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
Jayanthy, S.; Selvaganesh, M.; Kumar, S. Sakthi; Sathish, A. Manjunatha; Sabarisan, K. M.; Arasi, T. Senthamil
Generative AI Solution for CNC Machines and Robotics Code Generation Proceedings Article
In: Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 9798331536695 (ISBN).
Abstract | Links | BibTeX | Tags: Adaptive control systems, Adversarial networks, Automated Code Generation, Automatic programming, CNC machine, CNC Machines, CNC system, Codegeneration, Computer aided instruction, Computer control, Computer control systems, E-Learning, Edge computing, Federated learning, Flow control, GANs, Generative pre-trained transformer transformer, GPT Transformers, Industrial research, Industry 4.0, Innovative approaches, Intelligent robots, Learning algorithms, Personnel training, Reinforcement Learning, Reinforcement learnings, Robotic systems, Simulation platform, Smart manufacturing, Virtual Reality
@inproceedings{jayanthy_generative_2025,
title = {Generative AI Solution for CNC Machines and Robotics Code Generation},
author = {S. Jayanthy and M. Selvaganesh and S. Sakthi Kumar and A. Manjunatha Sathish and K. M. Sabarisan and T. Senthamil Arasi},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105011963078&doi=10.1109%2FICCIES63851.2025.11033032&partnerID=40&md5=fb9143cd22dc48ae6c557f722cc2d6ab},
doi = {10.1109/ICCIES63851.2025.11033032},
isbn = {9798331536695 (ISBN)},
year = {2025},
date = {2025-01-01},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The advent of Industry 4.0 has revolutionized the manufacturing landscape, driving significant advancements in automation and intelligence. This study introduces an innovative approach to automated code generation for CNC and robotic systems, leveraging Generative Adversarial Networks (GANs) and GPT(Generative Pre-trained Transformer) Transformers. These AI models enable precise and optimized code creation, minimizing manual errors. Adaptive process control, achieved through Reinforcement Learning (RL), allows real-time adjustments to operational parameters, enhancing performance in dynamic environments. The incorporation of natural language processing through Transformer models facilitates intuitive operator interactions via user-friendly interfaces. Immersive Virtual Reality (VR) technologies provide high-fidelity simulation and training platforms for realistic testing and control. Additionally, collaborative learning mechanisms, achieved through Federated Learning and Edge-cloud computing, support continuous improvement and scalable deployment. Impressive outcomes were attained by the system, including 90.5% training efficiency, 98.7% coding accuracy, 95.2% adaptability, and 93.4% operator satisfaction. Experimental results validate the system's superior accuracy, adaptability, and user-centric design, showcasing its potential to revolutionize manufacturing processes. This research sets a new benchmark for intelligent, efficient, and scalable automation in the Industry 4.0 era, paving the way for transformative innovations in smart manufacturing. © 2025 Elsevier B.V., All rights reserved.},
keywords = {Adaptive control systems, Adversarial networks, Automated Code Generation, Automatic programming, CNC machine, CNC Machines, CNC system, Codegeneration, Computer aided instruction, Computer control, Computer control systems, E-Learning, Edge computing, Federated learning, Flow control, GANs, Generative pre-trained transformer transformer, GPT Transformers, Industrial research, Industry 4.0, Innovative approaches, Intelligent robots, Learning algorithms, Personnel training, Reinforcement Learning, Reinforcement learnings, Robotic systems, Simulation platform, Smart manufacturing, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
The advent of Industry 4.0 has revolutionized the manufacturing landscape, driving significant advancements in automation and intelligence. This study introduces an innovative approach to automated code generation for CNC and robotic systems, leveraging Generative Adversarial Networks (GANs) and GPT(Generative Pre-trained Transformer) Transformers. These AI models enable precise and optimized code creation, minimizing manual errors. Adaptive process control, achieved through Reinforcement Learning (RL), allows real-time adjustments to operational parameters, enhancing performance in dynamic environments. The incorporation of natural language processing through Transformer models facilitates intuitive operator interactions via user-friendly interfaces. Immersive Virtual Reality (VR) technologies provide high-fidelity simulation and training platforms for realistic testing and control. Additionally, collaborative learning mechanisms, achieved through Federated Learning and Edge-cloud computing, support continuous improvement and scalable deployment. Impressive outcomes were attained by the system, including 90.5% training efficiency, 98.7% coding accuracy, 95.2% adaptability, and 93.4% operator satisfaction. Experimental results validate the system's superior accuracy, adaptability, and user-centric design, showcasing its potential to revolutionize manufacturing processes. This research sets a new benchmark for intelligent, efficient, and scalable automation in the Industry 4.0 era, paving the way for transformative innovations in smart manufacturing. © 2025 Elsevier B.V., All rights reserved.
