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 >

Rahul Raj^*

Journal on Artificial Intelligence, Vol.8, pp. 359-375, 2026, DOI:10.32604/jai.2026.083202 - 22 July 2026

Abstract Background: Unemployment Insurance (UI) fraud represents one of the most costly threats to social benefit integrity, with the U.S. DOL/ETA estimating improper payments exceeding $45 billion between 2020 and 2023. Existing detection systems fail to model the temporal evolution of claiming behavior or the relational topology connecting fraudulent actors across employer-claimant networks. This study introduces FRAUD-LENS, a hybrid deep learning framework delivering interpretable and scalable fraud detection for large-scale federal UI systems. Methods: FRAUD-LENS integrates three coordinated architectural modules: a Bidirectional Long Short-Term Memory (BiLSTM) network encoding temporal claim behavior sequences, a Graph Attention Network… More >

Nantian Huang^*, Jingyuan Zhang, Shicheng Ren, Hao Zhang, Bingling Li, Yaoyao Wang

Energy Engineering, Vol.123, No.8, 2026, DOI:10.32604/ee.2025.072993 - 12 July 2026

Abstract User electricity consumption behavior analysis and multi-load forecasting in integrated energy systems are crucial for system operation and scheduling. Traditional user electricity consumption behavior analysis fails to adequately incorporate meteorological factors, limiting the accuracy of characterizing user electricity consumption patterns. Traditional multi-load forecasting models do not consider the differentiated coupling relationships with meteorological factors across different seasons, which restricts the improvement of forecasting accuracy. To address the above issues, a method integrating data cleaning and meteorological correlation for electricity consumption behavior and multi-dimensional forecasting analysis is proposed. First, the Akima interpolation method is used to… More >

Miguel A. Millán^1,*, Rubén Galindo², Fausto Molina-Gómez¹

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.080219 - 30 June 2026

Abstract Low-density volcanic rocks have specific geomechanical properties that require complex laboratory tests and characterization that are not usually available in common geotechnical studies. A pyroclastic rock behaves at sufficiently “low” stress levels as if it were a conventional rock under the action of an external load, but when subjected to higher stresses, the bonds between its particles can break, leading to a sudden decrease in its volume and the reorganization of its particles, thus forming a more compact structure than the initial one. This process is known as “mechanical collapse” and involves a drastic change… More >

Anping Wan^1,2,3, Yingchang Gao^1,3, Weikang Liu¹, Rui Yin¹, Khalil Al-Bukhaiti^1,3,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081719 - 15 June 2026

Abstract Vertical roller mills are essential for energy-intensive grinding in cement, minerals, and metallurgy industries, consuming up to 50% of plant electricity and frequently experiencing operational instabilities (including excessive vibration and main motor current fluctuations) that drive unplanned downtime, increased wear, and reduced throughput. Despite their importance, real-time autonomous optimization remains challenging due to the nonlinear interactions among grinding pressure, feed rate, separator speed, and aerodynamic factors, which limit traditional control strategies under varying loads. This paper presents a real-time operational optimization system for large-scale vertical roller mills using big industrial data and artificial intelligence (AI).… More >

Arslan Qayyum Khan¹, Muhammad Dawood Rasheed², Muhammad Huzaifa Naveed², Amorn Pimanmas^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.083654 - 27 May 2026

Abstract The construction industry’s substantial carbon footprint, primarily attributed to the production of Ordinary Portland Cement, necessitates a transition toward more sustainable alternatives. Geopolymer concrete (GPC), an innovative binder synthesized from industrial by-products like fly ash (FA), offers a promising low-carbon solution but is hindered by performance variability and a lack of standardized design protocols. This research addresses this critical barrier by developing robust predictive models for the compressive strength of FA-based GPC. Six machine learning algorithms, including Bagging, Categorical Boosting (CatBoost), K-Nearest Neighbors (KNN), LightGBM, Random Forest Regressor (RFR), and eXtreme Gradient Boosting (XGBoost), were… More >

Salamat Ullah^1,2,*, Muhammad Zahid³, Khaled Aati⁴, Abdulrahman Abbadi⁴, Haroon Ijaz⁵, Ali Qabur⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080484 - 27 May 2026

Abstract Designing thin-walled plate structures is challenging due to their susceptibility to various forms of structural instability. In addition, the substantial computational cost of finite element analyses, especially in optimization scenarios, underscores the need for efficient and reliable surrogate models. To address this challenge, the present study employs machine learning (ML) techniques to predict the buckling response of thin plates under complex boundary conditions. Four ML models, including XGBoost, CatBoost, Light GBM, and Random Forest, are developed to predict the buckling coefficient based on input features, including aspect ratio, boundary condition, and compressive loading pattern. The… More >

Nahid Haghshenas, Ali Shamsoddini^*

Revue Internationale de Géomatique, Vol.35, pp. 249-272, 2026, DOI:10.32604/rig.2026.076139 - 21 May 2026

Abstract This study aimed to evaluate the potential of Sentinel-3 as an alternative to Moderate Resolution Imaging Spectroradiometer (MODIS) for generating high spatiotemporal resolution land surface temperature (LST) data. The Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) and the machine-learning-based Extreme Gradient Boosting (XGBoost) algorithm were independently assessed for fusing MODIS–Landsat and Sentinel-3–Landsat data. This comparison enabled the evaluation of each model’s capability to reconstruct spatiotemporal LST variations and assess the performance of the two sensors in the fusion process. The results showed that XGBoost outperformed ESTARFM in capturing complex and heterogeneous LST patterns,… More >

Sobhan Manjili¹, Saeid Jafarzadeh Ghoushchi¹, Mohammad Reza Maghami^2,*, Mazlan Mohamed^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.076217 - 27 April 2026

Abstract Accurate electricity load forecasting is crucial for optimizing power distribution networks, especially in rapidly growing cities like Tabriz (annual consumption growth of 7.2%). This study presents a hybrid AI framework integrating the Temporal Fusion Transformer (TFT) and XGBoost for residual error correction. The model is trained and evaluated using actual consumption data from Tabriz’s distribution network (2021–2023). Compared to a baseline TFT model, the proposed framework demonstrates a 11.2% reduction in RMSE (from 0.1249 to 0.1109) and a 10.7% decrease in MAE (from 0.0998 to 0.0891). Attention mechanism analysis reveals temperature (importance coefficient = 0.32), More >