Chuanlin Jian¹, Yuanchen Sun², Xiangcheng Liu¹, Xuming Huang³, Samaneh Beheshti Kashi⁴, Xing Hu^1,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086441 - 15 September 2026

Abstract Heterogeneous clusters now carry most artificial-intelligence, scientific-computing, cloud, and edge-assisted workloads, yet scheduling them well remains difficult: workload semantics, hardware capability, queue state, and energy behavior are tightly coupled. Existing schedulers, whether heuristic, learning-based, or built on reinforcement learning, tend to rely on coarse resource requests, overlook the compatibility between task types and node classes, or carry a heavy training and deployment cost. This paper presents Classification-Aware Scheduling for Heterogeneous clusters (CASH), a confidence-calibrated, classification-aware scheduling framework that integrates workload profiling, deep feature crossing, gradient-boosted boundary modeling, and risk-aware heterogeneous resource mapping. CASH constructs a… More >

S. M. Nihal Ahmed^*, Afrim Hossen Khan, Md. Mazbaur Rashid

Journal of Cyber Security, Vol.8, pp. 667-703, 2026, DOI:10.32604/jcs.2026.089357 - 14 September 2026

Abstract Phishing remains one of the most persistent attack vectors in cybersecurity, and the gradient-boosting models that now dominate its automated detection are frequently deployed as opaque classifiers, which limits analyst trust and slows incident response. This paper presents a cross-dataset, explainability-driven evaluation of three gradient boosting algorithms–XGBoost, LightGBM, and CatBoost—for phishing URL classification, paired with a SHAP (Shapley Additive Explanations)-based framework that attributes every prediction to specific, human-readable features rather than treating the classifier as a black box. To eliminate a data leakage risk present in an earlier evaluation protocol—in which hyperparameter tuning and final… More >

Mohammadreza Nehzati^*

Journal of Intelligent Medicine and Healthcare, Vol.4, pp. 155-177, 2026, DOI:10.32604/jimh.2026.082983 - 14 September 2026

Abstract Background: Exponential growth of diverse clinical data presents challenges for real-time predictive analytics in healthcare. Federated learning offers a paradigm for multi-institutional model training without centralized data sharing, but large-scale deployment across diverse healthcare settings with real-world electronic health record (EHR) integration challenges remains limited. Methods: We implemented Phase 1 of a federated learning network deploying federated histogram-based XGBoost across 47 U.S. healthcare institutions from January to June 2023 as a quality improvement initiative. The system processes clinical data locally, transmitting only gradient and Hessian histograms with differential privacy (ε = 1.0, δ = 10⁻⁵). Primary… More >

Yunlong Du¹, Shuyi Zhuang^2,*, Zhigang Ye², Qiangsheng Bu², Yun Chai¹, Yuanbing Wang³

Energy Engineering, Vol.123, No.10, 2026, DOI:10.32604/ee.2025.074702 - 30 August 2026

Abstract Under the dual challenges of global warming and energy transition, improving the short-term forecasting accuracy of surface solar radiation is of great practical importance for photovoltaic (PV) power integration. In this study, a short-term solar radiation forecasting model based on the XGBoost machine learning algorithm was developed for Jiangsu Province by integrating multispectral data from the Fengyun-4A (FY-4A) geostationary satellite with ground-based meteorological observations. The model incorporated 18 input features—including satellite reflectance, solar zenith angle, normalized difference vegetation index (NDVI), elevation, and land-cover data—to dynamically predict ground horizontal irradiance (GHI) with 0–4 h lead times.… More >

Ling Wang^1,2, Mohammad Faizuddin Md Noor^2,*, Yanan Zhang^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.084489 - 28 August 2026

