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  • Open Access

    ARTICLE

    A Proposed Feature Selection Particle Swarm Optimization Adaptation for Intelligent Logistics—A Supply Chain Backlog Elimination Framework

    Yasser Hachaichi1, Ayman E. Khedr1, Amira M. Idrees2,*

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4081-4105, 2024, DOI:10.32604/cmc.2024.048929

    Abstract The diversity of data sources resulted in seeking effective manipulation and dissemination. The challenge that arises from the increasing dimensionality has a negative effect on the computation performance, efficiency, and stability of computing. One of the most successful optimization algorithms is Particle Swarm Optimization (PSO) which has proved its effectiveness in exploring the highest influencing features in the search space based on its fast convergence and the ability to utilize a small set of parameters in the search task. This research proposes an effective enhancement of PSO that tackles the challenge of randomness search which… More >

  • Open Access

    ARTICLE

    CMAES-WFD: Adversarial Website Fingerprinting Defense Based on Covariance Matrix Adaptation Evolution Strategy

    Di Wang, Yuefei Zhu, Jinlong Fei*, Maohua Guo

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2253-2276, 2024, DOI:10.32604/cmc.2024.049504

    Abstract Website fingerprinting, also known as WF, is a traffic analysis attack that enables local eavesdroppers to infer a user’s browsing destination, even when using the Tor anonymity network. While advanced attacks based on deep neural network (DNN) can perform feature engineering and attain accuracy rates of over 98%, research has demonstrated that DNN is vulnerable to adversarial samples. As a result, many researchers have explored using adversarial samples as a defense mechanism against DNN-based WF attacks and have achieved considerable success. However, these methods suffer from high bandwidth overhead or require access to the target… More >

  • Open Access

    ARTICLE

    Chinese Adaptation and Psychometric Properties of the Belief in a Just World Scale for College Students

    Zhe Yu1,2, Shuping Yang1,*

    International Journal of Mental Health Promotion, Vol.26, No.4, pp. 271-278, 2024, DOI:10.32604/ijmhp.2024.048342

    Abstract This study aims to revise the Belief in a Just World Scale (BJWS) for Chinese college students and test its reliability and validity (construct validity, convergent and divergent validity). Two samples of 546 and 595 college students were selected, respectively, using stratified cluster random sampling. Item analysis, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), reliability analysis and convergent and divergent validity tests were carried out. The results showed that the 13 items of the BJWS have good item discrimination. The corrected item–total correlation in the general belief in a just world subscale was found… More >

  • Open Access

    ARTICLE

    Complementary-Label Adversarial Domain Adaptation Fault Diagnosis Network under Time-Varying Rotational Speed and Weakly-Supervised Conditions

    Siyuan Liu1,*, Jinying Huang2, Jiancheng Ma1, Licheng Jing2, Yuxuan Wang2

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 761-777, 2024, DOI:10.32604/cmc.2024.049484

    Abstract Recent research in cross-domain intelligence fault diagnosis of machinery still has some problems, such as relatively ideal speed conditions and sample conditions. In engineering practice, the rotational speed of the machine is often transient and time-varying, which makes the sample annotation increasingly expensive. Meanwhile, the number of samples collected from different health states is often unbalanced. To deal with the above challenges, a complementary-label (CL) adversarial domain adaptation fault diagnosis network (CLADAN) is proposed under time-varying rotational speed and weakly-supervised conditions. In the weakly supervised learning condition, machine prior information is used for sample annotation More >

  • Open Access

    ARTICLE

    Adaptation of Federated Explainable Artificial Intelligence for Efficient and Secure E-Healthcare Systems

    Rabia Abid1, Muhammad Rizwan2, Abdulatif Alabdulatif3,*, Abdullah Alnajim4, Meznah Alamro5, Mourade Azrour6

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3413-3429, 2024, DOI:10.32604/cmc.2024.046880

    Abstract Explainable Artificial Intelligence (XAI) has an advanced feature to enhance the decision-making feature and improve the rule-based technique by using more advanced Machine Learning (ML) and Deep Learning (DL) based algorithms. In this paper, we chose e-healthcare systems for efficient decision-making and data classification, especially in data security, data handling, diagnostics, laboratories, and decision-making. Federated Machine Learning (FML) is a new and advanced technology that helps to maintain privacy for Personal Health Records (PHR) and handle a large amount of medical data effectively. In this context, XAI, along with FML, increases efficiency and improves the More >

  • Open Access

    ARTICLE

    Sleep Quality and Emotional Adaptation among Freshmen in Elite Chinese Universities during Prolonged COVID-19 Lockdown: The Mediating Role of Anxiety Symptoms

