Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Journal of Computing Theories and Applications Journal of Future Artificial Intelligence and Technologies
Karunanayaka, Kasun
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
A Review on the Influence of Deep Learning and Generative AI in the Fashion Industry Imtiaz, Azma; Pathirana, Nethmi; Saheel, Shakir; Karunanayaka, Kasun; Trenado, Carlos
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 3 (2024): December 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.3048-3719-29

Abstract

Incorporating deep learning models has marked a significant advancement in integrating trends and technology within the fashion industry. These models are extensively applied in the realm of image recognition, product recommendation, and trend prediction, employing deep learning techniques such as Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), and Autoencoders. This paper aims to cover various aspects of the textile industry’s supply chain processes, highlighting these deep learning techniques' present influence and potential future directions. It includes a comprehensive analysis of some of the most recent and well-recognized studies in the industry that focus on different parts of a product’s lifecycle in the industry, such as Design and Trend Forecasting, Production and Quality Control, Marketing and Sales, and Distribution and Retail. While deep learning has significantly improved the efficiency of processes across the supply chain, our review highlights some of the existing challenges, such as dependency on large datasets, manual annotation needs, and limitations in creative design generation, encouraging future research to focus on more sophisticated models incorporating multimodal data and personalized factors like body types and aesthetic preferences. Additionally, areas like sewing pattern generation, body-aware designs, and ethical sourcing are critical areas of the fashion industry that require further exploration.
Immersive Interventions for Dementia: A Narrative Review of Virtual Reality's Role in Therapy, Well-Being, and Future Care Models Samarasekara, Prathibha; Karunanayaka, Kasun; Gunathilaka, Sanjani
Journal of Computing Theories and Applications Vol. 3 No. 3 (2026): JCTA 3(3) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.15310

Abstract

Dementia is a progressive neurocognitive disorder often accompanied by behavioral and psychological symptoms such as agitation, anxiety, and depression. Pharmacological treatments provide only modest benefits while introducing significant risks, which highlights the need for safer, non-pharmacological alternatives. This literature review examines the role of virtual reality in dementia care, with a focus on its integration with therapies such as music, reminiscence, sensory stimulation, and cognitive training. Evidence from prior research suggests that virtual reality can enhance cognitive functions, reduce symptoms, and improve emotional well-being while also strengthening patient–caregiver interaction. However, challenges related to usability, accessibility, cost, and long-term effectiveness continue to limit adoption. Gaps in research, including limited cultural diversity, inconsistent reporting of intervention design, and a lack of large-scale longitudinal trials, emphasize the need for future work exploring cross-cultural feasibility and AI-driven personalization. Overall, virtual reality represents a promising and evolving non-pharmacological intervention that has the potential to transform dementia care by improving quality of life and reducing reliance on medication.
Co-Authors Gunathilaka, Sanjani Imtiaz, Azma Pathirana, Nethmi Saheel, Shakir Samarasekara, Prathibha Trenado, Carlos