A machine learning model with crude estimation of property strategy for performance prediction of perovskite solar cells based on process optimization by Dan Li, Ernie Che Mid, Shafriza Nisha Basah, Xiaochun Liu, Jian Tang, Hongyan Cui, Huilong Su, Qianliang Xiao, Shiyin Gong

“…Notably, the coefficient of determination (R2) on the test set increased by 16.14%, while the root mean square error decreased by 20.44%, respectively. …”
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Deep learning-based sow posture classifier using colour and depth images by Verônica Madeira Pacheco, Tami M. Brown-Brandl, Rafael Vieira de Sousa, Gary A. Rohrer, Sudhendu Raj Sharma, Luciane Silva Martello

“…The best model used only depth images as input and presented an accuracy of 98.3 %. The mean precision and recall values were 97.04 % and 97.32 %, respectively (F1-score = 97.2 %). …”
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TomaFDNet: A multiscale focused diffusion-based model for tomato disease detection by Rijun Wang, Rijun Wang, Yesheng Chen, Fulong Liang, Xiangwei Mou, Xiangwei Mou, Guanghao Zhang, Hao Jin

“…Results and DiscussionExperimental results show that TomaFDNet reaches a mean average precision (mAP) of 83.1% in detecting Early_blight, Late_blight, and Leaf_Mold on tomato leaves, outperforming classical object detection algorithms, including Faster R-CNN (mAP = 68.2%) and You Only Look Once (YOLO) series (v5: mAP = 75.5%, v7: mAP = 78.3%, v8: mAP = 78.9%, v9: mAP = 79%, v10: mAP = 77.5%, v11: mAP = 79.2%). …”
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Development of Adaptive Testing Method Based on Neurotechnologies by E. V. Chumakova, D. G. Korneev, M. S. Gasparian

“…Adam showed the best results in terms of accuracy, while the MSE loss function (mean square error) was used together with the optimizer. …”
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First study of the RuPatient health information system with optical character recognition of medical records based on machine learning by A. A. Komkov, V. P. Mazaev, S. V. Ryazanova, D. N. Samochatov, E. V. Koshkina, E. V. Bushueva, O. M. Drapkina

“…The study included 77 pages of discharge reports of patients from various hospitals in Russia from 50 patients (men, 52%). The mean age of patients was 57,7±7,9 years. The number of reports with correctly recognized fields in various categories using the program algorithms was distributed as follows: Name — 14 (28%), Diagnosis — 13 (26%), Complaints — 40 (80%), Anamnesis — 14 (28%), Examination — 24 (48%), Recommendations — 46 (92%). …”
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Machine learning models for the prediction of preclinical coal workers’ pneumoconiosis: integrating CT radiomics and occupational health surveillance records by Yankun Ma, Fengtao Cui, Yulong Yao, Fuhai Shen, Hongyi Qin, Bing Li, Yan Wang

“…Second, two feature selection algorithms were applied to select critical features from both CT radiomics and occupational health data. …”
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Automated Deep Learning Based Cardiac Quantification in Hypertrophic Cardiomyopathy: A Comparative Study with Manual Segmentation by Shivam Angiras, Deb Kumar Boruah, Pranjal Phukan, Kalyan Sarma, Prince Das, Rajeev Bharadwaj, Harshit Jain, Ajmal Roshan

“…Recently, Deep Learning (DL) algorithms have emerged to automate cardiac quantification, but their performance in complex pathologies such as HCM still requires validation.Purpose: To compare the performance of a fully automated deep learning-based cardiac segmentation software (SW 2) (SuiteHEART) with conventional manual segmentation (SW 1) (syngo.Via) for quantifying crucial cardiac parameters in patients with HCM.Materials and Methods: In this prospective study, 25 consecutive adult patients (mean age 49±12 years) with HCM referred for CMR at our institute were included. …”
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Alpine Meadow Fractional Vegetation Cover Estimation Using UAV-Aided Sentinel-2 Imagery by Kai Du, Yi Shao, Naixin Yao, Hongyan Yu, Shaozhong Ma, Xufeng Mao, Litao Wang, Jianjun Wang

“…The results showed that: (1) Machine learning algorithms based on Sentinel-2 and UAV imagery effectively improved the accuracy of FVC estimation in alpine meadows. …”
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Development of an Intelligent System for Processing Semistructured Data: Industry Structuring and Advanced Analysis of Information Extracted from Comments to Video Clips in Social... by A. A. Poguda, H. Tape

“…Developing an intelligent system for analyzing semistructured data requires innovative methods and approaches that combine natural language processing (NLP), machine learning algorithms and big data analytics techniques. These methods include: automatic data extraction via API, preprocessing adapted for three languages (French, English and Russian), deep sentiment analysis using the Bert product and a probabilistic algorithm for statistical calculations, and clustering using K-Means, DBSCAN and Agglomerative algorithms. …”
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MEDIA-REALITY IN THE VISUAL ARTS / МЕДИАРЕАЛЬНОСТЬ В ИЗОБРАЗИТЕЛЬНОМ ИСКУССТВЕ by YUGAY INGA I. / ЮГАЙ И.И.

