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  • Open Access

    ARTICLE

    A Hybrid Approach for COVID-19 Detection Using Biogeography-Based Optimization and Deep Learning

    K. Venkatachalam1, Siuly Siuly2, M. Vinoth Kumar3, Praveen Lalwani1, Manas Kumar Mishra1, Enamul Kabir4,*

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3717-3732, 2022, DOI:10.32604/cmc.2022.018487

    Abstract The COVID-19 pandemic has created a major challenge for countries all over the world and has placed tremendous pressure on their public health care services. An early diagnosis of COVID-19 may reduce the impact of the coronavirus. To achieve this objective, modern computation methods, such as deep learning, may be applied. In this study, a computational model involving deep learning and biogeography-based optimization (BBO) for early detection and management of COVID-19 is introduced. Specifically, BBO is used for the layer selection process in the proposed convolutional neural network (CNN). The computational model accepts images, such as CT scans, X-rays, positron… More >

  • Open Access

    ARTICLE

    A Hybrid Approach for Network Intrusion Detection

    Mavra Mehmood1, Talha Javed2, Jamel Nebhen3, Sidra Abbas2,*, Rabia Abid1, Giridhar Reddy Bojja4, Muhammad Rizwan1

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 91-107, 2022, DOI:10.32604/cmc.2022.019127

    Abstract Due to the widespread use of the internet and smart devices, various attacks like intrusion, zero-day, Malware, and security breaches are a constant threat to any organization's network infrastructure. Thus, a Network Intrusion Detection System (NIDS) is required to detect attacks in network traffic. This paper proposes a new hybrid method for intrusion detection and attack categorization. The proposed approach comprises three steps to address high false and low false-negative rates for intrusion detection and attack categorization. In the first step, the dataset is preprocessed through the data transformation technique and min-max method. Secondly, the random forest recursive feature elimination… More >

  • Open Access

    ARTICLE

    Reliability Analysis of Piled Raft Foundation Using a Novel Hybrid Approach of ANN and Equilibrium Optimizer

    Abidhan Bardhan1, Priyadip Manna1, Vinay Kumar1, Avijit Burman1, Bojan Žlender2, Pijush Samui1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.3, pp. 1033-1067, 2021, DOI:10.32604/cmes.2021.015885

    Abstract In many civil engineering projects, Piled Raft Foundations (PRFs) are usually preferred where the incoming load from the superstructures is very high. In geotechnical engineering practice, the settlement of soil layers is a critical issue for the serviceability of the structures. Thus, assessment of risk associated with the structures corresponding to the maximum allowable settlement of soils needs to be carried out in the design phase. In this study, reliability analysis of PRF based on settlement criteria is performed using a high-performance hybrid soft computing model. The new approach is an integration of the artificial neural network (ANN) and a… More >

  • Open Access

    ARTICLE

    A Hybrid Approach for Performance and Energy-Based Cost Prediction in Clouds

    Mohammad Aldossary*

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3531-3562, 2021, DOI:10.32604/cmc.2021.017477

    Abstract With the striking rise in penetration of Cloud Computing, energy consumption is considered as one of the key cost factors that need to be managed within cloud providers’ infrastructures. Subsequently, recent approaches and strategies based on reactive and proactive methods have been developed for managing cloud computing resources, where the energy consumption and the operational costs are minimized. However, to make better cost decisions in these strategies, the performance and energy awareness should be supported at both Physical Machine (PM) and Virtual Machine (VM) levels. Therefore, in this paper, a novel hybrid approach is proposed, which jointly considered the prediction… More >

  • Open Access

    ARTICLE

    A Hybrid Approach for the Lung(s) Nodule Detection Using the Deformable Model and Distance Transform

    Ayyaz Hussain1, Mohammed Alawairdhi2, Fayez Alazemi3, Sajid Ali Khan4, Muhammad Ramzan2,*

    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 857-871, 2020, DOI:10.32604/iasc.2020.010120

    Abstract The Computer Aided Diagnosis (CAD) systems are gaining more recognition and being used as an aid by clinicians for detection and interpretation of diseases every passing day due to their increasing accuracy and reliability. The lung(s) nodule detection is a very crucial and difficult step for CAD systems. In this paper, a hybrid approach for the lung nodule detection using a deformable model and distance transform has been proposed. The proposed method has the ability to detect all major kinds of nodules such as the juxta-plueral, isolated, and the juxta-vescular, along with the non-solid nodules automatically and intelligently. Results show… More >

  • Open Access

    ARTICLE

    Conceptual Design of Natural Fiber Composites as a Side-Door Impact Beam Using Hybrid Approach

    M. A. Shaharuzaman1,2,3, S. M. Sapuan1,4,*, M. R. Mansor2,3, M. Y. M. Zuhri1,4

    Journal of Renewable Materials, Vol.8, No.5, pp. 549-563, 2020, DOI:10.32604/jrm.2020.08769

    Abstract This paper presents the conceptual design stage in the product development process of a natural fiber composites of the side-door impact beam, which starts from idea generation to the selection of the best design concept. This paper also demonstrates the use of the integrated Theory of Inventive Problem Solving (Function-Oriented Search) (TRIZ (FOS)) and Biomimetics method, as well as the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method. The aim of this study was to generate design concepts that were inspired by nature and to select the best design concept for the composite side-door impact beam. Subsequently, eight design concepts were… More >

  • Open Access

    ARTICLE

    A Hybrid Approach of TLBO and EBPNN for Crop Yield Prediction Using Spatial Feature Vectors

    Preeti Tiwari1, *, Piyush Shukla1

    Journal on Artificial Intelligence, Vol.1, No.2, pp. 45-58, 2019, DOI:10.32604/jai.2019.04444

    Abstract The prediction of crop yield is one of the important factor and also challenging, to predict the future crop yield based on various criteria’s. Many advanced technologies are incorporated in the agricultural processes, which enhances the crop yield production efficiency. The process of predicting the crop yield can be done by taking agriculture data, which helps to analyze and make important decisions before and during cultivation. This paper focuses on the prediction of crop yield, where two models of machine learning are developed for this work. One is Modified Convolutional Neural Network (MCNN), and the other model is TLBO (Teacher… More >

  • Open Access

    ARTICLE

    A Hybrid of Interval Wavelets and Wavelet Finite Element Model for Damage Detection in Structures

    Jiawei Xiang1, Toshiro Matsumoto2, Yanxue Wang3, Zhansi Jiang4

    CMES-Computer Modeling in Engineering & Sciences, Vol.81, No.3&4, pp. 269-294, 2011, DOI:10.3970/cmes.2011.081.269

    Abstract Damages occurred in a structure will lead to changes in modal parameters (natural frequencies and modal shapes). The relationship between modal parameters and damage parameters (locations and depths) is very complicated. Single detection method using natural frequencies or modal shapes can not obtain robust damage detection results from the inevitably noise-contaminated modal parameters. To eliminate the complexity, a hybrid approach using both of wavelets on the interval (interval wavelets) method and wavelet finite element model-based method is proposed to detect damage locations and depths. To avoid the boundary distortion phenomenon, Interval wavelets are employed to analyze the finite-length modal shape… More >

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