AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
2025
Wang, Z.; Aris, A.; Zhang, P.
Mobile-Driven Deep Learning Algorithm for Personalized Clothing Design Using Multi-Feature Attributes Journal Article
In: International Journal of Interactive Mobile Technologies, vol. 19, no. 18, pp. 146–160, 2025, ISSN: 18657923 (ISSN), (Publisher: International Federation of Engineering Education Societies (IFEES)).
Abstract | Links | BibTeX | Tags: Clothing design, Convolutional Neural Networks, Data privacy, Data visualization, Deep learning, E-Learning, Electronic commerce, Fashion design, Feature attributes, Hosiery manufacture, Learning algorithms, Learning platform, Learning systems, Mobile Learning, Mobile learning platform, Mobile-driven deep learning, Multi-feature attribute, multi-feature attributes, Multifeatures, Personalized clothing design, Personalized clothings, StyleFitNet, Textiles, Virtual Reality
@article{wang_mobile-driven_2025,
title = {Mobile-Driven Deep Learning Algorithm for Personalized Clothing Design Using Multi-Feature Attributes},
author = {Z. Wang and A. Aris and P. Zhang},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105017860148&doi=10.3991%2Fijim.v19i18.57239&partnerID=40&md5=de3ca359dd178d8ea59cf8da73a9c486},
doi = {10.3991/ijim.v19i18.57239},
issn = {18657923 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {International Journal of Interactive Mobile Technologies},
volume = {19},
number = {18},
pages = {146–160},
abstract = {Personalized fashion recommendation systems face significant challenges in balancing accurate style prediction, real-time mobile performance, and user privacy compliance. This study presents StyleFitNet, a novel mobile-driven deep learning framework that integrates multiple user feature attributes, including body measurements, fabric preferences, and temporal style evolution, to generate personalized clothing designs. The hybrid convolutional neural networks (CNNs)-recurrent neural networks (RNNs) architecture addresses key limitations of conventional recommendation systems by simultaneously processing spatial features and sequential preference patterns. A comprehensive evaluation demonstrates the system’s superiority in recommendation accuracy, design diversity, and user satisfaction compared to existing approaches. The implementation features GDPR-compliant data handling and a 3D virtual fitting room, significantly reducing return rates while maintaining robust privacy protections. Findings highlight the model’s ability to adapt to evolving fashion trends while preserving individual style preferences, offering both technical and business advantages for e-commerce platforms. The study concludes that StyleFitNet establishes a new standard for artificial intelligence (AI)-driven fashion recommendations, successfully merging advanced personalization with ethical data practices. Key implications include the demonstrated viability of hybrid deep learning models for mobile deployment and the importance of temporal analysis in preference modelling. Future research directions include cross-cultural validation and the integration of generative AI for enhanced visualization. © 2025 Elsevier B.V., All rights reserved.},
note = {Publisher: International Federation of Engineering Education Societies (IFEES)},
keywords = {Clothing design, Convolutional Neural Networks, Data privacy, Data visualization, Deep learning, E-Learning, Electronic commerce, Fashion design, Feature attributes, Hosiery manufacture, Learning algorithms, Learning platform, Learning systems, Mobile Learning, Mobile learning platform, Mobile-driven deep learning, Multi-feature attribute, multi-feature attributes, Multifeatures, Personalized clothing design, Personalized clothings, StyleFitNet, Textiles, Virtual Reality},
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}
Personalized fashion recommendation systems face significant challenges in balancing accurate style prediction, real-time mobile performance, and user privacy compliance. This study presents StyleFitNet, a novel mobile-driven deep learning framework that integrates multiple user feature attributes, including body measurements, fabric preferences, and temporal style evolution, to generate personalized clothing designs. The hybrid convolutional neural networks (CNNs)-recurrent neural networks (RNNs) architecture addresses key limitations of conventional recommendation systems by simultaneously processing spatial features and sequential preference patterns. A comprehensive evaluation demonstrates the system’s superiority in recommendation accuracy, design diversity, and user satisfaction compared to existing approaches. The implementation features GDPR-compliant data handling and a 3D virtual fitting room, significantly reducing return rates while maintaining robust privacy protections. Findings highlight the model’s ability to adapt to evolving fashion trends while preserving individual style preferences, offering both technical and business advantages for e-commerce platforms. The study concludes that StyleFitNet establishes a new standard for artificial intelligence (AI)-driven fashion recommendations, successfully merging advanced personalization with ethical data practices. Key implications include the demonstrated viability of hybrid deep learning models for mobile deployment and the importance of temporal analysis in preference modelling. Future research directions include cross-cultural validation and the integration of generative AI for enhanced visualization. © 2025 Elsevier B.V., All rights reserved.
