Luis Sánchez Calderón^*, David Valverde Burneo, Walter Hurtares Orrala

Structural Durability & Health Monitoring, Vol.20, No.3, 2026, DOI:10.32604/sdhm.2026.077137 - 18 May 2026

Abstract Accurate characterization of live load histories remains critical for structural safety and efficient design; however, traditional codes often overestimate in-service loads. This study introduced an AI-driven framework integrating YOLOv8 object detection and DeepFace gender classification with continuous video surveillance to monitor live loads in academic buildings. Gender classification used local anthropometric data (77 kg males, 61 kg females) for precise load estimation, with privacy ensured via local processing and anonymized metadata only. Observed peaks were substantially below Eurocode and IBC provisions, confirming code conservatism. Uncertainty propagation from detector errors (recall 0.57, ±0.02 Kn/m²) minimally impacted projections. More >

Thanh Tuan Nguyen¹, Cuong Nguyen Dinh Hoa^2,3,*

Intelligent Automation & Soft Computing, Vol.41, pp. 1-24, 2026, DOI:10.32604/iasc.2026.078344 - 12 May 2026

Abstract Accurate crude oil price forecasting is critical for global economic stability but remains an exceptionally challenging task due to the data’s complex, non-linear, and non-stationary nature. Deep learning models like LSTMs are widely favored. However, the dominant research trend currently focuses on increasingly complex hybrid and ensemble architectures. These models often suffer from high computational overhead, intricate tuning processes, and potential overfitting, raising critical questions about their necessity. In this paper, we challenged the assumption that complexity is required for high performance by proposing and evaluating a streamlined 1D-CNN model. We conducted a comprehensive evaluation… More >

Computers, Materials & Continua Editorial Office

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.083417 - 08 May 2026

Abstract This article has no abstract. More >

Giulio Caporro¹, Paolo Russo^2,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079331 - 08 May 2026

Abstract Monocular depth estimation (MDE) has become a practical alternative to active range sensing in many indoor scenarios, enabled by supervised deep learning models that predict dense depth maps from a single RGB image. However, most modern MDE systems assume mid-to-high resolution inputs and non-trivial compute budgets, limiting their direct applicability in embedded and bandwidth-constrained settings. This paper studies low resolution MDE, focusing on 96×96 inputs, where geometric cues are strongly degraded and naively downsizing high-resolution architectures often leads to unstable training and poor accuracy. We propose DeepEchoNet, a lightweight hybrid CNN-transformer model tailored to operate natively More > Graphic Abstract

Duong Do The^1,2, Duc-Nghia Tran³, Hoang-Dieu Vu⁴, Manh-Tuyen Vi^4,*, Duc-Tan Tran^4,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079056 - 08 May 2026

Abstract Road-surface identification is important for transportation monitoring and maintenance. However, this task is challenging due to the complexity of vibration signals, feature overlap among different surface types, and variations in real-world operating conditions. These challenges become more significant in time-series classification, where models must achieve high accuracy while remaining computationally efficient and suitable for low-cost hardware. This study investigates the design and evaluation of an automatic road-surface classification system using motion data collected from inertial sensors mounted on a vehicle, including accelerometers and gyroscopes. The system segments synchronized IMU signals into fixed-length windows and assigns… More >

Bing-Zhou Chen^1,2, Hai-Ying Zheng^1,2, Ao-Wen Wang^1,3, Ke-Lei Xia^1,2, Li-Feng Fan^1,3, Zhong-Yi Wang^1,3, Lan Huang^1,2,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.078894 - 08 May 2026

Abstract Many sensing and imaging modalities naturally yield complex-valued signals, where magnitude and phase jointly convey information. Complex-valued neural networks (CVNNs) possess unique advantages in processing phase-sensitive data (e.g., synthetic aperture radar (SAR) and magnetic resonance imaging (MRI)), yet their widespread adoption is hindered by significant computational overhead and training instability. To address these challenges, this paper presents the Wirtinger Derivative Complete Complex Network (WCCN), a unified and efficient framework for complex-valued deep learning. The proposed framework systematically addresses three key challenges in CVNNs: computational efficiency, parameter redundancy, and training stability. WCCN integrates three core components.… More >

Liping Wang¹, Jiang Wu^1,2,*, Liang Wang³

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.078591 - 08 May 2026

Abstract The rapid growth of networked systems and the increasing diversity of cyberattack behaviors have posed significant challenges to intrusion detection, particularly in scenarios characterized by high-dimensional features and severe class imbalance. Conventional detection approaches based on handcrafted rules or shallow representations often exhibit limited robustness under such conditions. To address these issues, this paper presents a hybrid deep learning framework for network intrusion detection that integrates complementary feature learning mechanisms within a dual-branch architecture. Specifically, a Transformer branch is employed to model long-range temporal dependencies in network traffic, while a convolutional neural network branch (CNN)… More >

Noor Mueen Mohammed Ali Hayder^1,2, Seyed Amin Hosseini Seno^2,*, Mehdi Ebady Manaa^3,4, Hamid Noori², Davood Zabihzadeh⁵

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.078546 - 08 May 2026

Abstract With the rapid expansion of networked systems, Distributed Denial-of-Service (DDoS) attacks have become a major threat to Internet security and service availability. Due to their limited scalability, incapacity to capture temporal and relational relationships, and decreased detection accuracy under dynamic and high-volume network traffic, traditional machine learning algorithms frequently fail in large-scale DDoS scenarios. This encourages the application of deep learning techniques that can simulate intricate relationships. This survey systematically reviews graph-based deep learning and Transformer models for DDoS detection. We categorize methods for transforming network traffic into graph representations and analyze key architectures, including… More >

Vinh Truong Hoang^*, Nghia Dinh, Luu Quang Phuong, Kiet Tran-Trung, Ha Duong Thi Hong, Bay Nguyen Van, Hau Nguyen Trung, Thien Ho Huong

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.078089 - 08 May 2026

Abstract Visual speech recognition (VSR) aims to infer spoken content from visual observations of articulatory movements. Despite significant progress, it remains a challenging task in computer vision and speech processing. Its difficulty arises from pronounced speaker-to-speaker variability, the presence of homophenes (phonemes that are visually indistinguishable), changes in illumination, and the intrinsically high-dimensional nature of spatiotemporal lip dynamics. In this work, we propose NestLipGNN, a graph-based framework that integrates Graph Neural Networks (GNNs) with a nested multi-granularity learning strategy for visual speech recognition. We construct dynamic lip graphs from facial landmarks to model both spatial relationships… More >

Duane G. Noé¹, Ku-Chin Lin², Chang-Lin Chuang³, Yung-Tsung Cheng^3,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.077838 - 08 May 2026

Abstract Steel manufacturing requires high-throughput and high-reliability surface inspection to minimize safety risks, scrap rates, and downstream quality reductions. Conventional rule-based vision and manual inspection are often impeded in real production environments by variable illumination, complex textures, subtle defect morphology, and stringent latency constraints imposed by production-line operation. Deep learning (DL) has become a dominant paradigm for the detection and classification of defects when inspecting steel, but many previous studies have performed broad architectural overviews without explicitly connecting model and pipeline choices to deployment-critical factors such as processing speed, hardware availability, annotation cost, and robustness during… More >