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
Mendoza, A. P.; Quiroga, K. J. Barrios; Celis, S. D. Solano; M., C. G. Quintero
NAIA: A Multi-Technology Virtual Assistant for Boosting Academic Environments—A Case Study Journal Article
In: IEEE Access, vol. 13, pp. 141461–141483, 2025, ISSN: 21693536 (ISSN), (Publisher: Institute of Electrical and Electronics Engineers Inc.).
Abstract | Links | BibTeX | Tags: Academic environment, Artificial intelligence, Case-studies, Computational Linguistics, Computer vision, Digital avatar, Digital avatars, Efficiency, Human computer interaction, Human-AI Interaction, Interactive computer graphics, Language Model, Large language model, large language model (LLM), Learning systems, Natural language processing systems, Personal digital assistants, Personnel training, Population statistics, Speech communication, Speech processing, Speech to text, speech to text (STT), Text to speech, text to speech (TTS), user experience, User interfaces, Virtual assistant, Virtual assistants, Virtual Reality
@article{mendoza_naia_2025,
title = {NAIA: A Multi-Technology Virtual Assistant for Boosting Academic Environments—A Case Study},
author = {A. P. Mendoza and K. J. Barrios Quiroga and S. D. Solano Celis and C. G. Quintero M.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105013598763&doi=10.1109%2FACCESS.2025.3597565&partnerID=40&md5=7ad6b037cfedb943fc026642c4854284},
doi = {10.1109/ACCESS.2025.3597565},
issn = {21693536 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Access},
volume = {13},
pages = {141461–141483},
abstract = {Virtual assistants have become essential tools for improving productivity and efficiency in various domains. This paper presents NAIA (Nimble Artificial Intelligence Assistant), an advanced multi-role and multi-task virtual assistant enhanced with artificial intelligence, designed to serve a university community case study. The system integrates AI technologies including Large Language Models (LLM), Computer Vision, and voice processing to create an immersive and efficient interaction through animated digital avatars. NAIA features five specialized roles: researcher, receptionist, personal skills trainer, personal assistant, and university guide, each equipped with specific capabilities to support different aspects of academic life. The system’s Computer Vision capabilities enable it to comment on users’ physical appearance and environment, enriching the interaction. Through natural language processing and voice interaction, NAIA aims to improve productivity and efficiency within the university environment while providing personalized assistance through a ubiquitous platform accessible across multiple devices. NAIA is evaluated through a user experience survey involving 30 participants with different demographic characteristics, this is the most accepted way by the community to evaluate this type of solution. Participants give their feedback after using one role of NAIA after using it for 30 minutes. The experiment showed that 90% of the participants considered NAIA-assisted tasks of higher quality and, on average, NAIA has a score of 4.27 out of 5 on user satisfaction. Participants particularly appreciated the assistant’s visual recognition, natural conversation flow, and user interaction capabilities. Results demonstrate NAIA’s capabilities and effectiveness across the five roles. © 2025 Elsevier B.V., All rights reserved.},
note = {Publisher: Institute of Electrical and Electronics Engineers Inc.},
keywords = {Academic environment, Artificial intelligence, Case-studies, Computational Linguistics, Computer vision, Digital avatar, Digital avatars, Efficiency, Human computer interaction, Human-AI Interaction, Interactive computer graphics, Language Model, Large language model, large language model (LLM), Learning systems, Natural language processing systems, Personal digital assistants, Personnel training, Population statistics, Speech communication, Speech processing, Speech to text, speech to text (STT), Text to speech, text to speech (TTS), user experience, User interfaces, Virtual assistant, Virtual assistants, Virtual Reality},
pubstate = {published},
tppubtype = {article}
}
Virtual assistants have become essential tools for improving productivity and efficiency in various domains. This paper presents NAIA (Nimble Artificial Intelligence Assistant), an advanced multi-role and multi-task virtual assistant enhanced with artificial intelligence, designed to serve a university community case study. The system integrates AI technologies including Large Language Models (LLM), Computer Vision, and voice processing to create an immersive and efficient interaction through animated digital avatars. NAIA features five specialized roles: researcher, receptionist, personal skills trainer, personal assistant, and university guide, each equipped with specific capabilities to support different aspects of academic life. The system’s Computer Vision capabilities enable it to comment on users’ physical appearance and environment, enriching the interaction. Through natural language processing and voice interaction, NAIA aims to improve productivity and efficiency within the university environment while providing personalized assistance through a ubiquitous platform accessible across multiple devices. NAIA is evaluated through a user experience survey involving 30 participants with different demographic characteristics, this is the most accepted way by the community to evaluate this type of solution. Participants give their feedback after using one role of NAIA after using it for 30 minutes. The experiment showed that 90% of the participants considered NAIA-assisted tasks of higher quality and, on average, NAIA has a score of 4.27 out of 5 on user satisfaction. Participants particularly appreciated the assistant’s visual recognition, natural conversation flow, and user interaction capabilities. Results demonstrate NAIA’s capabilities and effectiveness across the five roles. © 2025 Elsevier B.V., All rights reserved.
2024
Ansari, U.; Qureshi, H. A.; Soomro, N. A.; Memon, A. R.
Augmented Reality-Driven Reservoir Management Via Generative Ai: Transforming Pore-Scale Fluid Flow Simulation Proceedings Article
In: Soc. Pet. Eng. - ADIPEC, Society of Petroleum Engineers, 2024, ISBN: 9781959025498 (ISBN).
