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  • Open Access

    ARTICLE

    Structural Damage Identification Using Ensemble Deep Convolutional Neural Network Models

    Mohammad Sadegh Barkhordari1, Danial Jahed Armaghani2,*, Panagiotis G. Asteris3

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 835-855, 2023, DOI:10.32604/cmes.2022.020840 - 31 August 2022

    Abstract The existing strategy for evaluating the damage condition of structures mostly focuses on feedback supplied by traditional visual methods, which may result in an unreliable damage characterization due to inspector subjectivity or insufficient level of expertise. As a result, a robust, reliable, and repeatable method of damage identification is required. Ensemble learning algorithms for identifying structural damage are evaluated in this article, which use deep convolutional neural networks, including simple averaging, integrated stacking, separate stacking, and hybrid weighted averaging ensemble and differential evolution (WAE-DE) ensemble models. Damage identification is carried out on three types of More >

  • Open Access

    ARTICLE

    Metal Corrosion Rate Prediction of Small Samples Using an Ensemble Technique

    Yang Yang1,2,*, Pengfei Zheng3,4, Fanru Zeng5, Peng Xin6, Guoxi He1, Kexi Liao1

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 267-291, 2023, DOI:10.32604/cmes.2022.020220 - 24 August 2022

    Abstract Accurate prediction of the internal corrosion rates of oil and gas pipelines could be an effective way to prevent pipeline leaks. In this study, a proposed framework for predicting corrosion rates under a small sample of metal corrosion data in the laboratory was developed to provide a new perspective on how to solve the problem of pipeline corrosion under the condition of insufficient real samples. This approach employed the bagging algorithm to construct a strong learner by integrating several KNN learners. A total of 99 data were collected and split into training and test set More >

  • Open Access

    ARTICLE

    Pre Screening of Cervical Cancer Through Gradient Boosting Ensemble Learning Method

    S. Priya1,*, N. K. Karthikeyan1, D. Palanikkumar2

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2673-2685, 2023, DOI:10.32604/iasc.2023.028599 - 17 August 2022

    Abstract In recent years, cervical cancer is one of the most common diseases which occur in any woman regardless of any age. This is the deadliest disease since there were no symptoms shown till it is diagnosed to be the last stage. For women at a certain age, it is better to have a proper screening for cervical cancer. In most underdeveloped nations, it is very difficult to have frequent scanning for cervical cancer. Data Mining and machine learning methodologies help widely in finding the important causes for cervical cancer. The proposed work describes a multi-class More >

  • Open Access

    ARTICLE

    Prediction Model for a Good Learning Environment Using an Ensemble Approach

    S. Subha1,*, S. Baghavathi Priya2

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2081-2093, 2023, DOI:10.32604/csse.2023.028451 - 01 August 2022

    Abstract This paper presents an efficient prediction model for a good learning environment using Random Forest (RF) classifier. It consists of a series of modules; data preprocessing, data normalization, data split and finally classification or prediction by the RF classifier. The preprocessed data is normalized using min-max normalization often used before model fitting. As the input data or variables are measured at different scales, it is necessary to normalize them to contribute equally to the model fitting. Then, the RF classifier is employed for course selection which is an ensemble learning method and k-fold cross-validation (k = 10) is… More >

  • Open Access

    ARTICLE

    Intrusion Detection Method Based on Active Incremental Learning in Industrial Internet of Things Environment

    Zeyong Sun1, Guo Ran2, Zilong Jin1,3,*

    Journal on Internet of Things, Vol.4, No.2, pp. 99-111, 2022, DOI:10.32604/jiot.2022.037416 - 28 March 2023

    Abstract Intrusion detection is a hot field in the direction of network security. Classical intrusion detection systems are usually based on supervised machine learning models. These offline-trained models usually have better performance in the initial stages of system construction. However, due to the diversity and rapid development of intrusion techniques, the trained models are often difficult to detect new attacks. In addition, very little noisy data in the training process often has a considerable impact on the performance of the intrusion detection system. This paper proposes an intrusion detection system based on active incremental learning with… More >

  • Open Access

    ARTICLE

    Physical Layer Authentication Using Ensemble Learning Technique in Wireless Communications

