Zhichao Liu^1,2, Jun Zhang^2,*, Dongling Yu³, Libing Jin¹, Bingquan Song³

Structural Durability & Health Monitoring, Vol.20, No.5, 2026, DOI:10.32604/sdhm.2026.081186 - 24 August 2026

Abstract The chloride ion penetration resistance of manufactured sand concrete (MSC) critically determines the durability of marine concrete structures. However, its accurate prediction is challenging due to high uncertainty from complex influencing factors. To address this, a back-propagation neural network model optimized by a hybrid Whale Optimization Algorithm and Adaptive Moment Estimation strategy (WOA-Adam-BPNN) was developed to predict the electrical flux. The model was trained and tested on 245 experimental datasets covering eight key parameters and validated across four typical mix proportions. Results show that the WOA-Adam hybrid strategy effectively combines global search capability with adaptive More >

Anna Szymczak-Graczyk¹, Zijadin Guri², Ilir Canaj², Tomasz Garbowski^3,*

Structural Durability & Health Monitoring, Vol.20, No.5, 2026, DOI:10.32604/sdhm.2026.080104 - 24 August 2026

Abstract In this study, an energy-consistent analytical–numerical framework is proposed to determine the effective shear correction factor $k_s$ for non-homogeneous cross-sections within the Timoshenko beam theory, such as a porous cementitious matrix (e.g., perlite-based material) combined with an embedded steel I-section. The formulation enforces equivalence between the real heterogeneous shear strain energy, governed by a spatial shear modulus field $G(y,z)$, and its beam-theory representation based on $k_s(G_{\text{ref}}A)$. A pixel/voxel discretization is introduced to evaluate the generalized shear-energy integral and to quantify the deviation of $k_s$ from classical homogeneous benchmarks. The results demonstrate… More >

Xue Yang¹, Shunpeng Yang¹, Wanying Shi^2,*, Weizhong Yuan¹, Sihui Long¹, Ruixiao Sun³, Cheng Yang⁴

Structural Durability & Health Monitoring, Vol.20, No.5, 2026, DOI:10.32604/sdhm.2026.078468 - 24 August 2026

Abstract Unmanned aerial vehicle (UAV) imaging techniques have emerged as a promising solution to boost the accuracy and dependability of visual monitoring for railway facilities and peripheral ecological environments, garnering widespread research interest in recent years. Nevertheless, aerial images acquired by UAVs are prone to severe quality deterioration in fog and haze weather scenarios, which greatly hinders the progress and effectiveness of railway routine inspection work. As modern railway systems pursue higher operational safety benchmarks and intelligent rail transit technologies achieve iterative breakthroughs, video monitoring systems have evolved into indispensable core equipment for identifying and early… More >

Merve Sinem Karahan^*, Mehmet Karaköse

Journal of Quantum Computing, Vol.8, pp. 101-121, 2026, DOI:10.32604/jqc.2026.078969 - 21 August 2026

Abstract Accurate prediction of carbon emissions is essential for developing sustainable environmental policies and mitigating global warming. Road transportation represents one of the major sources of global CO₂ emissions due to its dependence on fossil fuels. This study presents a comparative framework that evaluates classical machine learning models alongside a hybrid quantum–classical learning architecture for vehicle-based CO₂ emission prediction. A large-scale vehicle emissions dataset containing 7385 samples collected over approximately seven years was obtained from the official open-data platform of the Government of Canada. Key vehicle characteristics, including engine size, fuel consumption, transmission type, and vehicle class,… More >

Dong-Fang Wu^1,2, Jiaojiao Deng^1,2, Zhiwei Ye^1,2,*, Rong Gao^1,2, Fan Ma^1,2, Dingfeng Song^1,2

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085308 - 13 August 2026

Abstract Anomaly detection is used to identify data points deviating from normal patterns and plays a significant role in fields such as fault diagnosis, biomedicine, and cybersecurity. However, anomaly detection tasks in practical applications often involve high-dimensional data which presents challenges such as severe feature redundancy, hidden anomaly patterns, and difficulty in capturing uncertainty. These issues make it difficult to effectively identify anomalies, thereby limiting the model’s discriminative power and stability. To address these challenges, we propose a high-dimensional anomaly detection model, MSFO-EIF, integrating multi-strategy feature optimization with rough set modeling. First, in the feature space… More >