2024
Do, M. D.; Dahlem, N.; Paulus, M.; Krick, M.; Steffny, L.; Werth, D.
“Furnish Your Reality” - Intelligent Mobile AR Application for Personalized Furniture Proceedings Article
In: J., Wei; G., Margetis (Ed.): Lect. Notes Comput. Sci., pp. 196–210, Springer Science and Business Media Deutschland GmbH, 2024, ISBN: 03029743 (ISSN); 978-303160457-7 (ISBN).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Augmented Reality, Augmented reality applications, Electronic commerce, Generative AI, generative artificial intelligence, Human computer interaction, Human computer interfaces, LiDAR, Mobile augmented reality, Mobile human computer interface, Mobile Human Computer Interfaces, Personalized product design, Personalized products, Phygital customer journey, Physical environments, Product design, Recommender system, Recommender systems, Sales, User centered design, User interfaces, User-centered design
@inproceedings{do_furnish_2024,
title = {“Furnish Your Reality” - Intelligent Mobile AR Application for Personalized Furniture},
author = {M. D. Do and N. Dahlem and M. Paulus and M. Krick and L. Steffny and D. Werth},
editor = {Wei J. and Margetis G.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196202642&doi=10.1007%2f978-3-031-60458-4_14&partnerID=40&md5=017510be06c286789867235cfd98bb36},
doi = {10.1007/978-3-031-60458-4_14},
isbn = {03029743 (ISSN); 978-303160457-7 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Lect. Notes Comput. Sci.},
volume = {14737 LNCS},
pages = {196–210},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Today’s online retailers are faced with the challenge of providing a convenient solution for their customers to browse through a wide range of products. Simultaneously, they must meet individual customer needs by creating unique, personalized, one-of-a-kind items. Technological advances in areas such as Augmented Reality (AR), Artificial Intelligence (AI) or sensors (e.g. LiDAR), have the potential to address these challenges by enhancing the customer experience in new ways. One option is to implement “phygital” commerce solutions, which combines the benefits of physical and digital environments to improve the customer journey. This work presents a concept for a mobile AR application that integrates LiDAR and an AI-powered recommender system to create a unique phygital customer journey in the context of furniture shopping. The combination of AR, LiDAR and AI enables an accurate immersive experience along with personalized product designs. This concept aims to deliver benefits in terms of usability, convenience, time savings and user experience, while bridging the gap between mass-produced and personalized products. The new possibilities for merging virtual with physical environments hold immense potential, but this work also highlights challenges for customers as well as for online platform providers and future researchers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},
keywords = {Artificial intelligence, Augmented Reality, Augmented reality applications, Electronic commerce, Generative AI, generative artificial intelligence, Human computer interaction, Human computer interfaces, LiDAR, Mobile augmented reality, Mobile human computer interface, Mobile Human Computer Interfaces, Personalized product design, Personalized products, Phygital customer journey, Physical environments, Product design, Recommender system, Recommender systems, Sales, User centered design, User interfaces, User-centered design},
pubstate = {published},
tppubtype = {inproceedings}
}
Today’s online retailers are faced with the challenge of providing a convenient solution for their customers to browse through a wide range of products. Simultaneously, they must meet individual customer needs by creating unique, personalized, one-of-a-kind items. Technological advances in areas such as Augmented Reality (AR), Artificial Intelligence (AI) or sensors (e.g. LiDAR), have the potential to address these challenges by enhancing the customer experience in new ways. One option is to implement “phygital” commerce solutions, which combines the benefits of physical and digital environments to improve the customer journey. This work presents a concept for a mobile AR application that integrates LiDAR and an AI-powered recommender system to create a unique phygital customer journey in the context of furniture shopping. The combination of AR, LiDAR and AI enables an accurate immersive experience along with personalized product designs. This concept aims to deliver benefits in terms of usability, convenience, time savings and user experience, while bridging the gap between mass-produced and personalized products. The new possibilities for merging virtual with physical environments hold immense potential, but this work also highlights challenges for customers as well as for online platform providers and future researchers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.