Browsing by Author "Silva, S"

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Solution to Measure Employee Productivity with Employee Emotion Detection
(IEEE, 2022-12-09) De Silva, T.R.S.; Dayananda, K.Y.; Galagama Arachchi, R.C.; Amerasekara, M.K.S.B.; Silva, S; Gamage, N
Health and safety of workers has become a top priority in modern businesses. The reason is that it will have an impact on both individual and team output. In the last few decades, automatic facial expression analysis using machine learning has emerged as a promising and bustling field of study. In this study, the system primarily evaluates the efficiency of workers and, through the detection of their emotional states, determines their levels of motivation. The task completion rate of employees is measured by the system in the first component, and the system predicts the level of satisfaction that the employees will have. In place of linear regression, this component makes use of random forest regression, which boasts a higher degree of precision than its counterpart. The performance of workers on their tasks will be evaluated periodically, about once every fifteen minutes, and the results will be shown on a dashboard. The system will pick up on the emotions of the staff members throughout the second phase of the process. These characteristics will be used to assess the level of motivation inside the organization, with the end goal of increasing overall productivity. The accuracy of this emotion detection will also be checked periodically, namely once every fifteen minutes. The following part of the process monitors the use of the PC and calculates the level of productivity. It will be possible to get an increase in productivity if one monitors and keeps track of the application usage of each employee. The final components monitor the websites that employees visit and how they use the network. This component makes it easier to generate reports based on the utilization of the internet and the network, as well as information on performance and reports that summarize website traffic. When it is fully operational as an integrated system, most businesses will rely on this system as their primary driver of success.
Embargo
Stress Analysis and Care Prediction System for Online Workers
(IEEE, 2021-12-09) Amarasinghe, A. A. S. M; Malassri, I. M. S; Weerasinghe, K. C. N; Jayasingha, I. B; Abeygunawardhana, P. K. W; Silva, S
Working from home (WFH) online during the covid-19 pandemic has caused increased stress level. Online workers/students have been affecting by the crisis according to new researches. Natural response of body, to external and internal stimuli is stress. Even though stress is a natural occurrence, prolonged exposure while working Online to stressors can lead to serious health problems if any action will not be applied to control it. Our research has been conducted deeply to identify the best parameters, which have connection with stress level of online workers. As a result of our research, a desktop application has been created to identify the users stress level in real time. According to the results, our overall system was able to provide outputs with more than 70% accuracy. It will give best predictions to avoid the health problems. Our main goal is to provide best solution for the online workers to have healthy lifestyles. Updates for the users will be provided according to the feedback we will have in the future from the users. Our System will be a most valuable application in the future among online workers.
Embargo
TrendiTex: An Intelligent Fashion Designer
(IEEE, 2019-12-05) Wickramarathne, P; De Silva, M; Weerasinghe, C; Nanayakkara, H; Abeygunawardhana, P. K. W; Silva, S
In a highly changing technical era, Intelligent Fashion Designing systems play a key role to bridge the gap between fashion designers and the customers. Most of the people specially females, are fond of fashion. Currently, fashion has become a way of defining a person's preferences and personality. Analyzing through a large number of fashion trends and selecting a one among them will be a highly time-consuming task. Even though most of the people are keen on fashion, with their busy schedules, spending time on selecting a cloth for an occasion among numerous numbers of designs available is a hard task. Therefore, it would be exhausting to select a proper design for an occasion for them. Prevailing the difficulty in finding the clothes up to the user's expectation, we propose a user-friendly fashion designing mobile application and a web application called "TrendiTex". Extracting user preference details, user's body shape predicting and recommending trending fashion designs according to their shape, generating the unique 2D new fashionable design for a specific event and the augmented fit-on facility are implemented in TrendiTex. This system represents an efficient approach to design new unique products according to user's preferences and gives augmented fit-on facility.