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Mathematics
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Advances and Applications of Artificial Intelligence Technologies
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Advances and Applications of Artificial Intelligence Technologies

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: closed (31 August 2024) | Viewed by 3043

Special Issue Editors

Prof. Dr. Xiaofeng Liao

E-Mail Website
Guest Editor
College of Computer Science, Chongqing University, Chongqing 400044, China
Interests: computer science; theoretical mathematics; optimization and control; machine learning; privacy protection; neural networks
Dr. Qingguo Lü

E-Mail Website
Guest Editor
College of Computer Science, Chongqing University, Chongqing 400044, China
Interests: multi-agent system; control and optimization; distributed algorithms; artificial intelligence; privacy security; smart grids; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The research landscape surrounding artificial intelligence (AI) technologies is both dynamic and promising. With the rapid evolution of AI, fueled by advances in machine learning, deep learning, and neural networks, researchers are making remarkable strides in understanding and replicating human cognitive functions. This progress has enabled AI systems to excel at various tasks, such as data mining, natural language processing, and mathematical optimization, etc. Currently, AI is increasingly integrated into our lives, and interdisciplinary collaborations and responsible AI development are gaining prominence to ensure its positive impact on society. Nevertheless, as AI applications continue to expand, the technical shortcomings alongside issues such as decision bias and safety concerns have instigated a crisis of confidence. Consequently, the research community is actively exploring cutting-edge AI technologies to enhance the dependability of these applications.

This Special Issue on “Advances and Applications of Artificial Intelligence Technologies” is aimed at propagating the latest research results and developments in AI, with special interest in its advanced theories and practical applications in mathematics, computer science, electronic information, industrial engineering, control science, communication engineering, and other fields. We kindly invite researchers and practitioners to contribute their high-quality original research or review articles discussing current cutting-edge research topics in AI. Analytical, numerical, and experimental works that contribute to the development of applications of AI technologies are welcome.

Prof. Dr. Xiaofeng Liao
Dr. Qingguo Lü
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. Mathematics 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 2600 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

  • artificial intelligence
  • mathematical optimization
  • computational intelligence
  • deep learning
  • signal processing
  • big data and data mining
  • information security
  • control theory and applications
  • neural networks
  • natural language processing

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Published Papers (3 papers)

