Tahira Khalil¹, Sadeeq Jan^2,*, Rania M. Ghoniem³, Muhammad Imran Khan Khalil¹

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-20, 2026, DOI:10.32604/cmc.2025.072655 - 09 December 2025

Abstract The contemporary era is characterized by rapid technological advancements, particularly in the fields of communication and multimedia. Digital media has significantly influenced the daily lives of individuals of all ages. One of the emerging domains in digital media is the creation of cartoons and animated videos. The accessibility of the internet has led to a surge in the consumption of cartoons among young children, presenting challenges in monitoring and controlling the content they view. The prevalence of cartoon videos containing potentially violent scenes has raised concerns regarding their impact, especially on young and impressionable minds.… More >

Hairul Aysa Abdul Halim Sithiq^1,*, Liyana Shuib^1,*, Muneer Ahmad², Chermaine Deepa Antony³

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-25, 2026, DOI:10.32604/cmc.2025.067781 - 10 November 2025

Abstract Honeycombing Lung (HCL) is a chronic lung condition marked by advanced fibrosis, resulting in enlarged air spaces with thick fibrotic walls, which are visible on Computed Tomography (CT) scans. Differentiating between normal lung tissue, honeycombing lungs, and Ground Glass Opacity (GGO) in CT images is often challenging for radiologists and may lead to misinterpretations. Although earlier studies have proposed models to detect and classify HCL, many faced limitations such as high computational demands, lower accuracy, and difficulty distinguishing between HCL and GGO. CT images are highly effective for lung classification due to their high resolution,… More >

Kunal Thakur¹, Ashu Taneja^1,*, Ali Alqahtani², Nayef Alqahtani³

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4827-4848, 2025, DOI:10.32604/cmc.2025.067901 - 23 October 2025

Abstract Accident detection plays a critical role in improving traffic safety by enabling timely emergency response and reducing the impact of road incidents. The main challenge lies in achieving real-time, reliable and highly accurate detection across diverse Internet-of-vehicles (IoV) environments. To overcome this challenge, this paper leverages deep learning to automatically learn patterns from visual data to detect accidents with high accuracy. A visual classification model based on the ResNet-50 architecture is presented for distinguishing between accident and non-accident images. The model is trained and tested on a labeled dataset and achieves an overall accuracy of… More >

Divya Arivalagan, Vignesh Ochathevan^*, Rubankumar Dhanasekaran

Congenital Heart Disease, Vol.20, No.4, pp. 477-501, 2025, DOI:10.32604/chd.2025.070372 - 18 September 2025

Abstract Background: The accurate identification of cardiac abnormalities is essential for proper diagnosis and effective treatment of cardiovascular diseases. Method: This work introduces an advanced methodology for detecting cardiac abnormalities and estimating electrocardiographic age (ECG Age) using sophisticated signal processing and deep learning techniques. This study looks at six main heart conditions found in 12-lead electrocardiogram (ECG) data. It addresses important issues like class imbalances, missing lead scenarios, and model generalizations. A modified residual neural network (ResNet) architecture was developed to enhance the detection of cardiac abnormalities. Results: The proposed ResNet demonst rated superior performance when compared with… More > Graphic Abstract

Hussain Dawood¹, Marriam Nawaz², Tahira Nazir³, Ali Javed², Abdul Khader Jilani Saudagar^4,*, Hatoon S. AlSagri⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 429-459, 2025, DOI:10.32604/cmes.2025.066415 - 31 July 2025

Abstract Reliable human action recognition (HAR) in video sequences is critical for a wide range of applications, such as security surveillance, healthcare monitoring, and human-computer interaction. Several automated systems have been designed for this purpose; however, existing methods often struggle to effectively integrate spatial and temporal information from input samples such as 2-stream networks or 3D convolutional neural networks (CNNs), which limits their accuracy in discriminating numerous human actions. Therefore, this study introduces a novel deep-learning framework called the ARNet, designed for robust HAR. ARNet consists of two main modules, namely, a refined InceptionResNet-V2-based CNN and… More >

