Intelligent anti-jamming communication technology with electromagnetic spectrum feature cognition. by Hui Zhao, Guobin Zhao, Xichun Wang, Zhonghui Zhang, Xianchao Xun

“…Experiments show that the proposed model achieves an accuracy rate of 95.23% in identifying interference signals and an anti-interference accuracy rate of 85.47%, significantly outperforming random forest and deep Q-network models. The study not only clarifies the limitations of existing solutions but also precisely defines the goals of the new model, which are to reduce error rates and improve adaptability in dynamic environments. …”
Get full text

A Bi-Temporal Airborne Lidar Shrub-to-Tree Aboveground Biomass Model for the Taiga of Western Canada: Un Modèle Bitemporal de Biomasse Aérienne D’arbuste à D’arbre Pour le Lidar Aé... by Linda Flade, Chris Hopkinson, Laura Chasmer

“…In addition, to ensure that modeled AGB changes do not incorporate systematic error due to differences between older and newer lidar technologies, model transfer tests are required. …”
Get full text

Machine learning analysis of rivaroxaban solubility in mixed solvents for application in pharmaceutical crystallization by Mohammed Alqarni, Ali Alqarni

“…The LGB model achieved the best results, with an R2 of 0.988 on the test set and low error rates (RMSE of 9.1284E-05 and MAE of 5.85322E-05), surpassing other models in predictive accuracy and generalizability. …”
Get full text

Enhancing peak performance forecasting in steam power plants through innovative AI-driven exergy-energy analysis by Muhammad Ali Ijaz Malik, Adeel Ikram, Sadaf Zeeshan, Muhammad Naqvi, Syed Qasim Raza Zahidi, Fayaz Hussain, Hayati Yassin, Atika Qazi

“…The model’s predictions for energy and exergy efficiencies are validated against experimental data, with root mean square error (RMSE) and coefficient of determination (R2) computed for accuracy evaluation. …”
Get full text

Synergistic mapping of urban tree canopy height using ICESat-2 data and GF-2 imagery by Xiaodi Xu, Ya Zhang, Peng Fu, Chaoya Dang, Bowen Cai, Qingwei Zhuang, Zhenfeng Shao, Deren Li, Qing Ding

“…The experimental results demonstrated a moderate correlation between estimated and actual canopy heights, with a coefficient of determination (R) = 0.53, root mean square error (RMSE) = 2.9 m, and mean absolute error (MAE) = 2.04 m. …”
Get full text

Inverse Design of Plasmonic Nanostructures Using Machine Learning for Optimized Prediction of Physical Parameters by Luana S. P. Maia, Darlan A. Barroso, Aêdo B. Silveira, Waleska F. Oliveira, André Galembeck, Carlos Alexandre R. Fernandes, Dayse G. C. Bandeira, Benoit Cluzel, Auzuir R. Alexandria, Glendo F. Guimarães

“…In this case, Random Forest presented the best performance, with a lower risk of overfitting. …”
Get full text

Predicting yellow mosaic disease severity in yardlong bean using visible imaging coupled with machine learning model by Abhishek Kumar Dubey, Prakash Kumar Jha, Kumari Shubha, RN Singh, Manisha Tamta, Sonam Sah, Santosh Kumar, Sanjeev Kumar, Rakesh Kumar, Kirti Saurabh, Rajeev Kumar, Anup Das, P. V. V. Prasad, Arbind Kumar Choudhary

“…Model performances was evaluated using R2, d-index, mean bias error, and normalized Root Mean Square Error (n-RMSE) metrics. …”
Get full text

UAV-Based Remote Sensing Monitoring of Maize Growth Using Comprehensive Indices by Tingrui Yang, Jinghua Zhao, Ming Hong, Mingjie Ma, Shijiao Ma, Yingying Yuan

“…Among the three machine learning methods, the RF model demonstrated superior performance, showing the highest accuracy in the growth monitoring model established with CGMICT, with a coefficient of determination (<inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula>) of 0.724, a root mean square error (RMSE) of 0.117, and mean absolute error (MAE) of 0.097. …”
Get full text

Prediction of hydrogen production in proton exchange membrane water electrolysis via neural networks by Muhammad Tawalbeh, Ibrahim Shomope, Amani Al-Othman, Hussam Alshraideh

“…The performance of the ANN model was evaluated against conventional regression models using key metrics: mean squared error (MSE), coefficient of determination (R2), and mean absolute error (MAE). …”
Get full text

Daily reference evapotranspiration prediction in Iran: A machine learning approach with ERA5-land data by Ali Asghar Zolfaghari, Maryam Raeesi, Giuseppe Longo-Minnolo, Simona Consoli, Miles Dyck

“…New hydrological insights for the region: Results demonstrated a high correlation between ERA5-Land temperature estimates and observed station data (Pearson correlation coefficient, r = 0.97; Root Mean Square Error, RMSE = 2.77°C), while relative humidity showed a weaker agreement (Normalized Root Mean Square Error, NRMSE = 21 %). …”
Get full text

