Browsing by Author "Perera, S."

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item: Article-Full-text
Exoskeletons for manual handling: A Scoping review
(IEEE, 2023) Perera, S.; Widanage, K. N. D.; Ranaweera, R. K. P. S.; Wijegunawardana, I. D.; Gopura, R. A. R. C.
The prevalence of work-related musculoskeletal disorders is a common issue in many occupations involving manual handling activities. In order to aid manual workers in reducing the burden on the musculoskeletal system, various wearable robotic technologies have been developed over the years. An increase in research work on wearable technologies has been observed, particularly in the last decade. In that context, this article presents a comprehensive review and a bibliometric analysis of the recorded occupational exoskeletons for manual handling since 2010. The review is aimed at identifying the paradigm shifts of research in the recent past and associating the trends pertaining to the applications, mechanisms, and control systems in the development of wearable devices for manual handling. The scope of the review limits itself to active and passive exoskeletons designed to support the upper extremity, lower extremity, and spine for performing load lifting, load carrying, or static holding. The analysis of the results revealed the emerging trends with the aim of providing researchers with areas for improvement and suggestions for different clusters of devices.
item: Conference-Full-text
Selecting a suitable variable combination to predict stock prices using support vector machines
(Business Research Unit (BRU), 2021-12-03) Indikadulle, P.; Hendahewa, D.; Perera, N.; De Meraal, D.; Perera, S.; Rathnayake, S.
With technological development, trading in stock markets has become more accessible to the general public. However, owing to the highly volatile nature of stock prices, stock price predictions remain a challenging task. Literature shows Support Vector Machines as a promising technique. This paper aims at identifying the best variable combination to predict the stock prices using Support Vector Machines along with the application of forward filling and linear interpolation as data filling methods and random search and grid search as hyper parameter optimization methods. After the individual evaluation of all models, data filling method of linear interpolation, hyper parameter optimization method of grid search and independent variable combinations with adjusted close price are found to give better results for prediction of stock prices.
item: Conference-Full-text
Sri Lanka's logistics and maritime industry: the gender wage inequality in Sri Lanka’s logistics and maritime industry
(Sri Lanka Society of Transport and Logistics, 2024) Perera, S.; Nayanalochana, C.; Gunaruwan, T. L.
This study investigates the impact of gender in determining the earnings in Sri Lanka's logistics and maritime industry, a crucial sector for the country's economy. The importance of this research is underscored by the logistics and maritime industry's significant role in economic development and the broader context of gender wage disparities. Previous research in Sri Lanka has shown a persistent gender pay gap in the labor force, even though females often have higher educational achievements. However, no specific studies have addressed the logistics and maritime sectors in Sri Lanka, though a few have examined the overall labor force. This study fills that gap by employing Ordinary Least Squares (OLS) regression to identify wage determinants and the Oaxaca-Blinder decomposition method to analyze the wage gap. Using data from 400 participants with a 97.5 percent response rate, the OLS results reveal that being female is associated with earning approximately 0.16 log points less per hour than a male counterpart. Decomposition analysis further shows that out of the overall wage difference of 0.3624 log points, 53.3 percent can be explained by productive characteristics such as education and experience, while 46.7 percent remains unexplained, suggesting potential gender-based discrimination. The fact that the explained portion of the wage gap is significantly higher indicates that productive characteristics have a more substantial impact on wage disparities.