Zhao, P.; Wei, X.
The Role of 3D Virtual Humans in Communication and Assisting Students' Learning in Transparent Display Environments: Perspectives of Pre-Service Teachers Proceedings Article
In: Chui, K. T.; Jaikaeo, C.; Niramitranon, J.; Kaewmanee, W.; Ng, K. -K.; Ongkunaruk, P. (Ed.): pp. 319–323, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 9798331595500 (ISBN).
Abstract | Links | BibTeX | Tags: 3D virtual human, Assistive technology, CDIO teaching model, Collaborative learning, Collaborative practices, Display environments, E-Learning, Educational Technology, Engineering education, feedback, Integration, Knowledge delivery, Knowledge transfer, Learning algorithms, Natural language processing systems, Preservice teachers, Psychology computing, Student learning, Students, Teaching, Teaching model, Transparent display environment, Transparent displays, Virtual Reality
@inproceedings{zhao_role_2025,
title = {The Role of 3D Virtual Humans in Communication and Assisting Students' Learning in Transparent Display Environments: Perspectives of Pre-Service Teachers},
author = {P. Zhao and X. Wei},
editor = {K. T. Chui and C. Jaikaeo and J. Niramitranon and W. Kaewmanee and K. -K. Ng and P. Ongkunaruk},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105015746241&doi=10.1109%2FISET65607.2025.00069&partnerID=40&md5=08c39b84fa6bd6ac13ddbed203d7b1d9},
doi = {10.1109/ISET65607.2025.00069},
isbn = {9798331595500 (ISBN)},
year = {2025},
date = {2025-01-01},
pages = {319–323},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The integration of transparent display and 3D virtual human technologies into education is expanding rapidly; however, their systematic incorporation into the CDIO teaching model remains underexplored, particularly in supporting complex knowledge delivery and collaborative practice. This study developed an intelligent virtual teacher assistance system based on generative AI and conducted a teaching experiment combining transparent display and 3D virtual human technologies. Feedback was collected through focus group interviews with 24 pre-service teachers. Results show that the virtual human, through natural language and multimodal interaction, significantly enhanced classroom engagement and contextual understanding, while its real-time feedback and personalized guidance effectively supported CDIO-based collaborative learning. Nonetheless, challenges remain in contextual adaptability and emotional feedback accuracy. Accordingly, the study proposes a path for technical optimization through the integration of multimodal emotion recognition, adaptive instructional algorithms, and nonintrusive data collection, offering empirical and theoretical insights into educational technology integration within the CDIO framework and future intelligent learning tools. © 2025 Elsevier B.V., All rights reserved.},
keywords = {3D virtual human, Assistive technology, CDIO teaching model, Collaborative learning, Collaborative practices, Display environments, E-Learning, Educational Technology, Engineering education, feedback, Integration, Knowledge delivery, Knowledge transfer, Learning algorithms, Natural language processing systems, Preservice teachers, Psychology computing, Student learning, Students, Teaching, Teaching model, Transparent display environment, Transparent displays, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
The integration of transparent display and 3D virtual human technologies into education is expanding rapidly; however, their systematic incorporation into the CDIO teaching model remains underexplored, particularly in supporting complex knowledge delivery and collaborative practice. This study developed an intelligent virtual teacher assistance system based on generative AI and conducted a teaching experiment combining transparent display and 3D virtual human technologies. Feedback was collected through focus group interviews with 24 pre-service teachers. Results show that the virtual human, through natural language and multimodal interaction, significantly enhanced classroom engagement and contextual understanding, while its real-time feedback and personalized guidance effectively supported CDIO-based collaborative learning. Nonetheless, challenges remain in contextual adaptability and emotional feedback accuracy. Accordingly, the study proposes a path for technical optimization through the integration of multimodal emotion recognition, adaptive instructional algorithms, and nonintrusive data collection, offering empirical and theoretical insights into educational technology integration within the CDIO framework and future intelligent learning tools. © 2025 Elsevier B.V., All rights reserved.
