Yongning Zhang¹, Dongxue Li^1,2,*, Cen Yang³, Yongwang Gui⁴

Structural Durability & Health Monitoring, Vol.20, No.5, 2026, DOI:10.32604/sdhm.2026.081767 - 24 August 2026

Abstract To address the challenge that the baseline state of FE models for operational highly statically indeterminate bridges is difficult to evaluate accurately, this paper proposes an intelligent multi-parameter inversion and updating framework driven by measured dynamic strains and a LSTM neural network. First, to tackle the complex environmental interferences coupled within short-term monitoring strain signals, a moving-window baseline detrending and refined thermal effect decoupling algorithm is employed. This successfully strips away long-term dead loads and temperature drift, extracting pure mechanical strain sequences with a high signal-to-noise ratio. Second, to overcome the mode omission issue caused… More >

Xue-Wei Liu¹, Shao-Qiang Ye², Feng Qin³, Kai-Qing Zhou^1,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085316 - 13 August 2026

Abstract Weighted fuzzy production rules (WFPRs) provide superior expressiveness and interpretability in knowledge engineering area. However, manual construction of WFPRs is labor-intensive, time-consuming, and inherently subjective, which greatly restricts their practical application. The back propagation neural network (BPNN) has been widely adopted for automatic WFPR extraction. Nevertheless, its high sensitivity to initial weight configurations frequently results in premature convergence to local optima, generating redundant, poorly interpretable rule sets that compromise the inherent interpretability advantage of WFPRs. This paper proposes an elite dynamic scout-guided grey wolf optimizer (EDSG-GWO) and integrates it into a BPNN-based WFPR extraction framework… More >

Guan Yang¹, Shiyan Kang¹, Bo Chen^2,3, Yu Wang^4,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084499 - 13 August 2026

Abstract Web honeypots serve as foundational technologies for active deception, attracting attackers and extracting actionable threat intelligence. To address the challenges associated with manual and labor-intensive frontend construction, this paper presents the HFG framework, a security-oriented frontend generation framework designed for the large-scale deployment of heterogeneous Web-service decoy nodes. HFG utilizes a vision-to-code architecture integrating a PVT-CoT visual encoder, multi-scale adaptive fusion, visual token compression, a visual prefix bridge, and a Qwen2-LoRA code decoder. The model is trained on WebSight-derived data and evaluated on both the WebSight-derived test set and the Design2Code benchmark. General reconstruction metrics,… More >

Israt Jahan Munny¹, Anup Majumder², Bibhas Roy Chowdhury Piyas^3,*, Fahmid Al Farid^4,5,*, Md. Rafsan Jani², Fatama Jannat Tisha³, Israt Jahan³, Abu Saleh Musa Miah⁶, Hezerul Abdul Karim^4,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083167 - 13 August 2026

Abstract Orange is one of the most economically significant citrus crops worldwide, which is essential for the global food distribution network and supports rural livelihoods. However, its high susceptibility to destructive diseases results in substantial yield losses and long-term economic damage. Despite recent advances in smart agriculture, early and precise disease diagnosis remains challenging due to visual resemblance among disease symptoms, high computational cost, and limited model interpretability. To overcome these difficulties, we introduce a novel lightweight and Region of Interest (ROI)-guided explainable machine learning framework to identify orange disease that integrates a strategic feature selection… More >

Isil Karabey Aksakalli^*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083166 - 13 August 2026

Abstract Open-source platforms and issue tracking systems such as GitHub and Jira generate large volumes of issue reports and code changes, making effective bug identification a challenging task. This study investigates software bug prediction by integrating various feature extraction methods, including Word2Vec, TF-IDF, FastText, GloVe, and Doc2Vec, with several lightweight ML algorithms. Hybrid feature sets are further enhanced using Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) and empirical results indicate that Word2Vec and Multi-Layer Perceptron (MLP) provide comparatively stronger performance. The study proposes a Hybrid Attention-Residual Multilayer Perceptron (HAR-MLP) model to automatically classify software… More >