Predictive performance and uncertainty analysis of ensemble models in gully erosion susceptibility assessment by Congtan Liu, Haoming Fan, Yixuan Wang

“…The ensemble model Transformer-RF-CNN employing PEWM demonstrated superior performance, validated by 10-fold cross-validation and 8 metrics: Efficiency (E), True Positive Rate (TPR), False Positive Rate (FPR), True Skill Statistics (TSS), Kappa coefficient (K), Area Under the receiver operating characteristic Curve (AUC), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The uncertainty associated with GESMs was quantified using the Coefficient of Variation (CV) map, resulting in a confidence map that classified 20 zones, with 75.976% of gullies located in high-susceptibility and low-uncertainty areas. …”
Get full text

Water Quality in the Ma’an Archipelago Marine Special Protected Area: Remote Sensing Inversion Based on Machine Learning by Zhixin Wang, Zhenqi Zhang, Hailong Li, Hong Jiang, Lifei Zhuo, Huiwen Cai, Chao Chen, Sheng Zhao

“…Specifically, the inversion results for Chl-a showed the coefficient of determination (R²) of 0.741, the root mean square error (RMSE) of 3.376 μg/L, and the mean absolute percentage error (MAPE) of 16.219%. …”
Get full text

Alfalfa stem count estimation using remote sensing imagery and machine learning on Google Earth Engine by Hazhir Bahrami, Karem Chokmani, Saeid Homayouni, Viacheslav I. Adamchuk, Md Saifuzzaman, Rami Albasha, Maxime Leduc

“…The results also indicated that alfalfa stem density can be estimated with an error of ∼ ±6-9 stems/foot2 (1 foot = 30.48 cm) using ML regression models. …”
Get full text

Estimation of Canopy Chlorophyll Content of Apple Trees Based on UAV Multispectral Remote Sensing Images by Juxia Wang, Yu Zhang, Fei Han, Zhenpeng Shi, Fu Zhao, Fengzi Zhang, Weizheng Pan, Zhiyong Zhang, Qingliang Cui

“…Additionally, the model performance was assessed by selecting the coefficient of determination (R²), root mean square error (RMSE) and mean absolute error (MAE). The results show that the SPAD estimation results vary from stage to stage, where the best estimation model for the flower-falling stage, fruit-setting stage and fruit-maturing stage is RF and those for the fruit expansion stage and fruit-coloring stage are PLSR and MLR, respectively. …”
Get full text

MeloDI: An Internet of Things Architecture to Evaluate Melon Quality by Means of Machine Learning Using Sensors Data and Drone Images by Angel Luis Perales Gomez, Juan Jesus Losada del Olmo, Pedro E. Lopez-de-Teruel, Alberto Ruiz, Felix J. Garcia Clemente, Andres Conesa Bueno

“…In particular, the best-performing model was Random Forest that achieved a Mean Square Error of 1.6111 and a corresponding error rate of 7.9944% on the test set.…”
Get full text

Fatigue strength prediction of Cobalt alloys using material composition and monotonic properties: ML-based approach by Subraya Krishna Bhat, Amritanshu Ranjan, Y S Upadhyaya, Vishwanath Managuli

“…Eight different ML models were developed and tested, including Linear Regression, Lasso Regression, Ridge Regression, Random Forest Method, Support Vector Regression, Gradient Boosting, XGBoost, and Artificial Neural Networks (ANN). …”
Get full text

Automatic Detection of Equatorial Plasma Bubbles in Airglow Images Using Two-Dimensional Principal Component Analysis and Explainable Artificial Intelligence by Moheb Yacoub, Moataz Abdelwahab, Kazuo Shiokawa, Ayman Mahrous

“…These bubbles can cause signal scintillation, leading to signal loss and errors in position calculations. EPBs can be detected in images captured by All-Sky Imager (ASI) systems. …”
Get full text

Artificial intelligence driven platform for rapid catalytic performance assessment of nanozymes by Wenjie Xuan, Xiaofo Li, Honglei Gao, Luyao Zhang, Jili Hu, Liping Sun, Hongxing Kan

“…This reduces manual effort and minimizes errors in large language model outputs, ensuring high-quality results.These innovations make AI-ZYMES a valuable tool for accelerating nanozyme research and application, including antimicrobial therapy, biosensing, and environmental remediation. …”
Get full text

Improving the quality of payment fraud detection by using a combined approach of transaction analysis by Світлана Гавриленко, Олексій Абдуллін

“…Conclusions: The scientific novelty of the results obtained is the combined use of data classification and clustering methods to detect fraudulent transactions, which reduced the number of type II errors. Assessing the informative value of features within different types (subclasses) of fraudulent transactions allows us to evaluate which features have the greatest impact on the object’s belonging to a particular subclass. …”
Get full text

A finger on the pulse of cardiovascular health: estimating blood pressure with smartphone photoplethysmography-based pulse waveform analysis by Ivan Shih-Chun Liu, Fangyuan Liu, Qi Zhong, Shiguang Ni

“…Random forest models further improved these values to 7.34, 5.79, and 4.45 mmHg. …”
Get full text