of the links in the given network and max value is the latest timestamp of the links. The scaled time difference of a link is considered as the temporal weight of the link, which reflects its activeness. TPA was evaluated in terms of its link prediction performance using well-known social network data sets. The results show that TPA performs well in link prediction compared to PA, and show a significant improvement in prediction accuracy.