Piotr Musznicki¹, Marek Turzyński¹, Lyu Guanghua², Ghulam E Mustafa Abro^3,*, Viola Gierszewska¹, Arsalan Muhammad Soomar¹, Syed Hadi Hussain Shah²

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.082254 - 13 August 2026

Abstract This paper presents an accurate and efficient methodology for parameter extraction in complex impedance models using Differential Evolution (DE), an evolutionary optimization technique. The proposed approach targets equivalent RLC circuit topologies and aims to match measured impedance characteristics across a wide frequency spectrum. By formulating the extraction process as a global optimization problem, DE enables precise identification of component values, even for high-order models with multiple resonances. The method is implemented in Python using open-source libraries, facilitating reproducibility and integration into broader modeling workflows. Validation is performed on both analytically derived resonant circuits and physically More >

Ying Cao¹, Penghui Zhao¹, Xinyu Qiao¹, Ningfan Zhan¹, Xiaomei Zou^2,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084057 - 23 July 2026

Abstract Multimodal Sentiment Analysis (MSA) integrates diverse modalities to identify emotional states, yet performance often suffers in scenarios with missing data. In this situation, despite the promising results of recent methods, the failure of part methods to fully exploit the latent valid information contained in incomplete modalities may degrade predictive performance. Besides, to address the oversight of varying contributions across modalities to sentiment understanding, the score-based weighting schemes in the exhibited methods remain overly sensitive to data fluctuations, leading to unstable and unreliable predictions. To this end, we propose a novel method, Data Mining and Uncertainty-Aware… More >

Luong Vuong Nguyen^1,*, Hoang Tran², Thuy-Trang Pham³

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082406 - 23 July 2026

Abstract In the era of information overload, cross-domain recommendations offer a promising solution by leveraging user preferences across domains to improve recommendation accuracy and relevance. This study proposes a novel approach to cross-domain recommendations based on cognitive similarity derived from user-based features. We construct comprehensive user profiles across multiple domains by defining cognitive similarity based on user interaction data, including ratings, reviews, and genre preferences. We employ advanced feature extraction techniques, including TF-IDF for textual data and matrix factorization for latent factors, to quantify similarities in user preferences across domains. These cognitive similarity measures are then More >

Lei Zeng¹, Wei Wei^1,2, Liu Yang¹, Zhihong Wang^1,*, Qiaoguang Li^2,*

Journal of Polymer Materials, Vol.43, No.2, 2026, DOI:10.32604/jpm.2026.079607 - 30 June 2026

Abstract Natural polymers (NPs) are widely distributed in plants, animals, and microorganisms. They can be classified into three habitat-based groups, namely terrestrial polymers, marine polymers, and extreme natural polymers. This review summarizes their structural features, environmental adaptation mechanisms, and functional attributes. It also outlines the evolution of extraction technologies from traditional acid–alkali and mechanical methods to modern green solvent systems. The roles of physical modification and chemical derivatization in performance regulation and functional enhancement are examined. Artificial intelligence methods are introduced to support structure–property prediction, formulation design, and process optimization. Molecular dynamics and other computational approaches More >

Zixin Lin¹, Cen Xiong², Yanli Yu¹, Sy-Yu Shiau^1,*

Phyton-International Journal of Experimental Botany, Vol.95, No.6, 2026, DOI:10.32604/phyton.2026.080430 - 29 June 2026

Abstract Anthocyanins (ACNs), a major class of water-soluble flavonoid pigments, are responsible for the vivid red, purple, and blue hues in many edible ornamental flowers. Recently, increasing attention has been directed toward these flowers not only for their aesthetic value but also for their nutritional and functional potential, such as antioxidant, anti-inflammatory, antidiabetic, anticancer, and cardioprotective activities. This review summarizes current knowledge on the source and biosynthesis pathways of ACNs in edible ornamental flowers, highlighting the key enzymes and regulatory genes involved. Factors affecting ACN stability, such as chemical structure, pH, temperature, light, oxygen, water activity,… More >