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}
}
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
Cronin, I.
Apress Media LLC, 2024, ISBN: 979-886880282-9 (ISBN); 979-886880281-2 (ISBN).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Augmented Reality, Autonomous system, Autonomous systems, Business applications, Computer vision, Decision making, Gaussian Splatting, Gaussians, Generative AI, Language processing, Learning algorithms, Learning systems, machine learning, Machine-learning, Natural Language Processing, Natural Language Processing (NLP), Natural language processing systems, Natural languages, Splatting
@book{cronin_understanding_2024,
title = {Understanding Generative AI Business Applications: A Guide to Technical Principles and Real-World Applications},
author = {I. Cronin},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105001777571&doi=10.1007%2f979-8-8688-0282-9&partnerID=40&md5=c0714ff3e1ad755596426ea092b830d6},
doi = {10.1007/979-8-8688-0282-9},
isbn = {979-886880282-9 (ISBN); 979-886880281-2 (ISBN)},
year = {2024},
date = {2024-01-01},
publisher = {Apress Media LLC},
series = {Understanding Generative AI Business Applications: A Guide to Technical Principles and Real-World Applications},
abstract = {This guide covers the fundamental technical principles and various business applications of Generative AI for planning, developing, and evaluating AI-driven products. It equips you with the knowledge you need to harness the potential of Generative AI for enhancing business creativity and productivity. The book is organized into three sections: text-based, senses-based, and rationale-based. Each section provides an in-depth exploration of the specific methods and applications of Generative AI. In the text-based section, you will find detailed discussions on designing algorithms to automate and enhance written communication, including insights into the technical aspects of transformer-based Natural Language Processing (NLP) and chatbot architecture, such as GPT-4, Claude 2, Google Bard, and others. The senses-based section offers a glimpse into the algorithms and data structures that underpin visual, auditory, and multisensory experiences, including NeRF, 3D Gaussian Splatting, Stable Diffusion, AR and VR technologies, and more. The rationale-based section illuminates the decision-making capabilities of AI, with a focus on machine learning and data analytics techniques that empower applications such as simulation models, agents, and autonomous systems. In summary, this book serves as a guide for those seeking to navigate the dynamic landscape of Generative AI. Whether you’re a seasoned AI professional or a business leader looking to harness the power of creative automation, these pages offer a roadmap to leverage Generative AI for your organization’s success. © 2024 by Irena Cronin.},
keywords = {Artificial intelligence, Augmented Reality, Autonomous system, Autonomous systems, Business applications, Computer vision, Decision making, Gaussian Splatting, Gaussians, Generative AI, Language processing, Learning algorithms, Learning systems, machine learning, Machine-learning, Natural Language Processing, Natural Language Processing (NLP), Natural language processing systems, Natural languages, Splatting},
pubstate = {published},
tppubtype = {book}
}
This guide covers the fundamental technical principles and various business applications of Generative AI for planning, developing, and evaluating AI-driven products. It equips you with the knowledge you need to harness the potential of Generative AI for enhancing business creativity and productivity. The book is organized into three sections: text-based, senses-based, and rationale-based. Each section provides an in-depth exploration of the specific methods and applications of Generative AI. In the text-based section, you will find detailed discussions on designing algorithms to automate and enhance written communication, including insights into the technical aspects of transformer-based Natural Language Processing (NLP) and chatbot architecture, such as GPT-4, Claude 2, Google Bard, and others. The senses-based section offers a glimpse into the algorithms and data structures that underpin visual, auditory, and multisensory experiences, including NeRF, 3D Gaussian Splatting, Stable Diffusion, AR and VR technologies, and more. The rationale-based section illuminates the decision-making capabilities of AI, with a focus on machine learning and data analytics techniques that empower applications such as simulation models, agents, and autonomous systems. In summary, this book serves as a guide for those seeking to navigate the dynamic landscape of Generative AI. Whether you’re a seasoned AI professional or a business leader looking to harness the power of creative automation, these pages offer a roadmap to leverage Generative AI for your organization’s success. © 2024 by Irena Cronin.
