Zhi-Yun Zhang^1,*, Yaqiong Wu¹, Chenshi Deng², Peng Wang³, Weiguaju Nong^4,*

International Journal of Mental Health Promotion, Vol.27, No.10, pp. 1533-1551, 2025, DOI:10.32604/ijmhp.2025.070883 - 31 October 2025

Abstract Objectives: Medical students often rely on recreational internet media to relieve the stress caused by immense academic and life pressures, and among these media, short-form videos, which are an emerging digital medium, have gradually become the mainstream choice of students to relieve their stress. However, the addiction caused by their usage has attracted the widespread attention of both academia and society, which is why the purpose of this study is to systematically explore the underlying mechanisms that link perceived stress, entertainment gratification, emotional gratification, short-form video usage intensity, and short-form video addiction based on multiple… More >

Sara H. Elsayed¹, Rodaina Abdelsalam¹, Mahmoud A. Ismail Shoman², Raed Alotaibi^3,*, Omar Reyad^4,5,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4995-5013, 2025, DOI:10.32604/cmc.2025.068429 - 23 October 2025

Abstract Ensuring the secure transmission of secret messages, particularly through video—one of the most widely used media formats—is a critical challenge in the field of information security. Relying on a single-layered security approach is often insufficient for safeguarding sensitive data. This study proposes a triple-lightweight cryptographic and steganographic model that integrates the Hill Cipher Technique (HCT), Rotation Left Digits (RLD), and Discrete Wavelet Transform (DWT) to embed secret messages within video frames securely. The approach begins with encrypting the secret text using a private key matrix (PK₁) of size 2 × 2 up to 6 × 6… More >

Moshiur Rahman Tonmoy¹, Md. Mithun Hossain¹, Mejdl Safran^2,*, Sultan Alfarhood², Dunren Che³, M. F. Mridha⁴

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5747-5763, 2025, DOI:10.32604/cmc.2025.067589 - 23 October 2025

Abstract Automated recognition of violent activities from videos is vital for public safety, but often raises significant privacy concerns due to the sensitive nature of the footage. Moreover, resource constraints often hinder the deployment of deep learning-based complex video classification models on edge devices. With this motivation, this study aims to investigate an effective violent activity classifier while minimizing computational complexity, attaining competitive performance, and mitigating user data privacy concerns. We present a lightweight deep learning architecture with fewer parameters for efficient violent activity recognition. We utilize a two-stream formation of 3D depthwise separable convolution coupled More >

Carlos Rojas¹, Cristian Bravo¹, Carlos Enrique Montenegro-Marín¹, Rubén González-Crespo^2,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5489-5507, 2025, DOI:10.32604/cmc.2025.067394 - 23 October 2025

Abstract The increasing prevalence of violent incidents in public spaces has created an urgent need for intelligent surveillance systems capable of detecting dangerous objects in real time. While traditional video surveillance relies on human monitoring, this approach suffers from limitations such as fatigue and delayed response times. This study addresses these challenges by developing an automated detection system using advanced deep learning techniques to enhance public safety. Our approach leverages state-of-the-art convolutional neural networks (CNNs), specifically You Only Look Once version 4 (YOLOv4) and EfficientDet, for real-time object detection. The system was trained on a comprehensive… More >

Yujie Wu¹, Xing Deng^1,*, Haijian Shao¹, Ke Cheng¹, Ming Zhang¹, Yingtao Jiang², Fei Wang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2709-2778, 2025, DOI:10.32604/cmes.2025.066647 - 30 September 2025

Abstract The application of generative artificial intelligence (AI) is bringing about notable changes in anime creation. This paper surveys recent advancements and applications of diffusion and language models in anime generation, focusing on their demonstrated potential to enhance production efficiency through automation and personalization. Despite these benefits, it is crucial to acknowledge the substantial initial computational investments required for training and deploying these models. We conduct an in-depth survey of cutting-edge generative AI technologies, encompassing models such as Stable Diffusion and GPT, and appraise pivotal large-scale datasets alongside quantifiable evaluation metrics. Review of the surveyed literature… More >

