A Comparative Study of Topic Modelling Approaches for User-generated Point of Interest Data by Ravi Satyappa Dabbanavar, Arindam Biswas

“…We also explore four text preprocessing steps to optimize the performance of the topic models. This study contributes to the field by providing a nuanced understanding of UFZs, paving the way for future data-driven urban planning and management. …”
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Prediction model of gastrointestinal tumor malignancy based on coagulation indicators such as TEG and neural networks by Fulong Yu, Chudi Sun, Liang Li, Xiaoyu Yu, Shumin Shen, Hao Qiang, Song Wang, Xianghua Li, Lin Zhang, Zhining Liu

“…This study builds various prediction models through machine learning methods based on the different coagulation statuses under varying malignancy levels of gastrointestinal tumors. …”
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Stratified allocation method for water injection based on machine learning: A case study of the Bohai A oil and gas field by Changlong Liu, Pingli Liu, Qiang Wang, Lu Zhang, Zechao Huang, Yuande Xu, Shaojiu Jiang, Le Zhang, Changxiao Cao

“…Second, the training and prediction effects of three machine learning prediction models—support vector machine, BP neural network, and random forest—were compared, and the BP neural network was selected as the machine learning mathematical model for injection allocation optimization. …”
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Experimental Investigation and NSGA-III Multi-Criteria Optimization of 60CrMoV18-5 Cold-Work Tool Steel Machinability Under Dry CNC Hard Turning Conditions by Nikolaos A. Fountas, Ioannis G. Papantoniou, Dimitrios E. Manolakos, Nikolaos M. Vaxevanidis

“…Finally, the NSGA-III algorithm was applied to simultaneously optimize the selected machinability parameters by providing beneficial values for determining cutting conditions. …”
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Leveraging Machine Learning to Forecast Neighborhood Energy Use in Early Design Stages: A Preliminary Application by Andrea Giuseppe di Stefano, Matteo Ruta, Gabriele Masera, Simi Hoque

“…This study identifies three key phases in a design process framework where machine learning can be applied to optimize energy consumption in early design stages. …”
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Concrete Dam Deformation Prediction Model Based on Attention Mechanism and Deep Learning by ZHANG Hongrui, CAO Xin, JIANG Chao, ZU Anjun, XU Mingxiang

“…The model comprehensively matched the multiple requirements of information weighting, temporal dependency modeling, and model optimization in concrete dam deformation prediction, forming a synergistic enhancement effect among methods.The attention mechanism operates on both feature and temporal dimensions to comprehensively enhance the model's focus on critical information. …”
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Hybrid procurement model for the construction of library literature and information resource procurement by Chuanyu Zhang, Changsheng Wang

“…To improve the efficiency of intelligent procurement of library literature and intelligence resources, the study conducts the design of literature and intelligence resources procurement model. The procurement model is constructed by using the support vector machine, and the optimal parameters of the support vector machine are obtained by using the genetic algorithm. …”
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An Optimization Framework for Waste Treatment Center Site Selection Considering Nighttime Light Remote Sensing Data and Waste Production Fluctuations by Junbao Xia, Yanping Liu, Haozhong Yang, Guodong Zhu

“…In conclusion, by incorporating nighttime light remote sensing data along with advanced machine learning techniques, this study markedly improves forecasting accuracy for waste production while offering effective optimization strategies for site selection and recovery route planning—thereby establishing a robust data foundation aimed at refining urban solid waste management systems.…”
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Robust asphaltene onset pressure prediction using ensemble learning by Jafar Khalighi, Alexey Cheremisin

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A machine learning-based nomogram for predicting graft survival in allograft kidney transplant recipients: a 20-year follow-up study by Jiamin He, Pinlin Liu, Lingyan Cao, Feng Su, Yifei Li, Tao Liu, Wenxing Fan

“…BackgroundKidney transplantation is the optimal form of renal replacement therapy, but the long-term survival rate of kidney graft has not improved significantly. …”
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Optimum Design and Research of the Vertical Manipulator Arm Rotation Structure by Sima Mingyang, Zhang Lin, Wang Baoping, Jiang Mingyang, Liu Hongbin

“…Therefore, a scale optimization model is established for the boom landing mechanism to minimize the maximum load of the driving cylinder as the objective function. …”
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The value of multi-phase CT based intratumor and peritumoral radiomics models for evaluating capsular characteristics of parotid pleomorphic adenoma by Qian Shen, Cong Xiang, Yongliang Han, Yongmei Li, Kui Huang