    Xinqiao Liu*, Linxin Zhang, Xinran Zhang

    International Journal of Mental Health Promotion, Vol.26, No.2, pp. 105-116, 2024, DOI:10.32604/ijmhp.2023.042359

    Abstract Under the effects of COVID-19 and a number of ongoing lockdown tactics, anxiety symptoms and poor sleep quality have become common mental health issues among college freshmen and are intimately related to their emotional adaptation. To explore this connection, this study gathered data from a sample of 256 freshmen enrolled in an elite university in China in September 2022. The association between sleep quality, anxiety symptoms, and emotional adaptation was clarified using correlation analysis. Additionally, the mediating function of anxiety symptoms between sleep quality and emotional adaptation was investigated using a structural equation model. The… More >

  • Open Access

    ARTICLE

    Bearing Fault Diagnosis Based on Deep Discriminative Adversarial Domain Adaptation Neural Networks

    Jinxi Guo1, Kai Chen1,2, Jiehui Liu1, Yuhao Ma2, Jie Wu2,*, Yaochun Wu2, Xiaofeng Xue3, Jianshen Li1

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2619-2640, 2024, DOI:10.32604/cmes.2023.031360

    Abstract Intelligent diagnosis driven by big data for mechanical fault is an important means to ensure the safe operation of equipment. In these methods, deep learning-based machinery fault diagnosis approaches have received increasing attention and achieved some results. It might lead to insufficient performance for using transfer learning alone and cause misclassification of target samples for domain bias when building deep models to learn domain-invariant features. To address the above problems, a deep discriminative adversarial domain adaptation neural network for the bearing fault diagnosis model is proposed (DDADAN). In this method, the raw vibration data are… More >

  • Open Access

    ARTICLE

    Tool Wear State Recognition with Deep Transfer Learning Based on Spindle Vibration for Milling Process

    Qixin Lan1, Binqiang Chen1,*, Bin Yao1, Wangpeng He2

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2825-2844, 2024, DOI:10.32604/cmes.2023.030378

    Abstract The wear of metal cutting tools will progressively rise as the cutting time goes on. Wearing heavily on the tool will generate significant noise and vibration, negatively impacting the accuracy of the forming and the surface integrity of the workpiece. Hence, during the cutting process, it is imperative to continually monitor the tool wear state and promptly replace any heavily worn tools to guarantee the quality of the cutting. The conventional tool wear monitoring models, which are based on machine learning, are specifically built for the intended cutting conditions. However, these models require retraining when… More >

  • Open Access

    ARTICLE

    An Effective Runge-Kutta Optimizer Based on Adaptive Population Size and Search Step Size

    Ala Kana, Imtiaz Ahmad*

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3443-3464, 2023, DOI:10.32604/cmc.2023.040775

    Abstract A newly proposed competent population-based optimization algorithm called RUN, which uses the principle of slope variations calculated by applying the Runge Kutta method as the key search mechanism, has gained wider interest in solving optimization problems. However, in high-dimensional problems, the search capabilities, convergence speed, and runtime of RUN deteriorate. This work aims at filling this gap by proposing an improved variant of the RUN algorithm called the Adaptive-RUN. Population size plays a vital role in both runtime efficiency and optimization effectiveness of metaheuristic algorithms. Unlike the original RUN where population size is fixed throughout… More >

  • Open Access

    ARTICLE

    L’expérience à long terme des parents endeuillés en oncologie pédiatrique : une étude rétrospective de 2 à 18 ans après le décès d’un enfant

    C.J. Bourque, E. Dumont, M. Martisella, L. Daoust, S. Cantin, M.-C. Levasseur, Q. de Steur, M. Duval, M.-A. Marquis, S. Sultan

    Psycho-Oncologie, Vol.17, No.2, pp. 85-94, 2023, DOI:10.3166/pson-2022-0222

    Abstract Objectifs : Cette étude rétrospective et transversale vise à comprendre l’expérience à long terme des parents endeuillés en oncologie pédiatrique et les différences du deuil parental en fonction du genre.
    Matériel et méthodes : Un questionnaire multisectionnel coconstruit avec des cliniciens et intervenants en suivi de deuil a été tenu en ligne en 2018 et 2019. Les participants au sondage étaient des parents dont l’enfant était décédé au service d’hématologie-oncologie du CHU Sainte-Justine 2 à 18 ans auparavant. Des sections spécifiques sur les réactions, les changements et les souvenirs ont fait l’objet d’analyses descriptives.
    Résultats : Les réponses… More >

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