ANN-based analysis of thin film Maxwell fluid dynamics with electro-osmotic and nonlinear thermal effects by Irfan Saif Ud Din, Imran Siddique, Zohaib Zahid, Muhammad Nadeem, S. Islam

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Downscaling of Soil Moisture Map using Sentinel Radar Satellite Images and Distribution Analysis in the West of Iran by Seyed Hossein Mirmosavi, kohzad Raispour, Muhammad Kamangar

“…After executing the algorithm implemented in Google Earth system, the soil moisture layer engine with high spatial resolution between 0.22 to 0.45 cubic meters per cubic meter showed that by comparing the pixels of the high resolution layer with the real data, square root error and correlation coefficient 0.1641 and 0.5012 were obtained, respectively. …”
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Aggregating flood damage functions: The peril of Jensen's gap by Seth Bryant, Jody Reimer, Heidi Kreibich, Bruno Merz

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Employing Neural Networks, Fuzzy Logic, and Weibull Analysis for the Evaluation of Recycled Brick Powder in Concrete Compositions by Mohammad Mohtasham Moein, Komeil Rahmati, Ali Mohtasham Moein, Ashkan Saradar, Sam E. Rigby, Amin Akhavan Tabassi

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You can simply rely on communities for a robust characterization of stances by Damián Ariel Furman, Santiago Marro, Cristian Cardellino, Diana Nicoleta Popa, Laura Alonso Alemany

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Evaluating the efficacy of bioelectrical impedance analysis using machine learning models for the classification of goats exposed to Haemonchosis by Aftab Siddique, Phaneendra Batchu, Arshad Shaik, Priyanka Gurrapu, Tharun Tej Erukulla, Cornileus Ellington, Andrea L. Rubio Villa, Davia Brown, Ajit Mahapatra, Sudhanshu Panda, Eric Morgan, Jan Van Wyk, David Shapiro-Ilan, Govind Kannan, Thomas H. Terrill

“…For regression tasks, BPNN outperformed other models, with a nearly perfect R2 value of 99.9% and a minimal Mean Squared Error (MSE) of 1.25e-04, followed by SVR with an R2 of 96.9%. …”
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Application of Machine Learning for Academic Outcome Prediction: A Methodological Comparative Study by Md. Wira Putra Dananjaya, Putu Gita Pujayanti

“…The models were evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R2) metrics. …”
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Clan Crypts on the Lower Don of the Early Catacomb Time and Genesis Issues of the Catacomb Rite by Alexey V. Kiyashko

“…The burials contained double tombs with a complex rite and prestigious accompanying items namely bronze tools and silver jewelry. The analysis of the rite and the inventory of the considered catacombs as well as the stratigraphic data allowed identifying their cultural affiliation and attributing them to two successive stages of the Early Catacomb period on the Lower Don: pre-Donetsk (No. 26) and early Donetsk (No. 42) cultures. …”
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A comparison of frequentist and Bayesian approaches to the Personalised Randomised Controlled Trial (PRACTical)—design and analysis considerations by Holly Jackson, Yiyun Shou, Nur Amira Binte Mohamed Azad, Jing Wen Chua, Rebecca Lynn Perez, Xinru Wang, Marlieke E. A. de Kraker, Yin Mo

“…Methods We simulated trial data to compare four targeted antibiotic treatments for multidrug resistant bloodstream infections as an example. …”
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Developing a brain inspired multilobar neural networks architecture for rapidly and accurately estimating concrete compressive strength by Bashar Alibrahim, Ahed Habib, Maan Habib

“…The study results indicated that the MLANN architecture significantly improves the estimation performance, reducing the root mean square error by up to 32.9% and mean absolute error by up to 25.9% while also enhancing the A20 index by up to 17.9%, ensuring a more robust and generalizable model. …”
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Machine learning insights for predicting density and hardness in centrifugal SHS synthesized ceramic coatings by N. Radhika, M. Sabarinathan, S. Sivaraman

“…The BR model achieved a Coefficient of Determination (R²) of 0.91, a Root Mean Square Error (RMSE) of 0.10, and a Mean Absolute Error (MAE) of 0.06 for density predictions. …”
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A Multidimensional Parameter Dynamic Evolution-Based Airdrop Target Prediction Method Driven by Multiple Models by Xuesong Wang, Jiapeng Yin, Jianbing Li, Yongzhen Li

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Degradation Modeling Based on Wiener Process Considering Multi-Source Heterogeneity by Meng Xiao, Youpeng Zhang, Yongjian Li, Wenxian Wang

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Predicting bitcoin cryptocurrency price behavior based on ARIMA and NNAR modelling by Patricia Virginia de Santana Lima, David Venâncio da Cruz, Albaro Ramon Paiva Sanz

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An interpretable deep learning framework using FCT-SMOTE and BO-TabNet algorithms for reservoir water sensitivity damage prediction by Yin-bo He, Ke-ming Sheng, Ming-liang Du, Guan-cheng Jiang, Teng-fei Dong, Lei Guo, Bo-tao Xu

“…The proposed framework exhibited superior predictive accuracy for the water sensitivity index on an independent test set, achieving a mean absolute percentage error (MAPE) of 4.4495% and a root mean square error (RMSE) of 4.05, underscoring its strong generalization capability. …”
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Prioritizing Higher Education Facilities Using TOPSIS Based on Student Preferences by Mutiara Novita Sari, Rahel Saffana Adya, Qurrotul Aini

“…Recognizing the critical role of facilities in enhancing student success and retention, this study evaluates key criteria such as needs, comfort, current conditions, accessibility, and frequency of use. Data were collected through a random sampling survey involving 98 active students, determined using Slovin's formula with a 10% margin of error. …”
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Alloys innovation through machine learning: a statistical literature review by Alireza Valizadeh, Ryoji Sahara, Maaouia Souissi

“…Through analysis, significant trends and disparities within the data discerned, while highlighting previously overlooked research gaps, thus underscoring areas that require further exploration. …”
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User Analysis of Info BMKG Application in The Perspective of Human Computer Interaction Using Support Vector Machine Algorithm by Ilham Fannani, Enggar Novianto, Alfin Syarifuddin Syahab

“…The results of the sentiment analysis process of testing 3000 review data using the fold = 5 test value in the Support Vector Machine (SVM) method obtained an accuracy of 85.54 % which produces predictions on data testing, namely 1500 positive reviews and 1500 negative reviews 1500 reviews.…”
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A comparative analysis of national water model versions 2.1 and 3.0 reveals advances and challenges in streamflow predictions during storm events by Sujana Timilsina, Paola Passalacqua

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Invited review: A review of some commonly used meta-analysis methods in dairy science research by R.J. Tempelman

“…In all cases, meta-estimates generated from the analysis of individual performance data (IPD), using the same statistical model as that used to simulate the data, were considered to be gold-standard references for meta-estimates derived from various meta-analysis strategies on aggregate data. …”
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