Table of Content

Bio-inspired Computer Modelling: Theories and Applications in Engineering and Sciences

Submission Deadline: 30 September 2022 (closed)

Guest Editors

Dr. Xiaochun Cheng, Middlesex University, UK
Prof. Mario J. Pérez Jiménez, University of Seville, Spain
Prof. Sun-Yuan Kung, Princeton University, USA

Summary

Bio-inspired computer modelling researches computerised solutions (such as data structures, algorithms, computation or visualisation operations with data, ways to control cyber physical operations, topological structures, decision support systems, multisource data communication and analysis, et al.) from the living phenomena or biological systems (such as cells, tissues, the brain, neural network, immune system, ant colony, genetic evolution, human and organisational behaviour, crowd, swarm, social network, frog, et al.). The areas of bio-inspired computer modelling include Neural Networks, Brain-inspired Computing, Neuromorphic Computing and Architectures, Cellular Automata and Cellular Neural Networks, Evolutionary Algorithms, Swarm Intelligence, Logics and Symbolic Systems, DNA and Molecular Computing, Membrane Computing, Artificial Intelligence, Machine Learning, Deep Learning. There are relevant potential applications in engineering and sciences, such as computer vision for medical engineering, pattern recognition in medicine, decision support in cybernetics, intelligent building, intelligent transportation, smart city, etc.

 

This special issue aims to attract latest research results and the latest solutions for bio-inspired computer modelling. Both theory focused and application driven studies are welcome, especially papers with good technical depth or with emerging applications in engineering and sciences.

 

Potential topics include, but are not limited to the following:

- Neural Networks

- Neuromorphic Computing and Architectures 

- Evolutionary Computing

- DNA and Molecular Computing

- Membrane Computing

- Cellular Automata and Cellular Neural Networks

- Swarm Intelligence

- Crowd Sourcing

- Artificial Immune System

- Frog Algorithm


Keywords

Neural Networks, Neuromorphic Computing, Evolutionary Computing, DNA and Molecular Computing, Membrane Computing, Swarm Intelligence, Frog Algorithm

Published Papers


  • Open Access

    ARTICLE

    Stochastic Analysis for the Dynamics of a Poliovirus Epidemic Model

    Ali Raza, Dumitru Baleanu, Zafar Ullah Khan, Muhammad Mohsin, Nauman Ahmed, Muhammad Rafiq, Pervez Anwar
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2023.023231
    (This article belongs to this Special Issue: Bio-inspired Computer Modelling: Theories and Applications in Engineering and Sciences)
    Abstract Most developing countries such as Afghanistan, Pakistan, India, Bangladesh, and many more are still fighting against poliovirus. According to the World Health Organization, approximately eighteen million people have been infected with poliovirus in the last two decades. In Asia, still, some countries are suffering from the virus. The stochastic behavior of the poliovirus through the transition probabilities and non-parametric perturbation with fundamental properties are studied. Some basic properties of the deterministic model are studied, equilibria, local stability around the stead states, and reproduction number. Euler Maruyama, stochastic Euler, and stochastic Runge-Kutta study the behavior of complex stochastic differential equations. The… More >

  • Open Access

    ARTICLE

    Using Hybrid Penalty and Gated Linear Units to Improve Wasserstein Generative Adversarial Networks for Single-Channel Speech Enhancement

    Xiaojun Zhu, Heming Huang
    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2155-2172, 2023, DOI:10.32604/cmes.2023.021453
    (This article belongs to this Special Issue: Bio-inspired Computer Modelling: Theories and Applications in Engineering and Sciences)
    Abstract Recently, speech enhancement methods based on Generative Adversarial Networks have achieved good performance in time-domain noisy signals. However, the training of Generative Adversarial Networks has such problems as convergence difficulty, model collapse, etc. In this work, an end-to-end speech enhancement model based on Wasserstein Generative Adversarial Networks is proposed, and some improvements have been made in order to get faster convergence speed and better generated speech quality. Specifically, in the generator coding part, each convolution layer adopts different convolution kernel sizes to conduct convolution operations for obtaining speech coding information from multiple scales; a gated linear unit is introduced to… More >

  • Open Access

    ARTICLE

    Application of a Parallel Adaptive Cuckoo Search Algorithm in the Rectangle Layout Problem

    Weimin Zheng, Mingchao Si, Xiao Sui, Shuchuan Chu, Jengshyang Pan
    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2173-2196, 2023, DOI:10.32604/cmes.2023.019890
    (This article belongs to this Special Issue: Bio-inspired Computer Modelling: Theories and Applications in Engineering and Sciences)
    Abstract The meta-heuristic algorithm is a global probabilistic search algorithm for the iterative solution. It has good performance in global optimization fields such as maximization. In this paper, a new adaptive parameter strategy and a parallel communication strategy are proposed to further improve the Cuckoo Search (CS) algorithm. This strategy greatly improves the convergence speed and accuracy of the algorithm and strengthens the algorithm’s ability to jump out of the local optimal. This paper compares the optimization performance of Parallel Adaptive Cuckoo Search (PACS) with CS, Parallel Cuckoo Search (PCS), Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Grey Wolf Optimizer… More >

  • Open Access

    ARTICLE

    IoMT-Cloud Task Scheduling Using AI

    Adedoyin A. Hussain, Fadi Al-Turjman
    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1345-1369, 2023, DOI:10.32604/cmes.2023.022783
    (This article belongs to this Special Issue: Bio-inspired Computer Modelling: Theories and Applications in Engineering and Sciences)
    Abstract The internet of medical things (IoMT) empowers patients to get adaptable, and virtualized gear over the internet. Task scheduling is the most fundamental problem in the IoMT-cloud since cloud execution commonly relies on it. Thus, a proposition is being made for a distinct scheduling technique to suitably meet these solicitations. To manage the scheduling issue, an artificial intelligence (AI) method known as a hybrid genetic algorithm (HGA) is proposed. The proposed AI method will be justified by contrasting it with other traditional optimization and AI scheduling approaches. The CloudSim is utilized to quantify its effect on various parameters like time,… More >

  • Open Access

    ARTICLE

    Bio-Inspired Optimal Dispatching of Wind Power Consumption Considering Multi-Time Scale Demand Response and High-Energy Load Participation

    Peng Zhao, Yongxin Zhang, Qiaozhi Hua, Haipeng Li, Zheng Wen
    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 957-979, 2023, DOI:10.32604/cmes.2022.021783
    (This article belongs to this Special Issue: Bio-inspired Computer Modelling: Theories and Applications in Engineering and Sciences)
    Abstract Bio-inspired computer modelling brings solutions from the living phenomena or biological systems to engineering domains. To overcome the obstruction problem of large-scale wind power consumption in Northwest China, this paper constructs a bio-inspired computer model. It is an optimal wind power consumption dispatching model of multi-time scale demand response that takes into account the involved high-energy load. First, the principle of wind power obstruction with the involvement of a high-energy load is examined in this work. In this step, highenergy load model with different regulation characteristics is established. Then, considering the multi-time scale characteristics of high-energy load and other demand-side… More >

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