Smart Computing and Systems Engineering - 2023 (SCSE 2023)
Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/27032
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Item Factors Influencing the Success of Software Startups in Sri Lanka: A Comparative Analysis using SmartPLS & SEMinR(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Attygalle, T.I.; Withanaarachchi, A.S.; Jayalal, S.IT industry is one of the fast-growing industries in Sri Lanka. In that industry the software development sector plays a massive role. Out of these software development firms, a considerable number of companies are startups. But compared to other countries, the contribution from software startups to the country’s economy is very low in Sri Lanka. Further with the current economic crisis Sri Lanka faces it is even harder for startups to continue their businesses and also it is challenging for an entrepreneur-minded person who wants to establish a software startup in Sri Lanka. This study focuses on the factors influencing the success of software startups in Sri Lanka and how those factors will be affected by the current economic crisis in Sri Lanka. The study has been conducted using a systematic literature review to discover and validate influential factors from past studies. Then the conceptual framework was formed to assess the variables. To validate the model, data was collected through an online questionnaire survey. Testing and validation of collected data were done using a comparative analysis between Smart PLS and SEMinR. The results of both studies show that the availability of finance is the only factor that has a significant relationship with the success of software startups in Sri Lanka. With that the study also recommends taking necessary actions to improve the availability of funds for software startup companies.Item Interpretation of Sri Lankan Sign Language: A Wearable Sensor-based Approach(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Kusalanga, M.N.; Jayalal, S.; Wijayasiriwardhane, T. K.Hearing-impaired and speech-impaired people communicate not only with themselves but also with ordinary people using visual languages. Sri Lankan Sign Language (SSL) is the standard visual language used in Sri Lanka. Like other sign languages, the SSL relies on a distinct combination of hand gestures, body movements, and facial expressions for communication. As a result, SSL is more challenging for individuals without knowledge of SSL to understand. On the other hand, the steep learning curve associated with SSL makes it even more difficult to acquire. Thus, the interpretation of SSL has become a need. However, Sri Lanka is suffering from a severe dearth in the availability of SSL interpreters. This justifies the need to use either vision- based or sensor-based technological approaches to help the interpretation of SSL. However, vision-based approaches are susceptible to conditions such as skin tone, background color, ambient light intensity, and real-time constraints, whilst the sensor-based solutions are generally better in gesture recognition. Further, there is no attempt has been made on developing a cost-effective, portable, and real-time solution to accurately interpret the hand gestures of SSL. In this paper, we, therefore, present a novel, wearable, sensor-based, real-time gesture recognition glove, and a machine-learning Long Short-Term Memory (LSTM) model to recognize the hand and finger positions in three-dimensional space for classification and interpretation of SSL. The proposed approach has achieved 320ms of lowest inference time while showing a promising result of 83% for categorical accuracy. Our aim is to help the interpretation of SSL with an affordable, portable as well as a real-time solution.