Conghuan Ye^1,*, Shenglong Tan¹, Shi Li¹, Jun Wang¹, Qiankun Zuo¹, Bing Xiong²

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2927-2946, 2025, DOI:10.32604/cmc.2025.062051 - 16 April 2025

Abstract With the fast development of multimedia social platforms, content dissemination on social media platforms is becoming more popular. Social image sharing can also raise privacy concerns. Image encryption can protect social images. However, most existing image protection methods cannot be applied to multimedia social platforms because of encryption in the spatial domain. In this work, the authors propose a secure social image-sharing method with watermarking/fingerprinting and encryption. First, the fingerprint code with a hierarchical community structure is designed based on social network analysis. Then, discrete wavelet transform (DWT) from block discrete cosine transform (DCT) directly… More >

Amjad A. Alsuwaylimi^1,*, Zaid S. Alenezi²

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3165-3185, 2025, DOI:10.32604/cmc.2025.061674 - 16 April 2025

Abstract Cyberbullying is a remarkable issue in the Arabic-speaking world, affecting children, organizations, and businesses. Various efforts have been made to combat this problem through proposed models using machine learning (ML) and deep learning (DL) approaches utilizing natural language processing (NLP) methods and by proposing relevant datasets. However, most of these endeavors focused predominantly on the English language, leaving a substantial gap in addressing Arabic cyberbullying. Given the complexities of the Arabic language, transfer learning techniques and transformers present a promising approach to enhance the detection and classification of abusive content by leveraging large and pretrained… More >

Mourad Benmalek^1,*,#,*, Abdessamed Seddiki^2,#, Kamel-Dine Haouam¹

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 1157-1184, 2025, DOI:10.32604/cmes.2025.062841 - 11 April 2025

Abstract The Internet of Medical Things (IoMT) connects healthcare devices and sensors to the Internet, driving transformative advancements in healthcare delivery. However, expanding IoMT infrastructures face growing security threats, necessitating robust Intrusion Detection Systems (IDS). Maintaining the confidentiality of patient data is critical in AI-driven healthcare systems, especially when securing interconnected medical devices. This paper introduces SNN-IoMT (Stacked Neural Network Ensemble for IoMT Security), an AI-driven IDS framework designed to secure dynamic IoMT environments. Leveraging a stacked deep learning architecture combining Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM), the model optimizes data management More >

Saad Alahmari^1,*, Abdulwhab Alkharashi²

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 849-873, 2025, DOI:10.32604/cmes.2025.062549 - 11 April 2025

Abstract The Internet of Things (IoT) is emerging as an innovative phenomenon concerned with the development of numerous vital applications. With the development of IoT devices, huge amounts of information, including users’ private data, are generated. IoT systems face major security and data privacy challenges owing to their integral features such as scalability, resource constraints, and heterogeneity. These challenges are intensified by the fact that IoT technology frequently gathers and conveys complex data, creating an attractive opportunity for cyberattacks. To address these challenges, artificial intelligence (AI) techniques, such as machine learning (ML) and deep learning (DL),… More >

Nguyen Minh Trieu, Nguyen Truong Thinh^*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 477-498, 2025, DOI:10.32604/cmes.2025.061339 - 11 April 2025

Abstract In robotics and human-robot interaction, a robot’s capacity to express and react correctly to human emotions is essential. A significant aspect of the capability involves controlling the robotic facial skin actuators in a way that resonates with human emotions. This research focuses on human anthropometric theories to design and control robotic facial actuators, addressing the limitations of existing approaches in expressing emotions naturally and accurately. The facial landmarks are extracted to determine the anthropometric indicators for designing the robot head and is employed to the displacement of these points to calculate emotional values using Fuzzy… More >

Chao Qu, Xiaoshan Jia, Haidong Zhu^*

International Journal of Mental Health Promotion, Vol.27, No.3, pp. 401-420, 2025, DOI:10.32604/ijmhp.2025.061059 - 31 March 2025

