Hybrid AI-Based Framework for Renewable Energy Forecasting: One-Stage Decomposition and Sample Entropy Reconstruction with Least-Squares Regression by Nahed Zemouri, Hatem Mezaache, Zakaria Zemali, Fabio La Foresta, Mario Versaci, Giovanni Angiulli

“…This study introduces a hybrid model that combines signal decomposition and artificial intelligence to enhance the prediction of solar radiation and wind speed. …”
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Blockchain-secured IoT-federated learning for industrial air pollution monitoring: A mechanistic approach to exposure prediction and environmental safety by Montaser N.A. Ramadan, Mohammed A.H. Ali, Hadi Jaber, Mohammad Alkhedher

“…Model validation indicated strong predictive reliability (R² = 0.89), significantly reducing prediction errors (Mean Absolute Error and Root Mean Square Error). Blockchain integration successfully ensured data integrity, identifying and rejecting over 98.7 % of unauthorized updates. …”
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The Harmony and Balance of the Facial Organs for a Natural Face Beauty: A Novel Perspective for Cosmetic/Aesthetic Interventions by Serdar Babacan, Mustafa Deniz

“…We believe that our study will guide medical professionals who perform cosmetic/aesthetic interventions and also inspire software or artificial intelligence applications related to facial or facial organ design.…”
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Prediction of total phosphorus in wastewater treatment plant effluent based on deep learning by AN Yuning, ZHU Sifu, LIU Jing, DU Liwei, LIU Changqing

“…The mean square error(MSE), root mean square error(RMSE), mean absolute error(MAE), and R2 of the LSTM model were 0.008 2, 0.090 5, 0.068 4 and 0.606 8 respectively, and the model prediction accuracy was high. …”
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Crude Oil and Hot-Rolled Coil Futures Price Prediction Based on Multi-Dimensional Fusion Feature Enhancement by Yongli Tang, Zhenlun Gao, Ya Li, Zhongqi Cai, Jinxia Yu, Panke Qin

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A supervised machine learning statistical design of experiment approach to modeling the barriers to effective snakebite treatment in Ghana. by Eric Nyarko, Edmund Fosu Agyemang, Ebenezer Kwesi Ameho, Louis Agyekum, José María Gutiérrez, Eduardo Alberto Fernandez

“…The results were compared using key metrics: Akaike Information Criterion corrected, Bayesian Information Criterion, Root Average Squared Error, and Fit Time in milliseconds.…”
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Orchard Navigation Method Based on RS-SC Loop Frame Search Method and SLAM Technology by Ning Xu, Qingshan Meng, Fengping Liu, Zhihe Li, Guangming Wang, Na Guo, Wenxuan Wu

“…With the rapid development of agricultural intelligence, the application of intelligent agricultural robots in orchard management has been widely concerned. …”
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Real-Time High-Resolution Global PWV Retrieval Based on Weather Forecast Foundation Models and Cross-Validation With Radiosonde, GNSS, and ERA5 by Junsheng Ding, Wu Chen, Junping Chen, Jungang Wang, Yize Zhang, Lei Bai

“…Results show the new scheme achieves 3.01 mm global root mean square error in real time, and the value reduce to 2.25 mm when focusing only on land areas, which is more accurate than most existing methods that rely on postprocessed surface-domain data. …”
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Wavelet Multiresolution Analysis-Based Takagi–Sugeno–Kang Model, with a Projection Step and Surrogate Feature Selection for Spectral Wave Height Prediction by Panagiotis Korkidis, Anastasios Dounis

“…Furthermore, it outperforms the second-best comparative model by approximately <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>49%</mn></mrow></semantics></math></inline-formula> in terms of root mean squared error. Comparative evaluations against powerful artificial intelligence models, using regression metrics and hypothesis test, underscore the effectiveness of the proposed methodology.…”
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Data efficiency assessment of generative adversarial networks in energy applications by Umme Mahbuba Nabila, Linyu Lin, Xingang Zhao, William L. Gurecky, Pradeep Ramuhalli, Majdi I. Radaideh

“…This study investigates the data requirements of generative artificial intelligence (AI), particularly generative adversarial networks (GANs), for reliable data augmentation in energy applications. …”
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A Comparative Study of Downscaling Methods for Groundwater Based on GRACE Data Using RFR and GWR Models in Jiangsu Province, China by Rihui Yang, Yuqing Zhong, Xiaoxiang Zhang, Aizemaitijiang Maimaitituersun, Xiaohan Ju

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Enhancing Satellite Image Coregistration Using Mirror Array as Artificial Point Source for Multisource Image Harmonization by Muhammad Daniel Iman bin Hussain, Vaibhav Katiyar, Masahiko Nagai, Dorj Ichikawa

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Evaluation of Time-Domain Acoustic Signature in TIG Welding of 5083 Aluminum Alloy: A Methodological Comparison of Feature Reduction Approaches by V M Gautham, A Sumesh, E V Jithin, K Rameshkumar, Dinu Thomas Thekkuden

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SSMSFuse: A Spectral and Spatial Multiscale Coupling Fusion Model for Hyperspectral and Multispectral Image by Siyuan Liu, Yingchao Fan, Qi Hu, Bing Li, Yudong Zhang, Shuaiqi Liu

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Multistep PV power forecasting using deep learning models and the reptile search algorithm by Sameer Al-Dahidi, Hussein Alahmer, Bilal Rinchi, Abdullah Bani-Abdullah, Mohammad Alrbai, Osama Ayadi, Loiy Al-Ghussain

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Developing an Equitable Machine Learning–Based Music Intervention for Older Adults At Risk for Alzheimer Disease: Protocol for Algorithm Development and Validation by Chelsea S Brown, Luna Dziewietin, Virginia Partridge, Jennifer Rae Myers

“…The recommendation accuracy of the ML algorithm will be assessed using multiple performance metrics, including root-mean-square error and normalized discounted cumulative gain as well as the mean acceptability score with a goal of 85% user acceptability. …”
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Prediction of lithium-ion battery SOC based on IGA-GRU and the fusion of multi-head attention mechanism by Pei Tang, Minnan Jiang, Weikai Xu, Zhengyu Ding, Mao Lv

“…Finally, the prediction performance of the fusion model proposed in this paper is verified by Pycharm simulation, and the average absolute error, root mean square error and maximum prediction error of the model are 1.62%, 1.55% and 0.5%, respectively, which proves that the model can accurately predict the SOC of lithium-ion battery. …”
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Advanced State-of-Health Estimation for Lithium-Ion Batteries Using Multi-Feature Fusion and KAN-LSTM Hybrid Model by Zhao Zhang, Runrun Zhang, Xin Liu, Chaolong Zhang, Gengzhi Sun, Yujie Zhou, Zhong Yang, Xuming Liu, Shi Chen, Xinyu Dong, Pengyu Jiang, Zhexuan Sun

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An Interpretable Machine Learning Framework for Unraveling the Dynamics of Surface Soil Moisture Drivers by Zahir Nikraftar, Esmaeel Parizi, Mohsen Saber, Mahboubeh Boueshagh, Mortaza Tavakoli, Abazar Esmaeili Mahmoudabadi, Mohammad Hassan Ekradi, Rendani Mbuvha, Seiyed Mossa Hosseini

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Guiding Field Measurement of Pine Tree Crowns: A Geometric Shape Comparison Using Drone Imagery by A. Hosingholizade, Y. Erfanifard, S. K. Alavipanah, V. García Millan, S. Pirasteh, S. Pirasteh

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