Hongrong Wang^1,2, Xinjian Li^3,4, Xingjing She¹, Wenjian Ma^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2039-2055, 2025, DOI:10.32604/cmes.2025.072572 - 26 November 2025

Abstract In modern complex systems, real-time regression prediction plays a vital role in performance evaluation and risk warning. Nevertheless, existing methods still face challenges in maintaining stability and predictive accuracy under complex conditions. To address these limitations, this study proposes an online prediction approach that integrates event tracking sensitivity analysis with machine learning. Specifically, a real-time event tracking sensitivity analysis method is employed to capture and quantify the impact of key events on system outputs. On this basis, a mutual-information–based self-extraction mechanism is introduced to construct prior weights, which are then incorporated into a LightGBM prediction More >

Ayaz Hussain¹, Asad Hayat², Muhammad Hasnain^1,*

Journal on Artificial Intelligence, Vol.7, pp. 485-498, 2025, DOI:10.32604/jai.2025.071933 - 17 November 2025

Abstract Violent hate speech and scapegoating people against one another have emerged as a rising worldwide issue. But identifying and combating such content is crucial to create safer and more inclusive societies. The current study conducted research using Machine Learning models to classify hate speech and overcome the limitations posed in the existing detection techniques. Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbour (KNN) and Decision Tree were used on top of a publicly available hate speech dataset. The data was preprocessed by cleaning the text and tokenization and using normalization techniques to efficiently train the… More >

Lihao Zhang^1,2, Lin Cao^1,2, Lige Huang^1,2, Jie Wang^1,2, Jiabing Li^2,3,*

Canadian Journal of Urology, Vol.32, No.5, pp. 457-468, 2025, DOI:10.32604/cju.2025.067364 - 30 October 2025

Abstract Background: Studies have indicated an association between inflammatory factors (IFs) in the blood and the development of bladder cancer (BC). This study aimed to explore the correlation and clinical significance of IFs with the pathological grading of BC. Methods: A retrospective analysis was conducted on the preoperative blood routine results, postoperative pathological findings, and baseline information of 163 patients. Patients were divided into high-grade and low-grade groups based on pathological grading. Group comparisons and logistic regression analyses were performed using R software version 4.1.3 to explore the relationships between IFs and BC pathological grading. Results: The… More >

Barkilean Jaismitha¹, Jagadeesan Sasikumar^2,*, Samad Noeiaghdam^3,*, Unai Fernandez-Gamiz⁴, Thirugnanasambandam Arunkumar¹

Frontiers in Heat and Mass Transfer, Vol.23, No.5, pp. 1529-1554, 2025, DOI:10.32604/fhmt.2025.067869 - 31 October 2025

Abstract The present study investigates the dynamic behavior of a ternary-hybrid nanofluid within a tapered asymmetric channel, focusing on the impact of unsteady oscillatory flow under the influence of a magnetic field. This study addresses temperature-sensitive water transport mechanisms relevant to industrial applications such as thermal management and energy-efficient fluid transport. By suspending nanoparticles of diverse shapes-platelets, blades, and spheres in a hybrid base fluid comprising cobalt ferrite, magnesium oxide, and graphene oxide, the study examines the influence of both small and large volume fraction values. The governing equations are converted into a dimensionless form. With More >

Victor Otieno Mony^*, Anselemo Peters Ikoha, Roselida O. Maroko

Journal of Cyber Security, Vol.7, pp. 395-415, 2025, DOI:10.32604/jcs.2025.070952 - 30 September 2025

Abstract The widespread adoption of Cloud-Based Services has significantly increased the surface area for cyber threats, particularly targeting authentication mechanisms, which remain among the most vulnerable components of cloud security. This study aimed to address these challenges by developing and evaluating an Intelligent Zero Trust Architecture model tailored to mitigate authentication-related threats in Cloud-Based Services environments. Data was sourced from public repositories, including Kaggle and the National Institute for Standards and Technology MITRE Corporation’s Adversarial Tactics, Techniques, & Common Knowledge (ATT&CK) framework. The study utilized two trust signals: Behavioral targeting system users and Contextual targeting system… More >

