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  • Open Access

    ARTICLE

    Social Robot Detection Method with Improved Graph Neural Networks

    Zhenhua Yu, Liangxue Bai, Ou Ye*, Xuya Cong

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1773-1795, 2024, DOI:10.32604/cmc.2023.047130

    Abstract Social robot accounts controlled by artificial intelligence or humans are active in social networks, bringing negative impacts to network security and social life. Existing social robot detection methods based on graph neural networks suffer from the problem of many social network nodes and complex relationships, which makes it difficult to accurately describe the difference between the topological relations of nodes, resulting in low detection accuracy of social robots. This paper proposes a social robot detection method with the use of an improved neural network. First, social relationship subgraphs are constructed by leveraging the user’s social network to disentangle intricate social… More >

  • Open Access

    ARTICLE

    Real-Time Spammers Detection Based on Metadata Features with Machine Learning

    Adnan Ali1, Jinlong Li1, Huanhuan Chen1, Uzair Aslam Bhatti2, Asad Khan3,*

    Intelligent Automation & Soft Computing, Vol.38, No.3, pp. 241-258, 2023, DOI:10.32604/iasc.2023.041645

    Abstract Spammer detection is to identify and block malicious activities performing users. Such users should be identified and terminated from social media to keep the social media process organic and to maintain the integrity of online social spaces. Previous research aimed to find spammers based on hybrid approaches of graph mining, posted content, and metadata, using small and manually labeled datasets. However, such hybrid approaches are unscalable, not robust, particular dataset dependent, and require numerous parameters, complex graphs, and natural language processing (NLP) resources to make decisions, which makes spammer detection impractical for real-time detection. For example, graph mining requires neighbors’… More >

  • Open Access

    ARTICLE

    Associations of Domain and Pattern of Sedentary Behaviors with Symptoms of Mental Disorders in Saudi Adults: ‘The Sedentary Behavior Paradox’

    Abdullah B. Alansare*

    International Journal of Mental Health Promotion, Vol.26, No.1, pp. 11-20, 2024, DOI:10.32604/ijmhp.2023.044656

    Abstract Emerging evidence suggests the existence of ‘paradoxical’ relationships between domain-specific sedentary behavior (SB) and health outcomes. This study assessed the associations of total and domain-specific SB, by pattern, with symptoms of mental disorders in Saudi adults. Participants (n = 554) completed a web-based survey between January 18th, 2023 and February 5th, 2023. Total SB was measured by using the Sedentary Behavior Questionnaire. Total SB was then partitioned into leisure, occupational, and commuting SB during weekdays and on weekend days. Symptoms of mental disorders including symptoms of depression, anxiety, and stress were evaluated by using the DASS-21 questionnaire. Adjusted linear regressions… More >

  • Open Access

    ARTICLE

    The Social Networking Addiction Scale: Translation and Validation Study among Chinese College Students

    Siyuan Bi1, Junfeng Yuan1,2, Lin Luo1,2,3,*

    International Journal of Mental Health Promotion, Vol.26, No.1, pp. 51-60, 2024, DOI:10.32604/ijmhp.2023.041614

    Abstract Purpose: The core component theory of addiction behavior provides a multidimensional theoretical model for measuring social networking addiction. Based on this theoretical model, the Social Networking Addiction Scale (SNAS) was developed. The aim of this study was to test the psychometric properties of the Chinese version of the SNAS (SNAS-C). Methods: This study used a sample of 3383 Chinese university students to conduct confirmatory factor analysis (CFA) to explore the structural validity of the SNAS-C. This study examined the Pearson correlations between the six subscales of the SNAS-C (i.e., salience, mood modification, tolerance, withdrawal symptoms, conflict, and relapse) and “social… More >

  • Open Access

    ARTICLE

    Enhancing Data Forwarding Efficiency in SIoT with Multidimensional Social Relations

    Fang Xu1,2,3, Songhao Jiang1,2, Yi Ma1,2,3,*, Manzoor Ahmed1,3,*, Zenggang Xiong1,2,3, Yuanlin Lyu1,2

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1095-1113, 2024, DOI:10.32604/cmc.2023.046577

    Abstract Effective data communication is a crucial aspect of the Social Internet of Things (SIoT) and continues to be a significant research focus. This paper proposes a data forwarding algorithm based on Multidimensional Social Relations (MSRR) in SIoT to solve this problem. The proposed algorithm separates message forwarding into intra- and cross-community forwarding by analyzing interest traits and social connections among nodes. Three new metrics are defined: the intensity of node social relationships, node activity, and community connectivity. Within the community, messages are sent by determining which node is most similar to the sender by weighing the strength of social connections… More >