“…The author stems from the fact that in the works of contemporary artists the influence of media reality becomes more and more discernible on the level of theme, means of presentation and application of expressive means. …”
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Application of machine learning techniques to predict the compressive strength of steel fiber reinforced concrete by Ala’a R. Al-Shamasneh, Arsalan Mahmoodzadeh, Faten Khalid Karim, Taoufik Saidani, Abdulaziz Alghamdi, Jasim Alnahas, Mohammed Sulaiman

“…Among the tested models, GPR consistently outperformed all others, achieving a maximum coefficient of determination (R²) of 0.93 and the lowest root mean square error (RMSE) of 16.54, thereby demonstrating superior capability in capturing the underlying nonlinear relationships within the data. …”
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Prediction of microbe-drug associations using a CNN-Bernoulli random forest model by Zihao Song, Qingnuo Li, Jincheng Zhao, Qinggang Bu, Zekang Bian, Jia Qu

“…The dual use of the Bernoulli distribution in BRF ensures algorithmic consistency and contributes to superior performance. …”
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Predicting cognitive decline in cognitively impaired patients with ischemic stroke with high risk of cerebral hemorrhage: a machine learning approach by Eun Namgung, Young Sun Kim, Sun U. Kwon, Dong-Wha Kang, Dong-Wha Kang

“…In the test set, CatBoost achieved a mean area under the curve (AUC) of 0.897, with an accuracy of 0.873; other models performed as follows: logistic regression (AUC 0.775), AdaBoost (AUC 0.767), and XGBoost (AUC 0.722). …”
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Estimation of Reference Crop Evapotranspiration in the Yellow River Basin Based on Machine Learning and Its Regional and Drought Adaptability Analysis by Jun Zhao, Huayu Zhong, Congfeng Wang

“…These models represent a range of algorithmic structures, from nonlinear ensemble methods (RF, GB) to kernel-based regression (SVR) and linear regularized regression (Ridge). …”
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Improving the Performance of Electrotactile Brain–Computer Interface Using Machine Learning Methods on Multi-Channel Features of Somatosensory Event-Related Potentials by Marija Novičić, Olivera Djordjević, Vera Miler-Jerković, Ljubica Konstantinović, Andrej M. Savić

“…We systematically tested classification performance using machine learning algorithms, including logistic regression, k-nearest neighbors, support vector machines, random forests, and artificial neural networks. …”
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Regularized Kaczmarz Solvers for Robust Inverse Laplace Transforms by Marta González-Lázaro, Eduardo Viciana, Víctor Valdivieso, Ignacio Fernández, Francisco Manuel Arrabal-Campos

“…In this work, we introduce a novel family of Kaczmarz-based ILT solvers that embed advanced regularization directly into the iterative projection framework. We propose three algorithmic variants—Tikhonov–Kaczmarz, total variation (TV)–Kaczmarz, and Wasserstein–Kaczmarz—each incorporating a distinct penalty to stabilize solutions and mitigate noise amplification. …”
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Adaptive strategies for the deployment of rapid diagnostic tests for COVID-19: a modelling study [version 2; peer review: 2 approved, 1 approved with reservations] by Emily Kendall, Lucia Cilloni, Nimalan Arinaminpathy, David Dowdy

“…Background Lateral flow assays (LFAs) for the rapid detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) provide an affordable, rapid and decentralised mean for diagnosing coronavirus disease 2019 (COVID-19). …”
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Comparison of Surface Current Measurement Between Compact and Square-Array Ocean Radar by Yu-Hsuan Huang, Chia-Yan Cheng

“…Baseline velocity comparisons between MABT and KNTN revealed a correlation coefficient of 0.77 and a root-mean-square deviation (RMSD) of 0.23 m/s, which are consistent with typical values reported in previous radar performance evaluations. …”
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Real world pharmacovigilance study of antineoplastic drug related vitiligo risks by Yixuan Yang, Hanzhang Xie, Dongtao Li, Ying Jia, Bingnan Cui, Jianhua Zou, Zhanshuo Xiao

“…The Reporting Odds Ratio, Proportional Reporting Ratio, Bayesian Confidence Propagation Neural Network, and Empirical Bayes Geometric Mean were calculated to assess the reported associations between available drugs and vitiligo. …”
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The effect of implementing parenteral nutrition guideline on growth and clinical outcomes in preterm infants: a comparative study by Majid Mahallei, Lida Gorbani, Mohammad Bagher Hoseini, Elnaz Shaseb, Bahareh Mehramuz, Khatereh Rezazadeh

“…These improvements paralleled higher mean daily energy and protein intakes during the early postnatal period. …”
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