Browsing by Author "Aththidiye, R"

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Open Access
Relationship Between Resilience and Optimism Among Young Adult Undergraduates: A Cross-Sectional Study in Sri Lanka Institute of Information Technology.
(Faculty of Humanities and Sciences, SLIIT, 2023-11-01) Jayaweera, T.A; Aththidiye, R
Young adulthood is an important developmental stage in which individuals must go through a positive trajectory for a healthy and successful life. Young adult undergraduates in Sri Lanka face many stressors and adversities which affect their lives negatively. Therefore, it is important to determine the factors contributing to their resilience. Optimism is a factor that significantly enhances resilience. Moreover, there is a paucity of positive psychological and gender-sensitive research in the Sri Lankan context. In this sense, the present study aims to determine the relationship between psychological resilience and dispositional optimism among young adult undergraduates in Sri Lanka, the gender differences in resilience, and the gender differences in optimism. The instruments used were the Brief Resilience Scale (BRS) to measure resilience and the Life Orientation Test-Revised (LOT-R) to measure dispositional optimism. A total of 124 participants were involved in the study. The findings suggest that (a) there is a statistically significant moderately positive relationship between resilience and optimism (b) there are no gender differences in resilience between males and females (c) there are no gender differences in optimism among males and females. Importantly, the research provides practical implications for mental health practitioners, researchers, and positive psychologists to develop gendersensitive interventions to cope with stressors and adversities.
Open Access
A User-oriented Ensemble Method for Multi-Modal Emotion Recognition
(SLAAI - International Conference on Artificial Intelligence, 2019-12-12) Iddamalgoda, N; Thrimavithana, P; Fernando, H; Ratnayake, T; Priyadarshana, Y. H. P. P; Aththidiye, R; Kasthurirathna, D
Emotions play a vital role in mental and physical activities of human lives. One of the biggest challenges in Human-Computer Interaction is emotion recognition. With the resurgence in the fields of Artificial Intelligence and Machine learning, a considerable number of studies have been carried out in order to address the challenge of emotion recognition. The individual heterogeneity of expressing emotions is a key problem that needs to be addressed in accurately detecting the emotional state of an individual. The purpose of this work is to propose a novel ensemble method to predict the emotions using a multimodal approach. The presented multimodal approach with the modalities of facial expressions, voice variations and, speech and social media content, are used to identify seven emotional states: anger, fear, disgust, happiness, sadness, surprise and neutral emotion. In this study, for the facial expression-based emotion recognition and voice variation-based emotion recognition, Deep Neural Network models have been used, and for emotion recognition using speech and social media content, Multinomial Naïve Bayesian algorithm is used. The mentioned three modalities were integrated using a novel ensemble method that captures the heterogeneity of individuals in how they express their emotions. The proposed ensemble method was evaluated with respect to real states of human emotions of a sample user group and the experimental results suggest that the suggested ensemble method may be more accurate in recognizing emotions. Accurate recognition of emotions may have myriad applications in domains such as healthcare, advertising and human resource management.