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Applied Sciences
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Trustworthy Artificial Intelligence (AI) and Robotics
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Trustworthy Artificial Intelligence (AI) and Robotics

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

Deadline for manuscript submissions: 30 April 2024 | Viewed by 12373

Special Issue Editors

Prof. Dr. Ayşegül Uçar

E-Mail Website
Guest Editor
Department of Mechatronics Engineering, Firat University, 23119 Elâzığ, Turkey
Interests: artificial intelligence and autonomous technologies; machine learning; robot vision; autonomous vehicles and autonomous robots; Human–robot interaction; modeling and control
Dr. Sergiu Dan Stan

E-Mail Website
Guest Editor
Associate Professor, Department of Mechatronics and Machine Dynamics, Technical University of Cluj-Napoca, Cluj-Napoca, Romania
Interests: mechatronics; parallel robots; robot programming; design of mechatronic systems; CAD; CAM; mechanisms and dynamics of machines; modelling and simulation; MATLAB/Simulink; VR; optimization; genetic algorithms
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Bogdan Mocan

E-Mail Website
Guest Editor
Department of Engineering Design and Robotics, Technical University of Cluj-Napoca, Cluj-Napoca, Romania
Interests: human-robot interaction; smart manufacturing; biomedical and rehabilitation engineering; AI; intelligent control; neuro-control; fuzzy control and their applications; robotics safety; robot-assisted medicine
Special Issues, Collections and Topics in MDPI journals
Dr. Milan Banić

E-Mail Website
Guest Editor
Associate Professor, Faculty of Mechanical Engineering, University of Nis, Niš 18000, Serbia
Interests: finite element analysis; mechanical engineering; structural analysis; finite element modeling; mechanical engineering design; computer-aided design; railway; CAE
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Milos Manic

E-Mail Website
Guest Editor
Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
Interests: applied research in computational intelligence algorithms, such as artificial neural networks, fuzzy logic systems, and unsupervised learning techniques in areas of energy, cyber security, human–machine interfacing, intelligent control systems, software-defined networks, robotics/mechatronics, visualizations, and others
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The special section on Trustworthy AI and Robotics is dedicated to exploring the cutting-edge advancements and challenges in the field of artificial intelligence and robotics. The section will cover a wide range of topics, including the latest research in AI and robotics, ethical considerations, and the impact of these technologies on society. The articles in this section will provide in-depth analysis and expert insights on the current state of AI and robotics, as well as their future potential. The goal of this section is to provide readers with a comprehensive understanding of the field and its implications for the future. It will be of interest to researchers, practitioners, policymakers, and anyone with a general interest in AI and robotics.

Both theoretical and experimental studies are welcome, as well as comprehensive reviews and survey articles.

Topics of interest for this Special Issue include but are not limited to:

  • Explainable AI: methods and techniques for making AI systems more transparent and understandable to humans.
  • Ethical considerations in AI: addressing the ethical implications of AI, such as bias, privacy, and autonomy.
  • Safety and security in AI and robotics: exploring the risks and challenges of AI and robotics, and methods for mitigating them.
  • Human–robot interaction: researching how humans and robots can work together effectively and safely.
  • Robotics in industry and society: examining the impact of robotics on various industries and society as a whole.
  • AI and robotics in healthcare: exploring the potential of AI and robotics in improving healthcare outcomes.
  • AI and robotics in transportation: studying the impact of AI and robotics on transportation systems, including self-driving cars and drones.
  • AI and robotics in the economy: analyzing the impact of AI and robotics on the economy and workforce.
  • AI and robotics in education and training: examining the impact of AI and robotics on education and training.
  • AI and robotics governance: discussing the need for governance and regulation of AI and robotics.

Prof. Dr. Ayşegül Uçar
Dr. Sergiu Dan Stan
Dr. Bogdan Mocan
Dr. Milan Banić
Prof. Dr. Milos Manic
Guest Editors

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.

Published Papers (4 papers)

