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 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},
pubstate = {published},
tppubtype = {article}
}
2023
Banafa, A.
Transformative AI: Responsible, Transparent, and Trustworthy AI Systems Book
River Publishers, 2023, ISBN: 978-877004018-1 (ISBN); 978-877004019-8 (ISBN).
Abstract | Links | BibTeX | Tags: 5G, Affective Computing, AI, AI Ethics, Alexa, Augment Reality, Autoencoders, Autonomous Cars, Autoregressive models, Big Data, Big Data Analytics, Bitcoin, Blockchain, C3PO, ChatGPT, Cloud computing, CNN, Computer vision, Conditional Automation, Convolutional Neural Networks, Cryptocurrency, Cybersecurity, Deep learning, Digital transformation, Driver Assistance, Driverless Cars, Entanglement, Ethereum, Explainable AI. Environment and sustainability, Facebook, Facial Recognition, Feedforward. Neural Networks, Fog Computing, Full Automation, General AI, Generative Adversarial Networks (GANs), Generative AI, Google, High Automation, Hybrid Blockchain, IEEE, IIoT, Industrial Internet of Things, Internet of Things, IoT, Jarvis, Long Short-Term Memory Networks, LTE, Machin Learning, Microsoft, Narrow AI, Natural Language Generation (NLG), Natural Language Processing (NLP), NetFlix, Network Security, Neural Networks, NYTimes, Open Source, Partial Automation, PayPal, Private Blockchain, Private Cloud Computing, Quantum Communications, Quantum Computing, Quantum Cryptography, Quantum Internet. Wearable Computing Devices (WCD). Autonomic Computing, Quantum Machine Learning (QML), R2D2, Reactive Machines . Limited Memory, Recurrent Neural Networks, Robots, Sci-Fi movies, Self-Aware, Siri, Small Data, Smart Contracts. Hybrid Cloud Computing, Smart Devices, Super AI, Superposition, Theory of Mind, Thick Data, Twitter, Variational Autoencoders (VAEs), Virtual Reality, Voice User Interface, VUI, Wearable Technology, Wi-Fi, Zero-Trust Model
@book{banafa_transformative_2023,
title = {Transformative AI: Responsible, Transparent, and Trustworthy AI Systems},
author = {A. Banafa},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180544759&partnerID=40&md5=c1fcd00f4b40e16156d9877185f66554},
isbn = {978-877004018-1 (ISBN); 978-877004019-8 (ISBN)},
year = {2023},
date = {2023-01-01},
publisher = {River Publishers},
series = {Transformative AI: Responsible, Transparent, and Trustworthy AI Systems},
abstract = {Transformative AI provides a comprehensive overview of the latest trends, challenges, applications, and opportunities in the field of Artificial Intelligence. The book covers the state of the art in AI research, including machine learning, natural language processing, computer vision, and robotics, and explores how these technologies are transforming various industries and domains, such as healthcare, finance, education, and entertainment. The book also addresses the challenges that come with the widespread adoption of AI, including ethical concerns, bias, and the impact on jobs and society. It provides insights into how to mitigate these challenges and how to design AI systems that are responsible, transparent, and trustworthy. The book offers a forward-looking perspective on the future of AI, exploring the emerging trends and applications that are likely to shape the next decade of AI innovation. It also provides practical guidance for businesses and individuals on how to leverage the power of AI to create new products, services, and opportunities. Overall, the book is an essential read for anyone who wants to stay ahead of the curve in the rapidly evolving field of Artificial Intelligence and understand the impact that this transformative technology will have on our lives in the coming years. © 2024 River Publishers. All rights reserved.},
keywords = {5G, Affective Computing, AI, AI Ethics, Alexa, Augment Reality, Autoencoders, Autonomous Cars, Autoregressive models, Big Data, Big Data Analytics, Bitcoin, Blockchain, C3PO, ChatGPT, Cloud computing, CNN, Computer vision, Conditional Automation, Convolutional Neural Networks, Cryptocurrency, Cybersecurity, Deep learning, Digital transformation, Driver Assistance, Driverless Cars, Entanglement, Ethereum, Explainable AI. Environment and sustainability, Facebook, Facial Recognition, Feedforward. Neural Networks, Fog Computing, Full Automation, General AI, Generative Adversarial Networks (GANs), Generative AI, Google, High Automation, Hybrid Blockchain, IEEE, IIoT, Industrial Internet of Things, Internet of Things, IoT, Jarvis, Long Short-Term Memory Networks, LTE, Machin