Multi-modal remote sensory learning for multi-objects over autonomous devices by Aysha Naseer, Naif Almudawi, Hanan Aljuaid, Abdulwahab Alazeb, Yahay AlQahtani, Asaad Algarni, Ahmad Jalal, Ahmad Jalal, Hui Liu, Hui Liu, Hui Liu

“…By simultaneously using deep learning model, the incorporation of Alex Net in the following classification phase enhances the model’s capacity to identify complex patterns in aerial images and adapt to a variety of object attributes.ResultsExperiments show that our method performs better than others in terms of classification accuracy and generalization, indicating its efficacy analysis on benchmark datasets such as UC Merced Land Use and AID.DiscussionSeveral performance measures were calculated to assess the efficacy of the suggested technique, including accuracy, precision, recall, error, and F1-Score. The assessment findings show a remarkable recognition rate of around 97.90% and 98.90%, on the AID and the UC Merced Land datasets, respectively.…”
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Machine learning for improvement of upper-tropospheric relative humidity in ERA5 weather model data by Z. Wang, Z. Wang, L. Bugliaro, K. Gierens, M. I. Hegglin, M. I. Hegglin, S. Rohs, A. Petzold, S. Kaufmann, C. Voigt, C. Voigt

“…The ANN shows excellent performance, and the predicted RHi in the UT has a mean absolute error (MAE) of 5.7 % and a coefficient of determination (<span class="inline-formula"><i>R</i>²</span>) of 0.95, which is significantly improved compared to ERA5 RHi (MAE of 15.8 %; <span class="inline-formula"><i>R</i>²</span> of 0.66). …”
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Spatiotemporal Soil Moisture Prediction Using a Causal-Guided Deep Learning Model by Tingtao Wu, Lei Xu, Ziwei Pan, Ruinan Cai, Jin Dai, Shuang Yang, Xihao Zhang, Xi Zhang, Nengcheng Chen

“…Experimental results show that Causal ST-SwinT significantly outperforms the classical convolutional long short-term memory model, reducing mean absolute error from 0.0146 to 0.0055 m³/m³ on the ERA5 dataset and from 0.0088 to 0.0046 m³/m³ on the SMAP dataset. …”
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Deep Learning-Driven Beam-Steering for Dual-Polarized 28 GHz Antenna Arrays in 5G Wireless Networks by Siti Zainab M. Zainab Hamzah, Norun Farihah Abdul Malek, Sarah Yasmin Mohamad, Farah Nadia Mohd Isa, Teddy Surya Gunawan, Kuo-Sheng Chin

“…The training and validation Root Mean Square Error (RMSE) and loss values converge to a minimum range of 1.3 to 2.3. …”
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Modeling Whole-Plant Carbon Stock in <i>Olea europaea</i> L. Plantations Using Logarithmic Nonlinear Seemingly Unrelated Regression by Yungang He, Weili Kou, Ning Lu, Yi Yang, Chunqin Duan, Ziyi Yang, Yongjun Song, Jiayue Gao, Weiyu Zhuang

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Fabrication of MPI-traceable alginate magnetic millirobots with multimodal selective-locomotion and heating capabilities by Armando Ramos-Sebastian, Ja-Sung Lee, Won-Il Song, Dong-Min Ji, So-Jung Gwak, Sung Hoon Kim

“…Alginate-based magnetic micro/millirobots have demonstrated significant potential for biomedical applications due to their flexible structures and capacity to carry various types of cargo, such as cells, enabling targeted therapy to specific diseased regions within the body. …”
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Optimization of Magnetic Finishing Process and Surface Quality Research for Inner Wall of MP35N Cobalt–Chromium Alloy Vascular Stent Tubing Based on Plasma-Fused Al₂O<su... by Yusheng Zhang, Yugang Zhao, Qilong Fan, Shimin Yang, Shuo Meng, Yu Tang, Guiguan Zhang, Haiyun Zhang

“…The optimized model predicted an Ra value of 0.104 μm, while the average Ra value verified experimentally was 0.107 μm, with the minimum error being 2.9%. Compared with the initial Ra of 0.486 μm, directly measured by the ultra-depth-of-field 3D microscope of model DSX1000, the surface roughness was reduced by 77.98%. …”
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Three-dimensional graph reconstruction of filamentous structures from z-stack images by Oscar Sten, Emanuela Del Dottore, Marilena Ronzan, Nicola Pugno, Barbara Mazzolai

“…The algorithm's 3D reconstruction capabilities are tested on 3D-printed structures used as ground truth with a filament radius of 0.5 mm, obtaining a Root Mean Square Error (RMSE) lower than the filament radius for most cases. …”
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An Online Estimating Framework for Ankle Actively Exerted Torque Under Multi-DOF Coupled Dynamic Motions via sEMG by Yu Zhou, Jianfeng Li, Shiping Zuo, Jie Zhang, Mingjie Dong, Zhongbo Sun

“…The results show that the proposed framework can estimate the actively exerted torque with a normalized root mean square error (NRMSE) of <inline-formula> <tex-math notation="LaTeX">${10}.{29}\% \pm {2}.{86}\%$ </tex-math></inline-formula> (mean &#x00B1; SD) for torque estimation under a single DOF, and NRMSE of <inline-formula> <tex-math notation="LaTeX">${11}.{35}\% \pm {4}.{51}\%$ </tex-math></inline-formula> under multiple DOFs, compared to the actual measured values. …”
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Comprehensive analysis of drilling responses in additively manufactured PLA using a regression—based statistical learning approach by Vishwadarshan, Gauthami Shetty, Raviraj Shetty, Supriya J P, Balaji V, Adithya Hegde