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Research

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30 pages, 1553 KiB  
Article
TER-CA-WGNN: Trimodel Emotion Recognition Using Cumulative Attribute-Weighted Graph Neural Network
by Hussein Farooq Tayeb Al-Saadawi and Resul Das
Appl. Sci. 2024, 14(6), 2252; https://doi.org/10.3390/app14062252 - 07 Mar 2024
Viewed by 710
Abstract
Affective computing is a multidisciplinary field encompassing artificial intelligence, natural language processing, linguistics, computer science, and social sciences. This field aims to deepen our comprehension and capabilities by deploying inventive algorithms. This article presents a groundbreaking approach, the Cumulative Attribute-Weighted Graph Neural Network, [...] Read more.
Affective computing is a multidisciplinary field encompassing artificial intelligence, natural language processing, linguistics, computer science, and social sciences. This field aims to deepen our comprehension and capabilities by deploying inventive algorithms. This article presents a groundbreaking approach, the Cumulative Attribute-Weighted Graph Neural Network, which is innovatively designed to integrate trimodal textual, audio, and visual data from the two multimodal datasets. This method exemplifies its effectiveness in performing comprehensive multimodal sentiment analysis. Our methodology employs vocal inputs to generate speaker embeddings trimodal analysis. Using a weighted graph structure, our model facilitates the efficient integration of these diverse modalities. This approach underscores the interrelated aspects of various emotional indicators. The paper’s significant contribution is underscored by its experimental results. Our novel algorithm achieved impressive performance metrics on the CMU-MOSI dataset, with an accuracy of 94% and precision, recall, and F1-scores above 92% for Negative, Neutral, and Positive emotion categories. Similarly, on the IEMOCAP dataset, the algorithm demonstrated its robustness with an overall accuracy of 93%, where exceptionally high precision and recall were noted in the Neutral and Positive categories. These results mark a notable advancement over existing state-of-the-art models, illustrating the potential of our approach in enhancing Sentiment Recognition through the synergistic use of trimodal data. This study’s comprehensive analysis and significant results demonstrate the proposed algorithm’s effectiveness in nuanced emotional state recognition and pave the way for future advancements in affective computing, emphasizing the value of integrating multimodal data for improved accuracy and robustness. Full article
(This article belongs to the Special Issue Trustworthy Artificial Intelligence (AI) and Robotics)
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16 pages, 456 KiB  
Article
Responsible AI in Farming: A Multi-Criteria Framework for Sustainable Technology Design
by Kevin Mallinger and Ricardo Baeza-Yates
Appl. Sci. 2024, 14(1), 437; https://doi.org/10.3390/app14010437 - 03 Jan 2024
Cited by 1 | Viewed by 1765
Abstract
The continuous fusion of artificial intelligence (AI) and autonomous farming machinery (e.g., drones and field robots) provides a significant shift in the daily work experience of farmers. Faced with new technological developments, many risks and opportunities arise that need to be carefully translated [...] Read more.
The continuous fusion of artificial intelligence (AI) and autonomous farming machinery (e.g., drones and field robots) provides a significant shift in the daily work experience of farmers. Faced with new technological developments, many risks and opportunities arise that need to be carefully translated into technological requirements to enable a sustainable production environment. Analyzing the complex relationship between social, ecological, and technological dependencies is a crucial step to understanding the different perspectives and systemic effects of technological functionalities. By providing a comprehensive overview of the state of the art, this article qualitatively analyzes the potential impact of AI on the autonomy of farmers and the technological developments to mitigate the risks. Fair data management practices, transparent AI approaches, and designs for an intuitive user experience are presented as key mechanisms for supporting responsible model development. Based on the defined social, technological, and ecological challenges in AI development, the knowledge to provide a high-level framework for the responsible creation of AI technologies is further systematized. By focusing on the multifaceted relationships and their effects on the autonomy of farmers, this article exemplifies the complex design decisions that must be faced in creating trustworthy and responsible AI tools. Full article
(This article belongs to the Special Issue Trustworthy Artificial Intelligence (AI) and Robotics)
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17 pages, 1375 KiB  
Article
Acceptance of Green Technology-Based Service: Consumers’ Risk-Taking Behavior in the Context of Indoor Smart Farm Restaurants
by Kyuhyeon Joo and Jinsoo Hwang
Appl. Sci. 2023, 13(20), 11433; https://doi.org/10.3390/app132011433 - 18 Oct 2023
Cited by 1 | Viewed by 990
Abstract
Smart farm technology contributes to sustainable environmental protection, and so it is important to investigate consumer behavior in this regard. Therefore, this paper constructs a theoretical model focusing on the consumers of indoor smart farm restaurants. The theoretical framework integrates the theory of [...] Read more.
Smart farm technology contributes to sustainable environmental protection, and so it is important to investigate consumer behavior in this regard. Therefore, this paper constructs a theoretical model focusing on the consumers of indoor smart farm restaurants. The theoretical framework integrates the theory of planned behavior and the perceived risk theory. The constructed framework is deepened by testing the moderating role of novelty seeking in the effects of perceived risks on attitudes. The results revealed that (1) psychological and quality risks negatively affect attitude, (2) subjective norm positively affects attitude, (3) attitudes, subjective norm, and perceived behavioral control positively affect behavioral intentions, and (4) the moderating impact of novelty seeking was discovered in the relationship between psychological risk and attitude. This is the first investigation of the perceived risks of indoor smart farm restaurants, and this study empirically proved the moderating role of novelty seeking in the risk-taking behavior context. This study consequently contributes to advancing state-of-the-art methods and presents practical marketing recommendations. Full article
(This article belongs to the Special Issue Trustworthy Artificial Intelligence (AI) and Robotics)
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Review

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35 pages, 2347 KiB  
Review
Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends
by Aysegul Ucar, Mehmet Karakose and Necim Kırımça
Appl. Sci. 2024, 14(2), 898; https://doi.org/10.3390/app14020898 - 20 Jan 2024
Viewed by 5685
Abstract
Predictive maintenance (PdM) is a policy applying data and analytics to predict when one of the components in a real system has been destroyed, and some anomalies appear so that maintenance can be performed before a breakdown takes place. Using cutting-edge technologies like [...] Read more.
Predictive maintenance (PdM) is a policy applying data and analytics to predict when one of the components in a real system has been destroyed, and some anomalies appear so that maintenance can be performed before a breakdown takes place. Using cutting-edge technologies like data analytics and artificial intelligence (AI) enhances the performance and accuracy of predictive maintenance systems and increases their autonomy and adaptability in complex and dynamic working environments. This paper reviews the recent developments in AI-based PdM, focusing on key components, trustworthiness, and future trends. The state-of-the-art (SOTA) techniques, challenges, and opportunities associated with AI-based PdM are first analyzed. The integration of AI technologies into PdM in real-world applications, the human–robot interaction, the ethical issues emerging from using AI, and the testing and validation abilities of the developed policies are later discussed. This study exhibits the potential working areas for future research, such as digital twin, metaverse, generative AI, collaborative robots (cobots), blockchain technology, trustworthy AI, and Industrial Internet of Things (IIoT), utilizing a comprehensive survey of the current SOTA techniques, opportunities, and challenges allied with AI-based PdM. Full article
(This article belongs to the Special Issue Trustworthy Artificial Intelligence (AI) and Robotics)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Design and configuration of unmanned ground vehicle with analyzing the accuracy of a RTK GPS for a field robotic Application
Author: Govindarajan
Highlights: Shared control , RTK GPS , ROS ,controls

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