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  • Open Access


    Enhanced Metaheuristics with Machine Learning Enabled Cyberattack Detection Model

    Ahmed S. Almasoud*

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2849-2863, 2023, DOI:10.32604/iasc.2023.039718

    Abstract The Internet of Things (IoT) is considered the next-gen connection network and is ubiquitous since it is based on the Internet. Intrusion Detection System (IDS) determines the intrusion performance of terminal equipment and IoT communication procedures from IoT environments after taking equivalent defence measures based on the identified behaviour. In this background, the current study develops an Enhanced Metaheuristics with Machine Learning enabled Cyberattack Detection and Classification (EMML-CADC) model in an IoT environment. The aim of the presented EMML-CADC model is to detect cyberattacks in IoT environments with enhanced efficiency. To attain this, the EMML-CADC model primarily employs a data… More >

  • Open Access


    Advanced Guided Whale Optimization Algorithm for Feature Selection in BlazePose Action Recognition

    Motasem S. Alsawadi1,*, El-Sayed M. El-kenawy2, Miguel Rio1

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2767-2782, 2023, DOI:10.32604/iasc.2023.039440

    Abstract The BlazePose, which models human body skeletons as spatiotemporal graphs, has achieved fantastic performance in skeleton-based action identification. Skeleton extraction from photos for mobile devices has been made possible by the BlazePose system. A Spatial-Temporal Graph Convolutional Network (STGCN) can then forecast the actions. The Spatial-Temporal Graph Convolutional Network (STGCN) can be improved by simply replacing the skeleton input data with a different set of joints that provide more information about the activity of interest. On the other hand, existing approaches require the user to manually set the graph’s topology and then fix it across all input layers and samples.… More >

  • Open Access


    Structural Damage Identification Using Modal Energy and Improved Hybrid Gradient-Based Optimizer

    Nizar Faisal Alkayem1, Maosen Cao2,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.26, No.4, pp. 1-1, 2023, DOI:10.32604/icces.2023.09367

    Abstract Structural damage identification is a key engineering technique that attempts to ensure structural reliability. In this regard, one of the major intelligent approaches is the inverse analysis of structural damage using metaheuristics. By considering the recent achievements, an efficient hybrid objective function that combines the modal kinetic energy and modal strain energy is developed. The objective function aims to extract maximum modal information from the structure and overcome noisy conditions. Moreover, the original methods are usually vulnerable to the associated high multimodality and uncertainty of the inverse problem. Therefore, the particle swarm algorithm (PSO) mechanism is combined with another newly… More >

  • Open Access


    Airfoil Shape Optimisation Using a Multi-Fidelity Surrogate-Assisted Metaheuristic with a New Multi-Objective Infill Sampling Technique

    Cho Mar Aye1, Kittinan Wansaseub2, Sumit Kumar3, Ghanshyam G. Tejani4, Sujin Bureerat1, Ali R. Yildiz5, Nantiwat Pholdee1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2111-2128, 2023, DOI:10.32604/cmes.2023.028632

    Abstract This work presents multi-fidelity multi-objective infill-sampling surrogate-assisted optimization for airfoil shape optimization. The optimization problem is posed to maximize the lift and drag coefficient ratio subject to airfoil geometry constraints. Computational Fluid Dynamic (CFD) and XFoil tools are used for high and low-fidelity simulations of the airfoil to find the real objective function value. A special multi-objective sub-optimization problem is proposed for multiple points infill sampling exploration to improve the surrogate model constructed. To validate and further assess the proposed methods, a conventional surrogate-assisted optimization method and an infill sampling surrogate-assisted optimization criterion are applied with multi-fidelity simulation, while their… More > Graphic Abstract

    Airfoil Shape Optimisation Using a Multi-Fidelity Surrogate-Assisted Metaheuristic with a New Multi-Objective Infill Sampling Technique

  • Open Access


    Billiards Optimization with Modified Deep Learning for Fault Detection in Wireless Sensor Network

    Yousif Sufyan Jghef1, Mohammed Jasim Mohammed Jasim2, Subhi R. M. Zeebaree3,*, Rizgar R. Zebari4

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1651-1664, 2023, DOI:10.32604/csse.2023.037449

    Abstract Wireless Sensor Networks (WSNs) gather data in physical environments, which is some type. These ubiquitous sensors face several challenges responsible for corrupting them (mostly sensor failure and intrusions in external agents). WSNs were disposed to error, and effectual fault detection techniques are utilized for detecting faults from WSNs in a timely approach. Machine learning (ML) was extremely utilized for detecting faults in WSNs. Therefore, this study proposes a billiards optimization algorithm with modified deep learning for fault detection (BIOMDL-FD) in WSN. The BIOMDLFD technique mainly concentrates on identifying sensor faults to enhance network efficiency. To do so, the presented BIOMDL-FD… More >

  • Open Access


    Modified Metaheuristics with Weighted Majority Voting Ensemble Deep Learning Model for Intrusion Detection System