“…The regression coefficients from response surface methodology suggest a predictive equation for the delamination factor, demonstrating an average prediction error of 3.17%. These findings contribute to enhanced understanding and optimization of drilling processes in 3D-printed PLA components, paving the way for improved quality and efficiency in manufacturing applications.…”
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Bridge Deformation Prediction Using KCC-LSTM With InSAR Time Series Data by Zechao Bai, Chang Shen, Yanping Wang, Yun Lin, Yang Li, Wenjie Shen

“…Our results demonstrate that compared to standard LSTM, the proposed approach reduces root mean square error of Bridge 1 from 3.6 to 0.5 mm and Bridge 2 from 3.6 to 1.3&#x00A0;mm, improving prediction accuracy. …”
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An Improved UAV RGB Image Processing Method for Quantitative Remote Sensing of Marine Green Macroalgae by Jinghu Li, Qianguo Xing, Liqiao Tian, Yingzhuo Hou, Xiangyang Zheng, Maham Arif, Lin Li, Shanshan Jiang, Jiannan Cai, Jun Chen, Yingcheng Lu, Dingfeng Yu, Jindong Xu

“…When the DN values were replaced by their corresponding <italic>E</italic> values to calculate the reflectance of green macroalgae under different illumination intensities, the errors in reflectance were reduced by &#x223C;21&#x0025;; for the corresponding green macroalgae indices, such as the red&#x2013;green band virtual baseline floating green algae height (RG-FAH), the <italic>E</italic>-value-based RG-FAH demonstrates more resistance to the impacts of sun glints; and the <italic>E</italic> values were further applied to estimate the coverage portion of macroalgae (POM, &#x0025;) in RGB videos; the illumination-induced deviations of the POM were effectively reduced by up to 33.06&#x0025;, showing an advantage in quantitative estimation of macroalgae biomass. …”
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Monitoring Double-Cropped Extent with Remote Sensing in Areas with High Crop Diversity by Hossein Noorazar, Michael P. Brady, Supriya Savalkar, Amin Norouzi Kandelati, Mingliang Liu, Perry Beale, Andrew M. McGuire, Timothy Waters, Kirti Rajagopalan

“…In particular, our (image-based) deep learning model was able to capture nuances in this crop-diverse environment and achieve a high accuracy (96–99% overall accuracy and 83–93% producer accuracy for the double-cropped class with a standard error of less than 2.5%) while also identifying double-cropping in the right crop types and locations based on expert knowledge. …”
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Expert identification blitz: A rapid high value approach for assessing and improving iNaturalist identification accuracy and data precision and confidence by Thomas Mesaglio, Kelly A. Shepherd, Juliet A. Wege, Russell L. Barrett, Hervé Sauquet, Will K. Cornwell

“…This collaboration between experts and citizen scientists provides a way to quantify identification error rates for downstream statistical analysis and improves uncertain or incorrect identifications. …”
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Dose prediction of CyberKnife Monte Carlo plan for lung cancer patients based on deep learning: robust learning of variable beam configurations by Yuchao Miao, Jiwei Li, Ruigang Ge, Chuanbin Xie, Yaoying Liu, Gaolong Zhang, Mingchang Miao, Shouping Xu

“…The AB model matched well with the clinical plan’s dose-volume histograms, and the average dose error for all organs was 1.65 ± 0.69%. Conclusions Our proposed new model signifies a crucial advancement in predicting CK 3D dose distributions for clinical applications. …”
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Healthcare workers' priorities of WHO snakebite strategic objectives for the control and prevention of snakebite envenoming in Ghana: A machine learning statistical design of exper... by Eric Nyarko, Iddrisu Abugbil Atubiga, Emmanuel Tetteh Siame, José María Gutiérrez, Eduardo Alberto Fernandez

“…To compare the performance of these models, we used several key metrics, including Akaike Information Criterion corrected (AICc), the Bayesian Information Criterion (BIC), the Root Average Squared Error (RASE), negative log-likelihood, and the total time taken to fit each model.…”
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Antihypertensive Drug Recommendations for Reducing Arterial Stiffness in Patients With Hypertension: Machine Learning–Based Multicohort (RIGIPREV) Study by Iván Cavero-Redondo, Arturo Martinez-Rodrigo, Alicia Saz-Lara, Nerea Moreno-Herraiz, Veronica Casado-Vicente, Leticia Gomez-Sanchez, Luis Garcia-Ortiz, Manuel A Gomez-Marcos

“…Model performance was evaluated using the coefficient of determination (R2) and mean squared error. ResultsThe random forest models exhibited strong predictive capabilities, with internal validation yielding R2 values between 0.61 and 0.74, while external validation showed a range of 0.26 to 0.46. …”
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Modeling of Ultrasonic Flaw Detection Processes in the Task of Searching and Visualizing Internal Defects in Assemblies and Structures by B. V. Sobol, A. N. Soloviev, P. V. Vasiliev, A. A. Lyapin

“…They have significant potential for scientific and practical applications.…”
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Iterative Forecasting of Financial Time Series: The Greek Stock Market from 2019 to 2024 by Evangelos Bakalis, Francesco Zerbetto

“…The forecasting points follow the same trend, are in good agreement with the actual data, and for most of the forecasts, the percentage error is less than 2%.…”
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Simulation and Test of Key Decorticating Components of Spiral Ramie Decorticator by Wenlong Zheng, Lan Ma, Jiajie Liu, Bo Yan, Yiping Duan, Sixun Chen, Jiangnan Lyu, Wei Xiang

“…Experimental results indicated that under this optimal parameter combination, the fiber percentage of fresh stalk of the spiral ramie decorticator can reach 5.03%, with a relative error of less than 3% compared to the theoretical model prediction value, thus confirming the accuracy of the model prediction. …”
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