Analysis on the difference of volatile flavor components in strong-flavor Baijiu base liquor with different grades by YUE Xiaolin, XIANG Shuangquan, QIAN Yu, LIAN Shengqiang, YANG Guorui, LAN Xiaoqin, TAN Wenyuan

“…The 3 discriminative models were constructed through machine learning, among them, the random forest model had the optimal discriminative effect with an accuracy of 100%.…”
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Sequential Design Process of a 350-kW Class Dual Three-Phase IPMSM for a Wheeled Armored Vehicle by Ji-Chang Son, Min-Su Kwon, Dong-Kuk Lim

“…Finally, in the optimal design stage, the optimal model is quickly derived using a machine learning method, and the stability of the model is examined through multiphysics analysis. …”
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Exploration of key genes associated with oxidative stress in polycystic ovary syndrome and experimental validation by Qinhua Li, Qinhua Li, Qinhua Li, Lei Liu, Yuhan Liu, Yuhan Liu, Yuhan Liu, Yuhan Liu, Tingting Zheng, Tingting Zheng, Tingting Zheng, Ningjing Chen, Ningjing Chen, Ningjing Chen, Peiyao Du, Peiyao Du, Peiyao Du, Hong Ye, Hong Ye, Hong Ye

“…Differentially expressed OS related genes (DE-OSRGs) associated with PCOS were obtained by overlapping DEGs, key module genes, and OSRGs. Subsequently, the optimal machine model was obtained to identify key genes by comparing the performance of the random forest model (RF), support vector machine model (SVM), and generalized linear model (GLM). …”
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Spatio-Temporal Graph Neural Networks for Streamflow Prediction in the Upper Colorado Basin by Akhila Akkala, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi, Pouya Hosseinzadeh, Ayman Nassar

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Impact of PM_2.5 Pollution on Solar Photovoltaic Power Generation in Hebei Province, China by Ankun Hu, Zexia Duan, Yichi Zhang, Zifan Huang, Tianbo Ji, Xuanhua Yin

“…To capture these complex aerosol–radiation–PV interactions, we developed and compared the following six machine learning models: Support Vector Regression, Random Forest, Decision Tree, K-Nearest Neighbors, AdaBoost, and Backpropagation Neural Network. …”
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PROBLEMS OF COMMERCIAL BANK ATM NETWORK FORMATION by J. V. Domashova

“…The problem of building an automatic teller machine (ATM) network of a commercial bank consists of both identifying analytical indicators of its efficiency, and developing its construction model. …”
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A Global Irradiance Prediction Model Using Convolutional Neural Networks, Wavelet Neural Networks, and Masked Multi-Head Attention Mechanism by Walid Mchara, Lazhar Manai, Mohamed Abdellatif Khalfa, Monia Raissi, Salah Hannechi

“…Existing approaches, including statistical methods, conventional machine learning models, and standalone deep learning techniques like LSTM, fail to integrate local features and long-term dependencies simultaneously, creating a need for more robust solutions. …”
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Pyrolysis Kinetics and Gas Evolution of Flame-Retardant PVC and PE: A TG-FTIR-GC/MS Study by Wen-Wei Su, Yang Li, Peng-Rui Man, Ya-Wen Sheng, Jian Wang

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Advanced climate model evaluation with ESMValTool v2.11.0 using parallel, out-of-core, and distributed computing by M. Schlund, B. Andela, J. Benke, R. Comer, B. Hassler, E. Hogan, P. Kalverla, A. Lauer, B. Little, S. Loosveldt Tomas, F. Nattino, P. Peglar, V. Predoi, S. Smeets, S. Worsley, M. Yeo, K. Zimmermann

“…In this paper, we describe recent significant improvements of ESMValTool's computational efficiency, which allow a more effective evaluation of these complex ESMs and also high-resolution models. These optimizations include parallel computing (execute multiple computation tasks simultaneously), out-of-core computing (process data larger than available memory), and distributed computing (spread computation tasks across multiple interconnected nodes or machines). …”
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VSG-FC: A Combined Virtual Sample Generation and Feature Construction Model for Effective Prediction of Surface Roughness in Polishing Processes by Dapeng Yang, Shenggao Ding, Lifang Pan, Yong Xu