Haramina, E.; Paladin, M.; Petričušić, Z.; Posarić, F.; Drobnjak, A.; Botički, I.
Learning Algorithms Concepts in a Virtual Reality Escape Room Proceedings Article
In: Babic, S.; Car, Z.; Cicin-Sain, M.; Cisic, D.; Ergovic, P.; Grbac, T. G.; Gradisnik, V.; Gros, S.; Jokic, A.; Jovic, A.; Jurekovic, D.; Katulic, T.; Koricic, M.; Mornar, V.; Petrovic, J.; Skala, K.; Skvorc, D.; Sruk, V.; Svaco, M.; Tijan, E.; Vrcek, N.; Vrdoljak, B. (Ed.): ICT Electron. Conv., MIPRO - Proc., pp. 2057–2062, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 9798350382495 (ISBN).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Computational complexity, Computer generated three dimensional environment, E-Learning, Education, Escape room, Extended reality, generative artificial intelligence, Learn+, Learning, Learning algorithms, Learning systems, Puzzle, puzzles, user experience, User study, User testing, Users' experiences, Virtual Reality
@inproceedings{haramina_learning_2024,
title = {Learning Algorithms Concepts in a Virtual Reality Escape Room},
author = {E. Haramina and M. Paladin and Z. Petričušić and F. Posarić and A. Drobnjak and I. Botički},
editor = {S. Babic and Z. Car and M. Cicin-Sain and D. Cisic and P. Ergovic and T. G. Grbac and V. Gradisnik and S. Gros and A. Jokic and A. Jovic and D. Jurekovic and T. Katulic and M. Koricic and V. Mornar and J. Petrovic and K. Skala and D. Skvorc and V. Sruk and M. Svaco and E. Tijan and N. Vrcek and B. Vrdoljak},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198221737&doi=10.1109%2FMIPRO60963.2024.10569447&partnerID=40&md5=ee56896e4128fd5a8bef03825469a46f},
doi = {10.1109/MIPRO60963.2024.10569447},
isbn = {9798350382495 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {ICT Electron. Conv., MIPRO - Proc.},
pages = {2057–2062},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Although the standard way to learn algorithms is by coding, learning through games is another way to obtain knowledge while having fun. Virtual reality is a computer-generated three-dimensional environment in which the player is fully immersed by having external stimuli mostly blocked out. In the game presented in this paper, players are enhancing their algorithms skills by playing an escape room game. The goal is to complete the room within the designated time by solving puzzles. The puzzles change for every playthrough with the use of generative artificial intelligence to provide every player with a unique experience. There are multiple types of puzzles such as. time complexity, sorting algorithms, searching algorithms, and code execution. The paper presents the results of a study indicating students' preference for learning through gaming as a method of acquiring algorithms knowledge. © 2024 Elsevier B.V., All rights reserved.},
keywords = {Artificial intelligence, Computational complexity, Computer generated three dimensional environment, E-Learning, Education, Escape room, Extended reality, generative artificial intelligence, Learn+, Learning, Learning algorithms, Learning systems, Puzzle, puzzles, user experience, User study, User testing, Users' experiences, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
Although the standard way to learn algorithms is by coding, learning through games is another way to obtain knowledge while having fun. Virtual reality is a computer-generated three-dimensional environment in which the player is fully immersed by having external stimuli mostly blocked out. In the game presented in this paper, players are enhancing their algorithms skills by playing an escape room game. The goal is to complete the room within the designated time by solving puzzles. The puzzles change for every playthrough with the use of generative artificial intelligence to provide every player with a unique experience. There are multiple types of puzzles such as. time complexity, sorting algorithms, searching algorithms, and code execution. The paper presents the results of a study indicating students' preference for learning through gaming as a method of acquiring algorithms knowledge. © 2024 Elsevier B.V., All rights reserved.
2023
Fuchs, A.; Appel, S.; Grimm, P.