Abstract | Links | BibTeX | Tags: 'current, AI techniques, Augmented Reality, Decision making, Decisions makings, Efficiency, Finance, Fluid flow simulation, Fluid-flow, Gasoline, High-accuracy, Management tasks, Petroleum refining, Petroleum reservoir evaluation, Pore scale, Real- time, User interaction
@inproceedings{ansari_augmented_2024,
title = {Augmented Reality-Driven Reservoir Management Via Generative Ai: Transforming Pore-Scale Fluid Flow Simulation},
author = {U. Ansari and H. A. Qureshi and N. A. Soomro and A. R. Memon},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215124881&doi=10.2118%2F222865-MS&partnerID=40&md5=ff4b2666367f118e8eea8199db88315a},
doi = {10.2118/222865-MS},
isbn = {9781959025498 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Soc. Pet. Eng. - ADIPEC},
publisher = {Society of Petroleum Engineers},
abstract = {The current revolution of generative artificial intelligence is transforming global dynamics which is also essential to petroleum engineers for effectively completing technical tasks.Henceforth the main aim of this study is to investigate the application of generative AI techniques for improving the efficiency of petroleum reservoir management.The outcomes of this study will help in developing and implementing generative AI algorithms tailored for reservoir management tasks, including reservoir modeling, production optimization, and decision support.In this study generative AI technique is employed to integrate with augmented reality (AR) to enhance reservoir management.The methodology involves developing a generative AI model to simulate pore-scale fluid flow, validated against experimental data.AR is utilized to visualize and interact with the simulation results in a real-time, immersive environment.The integration process includes data preprocessing, model training, and AR deployment.Performance metrics such as accuracy, computational efficiency, and user interaction quality are evaluated to assess the effectiveness of the proposed approach in transforming traditional reservoir management practices.The developed generative AI model demonstrated high accuracy in simulating pore-scale fluid flow, closely matching experimental data with a correlation coefficient of 0.95.The AR interface provided an intuitive visualization, significantly improving user comprehension and decision-making efficiency.Computational efficiency was enhanced by 40% compared to traditional methods, enabling real-time simulations and interactions.Moreover, it was observed that Users found the AR-driven approach more engaging and easier to understand, with a reported 30% increase in correct decision-making in reservoir management tasks.The integration of generative AI with AR allowed for dynamic adjustments and immediate feedback, which was particularly beneficial in complex scenarios requiring rapid analysis and response.Concludingly, the combination of generative AI and AR offers a transformative approach to reservoir management, enhancing both the accuracy of simulations and the effectiveness of user interactions.This methodology not only improves computational efficiency but also fosters better decision-making through immersive visualization.Future work will focus on refining the AI model and expanding the AR functionalities to cover a broader range of reservoir conditions and management strategies.This study introduces a novel integration of generative AI and augmented reality (AR) for reservoir management, offering a pioneering approach to pore-scale fluid flow simulation.By combining high-accuracy AI-driven simulations with real-time, immersive AR visualizations, this methodology significantly enhances user interaction and decision-making efficiency.This innovative framework transforms traditional practices, providing a more engaging, efficient, and accurate tool for managing complex reservoir systems. © 2025 Elsevier B.V., All rights reserved.},
keywords = {'current, AI techniques, Augmented Reality, Decision making, Decisions makings, Efficiency, Finance, Fluid flow simulation, Fluid-flow, Gasoline, High-accuracy, Management tasks, Petroleum refining, Petroleum reservoir evaluation, Pore scale, Real- time, User interaction},
pubstate = {published},
tppubtype = {inproceedings}
}
The current revolution of generative artificial intelligence is transforming global dynamics which is also essential to petroleum engineers for effectively completing technical tasks.Henceforth the main aim of this study is to investigate the application of generative AI techniques for improving the efficiency of petroleum reservoir management.The outcomes of this study will help in developing and implementing generative AI algorithms tailored for reservoir management tasks, including reservoir modeling, production optimization, and decision support.In this study generative AI technique is employed to integrate with augmented reality (AR) to enhance reservoir management.The methodology involves developing a generative AI model to simulate pore-scale fluid flow, validated against experimental data.AR is utilized to visualize and interact with the simulation results in a real-time, immersive environment.The integration process includes data preprocessing, model training, and AR deployment.Performance metrics such as accuracy, computational efficiency, and user interaction quality are evaluated to assess the effectiveness of the proposed approach in transforming traditional reservoir management practices.The developed generative AI model demonstrated high accuracy in simulating pore-scale fluid flow, closely matching experimental data with a correlation coefficient of 0.95.The AR interface provided an intuitive visualization, significantly improving user comprehension and decision-making efficiency.Computational efficiency was enhanced by 40% compared to traditional methods, enabling real-time simulations and interactions.Moreover, it was observed that Users found the AR-driven approach more engaging and easier to understand, with a reported 30% increase in correct decision-making in reservoir management tasks.The integration of generative AI with AR allowed for dynamic adjustments and immediate feedback, which was particularly beneficial in complex scenarios requiring rapid analysis and response.Concludingly, the combination of generative AI and AR offers a transformative approach to reservoir management, enhancing both the accuracy of simulations and the effectiveness of user interactions.This methodology not only improves computational efficiency but also fosters better decision-making through immersive visualization.Future work will focus on refining the AI model and expanding the AR functionalities to cover a broader range of reservoir conditions and management strategies.This study introduces a novel integration of generative AI and augmented reality (AR) for reservoir management, offering a pioneering approach to pore-scale fluid flow simulation.By combining high-accuracy AI-driven simulations with real-time, immersive AR visualizations, this methodology significantly enhances user interaction and decision-making efficiency.This innovative framework transforms traditional practices, providing a more engaging, efficient, and accurate tool for managing complex reservoir systems. © 2025 Elsevier B.V., All rights reserved.