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Applied Sciences
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Application of Machine Learning and Artificial Intelligence in Human-Computer Interaction
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Application of Machine Learning and Artificial Intelligence in Human-Computer Interaction

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 August 2025 | Viewed by 2260

Special Issue Editor

Dr. Pingping Zhu

E-Mail Website
Guest Editor
Department of Computer Science and Electrical Engineering, Marshall University, Huntington, WV, USA
Interests: signal processing; machine learning; intelligent control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the evolving technological landscape, the integration of machine learning (ML) in human-computer interactions (HCIs) stands as a cornerstone in enhancing user experience and interaction efficiency. This Special Issue explores innovative research and practical applications that bridge ML and HCI, illuminating how these technologies shape and transform user interfaces and experiences and interaction models.

We invite original research articles, case studies, and comprehensive reviews that address, but are not limited to, the following topics:

  • Natural Language Processing (NLP) in HCI: Studies on using NLP to improve voice- and text-based interfaces.
  • Ethics of AI in HCI: Critical analyses of ethical issues, including privacy, consent, and bias, in machine learning applied to HCI.
  • Interactive Machine Learning: Studies on systems that allow users to interactively train and refine machine learning models, enhancing personalized learning and adaptation.
  • Augmented and Virtual Reality: Papers on integrating ML in AR/VR to improve immersion, interaction, and the personalization of virtual environments.
  • Human–Swarm Interaction: Exploring theoretical and practical models for effective communication and control strategies between humans and swarms of robots.
  • Cooperative Control and Decision Making: Studying how humans and multiple-agent systems (MASs) can make decisions collaboratively, especially in scenarios where complex, dynamic tasks are divided among agents and humans.
  • Adaptive and Learning Agents: Exploring how agents in a multi-agent system can learn from interactions with humans and adapt their behavior to improve cooperation and performance.
  • Human-Centered Computing: Studying the design, development, and deployment of mixed-initiative human-computer systems.

Dr. Pingping Zhu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • human–computer interaction
  • machine learning
  • human–swarm interaction
  • human-centered computing

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Published Papers (3 papers)

