Adaptive Extended Kalman Prediction-Based SDN-FANET Segmented Hybrid Routing Scheme by Ke Sun, Mingyong Liu, Chuan Yin, Qian Wang

“…Further, to reduce the packet loss rate in high-speed motion scenes, an adaptive extended Kalman prediction algorithm is also proposed to track and predict the 3D movement of UAVs. …”
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Comparative Analysis of The Combined Model (Spatial and Temporal) and Regression Models for Predicting Murder Crime by Laith S. Ibrahim, Ghadeer Jasim Mohammed

“… This research dealt with the analysis of murder crime data in Iraq in its temporal and spatial dimensions, then it focused on building a new model with an algorithm that combines the characteristics associated with time and spatial series so that this model can predict more accurately than other models by comparing them with this model, which we called the Combined Regression model (CR), which consists of merging two models, the time series regression model with the spatial regression model, and making them one model that can analyze data in its temporal and spatial dimensions. …”
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Study of rectangular fin heat sink performance and prediction based on artificial neural network by Zheng Lan, Yu-hao Feng, Ying-wen Liu

“…Furthermore, a multi-layer feedforward neural network based on a genetic algorithm (GA-BP) is employed to predict both Nusselt number and friction factor; subsequently evaluating these predicted results. …”
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EUR Prediction for Shale Gas Wells Based on the ROA-CatBoost-AM Model by Weikang He, Xizhe Li, Yujin Wan, Honming Zhan, Nan Wan, Sijie He, Yaoqiang Lin, Longyi Wang, Wenxuan Yu, Liqing Chen

“…To address the challenges in accurately predicting shale gas EUR, this study analyzed production data from 200 wells in the CN block. …”
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Multiple programmed cell death patterns predict the prognosis and drug sensitivity in gastric cancer by Qiying Song, Shihe Liu, Di Wu, Aizhen Cai

“…By integrating PCDS with clinical characteristics, we constructed and validated a nomogram that demonstrated robust predictive performance.ResultsWe screened nine PCD-related genes (SERPINE1, PLPPR4, CDO1, MID2, NOX4, DYNC1I1, PDK4, MYB, TUBB2A) to create the PCDS. …”
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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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Prediction and Optimization of Bolt Parameters for Automotive Fuel Cell Stack under Random Vibration by Yao Junqi, Yin Yan, Wang Bowen, Zhang Fan, Qin Zhikun, Wang Zixuan, Guo Ting

“…By integrating XGBoost with a genetic algorithm (GA), a GA-XGBoost predictive model is developed to optimize the structural parameters of the fastening bolts. …”
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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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Computational-Based Approaches for Predicting Biochemical Oxygen Demand (BOD) Removal in Adsorption Process by Mohamed K. Mostafa, Ahmed S. Mahmoud, Mohamed S. Mahmoud, Mahmoud Nasr

“…An optimized ANN structure of 5–10–1, with the “trainlm” back-propagation learning algorithm, achieved the highest predictive performance for BOD removal (R2: 0.972, Adj-R2: 0.971, RMSE: 1.449, and SSE: 56.680). …”
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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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