Weiqiang Xin^1,2,3,#, Zheng Wang^4,#, Xi Chen^1,5, Yufeng Tang¹, Bing Li¹, Chunwei Tian^2,5,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2837-2849, 2025, DOI:10.32604/cmc.2025.069784 - 23 September 2025

Abstract Deep convolutional neural networks (CNNs) have demonstrated remarkable performance in video super-resolution (VSR). However, the ability of most existing methods to recover fine details in complex scenes is often hindered by the loss of shallow texture information during feature extraction. To address this limitation, we propose a 3D Convolutional Enhanced Residual Video Super-Resolution Network (3D-ERVSNet). This network employs a forward and backward bidirectional propagation module (FBBPM) that aligns features across frames using explicit optical flow through lightweight SPyNet. By incorporating an enhanced residual structure (ERS) with skip connections, shallow and deep features are effectively integrated,… More >

Pengpeng Yang^1,2,*, Chen Zhou¹, Dasara Shullani², Lanxi Liu¹, Daniele Baracchi²

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2487-2526, 2025, DOI:10.32604/cmc.2025.069129 - 23 September 2025

Abstract Images and videos play an increasingly vital role in daily life and are widely utilized as key evidentiary sources in judicial investigations and forensic analysis. Simultaneously, advancements in image and video processing technologies have facilitated the widespread availability of powerful editing tools, such as Deepfakes, enabling anyone to easily create manipulated or fake visual content, which poses an enormous threat to social security and public trust. To verify the authenticity and integrity of images and videos, numerous approaches have been proposed, which are primarily based on content analysis and their effectiveness is susceptible to interference… More >

Mingoo Kwon, Kyeongmin Kim, Minseok Song^*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 519-537, 2025, DOI:10.32604/cmc.2025.066754 - 29 August 2025

Abstract Efficient edge caching is essential for maximizing utility in video streaming systems, especially under constraints such as limited storage capacity and dynamically fluctuating content popularity. Utility, defined as the benefit obtained per unit of cache bandwidth usage, degrades when static or greedy caching strategies fail to adapt to changing demand patterns. To address this, we propose a deep reinforcement learning (DRL)-based caching framework built upon the proximal policy optimization (PPO) algorithm. Our approach formulates edge caching as a sequential decision-making problem and introduces a reward model that balances cache hit performance and utility by prioritizing More >

Xian Yu, Jianxun Zhang^*, Siran Tian, Xiaobao He

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1883-1897, 2025, DOI:10.32604/cmc.2025.065529 - 29 August 2025

Abstract In recent years, diffusion models have achieved remarkable progress in image generation. However, extending them to text-to-video (T2V) generation remains challenging, particularly in maintaining semantic consistency and visual quality across frames. Existing approaches often overlook the synergy between high-level semantics and low-level texture information, resulting in blurry or temporally inconsistent outputs. To address these issues, we propose Dual Consistency Training (DCT), a novel framework designed to jointly optimize semantic and texture consistency in video generation. Specifically, we introduce a multi-scale spatial adapter to enhance spatial feature extraction, and leverage the complementary strengths of CLIP and More >

Muhammad Javed¹, Zhaohui Zhang^1,*, Fida Hussain Dahri², Asif Ali Laghari^3,*, Martin Krajčík⁴, Ahmad Almadhor⁵

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1457-1493, 2025, DOI:10.32604/cmc.2025.062954 - 29 August 2025

Abstract Recent advances in artificial intelligence and the availability of large-scale benchmarks have made deepfake video generation and manipulation easier. Therefore, developing reliable and robust deepfake video detection mechanisms is paramount. This research introduces a novel real-time deepfake video detection framework by analyzing gaze and blink patterns, addressing the spatial-temporal challenges unique to gaze and blink anomalies using the TimeSformer and hybrid Transformer-CNN models. The TimeSformer architecture leverages spatial-temporal attention mechanisms to capture fine-grained blinking intervals and gaze direction anomalies. Compared to state-of-the-art traditional convolutional models like MesoNet and EfficientNet, which primarily focus on global facial… More >