“…The receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC) were used to evaluate the prediction performance of each model.ResultsAmong all the established machine learning prediction radiomics models, the model based on a three-phase combination had better prediction performance, and the model using a combination of intratumoral and peritumoral radiomics features achieved a higher AUC than the model with only intratumoral or peritumoral radiomics features, and the Tumor+External2 model based on LR was the optimal model, the AUC of the test set was 0.817 (95% CI = 0.712, 0.847), and its prediction performance was significantly higher (p < 0.05, DeLong’s test) than that with the Tumor model based on LDA (AUC of 0.772), the External2 model based on LR (AUC of 0.751), and the External5 model based on SVM (AUC of 0.667). …”
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An Improved IoT Based Hybrid Predictive Load Forecasting Model for a Greenhouse Integrated With Demand Side Management by Soumya Ranjan Biswal, Tanmoy Roy Choudhury, Babita Panda, Subhrajyoti Mishra

“…This study hypothesizes that combining hybrid machine learning models with IoT-based DSM can optimize energy consumption while maintaining critical microclimatic conditions for crop growth. …”
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Data-Driven Energy Consumption Analysis and Prediction of Real-World Electric Vehicles at Low Temperatures: A Case Study Under Dynamic Driving Cycles by Yifei Zhao, Hang Liu, Jinsong Li, Hongli Liu, Bin Li

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Predicting the Performance of Students Using Deep Ensemble Learning by Bo Tang, Senlin Li, Changhua Zhao

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Importance of drivetrain optimisation to maximise electrical power from wave energy converters by Nataliia Y. Sergiienko, Leandro Souza Pinheiro da Silva, Boyin Ding, Benjamin S. Cazzolato

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Exploring alternate coupling inputs of a data-driven model for optimum daily streamflow prediction in calibrated SWAT-BiLSTM rainfall-runoff modeling by Khalil Ahmad, Khalil Ahmad, Mudassar Iqbal, Muhammad Atiq Ur Rehman Tariq, Afed Ullah Khan, Abdullah Nadeem, Jinlei Chen, Kseniia Usanova, Kseniia Usanova, Hamad Almujibah, Hashem Alyami, Muhammad Abid

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Biomarker discovery and development of prognostic prediction model using metabolomic panel in breast cancer patients: a hybrid methodology integrating machine learning and explaina... by Fatma Hilal Yagin, Yasin Gormez, Fahaid Al-Hashem, Irshad Ahmad, Fuzail Ahmad, Luca Paolo Ardigò

“…SHapley Additive exPlanations (SHAP), an XAI method, was used to clinically explain the decisions of the optimal model in BC prediction.ResultsThe results revealed that variable selection increased the performance of ML models in BC classification, and the optimal model was obtained with the logistic regression (LR) classifier after support vector machine (SVM)-SHAP-based feature selection. …”
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Neural Networks for Phase Shift Optimization of Reconfigurable Intelligent Surfaces Under Imperfect Channel State Information by Pablo Fondo-Ferreiro, Firooz B. Saghezchi, Felipe Gil-Castineira, Jonathan Rodriguez

“…We evaluate the performance of the proposed Machine Learning (ML) model in terms of different key performance indicators (KPIs), including the system bit error rate (BER) and throughput, phase shift estimation mean square error (MSE), and the training time of the neural network itself. …”
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Fibrosis-4plus score: a novel machine learning-based tool for screening high-risk varices in compensated cirrhosis (CHESS2004): an international multicenter study by Bingtian Dong, Ruiling He, Shenghong Ju, Yuping Chen, Ivica Grgurevic, Jianzhong Ma, Ying Guo, Huizhen Fan, Qiang Yan, Chuan Liu, Huixiong Xu, Anita Madir, Kristian Podrug, Jia Wang, Linxue Qian, Zhengzi Geng, Shanghao Liu, Tao Ren, Guo Zhang, Kun Wang, Meiqin Su, Fei Chen, Sumei Ma, Liting Zhang, Zhaowei Tong, Yonghe Zhou, Xin Li, Fanbin He, Hui Huan, Wenjuan Wang, Yunxiao Liang, Juan Tang, Fang Ai, Tingyu Wang, Liyun Zheng, Zhongwei Zhao, Jiansong Ji, Wei Liu, Jiaojiao Xu, Bo Liu, Xuemei Wang, Yao Zhang, Qiong Yan, Hui Liu, Xiaomei Chen, Shuhua Zhang, Yihua Wang, Yang Liu, Li Yin, Yanni Liu, Yanqing Huang, Li Bian, Ping An, Xin Zhang, Shaoting Zhang, Jinhua Shao, Xiangman Zhang, Wei Rao, Chaoxue Zhang, Christoph Frank Dietrich, Won Kim, Xiaolong Qi

“…., age, aspartate aminotransferase, alanine aminotransferase, and platelet count [PLT]) were combined using machine learning algorithms (logistic regression [LR] and extreme gradient boosting [XGBoost]) to develop the LR-FIB-4plus and XGBoost-FIB-4plus models, respectively. …”
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