Immersive Spaces for Creativity: Smart Working Environments Proceedings Article
In: Yunanto, A. A.; Ramadhani, A. D.; Prayogi, Y. R.; Putra, P. A. M.; Ruswiansari, M.; Ridwan, M.; Gamar, F.; Rahmawati, W. M.; Rusli, M. R.; Humaira, F. M.; Adila, A. F. (Ed.): IES - Int. Electron. Symp.: Unlocking Potential Immersive Technol. Live Better Life, Proceeding, pp. 610–617, Institute of Electrical and Electronics Engineers Inc., 2023, ISBN: 9798350314731 (ISBN).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Generative AI, Human computer interaction, Immersive, Innovative approaches, Intelligent systems, Interactive Environments, Language Model, Language processing, Large language model, large language models, Learning algorithms, machine learning, Natural language processing systems, Natural languages, User behaviors, User interfaces, Virtual Reality, Working environment
@inproceedings{fuchs_immersive_2023,
title = {Immersive Spaces for Creativity: Smart Working Environments},
author = {A. Fuchs and S. Appel and P. Grimm},
editor = {A. A. Yunanto and A. D. Ramadhani and Y. R. Prayogi and P. A. M. Putra and M. Ruswiansari and M. Ridwan and F. Gamar and W. M. Rahmawati and M. R. Rusli and F. M. Humaira and A. F. Adila},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173627291&doi=10.1109%2FIES59143.2023.10242458&partnerID=40&md5=897696f2c0255ecc4fb2f859581c7619},
doi = {10.1109/IES59143.2023.10242458},
isbn = {9798350314731 (ISBN)},
year = {2023},
date = {2023-01-01},
booktitle = {IES - Int. Electron. Symp.: Unlocking Potential Immersive Technol. Live Better Life, Proceeding},
pages = {610–617},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {This paper presents an innovative approach to designing an immersive space that dynamically supports users (inter-)action based on users' behavior, voice, and mood, providing a personalized experience. The objective of this research is to explore how a space can communicate with users in a seamless, engaging, and interactive environment. Therefore, it integrates natural language processing (NLP), generative artificial intelligence applications and human computer interaction that utilizes a combination of sensors, microphones, and cameras to collect real-time data on users' behavior, voice, and mood. This data is then processed and analyzed by an intelligent system that employs machine learning algorithms to identify patterns and adapt the environment accordingly. The adaptive features include changes in lighting, sound, and visual elements to facilitate creativity, focus, relaxation, or socialization, depending on the user's topics and emotional state. The paper discusses the technical aspects of implementing such a system. Additionally, it highlights the potential applications of this technology in various domains such as education, entertainment, and workplace settings. In conclusion, the immersive creative space represents a paradigm shift in human-environment interaction, offering a dynamic and personalized space that caters to the diverse needs of users. The research findings suggest that this innovative approach holds great promise for enhancing user experiences, fostering creativity, and promoting overall well-being. © 2023 Elsevier B.V., All rights reserved.},
keywords = {Artificial intelligence, Generative AI, Human computer interaction, Immersive, Innovative approaches, Intelligent systems, Interactive Environments, Language Model, Language processing, Large language model, large language models, Learning algorithms, machine learning, Natural language processing systems, Natural languages, User behaviors, User interfaces, Virtual Reality, Working environment},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper presents an innovative approach to designing an immersive space that dynamically supports users (inter-)action based on users' behavior, voice, and mood, providing a personalized experience. The objective of this research is to explore how a space can communicate with users in a seamless, engaging, and interactive environment. Therefore, it integrates natural language processing (NLP), generative artificial intelligence applications and human computer interaction that utilizes a combination of sensors, microphones, and cameras to collect real-time data on users' behavior, voice, and mood. This data is then processed and analyzed by an intelligent system that employs machine learning algorithms to identify patterns and adapt the environment accordingly. The adaptive features include changes in lighting, sound, and visual elements to facilitate creativity, focus, relaxation, or socialization, depending on the user's topics and emotional state. The paper discusses the technical aspects of implementing such a system. Additionally, it highlights the potential applications of this technology in various domains such as education, entertainment, and workplace settings. In conclusion, the immersive creative space represents a paradigm shift in human-environment interaction, offering a dynamic and personalized space that caters to the diverse needs of users. The research findings suggest that this innovative approach holds great promise for enhancing user experiences, fostering creativity, and promoting overall well-being. © 2023 Elsevier B.V., All rights reserved.