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Research

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15 pages, 717 KiB  
Article
Integration of Causal Models and Deep Neural Networks for Recommendation Systems in Dynamic Environments: A Case Study in StarCraft II
by Fernando Moreira, Jairo Ivan Velez-Bedoya and Jeferson Arango-López
Appl. Sci. 2025, 15(8), 4263; https://doi.org/10.3390/app15084263 - 12 Apr 2025
Viewed by 224
Abstract
In the context of real-time strategy video games like StarCraft II, strategic decision-making is a complex challenge that requires adaptability and precision. This research creates a mixed recommendation system that uses causal models and deep neural networks to improve its ability to suggest [...] Read more.
In the context of real-time strategy video games like StarCraft II, strategic decision-making is a complex challenge that requires adaptability and precision. This research creates a mixed recommendation system that uses causal models and deep neural networks to improve its ability to suggest the best strategies based on the resources and conditions of the game. PySC2 and the official StarCraft II API collected data from 100 controlled matches, standardizing conditions with the Terran race. We created fake data using a Conditional Tabular Generative Adversarial Network to address data scarcity situations. These data were checked for accuracy using Kolmogorov–Smirnov tests and correlation analysis. The causal model, implemented with PyMC, captured key causal relationships between variables such as resources, military units, and strategies. These predictions were integrated as additional features into a deep neural network trained with PyTorch. The results show that the hybrid system is 1.1% more accurate and has a higher F1 score than a pure neural network. It also changes its suggestions based on the resources it has access to. However, certain limitations were identified, such as a bias toward offensive strategies in the original data. This approach highlights the potential of combining causal knowledge with machine learning for recommendation systems in dynamic environments. Full article
(This article belongs to the Special Issue Application of Machine Learning and Artificial Intelligence in Human-Computer Interaction)
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21 pages, 3928 KiB  
Article
Emotion Analysis AI Model for Sensing Architecture Using EEG
by Seung-Yeul Ji, Mi-Kyoung Kim and Han-Jong Jun
Appl. Sci. 2025, 15(5), 2742; https://doi.org/10.3390/app15052742 - 4 Mar 2025
Viewed by 1130
Abstract
The rapid advancement of artificial intelligence (AI) has spurred innovation across various domains—information technology, medicine, education, and the social sciences—and is likewise creating new opportunities in architecture for understanding human–environment interactions. This study aims to develop a fine-tuned AI model that leverages electroencephalography [...] Read more.
The rapid advancement of artificial intelligence (AI) has spurred innovation across various domains—information technology, medicine, education, and the social sciences—and is likewise creating new opportunities in architecture for understanding human–environment interactions. This study aims to develop a fine-tuned AI model that leverages electroencephalography (EEG) data to analyse users’ emotional states in real time and apply these insights to architectural spaces. Specifically, the SEED dataset—an EEG-based emotion recognition resource provided by the BCMI laboratory at Shanghai Jiao Tong University—was employed to fine-tune the ChatGPT model for classifying three emotional states (positive, neutral, and negative). Experimental results demonstrate the model’s effectiveness in differentiating these states based on EEG signals, although the limited number of participants confines our findings to a proof of concept. Furthermore, to assess the feasibility of the proposed approach in real architectural contexts, we integrated the model into a 360° virtual reality (VR) setting, where it showed promise for real-time emotion recognition and adaptive design. By combining AI-driven biometric data analysis with user-centred architectural design, this study aims to foster sustainable built environments that respond dynamically to human emotions. The results underscore the potential of EEG-based emotion recognition for enhancing occupant experiences and provide foundational insights for future investigations into human–space interactions. Full article
(This article belongs to the Special Issue Application of Machine Learning and Artificial Intelligence in Human-Computer Interaction)
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Other

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27 pages, 982 KiB  
Systematic Review
Artificial Intelligence Approaches for the Detection of Normal Pressure Hydrocephalus: A Systematic Review
by Luis R. Mercado-Diaz, Neha Prakash, Gary X. Gong and Hugo F. Posada-Quintero
Appl. Sci. 2025, 15(7), 3653; https://doi.org/10.3390/app15073653 - 26 Mar 2025
Viewed by 398
Abstract
Normal pressure hydrocephalus (NPH) is a neurological disorder characterized by altered cerebrospinal fluid accumulation in the brain’s ventricles, leading to symptoms such as gait disturbance and cognitive impairment. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), shows promise in diagnosing [...] Read more.
Normal pressure hydrocephalus (NPH) is a neurological disorder characterized by altered cerebrospinal fluid accumulation in the brain’s ventricles, leading to symptoms such as gait disturbance and cognitive impairment. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), shows promise in diagnosing NPH using medical images. In this systematic review, we examined 21 papers on the use of AI in detecting NPH. The studies primarily focused on differentiating NPH from other neurodegenerative disorders, such as Parkinson’s disease and Alzheimer’s disease. We found that traditional ML methods like Support Vector Machines, Random Forest, and Logistic Regression were commonly used, while DL methods, particularly Deep Convolutional Neural Networks, were also widely employed. The accuracy of these approaches varied, ranging from 70% to 95% in differentiating NPH from other conditions. Feature selection techniques were used to identify relevant parameters for diagnosis. MRI scans were more frequently used than CT scans, but both modalities showed promise. Evaluation metrics like Dice similarity coefficients and ROC-AUC were the most typical metrics of model performance. Challenges in implementing AI in clinical practice were identified, and the authors suggested that a hybrid deep-traditional ML framework could enhance NPH diagnosis. Further research is needed to maximize the benefits of AI while addressing limitations. Full article
(This article belongs to the Special Issue Application of Machine Learning and Artificial Intelligence in Human-Computer Interaction)
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