Forthcoming and Online First Articles

International Journal of Computational Intelligence Studies

International Journal of Computational Intelligence Studies (IJCIStudies)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Computational Intelligence Studies (8 papers in press)

Regular Issues

  • Solving differential equations with global optimization techniques   Order a copy of this article
    by Ioannis Tsoulos, Alexandros Tzallas, Dimitrios Tsalikakis 
    Abstract: The solution of differential equations finds many applications in a huge range of problems, and many techniques have been developed to approximate their solutions. For example, differential equations can be applied to physics problems, chemistry problems, economics, modelling, etc. This manuscript presents a number of global optimisation techniques that have been successfully applied to train machine learning models to approximate differential equation solutions. More specifically, two modified versions of genetic algorithms and particle swarm optimisation methods are proposed here. These methods have been successfully applied to solving ordinary differential equations and systems of differential equations as well as partial differential equations with Dirichlet boundary conditions.
    Keywords: differential equations; global optimisation; stochastic methods; machine learning.
    DOI: 10.1504/IJCISTUDIES.2024.10062128
     

Special Issue on: Interdisciplinary Applications and Technologies of Computer Vision

  • Performance prediction analysis of college aerobics course based on Back Propagation neural network   Order a copy of this article
    by Jianlin Su, Hao Zheng, Yanxi Chen 
    Abstract: To solve the problem of low accuracy of the performance prediction method of aerobics courses, the study proposes to combine the partial least squares regression (partial least squares
    Keywords: score prediction; BP neural network; relative error value; partial least squares regression.
    DOI: 10.1504/IJCISTUDIES.2022.10051976
     
  • Research on the optimal charging method of parallel power batteries for smart electric vehicles   Order a copy of this article
    by Yalin Li, Zhen Li 
    Abstract: To save charging cost on the premise of ensuring stable and efficient charging of electric vehicles, an optimal charging method for parallel power batteries of intelligent electric vehicles was proposed. Through mathematical model analysis, the RC network branch is added to construct a second-order RC equivalent circuit model. Optimise battery parameters based on CRUISE simulation platform. By optimising the control of battery current, current sharing and voltage stability are achieved. Finally, the local voltage equalising charging principle of the inverter is used to redesign the equalising charging of the electric vehicle battery. The experimental results show that the relative cost and charging stability of the proposed charging method are 0.58 and 0.62 respectively, which are better than the other two charging methods. The results show that the method can meet the requirements of intelligent electric vehicles for charging stability and efficiency, and has high application value.
    Keywords: electric vehicle; battery charging; equalisation charging; equivalent circuit model; charging power.
    DOI: 10.1504/IJCISTUDIES.2022.10051977
     
  • Research on spiking neural network in art visual image classification   Order a copy of this article
    by Yiping Zhang 
    Abstract: In the visual processing of artistic images, traditional CNN has a high resource demand, and SNN can solve this problem. The article selects SNN as the method for artistic visual image processing and combines it with CNN to simplify model training. After CNN adjustment and feedback adjustment algorithm processing, the classification accuracy of SNN can be improved. The results show that the accuracy of the adjusted CNN model is 80.25% and 79.60%, respectively, with an average training accuracy difference of 3.32%. Under the same pooling combination, the accuracy of the model with 11 and 12 iterations is 68.00% and 66.02%, respectively. The average classification accuracy of SNN is 78.80%, slightly lower than the adjusted CNN. SNN has a power consumption of approximately 0.0039 W per second in processing 742 images. The correlation classification method used in the article can reduce power consumption and has a high classification accuracy.
    Keywords: spiking neural network; SNN; convolutional neural network; CNN; artistic visual image; accuracy; classification.
    DOI: 10.1504/IJCISTUDIES.2023.10057739
     
