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  • Open Access

    ARTICLE

    Alternative Method of Constructing Granular Neural Networks

    Yushan Yin1, Witold Pedrycz1,2, Zhiwu Li1,*

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 623-650, 2024, DOI:10.32604/cmc.2024.048787

    Abstract Utilizing granular computing to enhance artificial neural network architecture, a new type of network emerges—the granular neural network (GNN). GNNs offer distinct advantages over their traditional counterparts: The ability to process both numerical and granular data, leading to improved interpretability. This paper proposes a novel design method for constructing GNNs, drawing inspiration from existing interval-valued neural networks built upon NNNs. However, unlike the proposed algorithm in this work, which employs interval values or triangular fuzzy numbers for connections, existing methods rely on a pre-defined numerical network. This new method utilizes a uniform distribution of information granularity to granulate connections with… More >

  • Open Access

    ARTICLE

    Probabilistic-Ellipsoid Hybrid Reliability Multi-Material Topology Optimization Method Based on Stress Constraint

    Zibin Mao1, Qinghai Zhao1,2,*, Liang Zhang1

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 757-792, 2024, DOI:10.32604/cmes.2024.048016

    Abstract This paper proposes a multi-material topology optimization method based on the hybrid reliability of the probability-ellipsoid model with stress constraint for the stochastic uncertainty and epistemic uncertainty of mechanical loads in optimization design. The probabilistic model is combined with the ellipsoidal model to describe the uncertainty of mechanical loads. The topology optimization formula is combined with the ordered solid isotropic material with penalization (ordered-SIMP) multi-material interpolation model. The stresses of all elements are integrated into a global stress measurement that approximates the maximum stress using the normalized p-norm function. Furthermore, the sequential optimization and reliability assessment (SORA) is applied to… More >

  • Open Access

    ARTICLE

    An Optimization Approach of IoD Deployment for Optimal Coverage Based on Radio Frequency Model

    Tarek Sheltami1,*, Gamil Ahmed1, Ansar Yasar2

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2627-2647, 2024, DOI:10.32604/cmes.2023.044973

    Abstract Recently, Internet of Drones (IoD) has garnered significant attention due to its widespread applications. However, deploying IoD for area coverage poses numerous limitations and challenges. These include interference between neighboring drones, the need for directional antennas, and altitude restrictions for drones. These challenges necessitate the development of efficient solutions. This research paper presents a cooperative decision-making approach for an efficient IoD deployment to address these challenges effectively. The primary objective of this study is to achieve an efficient IoD deployment strategy that maximizes the coverage region while minimizing interference between neighboring drones. In deployment problem, the interference increases as the… More >

  • Open Access

    ARTICLE

    IoT Smart Devices Risk Assessment Model Using Fuzzy Logic and PSO

    Ashraf S. Mashaleh1,2,*, Noor Farizah Binti Ibrahim1, Mohammad Alauthman3, Mohammad Almseidin4, Amjad Gawanmeh5

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2245-2267, 2024, DOI:10.32604/cmc.2023.047323

    Abstract Increasing Internet of Things (IoT) device connectivity makes botnet attacks more dangerous, carrying catastrophic hazards. As IoT botnets evolve, their dynamic and multifaceted nature hampers conventional detection methods. This paper proposes a risk assessment framework based on fuzzy logic and Particle Swarm Optimization (PSO) to address the risks associated with IoT botnets. Fuzzy logic addresses IoT threat uncertainties and ambiguities methodically. Fuzzy component settings are optimized using PSO to improve accuracy. The methodology allows for more complex thinking by transitioning from binary to continuous assessment. Instead of expert inputs, PSO data-driven tunes rules and membership functions. This study presents a… More >

  • Open Access

    ARTICLE

    Utilisation du système d’information géographique et modèle numérique de terrain dans l’analyse des caractéristiques hydro-morphométriques des sous-bassins versants de la rivière Tshopo, République démocratique du Congo

    Faidance Mashauri1,2,*, Mokili Mbuluyo1,3, Nsalambi Nkongolo2,4

    Revue Internationale de Géomatique, Vol.32, pp. 99-122, 2023, DOI:10.32604/rig.2023.044899

    Abstract L’analyse et la quantification des caractéristiques hydro-morphométriques sont essentielles pour une meilleure gestion des ressources en eau et une planification plus efficace des projets hydroélectriques dans le bassin de la Tshopo. Malheureusement, peu d’études ont été réalisées pour évaluer ces caractéristiques à l’échelle de ce bassin. Notre approche méthodologique consiste à utiliser les outils d’analyse des logiciels Système d’Information Géographique (SIG) appliqués au Modèle Numérique de Terrain (MNT) dérivé de l’image Advanced Land Observing Satellite (ALOS) World 3D-30m. Cela nous a permis d’extraire automatiquement le réseau hydrographique et de générer les sous-bassins versants de la Tshopo. Les résultats de cette… More >