    Mahmoud Ragab1,2,*, Sultanah M. Alshammari2,3, Abdullah S. Al-Malaise Al-Ghamdi2,4

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2497-2512, 2023, DOI:10.32604/csse.2023.041446

    Abstract The Internet of Things (IoT) system has confronted dramatic growth in high dimensionality and data traffic. The system named intrusion detection systems (IDS) is broadly utilized for the enhancement of security posture in an IT infrastructure. An IDS is a practical and suitable method for assuring network security and identifying attacks by protecting it from intrusive hackers. Nowadays, machine learning (ML)-related techniques were used for detecting intrusion in IoTs IDSs. But, the IoT IDS mechanism faces significant challenges because of physical and functional diversity. Such IoT features use every attribute and feature for IDS self-protection unrealistic and difficult. This study… More >

  • Open Access


    Artificial Humming Bird Optimization with Siamese Convolutional Neural Network Based Fruit Classification Model

    T. Satyanarayana Murthy1, Kollati Vijaya Kumar2, Fayadh Alenezi3, E. Laxmi Lydia4, Gi-Cheon Park5, Hyoung-Kyu Song6, Gyanendra Prasad Joshi7, Hyeonjoon Moon7,*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1633-1650, 2023, DOI:10.32604/csse.2023.034769

    Abstract Fruit classification utilizing a deep convolutional neural network (CNN) is the most promising application in personal computer vision (CV). Profound learning-related characterization made it possible to recognize fruits from pictures. But, due to the similarity and complexity, fruit recognition becomes an issue for the stacked fruits on a weighing scale. Recently, Machine Learning (ML) methods have been used in fruit farming and agriculture and brought great convenience to human life. An automated system related to ML could perform the fruit classifier and sorting tasks previously managed by human experts. CNN’s (convolutional neural networks) have attained incredible outcomes in image classifiers… More >

  • Open Access


    Leveraging Gradient-Based Optimizer and Deep Learning for Automated Soil Classification Model

    Hadeel Alsolai1, Mohammed Rizwanullah2,*, Mashael Maashi3, Mahmoud Othman4, Amani A. Alneil2, Amgad Atta Abdelmageed2

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 975-992, 2023, DOI:10.32604/cmc.2023.037936

    Abstract Soil classification is one of the emanating topics and major concerns in many countries. As the population has been increasing at a rapid pace, the demand for food also increases dynamically. Common approaches used by agriculturalists are inadequate to satisfy the rising demand, and thus they have hindered soil cultivation. There comes a demand for computer-related soil classification methods to support agriculturalists. This study introduces a Gradient-Based Optimizer and Deep Learning (DL) for Automated Soil Classification (GBODL-ASC) technique. The presented GBODL-ASC technique identifies various kinds of soil using DL and computer vision approaches. In the presented GBODL-ASC technique, three major… More >

  • Open Access


    Predictive Multimodal Deep Learning-Based Sustainable Renewable and Non-Renewable Energy Utilization

    Abdelwahed Motwakel1,*, Marwa Obayya2, Nadhem Nemri3, Khaled Tarmissi4, Heba Mohsen5, Mohammed Rizwanulla6, Ishfaq Yaseen6, Abu Sarwar Zamani6

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1267-1281, 2023, DOI:10.32604/csse.2023.037735

    Abstract Recently, renewable energy (RE) has become popular due to its benefits, such as being inexpensive, low-carbon, ecologically friendly, steady, and reliable. The RE sources are gradually combined with non-renewable energy (NRE) sources into electric grids to satisfy energy demands. Since energy utilization is highly related to national energy policy, energy prediction using artificial intelligence (AI) and deep learning (DL) based models can be employed for energy prediction on RE and NRE power resources. Predicting energy consumption of RE and NRE sources using effective models becomes necessary. With this motivation, this study presents a new multimodal fusion-based predictive tool for energy… More >

  • Open Access


    Chicken Swarm Optimization with Deep Learning Based Packaged Rooftop Units Fault Diagnosis Model

    G. Anitha1, N. Supriya2, Fayadh Alenezi3, E. Laxmi Lydia4, Gyanendra Prasad Joshi5, Jinsang You6,*

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 221-238, 2023, DOI:10.32604/csse.2023.036479

    Abstract Rooftop units (RTUs) were commonly employed in small commercial buildings that represent that can frequently do not take the higher level maintenance that chillers receive. Fault detection and diagnosis (FDD) tools can be employed for RTU methods to ensure essential faults are addressed promptly. In this aspect, this article presents an Optimal Deep Belief Network based Fault Detection and Classification on Packaged Rooftop Units (ODBNFDC-PRTU) model. The ODBNFDC-PRTU technique considers fault diagnosis as a multi-class classification problem and is handled using DL models. For fault diagnosis in RTUs, the ODBNFDC-PRTU model exploits the deep belief network (DBN) classification model, which… More >

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