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 >

Pengcheng Xia¹, Zhihong Pan^1,*, Ruoyu Chen¹, Linyuan Wang²

Structural Durability & Health Monitoring, Vol.20, No.5, 2026, DOI:10.32604/sdhm.2026.080742 - 24 August 2026

Abstract The grouting sleeves in prefabricated structures critically depend on the strength development of the grout; however, existing non-destructive testing methods struggle to capture its time-dependent evolution. This study proposes a hybrid prediction framework that combines the Synthetic Aperture Focusing Technique (SAFT) with a hyperparameter-optimized XGBoost model. Ultrasonic signals were collected at five curing stages (0, 1, 3, 7, and 28 days), from which SAFT-derived features and the area ratios of six color regions were extracted as input variables, with the curing age serving as the model output. Following a correlation analysis with compressive strength, three… More >

Usman Khalid¹, Nikhil Shah¹, Rajesh Kavia², Deepak Batura^2,*

Canadian Journal of Urology, Vol.33, No.4, pp. 735-752, 2026, DOI:10.32604/cju.2026.074820 - 21 August 2026

Abstract Bladder cancer (BCa) diagnosis relies heavily on cystoscopy and imaging. Both have limited sensitivity and accuracy, particularly for muscle-invasive disease. Artificial intelligence (AI) has emerged as a promising tool for improving detection, grading, and staging by extracting imaging features that exceed human perception. We conducted a narrative review of peer-reviewed, English-language studies published between 2015 and 2025. We identified 75 articles and synthesized data from 35 key studies retrieved via PubMed, Google Scholar, Scopus, and Embase. Data were synthesized narratively, emphasizing diagnostic performance, clinical relevance, and study limitations. In cystoscopy, AI models achieved high accuracy… 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 >

Animaw Kerie Aseres^1,2,*, Asrat Mulatu Beyene^3,2, Lemlem Kassa Tegegne^1,2

Journal of Cyber Security, Vol.8, pp. 559-576, 2026, DOI:10.32604/jcs.2026.086559 - 21 August 2026

Abstract Conventional multi-factor and one-time authentication approaches, such as passwords and one-time passwords (OTPs), have become increasingly vulnerable to advanced attack methods, motivating the need for continuous authentication (CA) systems that can verify user identity throughout an active session rather than only at login. For such a system to be effective, it must analyze user behavior reliably and in real time. This paper presents a novel approach to implementing CA on mobile devices using tap and swipe behavioral biometrics combined with machine learning (ML) and multimodal fusion. The dataset was collected from 400 volunteer participants using… More >

Issah Zabsonre Alhassan^1,2,*, Gaddafi Abdul-Salaam¹, Michael Asante¹, Yaw Marfo Missah¹, Alimatu Sadia Shirazu¹

Journal of Cyber Security, Vol.8, pp. 577-607, 2026, DOI:10.32604/jcs.2026.085303 - 21 August 2026

Abstract A hybrid approach to pseudorandom number generation that couples ensemble learning with the Residue Number System (RNS) is presented in this paper. Unlike conventional deterministic generators that depend solely on direct algorithmic transformation, the proposed method first maps a seed-driven integer sequence into its RNS representation under the coprime moduli set {3, 5, 7, 11}, whose dynamic range is M = 1155, thereby introducing modular non-linearity through a static, stateless feature transformation. A soft-voting ensemble of Logistic Regression, Random Forest, and Support Vector Machine then serves as a decision layer that classifies and re-maps the… More >

Peter Kipngeno Langat^*, Michael Kimwele, Dennis Kaburu

Journal of Cyber Security, Vol.8, pp. 541-558, 2026, DOI:10.32604/jcs.2026.083592 - 21 August 2026

Abstract Zero-day attacks pose a significant threat to computer systems and networks as they exploit weaknesses that have not been recognized by security professionals or software creators and for which there are no existing protective measures. This study introduced an innovative method for identifying Zero-day attacks through a Recurrent neural network model. To effectively mitigate these risks, not only is continuous monitoring essential, but also the implementation of machine learning. The model was trained on network traffic data and leveraged on the ability of Recurrent Neural Networks (RNNs) to learn complex patterns and identify anomalies that… More >

Jia Chen¹, Zhicheng Liao¹, Mengdi Hou², Jianbo Huang^1,3,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.086222 - 13 August 2026

Abstract Fly ash (FA) blended 3D printed concrete (3DPC) offers improved sustainability but requires strength prediction models validated at the mix-composition level rather than within familiar formulations. This study applies leave-one-mix-out (LOMO) cross-validation to benchmark eight machine learning algorithms on 126 experimental records spanning seven FA-blended 3DPC compositions (FA 0–15 wt.%, W/B 0.30–0.35, age 1–28 days). ExtraTrees and ElasticNet achieve the highest composition-level generalisation for compressive strength (CS, R2=0.786±0.253) and flexural strength (FS, R2=0.915±0.061, RMSE =0.301 MPa), respectively. SHAP analysis identifies FA replacement percentage as the dominant CS predictor (mean |SHAP|=3.92 MPa, negative… More >

Ali Hassan¹, Syed Rizwan Hassan^2,*, Ammar Rafiq³

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084672 - 13 August 2026

Abstract Artificial Intelligence of Things (AIoT) systems have emerged through the rapid integration of artificial intelligence (AI) and the Internet of Things (IoT), enabling intelligent sensing, distributed learning, and real-time decision-making across diverse application domains. However, this convergence also introduces a significantly expanded adversarial attack surface spanning sensing devices, communication networks, learning pipelines, and actuation environments. This paper presents a comprehensive systematic review of adversarial threats and defence mechanisms in AIoT systems using a novel 3D-AIoT-TT (Three-Dimensional AIoT Threat Taxonomy) framework. The proposed taxonomy jointly models three fundamental dimensions: (i) AI pipeline stages, (ii) IoT architectural… 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 >