Mohammed Alshahrani¹, Mohammed Al-Jabbar^1,*, Ebrahim Mohammed Senan^2,3, Fatima Ali Amer jid Almahri⁴, Sultan Ahmed Almalki¹, Eman A. Alshari^3,5

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3639-3675, 2025, DOI:10.32604/cmes.2025.064668 - 30 June 2025

Abstract Multiple Sclerosis (MS) poses significant health risks. Patients may face neurodegeneration, mobility issues, cognitive decline, and a reduced quality of life. Manual diagnosis by neurologists is prone to limitations, making AI-based classification crucial for early detection. Therefore, automated classification using Artificial Intelligence (AI) techniques has a crucial role in addressing the limitations of manual classification and preventing the development of MS to advanced stages. This study developed hybrid systems integrating XGBoost (eXtreme Gradient Boosting) with multi-CNN (Convolutional Neural Networks) features based on Ant Colony Optimization (ACO) and Maximum Entropy Score-based Selection (MESbS) algorithms for early… More >

Mengqi Li, Xiaodong Huang^*, Lifeng Wu

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 975-995, 2025, DOI:10.32604/cmc.2025.062621 - 09 June 2025

Abstract Micro-expressions, fleeting involuntary facial cues lasting under half a second, reveal genuine emotions and are valuable in clinical diagnosis and psychotherapy. Real-time recognition on resource-constrained embedded devices remains challenging, as current methods struggle to balance performance and efficiency. This study introduces a semi-lightweight multifunctional network that enhances real-time deployment and accuracy. Unlike prior simplistic feature fusion techniques, our novel multi-feature fusion strategy leverages temporal, spatial, and differential features to better capture dynamic changes. Enhanced by Residual Network (ResNet) architecture with channel and spatial attention mechanisms, the model improves feature representation while maintaining a lightweight design. More >

Vidivelli Soundararajan^*, Manikandan Ramachandran^*, Srivatsan Vinodh Kumar

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5259-5277, 2025, DOI:10.32604/cmc.2025.063059 - 19 May 2025

Abstract Many applications, including security systems, medical diagnostics, and human-computer interfaces, depend on eye gaze recognition. However, due to factors including individual variations, occlusions, and shifting illumination conditions, real-world scenarios continue to provide difficulties for accurate and consistent eye gaze recognition. This work is aimed at investigating the potential benefits of employing transfer learning to improve eye gaze detection ability and efficiency. Transfer learning is the process of fine-tuning pre-trained models on smaller, domain-specific datasets after they have been trained on larger datasets. We study several transfer learning algorithms and evaluate their effectiveness on eye gaze… More >

Shabana Ziyad Puthu Vedu^1,*, Wafaa A. Ghonaim², Naglaa M. Mostafa³, Pradeep Kumar Singh⁴

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2599-2617, 2025, DOI:10.32604/cmc.2025.062695 - 16 April 2025

Abstract Hearing and Speech impairment can be congenital or acquired. Hearing and speech-impaired students often hesitate to pursue higher education in reputable institutions due to their challenges. However, the development of automated assistive learning tools within the educational field has empowered disabled students to pursue higher education in any field of study. Assistive learning devices enable students to access institutional resources and facilities fully. The proposed assistive learning and communication tool allows hearing and speech-impaired students to interact productively with their teachers and classmates. This tool converts the audio signals into sign language videos for the… More >

Zengyu Cai^1,*, Yuming Dai¹, Jianwei Zhang^2,3,*, Yuan Feng⁴

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3335-3350, 2025, DOI:10.32604/cmc.2025.061206 - 16 April 2025

Abstract The rapid development and widespread adoption of Internet technology have significantly increased Internet traffic, highlighting the growing importance of network security. Intrusion Detection Systems (IDS) are essential for safeguarding network integrity. To address the low accuracy of existing intrusion detection models in identifying network attacks, this paper proposes an intrusion detection method based on the fusion of Spatial Attention mechanism and Residual Neural Network (SA-ResNet). Utilizing residual connections can effectively capture local features in the data; by introducing a spatial attention mechanism, the global dependency relationships of intrusion features can be extracted, enhancing the intrusion More >