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  • Open Access


    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 security of e-healthcare systems. The… More >

  • Open Access


    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 results reveal that: (1) Chinese… More >

  • Open Access


    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 firstly converted into frequency domain… More >

  • Open Access


    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 the cutting conditions undergo any… More >

  • Open Access


    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 the search process, Adaptive-RUN automatically… More >

  • Open Access


    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 de 48… More >

  • Open Access


    Une ontologie orientée objet pour la modélisation des socio-écosystèmes

    Éric Masson

    Revue Internationale de Géomatique, Vol.31, No.1, pp. 199-230, 2022, DOI:10.3166/RIG31.199-230

    Abstract Cet article de positionnement théorique est une contribution ontologique pour la modélisation des socio-écosystèmes (SES). C’est également une prise de position épistémique qui est ancrée sur la démarche orientée objet. Nous proposons ainsi un modèle d’organisation des connaissances qui permet d’intégrer plus de complexité dans la déconstruction pluridisciplinaire et l’analyse des SES. Cette proposition ontologique orientée objet s’appuie sur six concepts de haut niveau d’abstraction permettant une portabilité transdisciplinaire autour des structures, fonctions, connexions, phases, échelles et adaptations des SES. Après avoir défini notre positionnement sur la modélisation spatiotemporelle orientée objet, nous présentons les six concepts et nous explicitons l’intérêt… More >

  • Open Access


    ECG Heartbeat Classification Under Dataset Shift

    Zhiqiang He*

    Journal of Intelligent Medicine and Healthcare, Vol.1, No.2, pp. 79-89, 2022, DOI:10.32604/jimh.2022.036624

    Abstract Electrocardiogram (ECG) is widely used to detect arrhythmia. Atrial fibrillation, atrioventricular block, premature beats, etc. can all be diagnosed by ECG. When the distribution of training data and test data is inconsistent, the accuracy of the model will be affected. This phenomenon is called dataset shift. In the real-world heartbeat classification system, the heartbeat of the training set and test set often comes from patients of different ages and genders, so there are differences in the distribution of data sets. The main challenge in applying machine learning algorithms to clinical AI systems is dataset shift. Test-time adaptation (TTA) aims to… More >

  • Open Access


    Novel ARC-Fuzzy Coordinated Automatic Tracking Control of Four-Wheeled Mobile Robot

    G. Pandiaraj*, S. Muralidharan

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3713-3726, 2023, DOI:10.32604/iasc.2023.031463

    Abstract Four-wheeled, individual-driven, nonholonomic structured mobile robots are widely used in industries for automated work, inspection and exploration purposes. The trajectory tracking control of the four-wheel individual-driven mobile robot is one of the most blooming research topics due to its nonholonomic structure. The wheel velocities are separately adjusted to follow the trajectory in the old-fashioned kinematic control of skid-steered mobile robots. However, there is no consideration for robot dynamics when using a kinematic controller that solely addresses the robot chassis’s motion. As a result, the mobile robot has limited performance, such as chattering during curved movement. In this research work, a… More >

  • Open Access


    A Smart Room to Promote Autonomy of Disabled People due to Stroke

    Moeiz Miraoui1,2,*

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 677-692, 2023, DOI:10.32604/csse.2023.025799

    Abstract A cerebral vascular accident, known as common language stroke, is one of the main causes of mortality and remains the primary cause of acquired disabilities in adults. Those disabled people spend most of their time at home in their living rooms. In most cases, appliances of a living room (TV, light, cooler/heater, window blinds, etc.) are generally controlled by direct manipulation of a set of remote controls. Handling many remote controls can be disturbing and inappropriate for these people. In addition, in many cases these people could be alone at home and must open the door for visitors after their… More >

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