Sen Yang¹, Jie Zhong¹, Boyu Gan¹, Yi Sun¹, Changming Bu¹, Mingtao Zhang¹, Jiehong Li^1,*, Yang Yu^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2943-2967, 2025, DOI:10.32604/cmes.2025.067282 - 30 September 2025

Abstract Foam concrete is widely used in engineering due to its lightweight and high porosity. Its compressive strength, a key performance indicator, is influenced by multiple factors, showing nonlinear variation. As compressive strength tests for foam concrete take a long time, a fast and accurate prediction method is needed. In recent years, machine learning has become a powerful tool for predicting the compressive strength of cement-based materials. However, existing studies often use a limited number of input parameters, and the prediction accuracy of machine learning models under the influence of multiple parameters and nonlinearity remains unclear.… More >

Acácio M. R. Amaral^1,2,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3825-3859, 2025, DOI:10.32604/cmc.2025.067179 - 23 September 2025

Abstract Power converters are essential components in modern life, being widely used in industry, automation, transportation, and household appliances. In many critical applications, their failure can lead not only to financial losses due to operational downtime but also to serious risks to human safety. The capacitors forming the output filter, typically aluminum electrolytic capacitors (AECs), are among the most critical and susceptible components in power converters. The electrolyte in AECs often evaporates over time, causing the internal resistance to rise and the capacitance to drop, ultimately leading to component failure. Detecting this fault requires measuring the… More >

Nurul A. Asram¹, Eran S. S. Md Sadek^2,*

Revue Internationale de Géomatique, Vol.34, pp. 619-637, 2025, DOI:10.32604/rig.2025.065667 - 06 August 2025

Abstract Landslides are a frequent geomorphological hazard in tropical regions, particularly where steep terrain and high precipitation coincide. This study evaluates landslide susceptibility in the Jelapang area of Perak, Malaysia, using Shannon Entropy-weighted bivariate models (i.e., Frequency Ratio, Information Value, and Weight of Evidence), in comparison with Logistic Regression. Seven conditioning factors were selected based on their geomorphological relevance and tested for multicollinearity: slope gradient, slope aspect, curvature, vegetation cover, lineament density, terrain ruggedness index, and flow accumulation. Each model generated susceptibility maps, which were validated using Receiver Operating Characteristic curves and Area Under the Curve… More >

Fatma Bunyan Unel^1,*, Lutfiye Kusak¹, Murat Yakar¹, Abdullah Sahin², Hakan Dogan³, Fikret Demir⁴

Revue Internationale de Géomatique, Vol.34, pp. 433-460, 2025, DOI:10.32604/rig.2025.065502 - 29 July 2025

Abstract This study aimed to create a model for calculating the total reference crop evapotranspiration (ETo) in Mersin Province from May 2015 to 2020 and to generate maps using spatial analysis. Lemons from citrus play a significant role in Mersin’s agriculture, and because of lemons’ sensitivity to temperature, ETo is essential for them. It was observed that the ETo value () calculated using the Penman-Monteith (PM) method increased over the years. A model was developed using data from 36 Automated Weather Observing Systems (AWOS) in Mersin, Türkiye, which is located in a semi-arid climate zone. The… More >

S. M. Rezaul Karim^1,2, Md. Shouquat Hossain^1,3, Khadiza Akter¹, Debasish Sarker⁴, Md. Moniul Kabir ², Mamdouh Assad^5,*

Energy Engineering, Vol.122, No.8, pp. 3041-3054, 2025, DOI:10.32604/ee.2025.066867 - 24 July 2025

Abstract Knowing the influence of the size of datasets for regression models can help in improving the accuracy of a solar power forecast and make the most out of renewable energy systems. This research explores the influence of dataset size on the accuracy and reliability of regression models for solar power prediction, contributing to better forecasting methods. The study analyzes data from two solar panels, aSiMicro03036 and aSiTandem72-46, over 7, 14, 17, 21, 28, and 38 days, with each dataset comprising five independent and one dependent parameter, and split 80–20 for training and testing. Results indicate… More > Graphic Abstract