Ching-Lung Fan^*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077260 - 09 April 2026

Abstract Unmanned aerial vehicle (UAV) images have high spatial resolution and are cost-effective to acquire. UAV platforms are easy to control, and the prevalence of UAVs has led to an emerging field of remote sensing technologies. However, the details of high-resolution images often lead to fragmented classification results and significant scale differences between objects. Additionally, distinguishing between objects on the basis of shape or textural characteristics can be difficult. Conventional classification methods based on pixels and objects can indeed be ineffective at detecting complex and fine-scale land use and land cover (LULC) features. Therefore, in this More >

Nahdi Saubari^1,2,*, Kunfeng Wang^1,*, Rachmat Muwardi^3,*, Andri Pranolo⁴

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077087 - 09 April 2026

Abstract This study proposes an Adaptive Pooling method based on an alpha (α) parameter to enhance the effectiveness and stability of convolutional neural networks (CNNs) in image classification tasks. Conventional pooling techniques, such as max pooling and average pooling, often exhibit limited adaptability when applied to datasets with heterogeneous distributions and varying levels of complexity. To address this limitation, the proposed approach introduces an α parameter ranging from 0 to 1 that continuously regulates the contribution of maximum-based and average-based pooling operations in a unified and flexible framework. The proposed method is evaluated using two benchmark… More >

Mohammed A. Ahmed¹, Jian Dong^2,*, Ronghua Shi², Ammar Nassr³, Hani Almaqtari³, Ala A. Alsanabani³

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.075616 - 09 April 2026

Abstract Facial Expression Recognition (FER) is an essential endeavor in computer vision, applicable in human-computer interaction, emotion assessment, and mental health surveillance. Although Convolutional Neural Networks (CNNs) have proven effective in Facial Emotion Recognition, they encounter difficulties in capturing long-range connections and global context. To address these constraints, we propose Spatial Quad-Similarity Network (SQSNet), an innovative hybrid framework that integrates the local feature extraction capabilities of CNNs with the global contextual modeling efficacy of Swin Transformers via a cohesive fusion technique. SQSNet introduces the Spatial Quad-Similarity (SQS) module, a feature refinement approach that amplifies discriminative characteristics… More >

Wael A. Altabey^*

Structural Durability & Health Monitoring, Vol.20, No.2, 2026, DOI:10.32604/sdhm.2025.073949 - 31 March 2026

Abstract The study of long-term pavement performance is a fundamental topic in the field of highway engineering. Through comprehensive and in-depth research on the pavement system, the previous scattered, one-sided, superficial, and perceptual knowledge and experience are summarized and sublimated into a systematic and complete engineering theory, thereby providing powerful guidance and assistance for the practice of pavement design, construction, maintenance, operation, and management. In this research, the mentoring system deployment technology for automatic monitoring is carried out for long-term pavement performance. By burying a variety of sensors in different parts of the road surface, base,… More >

Qian Zhang¹, Lei Fu^1,2, Renzhou Chen³, Xu Zhan^4,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.074939 - 12 March 2026

Abstract Triply periodic minimal surface (TPMS) structures are widely utilized in engineering and biomedical fields owing to their superior mechanical and functional properties. However, limited by the current additive manufacturing (AM) techniques, insufficient wall thickness often leads to poor forming quality or even printing failure. Therefore, accurate prediction of wall thickness parameters during the design stage is essential. This study proposes a prediction approach for the wall thickness parameters of TPMS models by integrating a Convolutional Neural Network–Support Vector Regression (CNN-SVM) framework with Multiple Linear Regression (MLR). A total of 152 TPMS models were randomly generated,… More >

HeeSeok Choi¹, Joon-Min Gil^2,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.074511 - 12 March 2026

Abstract Given the growing number of vehicle accidents caused by unintended acceleration and braking failure, verifying Sudden Unintended Acceleration (SUA) incidents has become a persistent challenge. A central issue of debate is whether such events stem from mechanical malfunctions or driver pedal misapplications. However, existing verification procedures implemented by vehicle manufacturers often involve closed tests after vehicle recalls; thus raising ongoing concerns about reliability and transparency. Consequently, there is a growing need for a user-driven framework that enables independent data acquisition and verification. Although previous studies have addressed SUA detection using deep learning, few have explored… More >

Lin-Hui Liu¹, Dong-Jie Liu^1,*, Yin-Yan Zhang¹, Xiao-Bo Jin², Xiu-Cheng Wu³, Guang-Gang Geng¹

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.074253 - 12 March 2026

Abstract Phishing email detection represents a critical research challenge in cybersecurity. To address this, this paper proposes a novel Double-S (statistical-semantic) feature model based on three core entities involved in email communication: the sender, recipient, and email content. We employ strategic game theory to analyze the offensive strategies of phishing attackers and defensive strategies of protectors, extracting statistical features from these entities. We also leverage the Qwen large language model to excavate implicit semantic features (e.g., emotional manipulation and social engineering tactics) from email content. By integrating statistical and semantic features, our model achieves a robust More >

Seunggyu Byeon¹, Jung-hun Lee², Jong-Deok Kim^3,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.074033 - 12 March 2026

Abstract This paper introduces a fuzzy C-means-based pooling layer for convolutional neural networks that explicitly models local uncertainty and ambiguity. Conventional pooling operations, such as max and average, apply rigid aggregation and often discard fine-grained boundary information. In contrast, our method computes soft memberships within each receptive field and aggregates cluster-wise responses through membership-weighted pooling, thereby preserving informative structure while reducing dimensionality. Being differentiable, the proposed layer operates as standard two-dimensional pooling. We evaluate our approach across various CNN backbones and open datasets, including CIFAR-10/100, STL-10, LFW, and ImageNette, and further probe small training set restrictions More >

Zheng Zhao¹, Doo Heon Song², Kwang Baek Kim^1,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.073969 - 12 March 2026

Abstract Deep learning attention mechanisms have achieved remarkable progress in computer vision, but still face limitations when handling images with ambiguous boundaries and uncertain feature representations. Conventional attention modules such as SE-Net, CBAM, ECA-Net, and CA adopt a deterministic paradigm, assigning fixed scalar weights to features without modeling ambiguity or confidence. To overcome these limitations, this paper proposes the Fuzzy Attention Network Layer (FANL), which integrates intuitionistic fuzzy set theory with convolutional neural networks to explicitly represent feature uncertainty through membership (μ), non-membership (ν), and hesitation (π) degrees. FANL consists of four core modules: (1)… More >

Syed Nisar Hussain Bukhari^*, Sehar Altaf

Journal on Artificial Intelligence, Vol.8, pp. 137-152, 2026, DOI:10.32604/jai.2026.077943 - 11 March 2026

Abstract Knee osteoarthritis is a progressive degenerative joint disorder that leads to pain, stiffness, and reduced mobility, significantly affecting quality of life. Early and reliable diagnosis is essential for effective disease management, yet conventional radiographic assessment remains time-consuming and subject to inter-observer variability. This study presents a comparative deep learning (DL) based approach for automated severity classification of knee osteoarthritis using plain radiographic images. Multiple pretrained convolutional neural network architectures, including EfficientNetB3, InceptionNet, VGG19, ResNet, and EfficientNetV2S, were evaluated within a transfer learning paradigm. All models were trained and assessed on a publicly available dataset to More >