Ameer Hamza, Robertas Damaševičius^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-41, 2026, DOI:10.32604/cmc.2025.069721 - 10 November 2025

Abstract This systematic review aims to comprehensively examine and compare deep learning methods for brain tumor segmentation and classification using MRI and other imaging modalities, focusing on recent trends from 2022 to 2025. The primary objective is to evaluate methodological advancements, model performance, dataset usage, and existing challenges in developing clinically robust AI systems. We included peer-reviewed journal articles and high-impact conference papers published between 2022 and 2025, written in English, that proposed or evaluated deep learning methods for brain tumor segmentation and/or classification. Excluded were non-open-access publications, books, and non-English articles. A structured search was… More >

Xuanhong Wang¹, Hongyu Guo¹, Jiazhen Li¹, Mingchen Wang¹, Xian Wang¹, Yijun Zhang^2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-19, 2026, DOI:10.32604/cmc.2025.069482 - 10 November 2025

Abstract Over the past decade, large-scale pre-trained autoregressive and diffusion models rejuvenated the field of text-guided image generation. However, these models require enormous datasets and parameters, and their multi-step generation processes are often inefficient and difficult to control. To address these challenges, we propose CAFE-GAN, a CLIP-Projected GAN with Attention-Aware Generation and Multi-Scale Discrimination, which incorporates a pre-trained CLIP model along with several key architectural innovations. First, we embed a coordinate attention mechanism into the generator to capture long-range dependencies and enhance feature representation. Second, we introduce a trainable linear projection layer after the CLIP text… More >

Moneerah Alotaibi^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.069195 - 10 November 2025

Abstract Advanced traffic monitoring systems encounter substantial challenges in vehicle detection and classification due to the limitations of conventional methods, which often demand extensive computational resources and struggle with diverse data acquisition techniques. This research presents a novel approach for vehicle classification and recognition in aerial image sequences, integrating multiple advanced techniques to enhance detection accuracy. The proposed model begins with preprocessing using Multiscale Retinex (MSR) to enhance image quality, followed by Expectation-Maximization (EM) Segmentation for precise foreground object identification. Vehicle detection is performed using the state-of-the-art YOLOv10 framework, while feature extraction incorporates Maximally Stable Extremal… More >

Ginanjar Suwasono Adi¹, Samsul Huda^2,*, Griffani Megiyanto Rahmatullah³, Dodit Suprianto¹, Dinda Qurrota Aini Al-Sefy³, Ivon Sandya Sari Putri⁴, Lalu Tri Wijaya Nata Kusuma⁵

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.068619 - 10 November 2025

Abstract In the competitive retail industry of the digital era, data-driven insights into gender-specific customer behavior are essential. They support the optimization of store performance, layout design, product placement, and targeted marketing. However, existing computer vision solutions often rely on facial recognition to gather such insights, raising significant privacy and ethical concerns. To address these issues, this paper presents a privacy-preserving customer analytics system through two key strategies. First, we deploy a deep learning framework using YOLOv9s, trained on the RCA-TVGender dataset. Cameras are positioned perpendicular to observation areas to reduce facial visibility while maintaining accurate More >

Xinrui Ma, Shizhi Chen^*

Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1745-1767, 2025, DOI:10.32604/sdhm.2025.071148 - 17 November 2025

Abstract The rapid and accurate assessment of structural damage following an earthquake is crucial for effective emergency response and post-disaster recovery. Traditional manual inspection methods are often slow, labor-intensive, and prone to human error. To address these challenges, this study proposes STPEIC (Swin Transformer-based Framework for Interpretable Post-Earthquake Structural Classification), an automated deep learning framework designed for analyzing post-earthquake images. STPEIC performs two key tasks: structural components classification and damage level classification. By leveraging the hierarchical attention mechanisms of the Swin Transformer (Shifted Window Transformer), the model achieves 85.4% accuracy in structural component classification and 85.1% More >

Chuang Meng^1,*, Yutong Bi¹, Bingji Zhang¹, Wentao Fu², Guang Chen³

Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1681-1694, 2025, DOI:10.32604/sdhm.2025.068732 - 17 November 2025

Abstract The lift-and-transverse type parking equipment, with its core advantages such as high space utilization, modular and flexible layout, and intelligent operation, has become an efficient solution to alleviate the urban parking problem. However, existing research still lacks a systematic evaluation of its structural performance, particularly in areas such as the fatigue characteristics of steel frame materials, stress distribution under dynamic loads, and resonance risk analysis. The stress amplitude (S) and fatigue life (N) relationship curve of Q235 steel, the material used in the steel frame of the lift-and-transverse type parking equipment, was obtained through fatigue… More >

Dan Wang¹, Zhoubin Li¹, Yuze Xia^1,2,*, Zhenhua Yu^1,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5823-5845, 2025, DOI:10.32604/cmc.2025.071656 - 23 October 2025

Abstract Multimodal Aspect-Based Sentiment Analysis (MABSA) aims to detect sentiment polarity toward specific aspects by leveraging both textual and visual inputs. However, existing models suffer from weak aspect-image alignment, modality imbalance dominated by textual signals, and limited reasoning for implicit or ambiguous sentiments requiring external knowledge. To address these issues, we propose a unified framework named Gated-Linear Aspect-Aware Multimodal Sentiment Network (GLAMSNet). First of all, an input encoding module is employed to construct modality-specific and aspect-aware representations. Subsequently, we introduce an image–aspect correlation matching module to provide hierarchical supervision for visual-textual alignment. Building upon these components, More >

Yingying Guo¹, Ziyu Cui², Jibing Shen¹, Pei Li^3,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4519-4550, 2025, DOI:10.32604/cmc.2025.071504 - 23 October 2025

Abstract Accurate simulation of acoustic wave propagation in complex structures is of great importance in engineering design, noise control, and related research areas. Although traditional numerical simulation methods can provide precise results, they often face high computational costs when applied to complex models or problems involving parameter uncertainties, particularly in the presence of multiple coupled parameters or intricate geometries. To address these challenges, this study proposes an efficient algorithm for simulating the acoustic field of structures with adhered sound-absorbing materials while accounting for ground reflection effects. The proposed method integrates Catmull-Clark subdivision surfaces with the boundary… More >

Boyan Cui^1,2, Huijuan Wang^1,*, Yongjun Qi^1,*, Hongce Chen¹, Quanbo Yuan^1,3, Dongran Liu¹, Xuehua Zhou¹

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5531-5553, 2025, DOI:10.32604/cmc.2025.070101 - 23 October 2025

Abstract The proliferation of malware and the emergence of adversarial samples pose severe threats to global cybersecurity, demanding robust detection mechanisms. Traditional malware detection methods suffer from limited feature extraction capabilities, while existing Vision Transformer (ViT)-based approaches face high computational complexity due to global self-attention, hindering their efficiency in handling large-scale image data. To address these issues, this paper proposes a novel hybrid enhanced Vision Transformer architecture, HERL-ViT, tailored for malware detection. The detection framework involves five phases: malware image visualization, image segmentation with patch embedding, regional-local attention-based feature extraction, enhanced feature transformation, and classification. Methodologically,… 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 >