Abstract Ultra-high-performance concrete (UHPC) relies on multivariable mix design and curing regimes, which makes empirical estimation of compressive strength increasingly unreliable when material systems vary. In this context, unlike previous studies that only applied standard eXtreme Gradient Boosting (XGBoost), this study introduces an advanced hybrid optimization strategy, Bayesian Optimization and Hyperband (BOHB), which combines the sample efficiency of Bayesian optimization with the resource allocation mechanism of Hyperband, and incorporates SHapley Additive exPlanations (SHAP) for influencing factor analysis, thereby proposing a BOHB-XGBoost framework integrated with SHAP analysis. The proposed model demonstrates excellent predictive accuracy and stability, achieving… More >

Kun-Lin Tsai¹, Shih-Ting Chiu², Chihhsiong Shih², Fang-Yie Leu^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.083958 - 28 August 2026

Abstract With the quick development of 5G networks, network slicing and Open Radio Access Network (O-RAN) have become key technologies for improving network resource-allocation efficiency and flexibility. However, network slicing also faces intrusion-detection challenges, particularly for detecting DDoS attacks, which are difficult to detect due to traffic being silently transmitted across multiple sub-slices. To address this problem, this paper proposes a 5G network slicing intrusion detection mechanism, called the DDoS Defense Model on 5G Network Slices (2D5NS) which integrates machine learning and real-time traffic monitoring techniques to detect and mitigate DDoS attacks within an O-RAN. This… 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 >

Qisen Jin^1,2, Xiaoping Wang¹, Feng Zhang², Yu Zeng², Jia Guo^3,4,5,*, Jiacheng Li^6,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.080077 - 13 August 2026

Abstract This paper presents a Sine-Lucas Oscillation Particle Swarm Optimization (SLOPSO) XGBoost algorithm for landslide susceptibility prediction. SLOPSO extends canonical PSO by introducing an oscillation factor constructed from a sine function and the Lucas number sequence into the position update, which raises swarm diversity and improves the global search. SLOPSO is then applied to tune six XGBoost hyperparameters using K-fold cross-validation accuracy as the fitness function. In experimental evaluation, SLOPSO-XGBoost reaches a mean test Accuracy of 0.8113, F1 of 0.8182, and AUC of 0.8856 over 10 independent runs, outperforming standard PSO-XGBoost and the default Random Forest, More >

Mubariz Khan¹, Hafeez Ur Rehman Siddiqui², Adil Ali Saleem², Muhammad Amjad Raza^2,3, Lázaro Javier Hernández Rodríguez^4,5,6,7, Pablo Herrero García^4,8,9, Isabel de la Torre Díez^10,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084269 - 23 July 2026

Abstract Accurate forecasting of cryptocurrency prices remains an open challenge because classical statistical models cannot capture the non-linear, sentiment-driven dynamics of these markets. This study compares three hybrid deep learning architectures—VAR-LSTM, XGBoost-LSTM, and CNN-LSTM—to determine which best forecasts Bitcoin (BTC), Ethereum (ETH), and Dogecoin (DOGE) closing prices, and to quantify the marginal predictive value of Twitter sentiment integration. Six years of hourly OHLCV data (2017–2023) are augmented with VADER-scored Twitter sentiment polarity. Each model is formulated mathematically, implemented with documented hyperparameters (epochs, dropout, units;), and trained for one-step-ahead next-hour price prediction. Performance is measured by RMSE,… More >

Abdulaziz A. Alsulami¹, Qasem Abu Al-Haija^2,*, Rayed Alakhtar³, Ahmad J. Tayeb³, Badraddin Alturki³, Huda Alsobhi⁴, Rayan A. Alsemmeari³

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083321 - 23 July 2026

Abstract The rapid growth of the Internet of Things (IoT) devices has increased the attack area of modern networks, which makes effective intrusion detection systems (IDSs) essential to detect attacks that target IoT infrastructures. Federated learning is a promising approach for collaborative model training in the absence of centralized raw data. Conventional federated approaches rely on fixed client participation and static training configurations, which ensure symmetric treatment of clients despite heterogeneous local data distributions. This can limit convergence and degrade detection performance in non-IID conditions. This paper proposes an Adaptive Action-Based Federated Learning (AA-FL) framework for… More >