Abstract Objectives: Attachment is a profound and enduring connection to the emotion children progressively form with their parents as they mature. It significantly impacts the social and psychological development of kids and teenagers. This study aimed to explore the latent profiles and longitudinal transition patterns of parent-child and peer attachments among adolescents. Methods: A cohort of 914 participants from China completed the measures with a twelve-month interval. There were 46.8% boys and 53.2% girls in this survey. Latent profile analysis (LPA) was adopted to explore the distinct profiles reflecting different parent-child and peer attachment response patterns… More >

Jun Zhang^1,#,*, Li Wang^1,#, Shan Liu¹, Yurong Yang², Jingyi Fan³, Yijia Zhang¹

International Journal of Mental Health Promotion, Vol.27, No.3, pp. 287-302, 2025, DOI:10.32604/ijmhp.2025.060064 - 31 March 2025

Abstract Background: Parents of children with autism are susceptible to parenting burnout due to tremendous parenting burden and parenting challenges. Parenting burnout has a detrimental effect on both children with autism and their parents. However, the underlying mechanisms that lead to parenting burnout remain unclear. This study aimed to investigate the relationship between parenting stress and parenting burnout, along with the serial mediation effect of social support and coping strategies in the context of families with autistic children. Methods: We conducted a cross-sectional study in 231 parents of autistic children in four autism facilities located in… More >

Wan Ying Gan^1,#,*, Wei-Leng Chin^2,3,#, Shih-Wei Huang^4,5, Serene En Hui Tung⁶, Ling Jun Lee¹, Wai Chuen Poon⁷, Yan Li Siaw⁸, Kerry S. O’Brien⁹, Iqbal Pramukti¹⁰, Kamolthip Ruckwongpatr¹¹, Jung-Sheng Chen¹², Mark D. Griffiths¹³, Chung-Ying Lin^{10,11,14,15,*}

International Journal of Mental Health Promotion, Vol.27, No.3, pp. 319-331, 2025, DOI:10.32604/ijmhp.2025.060049 - 31 March 2025

Abstract Background: Weight-related self-stigma (WRSS) is prevalent among individuals with different types of weight status and is associated with a range of negative health outcomes. Social support and coping models explain how individuals may use different coping methods to deal with their mental health needs. Psychological distress (e.g., depression and stress) could lead to overuse of social media and smartphones. When using social media or smartphones, individuals are likely to be exposed to negative comments regarding weight/shape/size posted on the social media. Consequently, individuals who experience problematic social media use (PSMU) or problematic smartphone use (PSPU)… More >

Juan Yang^1,2,#, Jiahui Zhang^3,#, Le Zhang^1,2,*, Zhenshan Yang^4,*

BIOCELL, Vol.49, No.3, pp. 399-417, 2025, DOI:10.32604/biocell.2025.059908 - 31 March 2025

Abstract With the prevalence of obesity, metabolic dysfunction-associated steatotic liver disease (MASLD) has become the most common chronic liver disease worldwide and can cause a series of serious complications. The pathogenesis of MASLD is complex, characterized by oxidative stress, impaired mitochondrial function and lipid metabolism, and cellular inflammation. Mitochondrial biology and function are central to the physiology of the liver. It has been suggested that mitochondrial oxidative stress plays a crucial role in MASLD progression. Excessive oxidative stress response is an important trigger for the occurrence and development of MASLD. In this review, we aim to More >

Yi-Feng Luo¹, Guan-Jhu Chen^2,*, Chun-Liang Liu³, Yen-Tse Chung⁴

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 823-844, 2025, DOI:10.32604/cmc.2025.061498 - 26 March 2025

Abstract As lithium-ion batteries become increasingly prevalent in electric scooters, vehicles, mobile devices, and energy storage systems, accurate estimation of remaining battery capacity is crucial for optimizing system performance and reliability. Unlike traditional methods that rely on static alternating internal resistance (SAIR) measurements in an open-circuit state, this study presents a real-time state of charge (SOC) estimation method combining dynamic alternating internal resistance (DAIR) with artificial neural networks (ANN). The system simultaneously measures electrochemical impedance |Z| at various frequencies, discharge C-rate, and battery surface temperature during the discharge process, using these parameters for ANN training. The… More >