Single Valued Neutrosophic Number Ensemble Learning Model for Stability Classification of Open Pit Mine Slopes by Hanzhong Wang, Jun Ye, Rui Yong

“…A comparison with the k-nearest neighbor, support vector machine and random forest methods reveals that the performance metrics of the proposed SVNN-EL model are superior to those of existing methods.…”
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Innovative Mining of User Requirements Through Combined Topic Modeling and Sentiment Analysis: An Automotive Case Study by Yujia Liu, Dong Zhang, Qian Wan, Zhongzhen Lin

“…The findings categorize user requirements into four main areas—performance, comfort and experience, price sensitivity, and safety—while also reflecting the increasing relevance of advanced features, such as sensors, powertrain performance, and other technologies. …”
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A machine learning model for predicting anatomical response to Anti-VEGF therapy in diabetic macular edema by Wenrui Lu, Kunhong Xiao, Xuemei Zhang, Yuqing Wang, Wenbin Chen, Xierong Wang, Yunxi Ye, Yan Lou, Li Li

“…The model identified key features associated with treatment outcomes, providing a valuable tool for personalized therapeutic planning. …”
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An interpretable CT-based machine learning model for predicting recurrence risk in stage II colorectal cancer by Ziqi Wu, Liya Gong, Jingwen Luo, Xiaobo Chen, Fan Yang, Junyan Wen, Yanyu Hao, Zhishan Wang, Ruozhen Gu, Yuqin Zhang, Hai Liao, Ge Wen

“…Model performance was assessed using the area under the curve (AUC), and feature contributions were qualified using the Shapley additive explanation (SHAP) algorithm. …”
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A stacked ensemble machine learning model for the prediction of pentavalent 3 vaccination dropout in East Africa by Meron Asmamaw Alemayehu, Shimels Derso Kebede, Agmasie Damtew Walle, Daniel Niguse Mamo, Ermias Bekele Enyew, Jibril Bashir Adem

“…The experimental design was employed to present the results.ResultsFour experiments were conducted to evaluate feature selection and HPO methods. All stacked ensemble models outperformed individual learners, with the XGBoost meta-learner optimized with grid search and LASSO FS achieving the highest performance: 93.9% accuracy and 99.4% AUC. …”
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An integration of ensemble deep learning with hybrid optimization approaches for effective underwater object detection and classification model by G. Abirami, S. Nagadevi, J. D. Dorathi Jayaseeli, T. Prabhakara Rao, R S M Lakshmi Patibandla, Rajanikanth Aluvalu, K Srihari

“…Extensive experimentation is conducted on the UOD dataset to illustrate the robust classification performance of the UODC-EDLHOA model. The performance validation of the UODC-EDLHOA model portrayed a superior accuracy value of 92.78% over existing techniques.…”
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Predicting lung cancer bone metastasis using CT and pathological imaging with a Swin Transformer model by Wanling Li, Xin Zou, Jie Zhang, Minghong Hu, Guanfeng Chen, Shanshan Su

“…The Swin-Dual Fusion Model achieved superior performance compared to single-modality models and conventional architectures such as ResNet50, with an AUC of 0.966 on the test data and 0.967 on the training data. …”
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Interpretable Deep Learning Models for Arrhythmia Classification Based on ECG Signals Using PTB-X Dataset by Ahmed E. Mansour Atwa, El-Sayed Atlam, Ali Ahmed, Mohamed Ahmed Atwa, Elsaid Md. Abdelrahim, Ali I. Siam

“…Advanced preprocessing techniques, combined with demographic features, significantly enhanced performance. <b>Results</b>: The CNN model achieved up to 97.78% accuracy in binary classification and 79.7% in multiclass tasks, outperforming the VGG16 model (97.38% and 76.53%, respectively) and state-of-the-art benchmarks like CNN-LSTM and CNN entropy features. …”
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FruitQuery: A lightweight query-based instance segmentation model for in-field fruit ripeness determination by Ziang Zhao, Yulia Hicks, Xianfang Sun, Chaoxi Luo

“…These results highlight FruitQuery's compelling balance between segmentation performance and model size, offering the potential for in-field application.…”
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A hybrid CNN-LSTM model with adaptive instance normalization for one shot singing voice conversion by Assila Yousuf, David Solomon George

