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Applied Sciences
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Recent Advances in Hybrid Artificial Intelligence
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Recent Advances in Hybrid Artificial Intelligence

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

Deadline for manuscript submissions: closed (20 August 2023) | Viewed by 1929

Special Issue Editors

Dr. Ruibin Feng

E-Mail Website
Guest Editor
Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Mailcode 5687, 453 Quarry Road, Palo Alto, CA 94304, USA
Interests: machine learning; medical imaging; cardiovascular medicine
Special Issues, Collections and Topics in MDPI journals
Dr. Hongming Shan

E-Mail Website
Guest Editor
Institute of Science and Technology for Brain-inspired Intelligence and the MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China
Interests: machine learning; medical imaging; computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Hybrid artificial intelligence (AI) is a field that seeks to synergize the best aspects of neural networks and other domain techniques, such as symbolic AI and human knowledge. The hybrid AI model not only utilizes neural networks' ability to extract patterns from massive data sets, but also incorporates symbolic AI techniques and human knowledge to overcome the limitations of neural networks. By combining the strengths of different approaches, hybrid AI is expected to address the most challenging problems in the field by capturing, mapping, structuring, and delivering knowledge or data in an understandable, readable, and "machine-retrievable" format.

This Special Issue aims to highlight the recent advances in hybrid AI, including both developments in individual domains and innovative hybrid AI techniques that incorporate domain knowledge or symbolic AI from both theoretical and practical perspectives. We welcome original research papers and comprehensive literature reviews that provide unique scientific insights into the topic. We encourage submissions that showcase hybrid AI's potential to transform various domains, including healthcare, finance, engineering, etc. Furthermore, this Special Issue aims to foster discussions and collaborations among researchers and practitioners to push the boundaries of hybrid AI further and accelerate its applications in real-world scenarios. We welcome papers that present innovative applications of hybrid AI in industry or academia, as well as those that demonstrate the scalability, interpretability, and generalization of hybrid AI models. Ultimately, we believe that this Special Issue will provide a platform for researchers to exchange ideas, share best practices, and shape the future of hybrid AI.

Dr. Ruibin Feng
Dr. Hongming Shan
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.

Keywords

  • hybrid AI
  • machine learning
  • deep learning
  • neural networks
  • nature intelligence
  • domain knowledge
  • data analysis
  • pattern recognition
  • symbolic AI
  • interpretable AI
  • generalizable AI
  • scalable AI

Published Papers (2 papers)

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Research

14 pages, 1916 KiB  
Article
A Stacking Model-Based Classification Algorithm Is Used to Predict Social Phobia
by Changchang Li, Botao Xu, Zhiwei Chen, Xiaoou Huang, Jing (Selena) He and Xia Xie
Appl. Sci. 2024, 14(1), 433; https://doi.org/10.3390/app14010433 - 03 Jan 2024
Viewed by 602
Abstract
University students, as a special group, face multiple psychological pressures and challenges, making them susceptible to social anxiety disorder. However, there are currently no articles using machine learning algorithms to identify predictors of social anxiety disorder in university students. This study aims to [...] Read more.
University students, as a special group, face multiple psychological pressures and challenges, making them susceptible to social anxiety disorder. However, there are currently no articles using machine learning algorithms to identify predictors of social anxiety disorder in university students. This study aims to use a stacked ensemble model to predict social anxiety disorder in university students and compare it with other machine learning models to demonstrate the effectiveness of the proposed model. AUC and F1 are used as classification evaluation metrics. The experimental results show that in this dataset, the model combining logistic regression, Naive Bayes, and KNN algorithms as the first layer and Naive Bayes as the second layer performs better than traditional machine learning algorithms. This provides a new approach to studying social anxiety disorder. Full article
(This article belongs to the Special Issue Recent Advances in Hybrid Artificial Intelligence)
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16 pages, 423 KiB  
Article
A Comparative Study on the Schedulability of the EDZL Scheduling Algorithm on Multiprocessors
by Sangchul Han, Woojin Paik, Myeong-Cheol Ko and Minkyu Park
Appl. Sci. 2023, 13(18), 10131; https://doi.org/10.3390/app131810131 - 08 Sep 2023
Viewed by 517
Abstract
As multiprocessor (or multicore) real-time systems become popular, there has been much research on multiprocessor real-time scheduling algorithms. This work evaluates EDZL (Earliest Deadline until Zero Laxity), a scheduling algorithm for real-time multiprocessor systems. First, we compare the performance of EDZL schedulability tests. [...] Read more.
As multiprocessor (or multicore) real-time systems become popular, there has been much research on multiprocessor real-time scheduling algorithms. This work evaluates EDZL (Earliest Deadline until Zero Laxity), a scheduling algorithm for real-time multiprocessor systems. First, we compare the performance of EDZL schedulability tests. We measure and compare the ratio of task sets admitted by each test. We also investigate the dominance between EDZL schedulability tests and discover that the union of the demand-based test and the utilization-based test is an effective combination. Second, we compare the schedulability of EDZL and EDF(k). We prove that the union of the EDZL schedulability tests dominates the EDF(k) schedulability test, i.e., the union of the EDZL schedulability tests can admit all task sets admitted by the EDF(k) schedulability test. We also compare the schedulability of EDZL and EDF(k) through scheduling simulation by measuring the ratio of successfully scheduled task sets. EDZL can successfully schedule 7.0% more task sets than EDF(k). Full article
(This article belongs to the Special Issue Recent Advances in Hybrid Artificial Intelligence)
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