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 >

Lin-Hui Liu¹, Dong-Jie Liu^1,*, Yin-Yan Zhang¹, Xiao-Bo Jin², Xiu-Cheng Wu³, Guang-Gang Geng¹

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.074253 - 12 March 2026

Abstract Phishing email detection represents a critical research challenge in cybersecurity. To address this, this paper proposes a novel Double-S (statistical-semantic) feature model based on three core entities involved in email communication: the sender, recipient, and email content. We employ strategic game theory to analyze the offensive strategies of phishing attackers and defensive strategies of protectors, extracting statistical features from these entities. We also leverage the Qwen large language model to excavate implicit semantic features (e.g., emotional manipulation and social engineering tactics) from email content. By integrating statistical and semantic features, our model achieves a robust More >

Hasan Kaan Kucukerdem^*

Phyton-International Journal of Experimental Botany, Vol.95, No.1, 2026, DOI:10.32604/phyton.2026.074188 - 30 January 2026

Abstract Root-zone temperature (RZT) strongly affects plant growth, nutrient uptake and tolerance to environmental stress, making its regulation a key challenge in greenhouse cultivation in cold climates. This study aimed to assess the potential of passive techniques, namely black polyethylene mulch and row covers, for modifying RZT dynamics in lettuce (Lactuca sativa L.) production and to evaluate the predictive performance of the eXtreme Gradient Boosting (XGBoost) algorithm. Experiments were conducted in Iğdır, Türkiye, over a 61-day period, with soil temperature continuously monitored at depths of 1–30 cm under mulched and non-mulched conditions, alongside measurements of greenhouse air… More >

Salahudin Robo^1,2, Triyanna Widiyaningtyas^1,*, Wahyu Sakti Gunawan Irianto¹

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.073011 - 09 December 2025

Abstract Recommendation systems are an integral and indispensable part of every digital platform, as they can suggest content or items to users based on their respective needs. Collaborative filtering is a technique often used in various studies, which produces recommendations by analyzing similarities between users and items based on their behavior. Although often used, traditional collaborative filtering techniques still face the main challenge of sparsity. Sparsity problems occur when the data in the system is sparse, meaning that only a portion of users provide feedback on some items, resulting in inaccurate recommendations generated by the system.… More >

Yağmur Yılan, Ahad Beykent^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-16, 2026, DOI:10.32604/cmc.2025.068440 - 10 November 2025

Abstract Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies, hedge risk and plan generation schedules. By leveraging advanced data analytics and machine learning methods, accurate and reliable price forecasts can be achieved. This study forecasts day-ahead prices in Türkiye’s electricity market using eXtreme Gradient Boosting (XGBoost). We benchmark XGBoost against four alternatives—Support Vector Machines (SVM), Long Short-Term Memory (LSTM), Random Forest (RF), and Gradient Boosting (GBM)—using 8760 hourly observations from 2023 provided by Energy Exchange Istanbul (EXIST). All models were trained on an identical chronological 80/20 train–test split, with hyperparameters More >

Şeref Barbaros Arik^1,2,*, İnanç Güvenç^1,2

Canadian Journal of Urology, Vol.32, No.6, pp. 569-578, 2025, DOI:10.32604/cju.2025.068094 - 30 December 2025

Abstract Objectives: Differentiating benign from malignant testicular lesions is essential to avoid unnecessary surgery and ensure timely intervention. While conventional ultrasound remains the first-line imaging method, elastography and MRI provide additional functional and structural information. This study assesses the diagnostic utility of testicular elastography and magnetic resonance imaging (MRI) in differentiating benign and malignant testicular lesions. Methods: Patients with sonographically detected testicular masses were retrospectively evaluated using elastography, scrotal MRI, and tumor markers. Quantitative and qualitative imaging findings, lesion size, and laboratory values were recorded. Statistical analyses included Fisher’s exact test, logistic regression, Receiver operating characteristic… More >

Yassir M. Abbas^1,*, Ammar Babiker², Fouad Ismail Ismail³

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3279-3307, 2025, DOI:10.32604/cmes.2025.074069 - 23 December 2025

Abstract Accurately predicting the compressive strength of recycled aggregate concrete (RAC) incorporating supplementary cementitious materials (SCMs) remains a critical challenge due to the heterogeneous nature of recycled aggregates (RA) and the complex interactions among multiple binder constituents. This study advances the field by developing the most extensive and rigorously preprocessed database to date, which comprises 1243 RAC mixtures containing silica fume, fly ash, and ground-granulated blast-furnace slag. A hybrid, domain-informed machine-learning framework was then proposed, coupling optimized Extreme Gradient Boosting (XGBoost) with civil engineering expertise to capture the complex chemical and microstructural mechanisms that govern RAC… More >

Hongrong Wang^1,2, Hang Geng^1,*, Jing Yuan¹, Wen Zhang², Hanmin Sheng¹, Qiuhua Wang³, Xinjian Li^4,5

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3701-3721, 2025, DOI:10.32604/cmes.2025.073513 - 23 December 2025

Abstract Machine learning has emerged as a key approach in wildfire risk prediction research. However, in practical applications, the scarcity of data for specific regions often hinders model performance, with models trained on region-specific data struggling to generalize due to differences in data distributions. While traditional methods based on expert knowledge tend to generalize better across regions, they are limited in leveraging multi-source data effectively, resulting in suboptimal predictive accuracy. This paper addresses this challenge by exploring how accumulated domain expertise in wildfire prediction can reduce model reliance on large volumes of high-quality data. An active More >

Ricardo Dominico Da Silva^1,2, Jangkung Raharjo^1,3,*, Sudarmono Sasmono^1,3

Energy Engineering, Vol.122, No.12, pp. 5073-5090, 2025, DOI:10.32604/ee.2025.071545 - 27 November 2025

Abstract The rapid development of technology has led to an ever-increasing demand for electrical energy. In the context of Timor-Leste, which still relies on fossil energy sources with high operational costs and significant environmental impacts, electricity load forecasting is a strategic measure to support the energy transition towards the Net Zero Emission (NZE) target by 2050. This study aims to utilize historical electricity load data for the period 2013–2024, as well as data on external factors affecting electricity consumption, to forecast electricity load in Timor-Leste in the next 10 years (2025–2035). The forecasting results are expected… More >