Learning, Microsoft, Narrow AI, Natural Language Generation (NLG), Natural Language Processing (NLP), NetFlix, Network Security, Neural Networks, NYTimes, Open Source, Partial Automation, PayPal, Private Blockchain, Private Cloud Computing, Quantum Communications, Quantum Computing, Quantum Cryptography, Quantum Internet. Wearable Computing Devices (WCD). Autonomic Computing, Quantum Machine Learning (QML), R2D2, Reactive Machines . Limited Memory, Recurrent Neural Networks, Robots, Sci-Fi movies, Self-Aware, Siri, Small Data, Smart Contracts. Hybrid Cloud Computing, Smart Devices, Super AI, Superposition, Theory of Mind, Thick Data, Twitter, Variational Autoencoders (VAEs), Virtual Reality, Voice User Interface, VUI, Wearable Technology, Wi-Fi, Zero-Trust Model},
pubstate = {published},
tppubtype = {book}
}
2017
Vella, Filippo; Augello, Agnese; Maniscalco, Umberto; Bentivenga, Vincenzo; Gaglio, Salvatore
Classification of Indoor Actions through Deep Neural Networks Proceedings Article
In: G., Dipanda A. Chbeir R. Gallo L. Yetongnon K. De Pietro (Ed.): Proceedings - 12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016, pp. 82–87, Institute of Electrical and Electronics Engineers Inc., 2017, ISBN: 978-1-5090-5698-9.
Abstract | Links | BibTeX | Tags: Action Recognition, Convolutional Neural Networks, Deep learning, RGB-D
@inproceedings{vellaClassificationIndoorActions2017,
title = {Classification of Indoor Actions through Deep Neural Networks},
author = { Filippo Vella and Agnese Augello and Umberto Maniscalco and Vincenzo Bentivenga and Salvatore Gaglio},
editor = { Dipanda A. Chbeir R. Gallo L. Yetongnon K. De Pietro G.},
doi = {10.1109/SITIS.2016.22},
isbn = {978-1-5090-5698-9},
year = {2017},
date = {2017-01-01},
booktitle = {Proceedings - 12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016},
pages = {82--87},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The raising number of elderly people urges the research of systems able to monitor and support people inside their domestic environment. An automatic system capturing data about the position of a person in the house, through accelerometers and RGBd cameras can monitor the person activities and produce outputs associating the movements to a given tasks or predicting the set of activities that will be executes. We considered, for the task the classification of the activities a Deep Convolutional Neural Network. We compared two different deep network and analyzed their outputs. textcopyright 2016 IEEE.},
keywords = {Action Recognition, Convolutional Neural Networks, Deep learning, RGB-D},
pubstate = {published},
tppubtype = {inproceedings}
}
Vella, Filippo; Augello, Agnese; Maniscalco, Umberto; Bentivenga, Vincenzo; Gaglio, Salvatore
Classification of Indoor Actions through Deep Neural Networks Proceedings Article
In: G., Chbeir R. Dipanda A. De Pietro (Ed.): Proceedings - 12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016, pp. 82–87, Institute of Electrical and Electronics Engineers Inc., 2017, ISBN: 978-1-5090-5698-9.
Abstract | Links | BibTeX | Tags: Action Recognition, Convolutional Neural Networks, Deep learning, RGB-D
@inproceedings{vella_classification_2017,
title = {Classification of Indoor Actions through Deep Neural Networks},
author = {Filippo Vella and Agnese Augello and Umberto Maniscalco and Vincenzo Bentivenga and Salvatore Gaglio},
editor = {Chbeir R. Dipanda A. De Pietro G.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019213644&doi=10.1109%2fSITIS.2016.22&partnerID=40&md5=329d35941a322add5df73469e33e0f07},
doi = {10.1109/SITIS.2016.22},
isbn = {978-1-5090-5698-9},
year = {2017},
date = {2017-01-01},
booktitle = {Proceedings - 12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016},
pages = {82–87},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The raising number of elderly people urges the research of systems able to monitor and support people inside their domestic environment. An automatic system capturing data about the position of a person in the house, through accelerometers and RGBd cameras can monitor the person activities and produce outputs associating the movements to a given tasks or predicting the set of activities that will be executes. We considered, for the task the classification of the activities a Deep Convolutional Neural Network. We compared two different deep network and analyzed their outputs. © 2016 IEEE.},
keywords = {Action Recognition, Convolutional Neural Networks, Deep learning, RGB-D},
pubstate = {published},
tppubtype = {inproceedings}
}