  • Open Access

    ARTICLE

    Application of DSAPSO Algorithm in Distribution Network Reconfiguration with Distributed Generation

    Caixia Tao, Shize Yang*, Taiguo Li

    Energy Engineering, Vol.121, No.1, pp. 187-201, 2024, DOI:10.32604/ee.2023.042421

    Abstract With the current integration of distributed energy resources into the grid, the structure of distribution networks is becoming more complex. This complexity significantly expands the solution space in the optimization process for network reconstruction using intelligent algorithms. Consequently, traditional intelligent algorithms frequently encounter insufficient search accuracy and become trapped in local optima. To tackle this issue, a more advanced particle swarm optimization algorithm is proposed. To address the varying emphases at different stages of the optimization process, a dynamic strategy is implemented to regulate the social and self-learning factors. The Metropolis criterion is introduced into the simulated annealing algorithm to… More >

  • Open Access

    ARTICLE

    An Optimized System of Random Forest Model by Global Harmony Search with Generalized Opposition-Based Learning for Forecasting TBM Advance Rate

    Yingui Qiu1, Shuai Huang1, Danial Jahed Armaghani2, Biswajeet Pradhan3, Annan Zhou4, Jian Zhou1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2873-2897, 2024, DOI:10.32604/cmes.2023.029938

    Abstract As massive underground projects have become popular in dense urban cities, a problem has arisen: which model predicts the best for Tunnel Boring Machine (TBM) performance in these tunneling projects? However, performance level of TBMs in complex geological conditions is still a great challenge for practitioners and researchers. On the other hand, a reliable and accurate prediction of TBM performance is essential to planning an applicable tunnel construction schedule. The performance of TBM is very difficult to estimate due to various geotechnical and geological factors and machine specifications. The previously-proposed intelligent techniques in this field are mostly based on a… More >

  • Open Access

    ARTICLE

    Deep Learning-Enhanced Brain Tumor Prediction via Entropy-Coded BPSO in CIELAB Color Space

    Mudassir Khalil1, Muhammad Imran Sharif2,*, Ahmed Naeem3, Muhammad Umar Chaudhry1, Hafiz Tayyab Rauf4,*, Adham E. Ragab5

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2031-2047, 2023, DOI:10.32604/cmc.2023.043687

    Abstract Early detection of brain tumors is critical for effective treatment planning. Identifying tumors in their nascent stages can significantly enhance the chances of patient survival. While there are various types of brain tumors, each with unique characteristics and treatment protocols, tumors are often minuscule during their initial stages, making manual diagnosis challenging, time-consuming, and potentially ambiguous. Current techniques predominantly used in hospitals involve manual detection via MRI scans, which can be costly, error-prone, and time-intensive. An automated system for detecting brain tumors could be pivotal in identifying the disease in its earliest phases. This research applies several data augmentation techniques… More >

  • Open Access

    ARTICLE

    System Reliability Analysis Method Based on T-S FTA and HE-BN

    Qing Xia1, Yonghua Li2,*, Dongxu Zhang2, Yufeng Wang2

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1769-1794, 2024, DOI:10.32604/cmes.2023.030724

    Abstract For high-reliability systems in military, aerospace, and railway fields, the challenges of reliability analysis lie in dealing with unclear failure mechanisms, complex fault relationships, lack of fault data, and uncertainty of fault states. To overcome these problems, this paper proposes a reliability analysis method based on T-S fault tree analysis (T-S FTA) and Hyper-ellipsoidal Bayesian network (HE-BN). The method describes the connection between the various system fault events by T-S fuzzy gates and translates them into a Bayesian network (BN) model. Combining the advantages of T-S fault tree modeling with the advantages of Bayesian network computation, a reliability modeling method… More >

  • Open Access

    ARTICLE

    An Improved Lung Cancer Segmentation Based on Nature-Inspired Optimization Approaches

    Shazia Shamas1, Surya Narayan Panda1,*, Ishu Sharma1,*, Kalpna Guleria1, Aman Singh2,3,4, Ahmad Ali AlZubi5, Mallak Ahmad AlZubi6

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1051-1075, 2024, DOI:10.32604/cmes.2023.030712

    Abstract The distinction and precise identification of tumor nodules are crucial for timely lung cancer diagnosis and planning intervention. This research work addresses the major issues pertaining to the field of medical image processing while focusing on lung cancer Computed Tomography (CT) images. In this context, the paper proposes an improved lung cancer segmentation technique based on the strengths of nature-inspired approaches. The better resolution of CT is exploited to distinguish healthy subjects from those who have lung cancer. In this process, the visual challenges of the K-means are addressed with the integration of four nature-inspired swarm intelligent techniques. The techniques… More >

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