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17 pages, 6601 KiB  
Article
Deep Learning-Driven Virtual Furniture Replacement Using GANs and Spatial Transformer Networks
by Resmy Vijaykumar, Muneer Ahmad, Maizatul Akmar Ismail, Iftikhar Ahmad and Neelum Noreen
Mathematics 2024, 12(22), 3513; https://doi.org/10.3390/math12223513 - 11 Nov 2024
Viewed by 467
Abstract
This study proposes a Generative Adversarial Network (GAN)-based method for virtual furniture replacement within indoor scenes. The proposed method addresses the challenge of accurately positioning new furniture in an indoor space by combining image reconstruction with geometric matching through combining spatial transformer networks [...] Read more.
This study proposes a Generative Adversarial Network (GAN)-based method for virtual furniture replacement within indoor scenes. The proposed method addresses the challenge of accurately positioning new furniture in an indoor space by combining image reconstruction with geometric matching through combining spatial transformer networks and GANs. The system leverages deep learning architectures like Mask R-CNN for executing image segmentation and generating masks, and it employs DeepLabv3+, EdgeConnect algorithms, and ST-GAN networks for carrying out virtual furniture replacement. With the proposed system, furniture shoppers can obtain a virtual shopping experience, providing an easier way to understand the aesthetic effects of furniture rearrangement without putting in effort to physically move furniture. The proposed system has practical applications in the furnishing industry and interior design practices, providing a cost-effective and efficient alternative to physical furniture replacement. The results indicate that the proposed method achieves accurate positioning of new furniture in indoor scenes with minimal distortion or displacement. The proposed system is limited to 2D front-view images of furniture and indoor scenes. Future work would involve synthesizing 3D scenes and expanding the system to replace furniture images photographed from different angles. This would enhance the efficiency and practicality of the proposed system for virtual furniture replacement in indoor scenes. Full article
(This article belongs to the Special Issue Advances and Applications of Artificial Intelligence Technologies)
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16 pages, 6109 KiB  
Article
A Photovoltaic Prediction Model with Integrated Attention Mechanism
by Xiangshu Lei
Mathematics 2024, 12(13), 2103; https://doi.org/10.3390/math12132103 - 4 Jul 2024
Cited by 4 | Viewed by 759
Abstract
Solar energy has become a promising renewable energy source, offering significant opportunities for photovoltaic (PV) systems. Accurate and reliable PV generation forecasts are crucial for efficient grid integration and optimized system planning. However, the complexity of environmental factors, including seasonal and daily patterns, [...] Read more.
Solar energy has become a promising renewable energy source, offering significant opportunities for photovoltaic (PV) systems. Accurate and reliable PV generation forecasts are crucial for efficient grid integration and optimized system planning. However, the complexity of environmental factors, including seasonal and daily patterns, as well as social behaviors and user habits, presents significant challenges. Traditional prediction models often struggle with capturing the complex nonlinear dynamics in multivariate time series, leading to low prediction accuracy. To address this issue, this paper proposes a new PV power prediction method that considers factors such as light, air pressure, wind direction, and social behavior, assigning different weights to them to accurately extract nonlinear feature relationships. The framework integrates long short-term memory (LSTM) and gated recurrent units (GRU) to capture local time features, while bidirectional LSTM (BiLSTM) and an attention mechanism extract global spatiotemporal relationships, effectively capturing key features related to historical output. This improves the accuracy of multi-step predictions. To verify the feasibility of the method for multivariate time series, we conducted experiments using PV power prediction as a scenario and compared the results with LSTM, CNN, BiLSTM, CNN-LSTM and GRU models. The experimental results show that the proposed method outperforms these models, with a mean absolute error (MAE) of 12.133, root mean square error (RMSE) of 14.234, mean absolute percentage error (MAPE) of 2.1%, and a coefficient of determination (R2) of 0.895. These results indicate the effectiveness and potential of the method in PV prediction tasks. Full article
(This article belongs to the Special Issue Advances and Applications of Artificial Intelligence Technologies)
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20 pages, 798 KiB  
Article
The Synchronisation Problem of Chaotic Neural Networks Based on Saturation Impulsive Control and Intermittent Control
by Zhengran Cao, Chuandong Li and Man-Fai Leung
Mathematics 2024, 12(1), 151; https://doi.org/10.3390/math12010151 - 2 Jan 2024
Cited by 1 | Viewed by 1191
Abstract
This paper primarily focuses on the chaos synchronisation analysis of neural networks (NNs) under a hybrid controller. Firstly, we design a suitable hybrid controller with saturated impulse control, combined with time-dependent intermittent control. Both controls are low-energy consumption and discrete, aligning well with [...] Read more.
This paper primarily focuses on the chaos synchronisation analysis of neural networks (NNs) under a hybrid controller. Firstly, we design a suitable hybrid controller with saturated impulse control, combined with time-dependent intermittent control. Both controls are low-energy consumption and discrete, aligning well with industrial development needs. Secondly, the saturation function in the chaotic neural network is addressed using the polyhedral representation method and the sector nonlinearity method, respectively. By integrating the Lyapunov stability theory, Jensen’s inequality, the mathematical induction method, and the inequality reduction technique, we establish suitable time-dependent Lyapunov generalised equations. This leads to the estimation of the domain of attraction and the derivation of local exponential stability conditions for the error system. The validity of the achieved theoretical criteria is eventually demonstrated through numerical experiment simulations. Full article
(This article belongs to the Special Issue Advances and Applications of Artificial Intelligence Technologies)
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