Special lssues

Trustworthy AI and Its Applications

Submission Deadline: 31 January 2023 (closed)

Guest Editors

Dr. Man-Fai Leung, Anglia Ruskin University, Cambridge, U.K.
Dr. Wenming Cao, City University of Hong Kong, Hong Kong.
Dr. Keping Yu, Hosei University, Japan.

Summary

In the era of artificial intelligence (AI) and big data, the approaches for data analysis, information extraction, and underlying event analysis with state-of-the-art machine learning algorithms have grown radically. AI is a major research area with many real-world applications. For example, the introduction of different new techniques in machine learning and data mining provide efficient tools to assisted systems in healthcare such as medical diagnostics and patient monitoring. The techniques are widely used as a screening tool or as an aid to diagnosis so that fast and informed decisions could be made, especially those with big data sets from multiple sources. However, the black-box nature of AI hinders its applications in industry. This situation is even severely worse in complex data analytics. It is imperative to develop explainable AI models to provide safe, reliable, and efficient solutions integrated into applications. This Special Issue will accept original research and review articles on explainable AI techniques and their applications.

 

Potential topics include but are not limited to the following:

1. Explainable AI models

2. AI for daily living activities

3. Neural networks and fuzzy logic based systems

4. AI models for pattern recognition

5. Novel AI theory and algorithm

6. Intelligent wearable and assistive robotic devices

7. AI Based recommender system

8. Big data analytics


Keywords

Machine Learning; deep learning; optimization; neural networks; big data analytics; data mining; pattern recognition

Published Papers


  • Open Access

    ARTICLE

    Multi-Feature Fusion Book Recommendation Model Based on Deep Neural Network

    Zhaomin Liang, Tingting Liang
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 205-219, 2023, DOI:10.32604/csse.2023.037124
    (This article belongs to this Special Issue: Trustworthy AI and Its Applications)
    Abstract The traditional recommendation algorithm represented by the collaborative filtering algorithm is the most classical and widely recommended algorithm in the practical industry. Most book recommendation systems also use this algorithm. However, the traditional recommendation algorithm represented by the collaborative filtering algorithm cannot deal with the data sparsity well. This algorithm only uses the shallow feature design of the interaction between readers and books, so it fails to achieve the high-level abstract learning of the relevant attribute features of readers and books, leading to a decline in recommendation performance. Given the above problems, this study uses deep learning technology to model… More >

  • Open Access

    ARTICLE

    Application of Depth Learning Algorithm in Automatic Processing and Analysis of Sports Images

    Kai Yang
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 317-332, 2023, DOI:10.32604/csse.2023.037266
    (This article belongs to this Special Issue: Trustworthy AI and Its Applications)
    Abstract With the rapid development of sports, the number of sports images has increased dramatically. Intelligent and automatic processing and analysis of moving images are significant, which can not only facilitate users to quickly search and access moving images but also facilitate staff to store and manage moving image data and contribute to the intellectual development of the sports industry. In this paper, a method of table tennis identification and positioning based on a convolutional neural network is proposed, which solves the problem that the identification and positioning method based on color features and contour features is not adaptable in various… More >

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