  • Open Access

    ARTICLE

    A Weighted Multi-Layer Analytics Based Model for Emoji Recommendation

    Amira M. Idrees1,*, Abdul Lateef Marzouq Al-Solami2

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1115-1133, 2024, DOI:10.32604/cmc.2023.046457

    Abstract The developed system for eye and face detection using Convolutional Neural Networks (CNN) models, followed by eye classification and voice-based assistance, has shown promising potential in enhancing accessibility for individuals with visual impairments. The modular approach implemented in this research allows for a seamless flow of information and assistance between the different components of the system. This research significantly contributes to the field of accessibility technology by integrating computer vision, natural language processing, and voice technologies. By leveraging these advancements, the developed system offers a practical and efficient solution for assisting blind individuals. The modular design ensures flexibility, scalability, and… More >

  • Open Access

    REVIEW

    Social Media-Based Surveillance Systems for Health Informatics Using Machine and Deep Learning Techniques: A Comprehensive Review and Open Challenges

    Samina Amin1, Muhammad Ali Zeb1, Hani Alshahrani2,*, Mohammed Hamdi2, Mohammad Alsulami2, Asadullah Shaikh3

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1167-1202, 2024, DOI:10.32604/cmes.2023.043921

    Abstract Social media (SM) based surveillance systems, combined with machine learning (ML) and deep learning (DL) techniques, have shown potential for early detection of epidemic outbreaks. This review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their performance. Since, every year, a large amount of data related to epidemic outbreaks, particularly Twitter data is generated by SM. This paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM, along with the ML and DL techniques that have been configured for the… More >

  • Open Access

    ARTICLE

    Automatic SOC Equalization Strategy of Energy Storage Units with DC Microgrid Bus Voltage Support

    Jingjing Tian1, Shenglin Mo1,*, Feng Zhao1, Xiaoqiang Chen2

    Energy Engineering, Vol.121, No.2, pp. 439-459, 2024, DOI:10.32604/ee.2023.029956

    Abstract In this paper, an improved sag control strategy based on automatic SOC equalization is proposed to solve the problems of slow SOC equalization and excessive bus voltage fluctuation amplitude and offset caused by load and PV power variations in a stand-alone DC microgrid. The strategy includes primary and secondary control. Among them, the primary control suppresses the DC microgrid voltage fluctuation through the Ⅰ and Ⅱ section control, and the secondary control aims to correct the P-U curve of the energy storage system and the PV system, thus reducing the steady-state bus voltage excursion. The simulation results demonstrate that the… More >

  • Open Access

    ARTICLE

    Is There a Specific Profile of COVID-19 Risk Perception among People with Cancer? A Cross-Sectional Study

    Existe-t-il un profil spécifique de perception du risque de COVID-19 chez les personnes atteintes d’un cancer ? une étude transversale

    Renaud Mabire-Yon1,*, Arnaud Siméone1, Thibaud Marmorat2, Anne-Sophie Petit1, Mathilde Perray1, Costanza Puppo1, Charlotte Bauquier1, Claire Della Vecchia1, Hervé Picard3, Marie Préau1

    Psycho-Oncologie, Vol.17, No.4, pp. 245-256, 2023, DOI:10.32604/po.2023.042296

    Abstract Aims: This study aimed to determine if people with cancer (PWC) exhibit a unique COVID-19 risk perception profile and identify psychosocial factors characterizing PWC who do not conform to the majority risk perception profile. Procedure: A cross-sectional online self-questionnaire study was conducted in France from April 25 to May 07, 2020, with a sample (n = 748) comprising PWC, individuals not currently receiving cancer treatment, and those without a history of cancer. Latent profiles of COVID-19 risk perception (PCRP) were established. Methods: A multivariate multinomial logistic regression was performed to assess the association between cancer status and PCRP membership. Characteristics… More >

  • Open Access

    ARTICLE

    Associations between Mental Health Outcomes and Adverse Childhood Experiences and Character Strengths among University Students in Southern China

    Yulan Yu1,2, Rassamee Chotipanvithayakul3, Hujiao Kuang4, Wit Wichaidit3,*, Chonghua Wan1,2,*

    International Journal of Mental Health Promotion, Vol.25, No.12, pp. 1343-1351, 2023, DOI:10.32604/ijmhp.2023.043446

    Abstract Adverse childhood experiences (ACEs) can negatively affect mental health, whereas character strengths seem to be positively correlated with mental health. Detailed information on the history of ACEs among university students in China and the extent which mental health is associated with ACEs and character strengths can contribute to the needed empirical evidence for relevant stakeholders. Objectives of this study are 1) to estimate the prevalence of ACEs among undergraduate students in Southern China; and 2) to assess the extent which mental health outcomes (positive growth, well-being, and depression) are associated with ACEs and character strengths among undergraduate students in Southern… More >

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