Prediction and Optimization for Multi-Product Marketing Resource Allocation in Cross-Border E-Commerce by Yi Xie, Heng-Qing Ye, Wenbin Zhu

“…We propose a two-stage optimization framework that integrates predictive models with constrained optimization. In the first stage, predictive models estimate user purchase probabilities and determine upper bounds on product-specific sending volumes. …”
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Relationship between urban traffic crashes and temporal/meteorological conditions: understanding and predicting the effects by Xiao Tang, Zihan Liu, Zhenlin Wei

“…Further, by incorporating a diverse set of the features, a prediction model leveraging the random forest algorithm is proposed and proved effective in anticipating accident occurrences on the district level. …”
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Application of BITCN-BIGRU Neural Network Based on ICPO Optimization in Pit Deformation Prediction by Yong Liu, Cheng Liu, Xianguo Tuo, Xiang He

“…To enhance the prediction of pit deformation and improve accuracy and precision, an Improved Crown Porcupine Optimization Algorithm (ICPO) based on a Bidirectional Time Convolution Network–Bidirectional Gated Recirculation Unit (BITCN-BIGRU) is developed. …”
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Deep learning signature to predict postoperative anxiety in patients receiving lung cancer surgery by Qingqing Ji, Guohua Zhou, Xiangxiang Sun

“…Preoperative MRI-T1WI images were collected to train the deep learning signature utilized the ResNet-152 algorithm. The relationships between clinical variables and postoperative anxiety were explored via Logistic regression and the predictive performances of the developed deep learning signature were evaluated via receiver operating characteristic analysis. …”
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De novo structure prediction of globular proteins aided by sequence variation-derived contacts. by Tomasz Kosciolek, David T Jones

“…Here, we investigate the potential benefits of combining a well-established fragment-based folding algorithm--FRAGFOLD, with PSICOV, a contact prediction method which uses sparse inverse covariance estimation to identify co-varying sites in multiple sequence alignments. …”
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Remaining Useful Life Prediction Based on Wear Monitoring with Multi-Attribute GAN Augmentation by Xiaojun Zhu, Yan Pan, Bin Lan, He Wang, Huixin Huang

“…With the growing imperative for advanced prognostics and health management (PHM) systems, remaining useful life (RUL) prediction through lubricating oil monitoring has become pivotal for intelligent preventive maintenance. …”
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An extreme forecast index-driven runoff prediction approach using stacking ensemble learning by Zhiyuan Leng, Lu Chen, Binlin Yang, Siming Li, Bin Yi

“…The stacking ensemble learning framework comprises four base-models and a meta-model, and model hyperparameters are re-optimized using the particle swarm optimization algorithm. The approach focuses on predicting the inflow processes of the Geheyan Reservoir in the Qing River using EFI and runoff time series. …”
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An Enhanced Approach for Remaining Useful Life Prediction of Bearings Using Incomplete Lifecycle Data by Xunmeng An, Chao Zhang, Caiye Liu, Wentao Zhao

“…Experimental results demonstrate that the proposed algorithm effectively captures the complex dynamic behavior of rolling bearings, achieving high accuracy and generalization in RUL prediction.…”
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Joint Prediction of U.S. Rice Yields and Methane Emissions: A Machine Learning Approach by Jameson Augustin, Munisamy Gopinath, Berna Karali, Yuhan Rao

“…Despite the United States’s major role as a rice exporter with significant economic and environmental impacts, most remote sensing and machine learning research on rice yield prediction has focused on Asian production regions. …”
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Ensemble Method of Triple Naïve Bayes for Plastic Type Prediction in Sorting System Automation by Irsyadi Yani, Ismail Thamrin, Dewi Puspitasari, Barlin, Yulia Resti

“…The plastic sorting system automation requires intelligent computing as a software system that can predict the type of plastic accurately. The ensemble method is a method that combines several single prediction methods based on machine learning into an algorithm to obtain better performance. …”
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A Hybrid FEM-CNN for Image-Based Severity Prediction of Corroded Offshore Pipelines by Mohammad Fadzil Najwa, Muda Mohd Fakri, Abdul Shahid Muhammad Daniel, Aziz Norheliena, Mohd Mohd Hairil, Mohd Amin Norliyati, Mohd Hashim Mohd Hisbany

“…Moreover, the model was validated for prediction with irregular-sized corroded pipelines (50x100 mm and 10x100 mm). …”
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COMPARISON OF K-NEAREST NEIGHBOR AND NEURAL NETWORK FOR PREDICTION INTERNATIONAL VISITOR IN EAST JAVA by Dina Novita, Teguh Herlambang, Vaizal Asy’ari, Arasy Alimudin, Hamzah Arof

“…So, it is recommended that the k-NN algorithm be used to predict the number of international visitors in East Java. …”
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Combined Prediction Method of Short-Term Distance Headway Based on EB-GRA-TCN by Chun Wang, Weihua Zhang, Cong Wu, Heng Hu, Wenjia Zhu

“…In the model, the EB-GRA is adopted to calculate the correlation between the target DHW and historical DHW sequences, and the DHW data with high correlation are dynamically selected as the optimal input of the DHW prediction model. Then, the TCN algorithm is used to train the DHW prediction model. …”
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Predicting sensory evaluation of spinach freshness using machine learning model and digital images. by Kento Koyama, Marin Tanaka, Byeong-Hyo Cho, Yusaku Yoshikawa, Shige Koseki

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A stacked ensemble model for traffic conflict prediction using emerging sensor data by Bowen Cai, Léah Camarcat, Nicolette Formosa, Mohammed Quddus

“…This model integrates a Random Forest (RF), three-layer Deep Neural Networks (DNN), Support Vector Machine Radial (SVM-R), and a Gradient Boosting Model (GBM) meta layer to enhance prediction accuracy. The Recursive Feature Elimination (RFE) algorithm is then employed to identify the most influential SSMs for conflict prediction in each scenario. …”
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Capturing the Characteristics of Car-Sharing Users: Data-Driven Analysis and Prediction Based on Classification by Jun Bi, Ru Zhi, Dong-Fan Xie, Xiao-Mei Zhao, Jun Zhang

“…We also propose a model that predicts the driver cluster based on the decision tree. …”
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Enhancing Cloud Job Failure Prediction With a Novel Multilayer Voting-Based Framework by Ahmed Elkaradawy, Ayman Elshenawy, Hany Harb

“…In modern cloud data centers, accurately predicting job failures before they occur is essential for ensuring system reliability, availability, and efficiency. …”
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Oilfield Production Prediction Method Based on Multi-Input CNN-LSTM With Attention Mechanism by Lihui Tang, Zhenpeng Wang, Yajun Gao, Hao Wu, Wenbo Zhang, Xiaoqing Xie

“…Oil production prediction is crucial for the formulation of adjustment strategies, enhancement of recovery rates, and guidance of production in oilfields. …”
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Biochemical Oxygen Demand Prediction Based on Three-Dimensional Fluorescence Spectroscopy and Machine Learning by Xu Zhang, Yihao Zhang, Xuanyi Yang, Zhiyun Wang, Xianhua Liu

“…The aim of this study was to propose a facile method for predicting biochemical oxygen demand by fluorescence signals using three-dimensional fluorescence spectroscopy and parallel factor analysis in combination with a machine learning algorithm. …”
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Radiodosiomics Prediction of Treatment Failures Prior to Chemoradiotherapy in Head-and-Neck Squamous Cell Carcinoma by Hidemi Kamezawa, Hidetaka Arimura

“…Moreover, deep (<i>D</i>) features were extracted from a deep learning-based prediction model. The Coxnet algorithm was employed to select significant features. …”
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