  • Application of binocular image reconstruction method in the construction of 3D model of wooden arch corridor bridge structure   Order a copy of this article
    by Hua Deng 
    Abstract: In order to better understand the structure of wooden-arch corridor bridges and preserve cultural buildings, the study proposes a three-dimensional model construction method based on binocular image reconstruction method. Facing the problem of poor image edge feature extraction, the study uses Laplace combined with the Sobel operator (Laplace-Sobel) for image edge feature extraction. The SIFT structural feature matching algorithm is improved to eliminate the mis-matching points in stereo matching. The results show that as the active window size increases, the number of detected edge feature points gradually increases and the number of feature stereo matching pairs also increases, but the number of matching pairs is lower than the number of feature points; the number of features is searched for the most and the matching cost is the least when the neighbourhood range of pixels is 15
    Keywords: binocular image reconstruction method; three-dimensional model; wooden arch gallery bridge structure; matching algorithm.
    DOI: 10.1504/IJCISTUDIES.2023.10058108
     
  • Research on Intelligent Access Control Technology of Face Recognition Model Based on Parameter Sharing and Dense Connection   Order a copy of this article
    by Yonghua Xu 
    Abstract: This study proposes a laboratory intelligent facial recognition system based on improved CNN, which significantly improves the accuracy of facial recognition by optimising the portrait recognition algorithm, improving CNN calculation and large parameter scale, and utilising perspective projection to improve portrait effect and sample utilisation. The experimental results show that the recognition rate has been improved by 10%, the CPU usage rate is less than 100%, and the model parameters have been reduced by more than 95%. This system can effectively and accurately recognise faces, making it suitable for embedded facial recognition devices.
    Keywords: convolutional neural network; CNN; face recognition; liveness detection; intelligent access control.
    DOI: 10.1504/IJCISTUDIES.2023.10060752
     
  • New media interaction in art design based on deep learning binocular stereo vision   Order a copy of this article
    by Yongchao Liu, Ziping Zhao 
    Abstract: Advances in computer vision technology and the widespread promotion of artworks have led to a profound connection between the two. Highly imitated fakes are often found in art works, which damage consumers as well as creators’ own interests, so the study improves the accuracy of art works authenticity identification through the development of computer vision technology. The research is based on machine binocular stereo vision technology, the convolution neural network structure of single shot multi-box detector (SSD) is fused and trained to establish a high-precision object recognition model, which recognises objects by matching the feature points of binocular images. In the experiment, the SSD model has a stable loss value of 0.9 in the loss function performance test, and the overlap rate of the model is around 0.85, which indicates that the model has a high accuracy of object recognition. In the feature point matching algorithm, the parallax value of the multi-feature point fusion matching algorithm is stable in the range of 67 to 75 after filtering. The model proposed in the study has high accuracy in object recognition, which can play an important role in artwork authenticity identification.
    Keywords: single shot multi-box detector; SDD; convolutional neural network; object recognition; art design; multi-feature point fusion matching algorithm.
    DOI: 10.1504/IJCISTUDIES.2023.10060753
     
  • Nonlinear Modeling and Analysis of Stable Behavior of Robot Gait Control System Based on Image Processing Technology   Order a copy of this article
    by Jin Wang, Hui Lin, Wenbing Yang 
    Abstract: Walking stability is an important index to measure the walking performance of robot, and is the key to realise its wide application. However, the traditional gait control system has low control accuracy and long control time. Therefore, aiming at the above problems, a nonlinear modelling and analysis method of stable behaviour of robot gait control system based on image processing technology is studied and designed. The robot gait control system is composed of main control system hardware, wireless control system, robot debugging software and image pre-processing module. The hardware design of the main control system includes the DSP minimum system and the steering gear control board. The image pre-processing module pre-processes the image collected by the CCD camera to remove the noise in the image. Finally, the speed of the robot is controlled by visual servo control method, and a nonlinear sliding mode control closed-loop system of the robot is constructed. The simulation results show that the designed method has the highest accuracy of 100%, and the longest time is only 27.96 s. It has high control accuracy and control efficiency, which provides a method reference for further realising the optimal control of the robot.
    Keywords: image processing techniques; robot; gait control; stable behaviour; nonlinear analysis.
    DOI: 10.1504/IJCISTUDIES.2023.10061033