    Muhammad Waqas1,3,*, Shehr Bano2, Fatima Hassan2, Shanshan Tu1, Ghulam Abbas2, Ziaul Haq Abbas4

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4489-4499, 2022, DOI:10.32604/cmc.2022.029539 - 28 July 2022

    Abstract Cyber-physical wireless systems have surfaced as an important data communication and networking research area. It is an emerging discipline that allows effective monitoring and efficient real-time communication between the cyber and physical worlds by embedding computer software and integrating communication and networking technologies. Due to their high reliability, sensitivity and connectivity, their security requirements are more comparable to the Internet as they are prone to various security threats such as eavesdropping, spoofing, botnets, man-in-the-middle attack, denial of service (DoS) and distributed denial of service (DDoS) and impersonation. Existing methods use physical layer authentication (PLA), the… More >

  • Open Access

    ARTICLE

    Emotion Recognition from Occluded Facial Images Using Deep Ensemble Model

    Zia Ullah1, Muhammad Ismail Mohmand1, Sadaqat ur Rehman2,*, Muhammad Zubair3, Maha Driss4, Wadii Boulila5, Rayan Sheikh2, Ibrahim Alwawi6

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4465-4487, 2022, DOI:10.32604/cmc.2022.029101 - 28 July 2022

    Abstract Facial expression recognition has been a hot topic for decades, but high intraclass variation makes it challenging. To overcome intraclass variation for visual recognition, we introduce a novel fusion methodology, in which the proposed model first extract features followed by feature fusion. Specifically, RestNet-50, VGG-19, and Inception-V3 is used to ensure feature learning followed by feature fusion. Finally, the three feature extraction models are utilized using Ensemble Learning techniques for final expression classification. The representation learnt by the proposed methodology is robust to occlusions and pose variations and offers promising accuracy. To evaluate the efficiency More >

  • Open Access

    ARTICLE

    Text-Independent Algorithm for Source Printer Identification Based on Ensemble Learning

    Naglaa F. El Abady1,*, Mohamed Taha1, Hala H. Zayed1,2

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1417-1436, 2022, DOI:10.32604/cmc.2022.028044 - 18 May 2022

    Abstract Because of the widespread availability of low-cost printers and scanners, document forgery has become extremely popular. Watermarks or signatures are used to protect important papers such as certificates, passports, and identification cards. Identifying the origins of printed documents is helpful for criminal investigations and also for authenticating digital versions of a document in today’s world. Source printer identification (SPI) has become increasingly popular for identifying frauds in printed documents. This paper provides a proposed algorithm for identifying the source printer and categorizing the questioned document into one of the printer classes. A dataset of 1200… More >

  • Open Access

    ARTICLE

    ENSOCOM: Ensemble of Multi-Output Neural Network’s Components for Multi-Label Classification

    Khudran M. Alzhrani*

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5459-5479, 2022, DOI:10.32604/cmc.2022.028512 - 21 April 2022

    Abstract Multitasking and multioutput neural networks models jointly learn related classification tasks from a shared structure. Hard parameters sharing is a multitasking approach that shares hidden layers between multiple task-specific outputs. The output layers’ weights are essential in transforming aggregated neurons outputs into tasks labels. This paper redirects the multioutput network research to prove that the ensemble of output layers prediction can improve network performance in classifying multi-label classification tasks. The network’s output layers initialized with different weights simulate multiple semi-independent classifiers that can make non-identical label sets predictions for the same instance. The ensemble of… More >

  • Open Access

    ARTICLE

    Movie Recommendation Algorithm Based on Ensemble Learning

    Wei Fang1,2,*, Yu Sha1, Meihan Qi1, Victor S. Sheng3

    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 609-622, 2022, DOI:10.32604/iasc.2022.027067 - 15 April 2022

    Abstract With the rapid development of personalized services, major websites have launched a recommendation module in recent years. This module will recommend information you are interested in based on your viewing history and other information, thereby improving the economic benefits of the website and increasing the number of users. This paper has introduced content-based recommendation algorithm, K-Nearest Neighbor (KNN)-based collaborative filtering (CF) algorithm and singular value decomposition-based (SVD) collaborative filtering algorithm. However, the mentioned recommendation algorithms all recommend for a certain aspect, and do not realize the recommendation of specific movies input by specific users which… More >

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