“…Additionally, the fusion of LSTM with CNN can enhance voice conversion models by capturing both local and contextual features. …”
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A deep learning model for fault detection in distribution networks with high penetration of electric vehicle chargers by Seyed Amir Hosseini, Behrooz Taheri, Seyed Hossein Hesamedin Sadeghi, Adel Nasiri

“…In this method, first, the features of voltage and current waveforms in various operational scenarios are extracted through a two-dimensional modeling. …”
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A traceability model for upper corner gas in fully mechanized mining faces based on XGBoost-SHAP by SHENG Wu, WANG Lingzi

“…Case analysis results showed that: ① the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) of the XGBoost model were 0.93, 0.007, and 0.008, respectively, indicating the highest goodness of fit and the lowest errors compared with random forest (RF), support vector regression (SVR), and gradient boosting decision tree (GBDT). ② The mean relative error of the XGBoost model was 4.478%, demonstrating higher accuracy and better generalization performance compared with the other models. ③ Based on the mean absolute SHAP values of input features, the gas concentration at T1 on the working face had the greatest influence on the gas concentration in the upper corner, followed by the gas concentration in the upper corner extraction pipeline, with the gas content and roof pressure of the mining coal seam following closely. …”
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Machine learning models for accurately predicting properties of CsPbCl3 Perovskite quantum dots by Mehmet Sıddık Çadırcı, Musa Çadırcı

“…Although all models performed highly accurate results, SVR and NND demonstrated the best accurate property prediction by achieving excellent performance on the test and training datasets, with high R2, low Root Mean Squared Error (RMSE) and low Mean Absolute Error (MAE) metric values. …”
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Enhanced detection of accounting fraud using a CNN-LSTM-Attention model optimized by Sparrow search by Peifeng Wu, Yaqiang Chen

“…This paper proposes an enhanced approach to fraud detection by integrating convolutional neural networks (CNN) and long short-term memory (LSTM) networks, complemented by an attention mechanism to prioritize relevant features. To further improve the model’s performance, the sparrow search algorithm (SSA) is employed for parameter optimization, ensuring the best configuration of the CNN-LSTM-Attention framework. …”
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RETRACTED: Post COVID green intellectual capital management with the mediation of organizational learning capability by Elena Rostislavovna Schislyaeva, Inna Petrovna Krasovskaya, Kristina Sergeevna Plis

“…This study aims to investigate the features of managing intellectual capital regarding the influence on firm performance in the Russian banking sector after COVID-19. …”
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Progression risk of adolescent idiopathic scoliosis based on SHAP-Explained machine learning models: a multicenter retrospective study by Xinyi Fang, Ting Weng, Zhehao Zhang, Wanfeng Gong, Yu Zhang, Mei Wang, Jianhua Wang, Zhongxiang Ding, Can Lai

“…Abstract Objective To develop an interpretable machine learning model, explained using SHAP, based on imaging features of adolescent idiopathic scoliosis extracted by convolutional neural networks (CNNs), in order to predict the risk of curve progression and identify the most accurate predictive model. …”
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Machine learning-based predictive model for acute pancreatitis-associated lung injury: a retrospective analysis by Zhaohui Du, Qiaoling Ying, Yisen Yang, Huicong Ma, Hongchang Zhao, Jie Yang, Zhenjie Wang, Chuanming Zheng, Shurui Wang, Qiang Tang

“…The global interpretability of the XGBoost and RF models, along with these six features, is shown in the SHAP summary plot. …”
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VID: Human identification through vein patterns captured from commodity depth cameras by Syed W. Shah, Salil S. Kanhere, Jin Zhang, Lina Yao

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Hyperspectral Images Fusion Classification Based on the DS Evidence Theory by LI Hao, YU Hong, RAO Tong, ZHOU Shuai, SHEN Feng

“…Since each classification model has different classification performance, effectively utilizing the difference in the performance of each classification model is an essential step to achieve fusion classification. …”
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Combined clinical and MRI-based radiomics model for predicting acute hematologic toxicity in gynecologic cancer radiotherapy by Lumeng Luo, Lumeng Luo, Jiahao Wang, Jiahao Wang, Hongling Xie, Hongling Xie, Bingxin Chen, Bingxin Chen, Hui Wang, Hui Wang, Qiu Tang, Qiu Tang

“…Feature selection was performed using LASSO and random forest algorithms, followed by comparison across multiple classification models. …”
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