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  • Open Access


    Tensile Strain Capacity Prediction of Engineered Cementitious Composites (ECC) Using Soft Computing Techniques

    Rabar H. Faraj1,*, Hemn Unis Ahmed2,3, Hardi Saadullah Fathullah4, Alan Saeed Abdulrahman2, Farid Abed5

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2925-2954, 2024, DOI:10.32604/cmes.2023.029392

    Abstract Plain concrete is strong in compression but brittle in tension, having a low tensile strain capacity that can significantly degrade the long-term performance of concrete structures, even when steel reinforcing is present. In order to address these challenges, short polymer fibers are randomly dispersed in a cement-based matrix to form a highly ductile engineered cementitious composite (ECC). This material exhibits high ductility under tensile forces, with its tensile strain being several hundred times greater than conventional concrete. Since concrete is inherently weak in tension, the tensile strain capacity (TSC) has become one of the most… More >

  • Open Access


    Metaheuristic Optimization of Time Series Models for Predicting Networks Traffic

    Reem Alkanhel1, El-Sayed M. El-kenawy2,3, D. L. Elsheweikh4, Abdelaziz A. Abdelhamid5,6, Abdelhameed Ibrahim7, Doaa Sami Khafaga8,*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 427-442, 2023, DOI:10.32604/cmc.2023.032885

    Abstract Traffic prediction of wireless networks attracted many researchers and practitioners during the past decades. However, wireless traffic frequently exhibits strong nonlinearities and complicated patterns, which makes it challenging to be predicted accurately. Many of the existing approaches for predicting wireless network traffic are unable to produce accurate predictions because they lack the ability to describe the dynamic spatial-temporal correlations of wireless network traffic data. In this paper, we proposed a novel meta-heuristic optimization approach based on fitness grey wolf and dipper throated optimization algorithms for boosting the prediction accuracy of traffic volume. The proposed algorithm More >

  • Open Access


    Improved Network Validity Using Various Soft Computing Techniques

    M. Yuvaraju*, R. Elakkiyavendan

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1465-1477, 2023, DOI:10.32604/iasc.2023.032417

    Abstract Nowadays, when a life span of sensor nodes are threatened by the shortage of energy available for communication, sink mobility is an excellent technique for increasing its lifespan. When communicating via a WSN, the use of nodes as a transmission method eliminates the need for a physical medium. Sink mobility in a dynamic network topology presents a problem for sensor nodes that have reserved resources. Unless the route is revised and changed to reflect the location of the mobile sink location, it will be inefficient for delivering data effectively. In the clustering strategy, nodes are… More >

  • Open Access


    Improving Power Quality by DSTATCOM Based DQ Theory with Soft Computing Techniques

    V. Nandagopal1,*, T. S. Balaji Damodhar2, P. Vijayapriya3, A. Thamilmaran3

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1315-1329, 2023, DOI:10.32604/iasc.2023.032039


    The development of non-linear loads at consumers has significantly impacted power supply systems. Since, the poor power quality has been found in the three-phase distribution system due to unbalanced loads, harmonic current, undesired voltage regulation, and extreme reactive power demand. To overcome this issue, Distributed STATicCOMpensator (DSTATCOM) is implemented. DSTATCOM is a shunt-connected Voltage Source Converter (VSC) that has been utilized in distribution networks to balance the bus voltage in terms of enhancing reactive power control and power factor. DSTATCOM can provide both rapid and continuous capacitive and inductive mode compensation. A rectified resistive and

    More >

  • Open Access


    Introduction to the Special Issue on Soft Computing Techniques in Materials Science and Engineering

    Panagiotis G. Asteris1,*, Danial Jahed Armaghani2, Liborio Cavaleri3, Hoang Nguyen4

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 839-841, 2023, DOI:10.32604/cmes.2023.025694

    Abstract This article has no abstract. More >

  • Open Access


    IC Pattern Based Power Factor Maximization Model for Improved Power Stabilization

    N. Hariharan1,*, Y. Sukhi2, N. Kalaiarasi1

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 401-414, 2023, DOI:10.32604/iasc.2023.030768