“…Additionally, the proposed model is explainable and could successfully identify key influencing machining factors.…”
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Leveraging petrophysical and geological constraints for AI-driven predictions of total organic carbon (TOC) and hardness in unconventional reservoir prospects by Nandito Davy, Ammar El-Husseiny, Umair bin Waheed, Korhan Ayranci, Manzar Fawad, Mohamed Mahmoud, Nicholas B. Harris

“…Petrophysical constraints were derived from triple combo well logs (gamma ray, bulk density, neutron porosity), while geological constraints included stratigraphic data or spatial distance between training and target wells—petrophysical constraints most improved predictions, while stratigraphic and spatial constraints had progressively less impact. Our optimized models achieved R2 (coefficient of determination) of 0.89 and RMSE (root-mean-square error) of 0.47 for TOC predictions and 0.90 and 34.8 for hardness predictions, reducing RMSE by up to 13.52% compared to the unconstrained model. …”
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Data augmented lung cancer prediction framework using the nested case control NLST cohort by Yifan Jiang, Yifan Jiang, Venkata S. K. Manem, Venkata S. K. Manem, Venkata S. K. Manem

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A Study on the Effect of Cutting Temperature on CFRP Hole Wall Damage in Continuous Drilling Process by Chong Zhang, Feiyu Chen, Dongxue Song, Jiale Liu, Qingsong Xu, Qunli Zhou, Haoyu Wang

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Risk Prediction of Postoperative Renal Dysfunction Based on Preoperative Lipid Profiles in Renal Transplant Recipients: A Retrospective Cohort Study by Zhang H, Zhang H, Li R, Zhuo L, Liu L, Tan L, Li R, Zhang S

“…It demonstrated good diagnostic performance, with an area under the curve (AUC) of 0.87 (95% CI, 0.85– 0.89) in the training group and 0.81 (95% CI, 0.78– 0.83) in the validation group.Conclusion: Our study developed a risk prediction model to identify RTRs at high risk of renal dysfunction based on preoperative lipid profiles, which is crucial for optimizing patient management and improving the prognosis.Keywords: kidney transplantation, renal dysfunction, eGFR, risk prediction, blood lipid levels, machine learning, nomogram…”
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Efficient sepsis detection using deep learning and residual convolutional networks by Ahmed S. Almasoud, Ghada Moh Samir Elhessewi, Munya A. Arasi, Abdulsamad Ebrahim Yahya, Menwa Alshammeri, Donia Badawood, Faisal Mohammed Nafie, Mohammed Assiri

“…In this article, we present a new deep learning model to detect the occurrence of sepsis and the African vulture optimization algorithm (AVOA) to enhance the model performance. …”
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Field Rice Growth Monitoring and Fertilization Management Based on UAV Spectral and Deep Image Feature Fusion by Bingnan Chen, Qihe Su, Yansong Li, Rui Chen, Wanneng Yang, Chenglong Huang

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Physiological Sensor Modality Sensitivity Test for Pain Intensity Classification in Quantitative Sensory Testing by Wenchao Zhu, Yingzi Lin

“…Plan 1 utilized a grid search methodology with a 10-fold cross-validation framework to optimize time windows (1–5 s) and machine learning hyperparameters for pain classification tasks. …”
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Research and application of mining AI video edge computing technology by ZHANG Liya, HAO Bonan, MA Zheng, YANG Zhifang

“…After deploying the registration machine SDK and optimizing the YOLOv7 model, the average inference latency was 28 ms, 52% and 44% lower than that of React Native+YOLOv7 and MobileNet, respectively. …”
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Brain CT image classification based on mask RCNN and attention mechanism by Shoulin Yin, Hang Li, Lin Teng, Asif Ali Laghari, Ahmad Almadhor, Michal Gregus, Gabriel Avelino Sampedro

“…Abstract Along with the computer application technology progress, machine learning, and block-chain techniques have been applied comprehensively in various fields. …”
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On Predicting Marine Engine Measurements with Synthetic Data in Scarce Dataset by Sandi Baressi Šegota, Igor Poljak, Nikola Anđelić, Vedran Mrzljak

“…These were used to train multilayer perceptron (MLP) regression models, which were optimized via grid search and validated through five-fold cross-validation. …”
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