Ye Lu¹, Haoyang Hu^1,*, Wenyi Chang¹, Wanbin Liu¹, Wenyuan Zhang²

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084490 - 13 August 2026

Abstract Fine-grained multiclass intrusion detection over flow-level traffic remains difficult, largely because class boundaries are often entangled, temporal dependence is non-negligible, and the label distribution is heavily long-tailed. In this study, a compact temporal convolutional network (TCN)-bidirectional gated recurrent unit (BiGRU)-TinyTransformer framework is developed to bring these issues into a single modeling pipeline: the TCN branch focuses on short-range anomalous patterns, the BiGRU branch captures bidirectional temporal structure, and the TinyTransformer branch complements them with broader contextual interaction learning. To reduce the bias induced by extreme imbalance, training is not driven by a single correction mechanism, More >

Ren-Jieh Kuo^1,*, Muhammad Rizki¹, Ferani Eva Zulvia², Eddy Roflin³

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084436 - 13 August 2026

Abstract Imbalanced data remain a critical challenge in classification, as skewed distributions bias models toward majority classes and diminish sensitivity to minority classes, which are often the most critical. To address this issue, this paper proposes the Information Filtered Hybrid Algorithm (IF-HA), a novel entropy-based sampling method that integrates undersampling and oversampling guided by information theory. IF-HA quantifies instance importance through an instance-wise difference statistic. In the undersampling stage, majority of instances with low difference statistics in the border area are eliminated, while in the oversampling stage, synthetic samples are generated from two minority core points… More >

Carlos Rosa-Remedios^*, Pino Caballero-Gil^*, Jezabel Molina-Gil

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084352 - 13 August 2026

Abstract Increasing digitalization exposes critical infrastructure to sophisticated cyber threats, requiring new approaches to improving security and resilience. While classical machine learning techniques have shown promise in anomaly detection and threat mitigation, emerging quantum-inspired methods offer new opportunities to enhance detection capabilities by leveraging principles derived from quantum computing. The objective of this work is to propose a model for the early detection of Telephony Denial of Service attacks using a combination of classical algorithms and quantum computing-based techniques. Call records are embedded into a low-dimensional quantum feature space using spatial and temporal attributes, mapped through… More >

Yunrui Bi^1,*, Qiliang Yang¹, Qinglin Ding¹, Bin Ran², Kun Liu¹, Mingjie Zhang¹

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084267 - 13 August 2026

Abstract To improve regional traffic signal coordination under uncertain and dynamic traffic conditions, this paper proposes a hybrid Type-2 fuzzy reinforcement learning framework integrated with Beetle Antennae Search (BAS) and Deep Q-Network (DQN), named Type-2 fuzzy Beetle Antennae Search and Deep Q-Network (T2-BAS-DQN). In this framework, DQN remains active during online signal control, while the Type-2 fuzzy module provides uncertainty-aware correction for phase selection and green-time adjustment. BAS is used only in the offline training stage to optimize a low-dimensional parameter vector related to fuzzy correction, reward adjustment, and coordination pressure. A 3 × 3 Simulation… More >

Sultan Shutyan Albalawi¹, Mohd Yamani Idna Idris^1,2,*, Ainuddin Wahid Bin Abdul Wahab¹

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083860 - 13 August 2026

Abstract As Distributed Denial-of-Service (DDoS) attacks grow in size and complexity, standard intrusion detection systems are hitting a wall. Most struggle to generalize well, require too much computational power, or lack model diversity. To tackle this, we developed TS-SCHO-TSE, a unified hybrid framework for DDoS detection. Unlike traditional methods that treat feature selection and ensemble building as isolated, step-by-step tasks, our approach combines everything. We map feature masks, classifier states, voting weights, and hyperparameters into a single mixed discrete-continuous search space to optimize them all at once. We tested our system against classical functions (F1–F23), the More >