Advanced sentiment analysis in online shopping: Implementing LSTM models analyzing E-commerce user sentiments by Lu Liyuan

“…Sarcasm and irony accounted for 22% of the classification errors, while mixed sentiment accounted for 18%, and implicit accounted for 15%. …”
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Predictive Modeling of River Water Temperatures in Catu River: A Neural Network-Based Approach by Carmen Goncalves de Macedo e Silva, José Roberto de Araújo Fontoura, Alarcon Matos de Oliveira, Thais de Souza Neri, Roberto Luiz Souza Monteiro, Thiago Barros Murari, Alexandre do Nascimento Silva, Leandro Brito Santos, Marcos Batista Figueredo

“…The results show that the BiLSTM model achieved the best performance, with a root mean square error (RMSE) of 0.12°C and R2 = 0.98, followed by BPNN with an RMSE of 0.18°C and R2 = 0.91, and the Random Forest model, which obtained an NSE of 0.95. …”
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A Comparative Analysis of Buckling Pressure Prediction in Composite Cylindrical Shells Under External Loads Using Machine Learning by Hyung Gi Lee, Jung Min Sohn

“…</b> The results demonstrated that the random forest model and XGBoost regression achieved superior accuracy with minimal prediction errors. …”
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Estimating Canopy Chlorophyll Content of Potato Using Machine Learning and Remote Sensing by Xiaofei Yang, Hao Zhou, Qiao Li, Xueliang Fu, Honghui Li

“…At various potato fertility stages, Random Forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost) inversion models were constructed. …”
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Outliers and anomalies in training and testing datasets for AI-powered morphometry—evidence from CT scans of the spleen by Yuriy Vasilev, Yuriy Vasilev, Anastasia Pamova, Tatiana Bobrovskaya, Anton Vladzimirskyy, Anton Vladzimirskyy, Olga Omelyanskaya, Elena Astapenko, Artem Kruchinkin, Novik Vladimir, Kirill Arzamasov, Kirill Arzamasov

“…Using visual methods (1.5 interquartile range; heat map; boxplot; histogram; scatter plot), machine learning algorithms (Isolation forest; Density-Based Spatial Clustering of Applications with Noise; K-nearest neighbors algorithm; Local outlier factor; One-class support vector machines; EllipticEnvelope; Autoencoders), and mathematical statistics (z-score, Grubb’s test; Rosner’s test).ResultsWe identified measurement errors, input errors, abnormal size values and non-standard shapes of the organ (sickle-shaped, round, triangular, additional lobules). …”
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Quantitative assessment of the scale conversion from instantaneous to daily GPP under various sky conditions based on MODIS local overpassing time by Junghee Lee, Jungho Im

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Predicting Weaning Weight of Romanov Lambs From Biometric Measurements Before Weaning Age Using Machine Learning Algorithms by Mehmet Eroğlu, Ali Osman Turgut, Mürsel Küçük, Muhammed Furkan Önen

“…In contrast, the random forest and CatBoost models showed lower predictive performance, with higher errors in the test data. …”
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Design of Chinese traditional Jiaoyi (Folding chair) based on Kansei Engineering and CNN-GRU-attention by Xinyan Yang, Nan Zhang, Jiufang Lv, Jiufang Lv, Jiufang Lv

“…Based on the top-ranked preference encoding, we designed a new Jiaoyi chair prototype, achieving significantly reduced prediction errors in final user testing (RMSE = 0.0034127, 0.0026915, 0.0035955).ConclusionThis research establishes a quantifiable intelligent design paradigm for modernizing cultural heritage through computational design.…”
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Hybrid approaches enhance hydrological model usability for local streamflow prediction by Yiheng Du, Ilias G. Pechlivanidis

“…We investigate various post-processing methods, such as random forest, long short-term memory model, quantile mapping and generalised linear model, demonstrating notable improvements in model performance, in terms of reducing errors in total volume and extremes and increasing robustness across diverse climatic and geographic conditions. …”
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Everyone Knows Who is Stupid Around Here by Sinan Cem Kızıl, Bengisu Derebaşı

“…To specify what architectural stupidity is, we must acknowledge that not all failures of architecture are ‘errors’, some are worse. This article discusses the already architecturally situated concept of error and distinguishes it from stupidity in terms of ‘technicities’ that fail. …”
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Construction and application of a TCN-LSTM-SVM-based time series prediction model for water inflow in coal seam roofs by Xuan LIU, Yadong JI, Kaipeng ZHU, Chunhu ZHAO, Kai LI, Chaofeng LI, Chenhan YUAN, Panpan LI, Pengzhen YAN