    Abstract The voltage fluctuation in electric circuits has been identified as key issue in different electric systems. As the usage of electricity growing in rapid way, there exist higher fluctuations in power flow. To maintain the flow or stability of power in any electric circuit, there are many circuit models are discussed in literature. However, they suffer to maintain the output voltage and not capable of maintaining power stability. To improve the performance in power stabilization, an efficient IC pattern based power factor maximization model (ICPFMM) in this article. The model is focused on improving the… More >

  • Open Access


    Novel Soft Computing Model for Predicting Blast-Induced Ground Vibration in Open-Pit Mines Based on the Bagging and Sibling of Extra Trees Models

    Quang-Hieu Tran1,2,*, Hoang Nguyen1,2, Xuan-Nam Bui1,2

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 2227-2246, 2023, DOI:10.32604/cmes.2022.021893

    Abstract This study considered and predicted blast-induced ground vibration (PPV) in open-pit mines using bagging and sibling techniques under the rigorous combination of machine learning algorithms. Accordingly, four machine learning algorithms, including support vector regression (SVR), extra trees (ExTree), K-nearest neighbors (KNN), and decision tree regression (DTR), were used as the base models for the purposes of combination and PPV initial prediction. The bagging regressor (BA) was then applied to combine these base models with the efforts of variance reduction, overfitting elimination, and generating more robust predictive models, abbreviated as BA-ExTree, BAKNN, BA-SVR, and BA-DTR. It… More >

  • Open Access


    A Hybrid Deep Features PSO-ReliefF Based Classification of Brain Tumor

    Alaa Khalid Alduraibi*

    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1295-1309, 2022, DOI:10.32604/iasc.2022.026601

    Abstract With technological advancements, deep machine learning can assist doctors in identifying the brain mass or tumor using magnetic resonance imaging (MRI). This work extracts the deep features from 18-pre-trained convolutional neural networks (CNNs) to train the classical classifiers to categorize the brain MRI images. As a result, DenseNet-201, EfficientNet-b0, and DarkNet-53 deep features trained support vector machine (SVM) model shows the best accuracy. Furthermore, the ReliefF method is applied to extract the best features. Then, the fitness function is defined to select the number of nearest neighbors of ReliefF algorithm and feature vector size. Finally, More >

  • Open Access


    Soft Computing Based Metaheuristic Algorithms for Resource Management in Edge Computing Environment

    Nawaf Alhebaishi1, Abdulrhman M. Alshareef1, Tawfiq Hasanin1, Raed Alsini1, Gyanendra Prasad Joshi2, Seongsoo Cho3, Doo Ill Chul4,*

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5233-5250, 2022, DOI:10.32604/cmc.2022.025596

    Abstract In recent times, internet of things (IoT) applications on the cloud might not be the effective solution for every IoT scenario, particularly for time sensitive applications. A significant alternative to use is edge computing that resolves the problem of requiring high bandwidth by end devices. Edge computing is considered a method of forwarding the processing and communication resources in the cloud towards the edge. One of the considerations of the edge computing environment is resource management that involves resource scheduling, load balancing, task scheduling, and quality of service (QoS) to accomplish improved performance. With this… More >

  • Open Access


    Self-Balancing Vehicle Based on Adaptive Neuro-Fuzzy Inference System

    M. L. Ramamoorthy1, S. Selvaperumal2,*, G. Prabhakar3

    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 485-497, 2022, DOI:10.32604/iasc.2022.025824

    Abstract The scope of this research is to design and fuse the sensors used in the self-balancing vehicle through Adaptive Neuro-Fuzzy Inference systems (ANFIS) algorithm to optimize the output. The self-balancing vehicle is a wheeled inverted pendulum, which is extremely complex, nonlinear and unstable. Homogeneous and Heterogeneous sensors are involved in this sensor fusion research to identify the best feasible value among them. The data fusion algorithm present inside the controller of the self-balancing vehicle makes the inputs of the homogeneous sensors and heterogeneous sensors separately for ameliorate surrounding perception. Simulation is performed by modeling the… More >

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