“…This model exhibited more accurate prediction results compared to the commonly used prediction models like backpropagation neural network (BPNN), random forest (RF), and Transformer while avoiding excessive errors produced by most of these models on the validation and test sets. …”
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Impact of atmospheric corrections on satellite imagery for corn yield prediction using machine learning by Octávio Pereira da Costa, Franklin Daniel Inácio, Jéssica Elaine da Silva, Thiago Orlando Costa Barboza, Wender Henrique Batista da Silva, Lorena Nunes Lacerda, Adão Felipe dos Santos

“…RF remained the best-performing model, with R² values exceeding 0.80 and errors below 0.20 t ha−1.…”
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Construction and Evaluation of a Cross-Regional and Cross-Year Monitoring Model for Millet Canopy Phenotype Based on UAV Multispectral Remote Sensing by Peng Zhao, Yuqiao Yan, Shujie Jia, Jie Zhao, Wuping Zhang

“…Various modeling approaches, including Random Forest, Gradient Boosting, and regularized regressions (e.g., Ridge and Lasso), were evaluated for cross-regional and cross-year extrapolation. …”
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A Fault Identification Method for Electric Submersible Pumps Based on DAE-SVM by Peihao Yang, Jiarui Chen, Hairong Zhang, Sheng Li

“…Thirdly, the real-time status of the production data of ESP was monitored with reconstruction errors to detect the point when an abnormality occurs signifying a pending fault. …”
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Comparative Assessment of Several Effective Machine Learning Classification Methods for Maternal Health Risk by Md Nurul Raihen, Sultana Akter

“…Early identification and classification of risk variables can successfully prevent pregnancy-related issues by reducing the number of errors. Maternal risk analysis can improve prenatal care, improve mother and baby health, and optimize healthcare resources by identifying misclassified observations using machine learning algorithms such as LDA, QDA, KNN, Decision Tree, Random Forest, Bagging, and Support Vector Machine, all of which have a significant impact on maternity health risk assessment. …”
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Linking Electrocardiogram and Echocardiogram: Comparing Classical Machine Learning and Deep Learning Neural Networks for the Detection of Regional Wall Motion Abnormalities by Shantanu M. Joshi, Hana R. Shaik, Shivam Rai Sharma, Philip Strong, Uma Srivatsa, Imo Ebong, Hyoyoung Jeong, Chen-Nee Chuah, Lihong Mo

“…Our results indicate that the optimized 1D CNN model achieved an area under the receiver operating characteristic curve (AUROC) of 73.40% (1D CNN) versus 63.58% (Random Forest), an area under the precision-recall curve (AUPRC) of 72.45% (1D CNN) versus 72.64% (Random Forest), and accuracy of 71.15% (1D CNN) versus 63.58% (Random Forest). …”
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Machine Learning-Driven Rapid Flood Mapping for Tropical Storm Imelda Using Sentinel-1 SAR Imagery by Reda Amer

“…The Random Forest classifier achieved an overall flood mapping accuracy exceeding 94%, with Cohen’s kappa coefficients of approximately 0.75–0.80, demonstrating the approach’s reliability in distinguishing transient floodwaters from permanent water bodies. …”
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Simulation of snow accumulation and melting in the Kama river basin using data from global prognostic models by S. V. Pyankov, A. N. Shikhov, P. G. Mikhaylyukova

“…Materials of 40 field and 27 forest snow-measuring routes were taken into account to assess the reliability of the calculation of snow storages (SWE). …”
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Revolutionizing Chinese medicine granule placebo with a machine learning four-color model by Tingting Teng, Jingze Zhang, Peiqi Miao, Lipeng Liang, Xinbo Song, Dailin Liu, Junhua Zhang

“…Among these models, the average R2 of the random forest model reached 0.9249, outperforming other models. …”
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A Neighborhood Approach for Using Remotely Sensed Data to Estimate Current Ranges for Conservation Assessments by Bethany A. Johnson, Gonzalo E. Pinilla‐Buitrago, Robert P. Anderson

“…We implement its use for a forest‐dwelling species (Handleyomys chapmani) considered threatened by the IUCN. …”
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