Clinical, radiological, and radiomics feature-based explainable machine learning models for prediction of neurological deterioration and 90-day outcomes in mild intracerebral hemor... by Weixiong Zeng, Jiaying Chen, Linling Shen, Genghong Xia, Jiahui Xie, Shuqiong Zheng, Zilong He, Limei Deng, Yaya Guo, Jingjing Yang, Yijun Lv, Genggeng Qin, Weiguo Chen, Jia Yin, Qiheng Wu

“…Additionally, we incorporated the Shapley Additive Explanation (SHAP) method to display key features and visualize the decision-making process of the model for each individual. …”
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Construction of Clinical Predictive Models for Heart Failure Detection Using Six Different Machine Learning Algorithms: Identification of Key Clinical Prognostic Features by Qu FZ, Ding J, An XF, Peng R, He N, Liu S, Jiang X

“…Following the elimination of features with significant missing values, the remaining features were utilized to construct predictive models employing six machine learning algorithms. …”
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An Improved MPC-based energy management strategy for hydrogen fuel cell EVs featuring dual-motor coupling powertrain by Xinyu Luo, Henry Shu-Hung Chung

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LQ-MixerNeT: A CNN-Transformer Deep Fusion-Based Model for Object Detection in Optical Remote Sensing Images by Wenxuan Zheng, Ying Yang

“…The framework also incorporates an enhanced Asymptotic Feature Pyramid Network (AFPN) and Coordinate Attention (CA) mechanisms, synergistically optimizing spatial-semantic alignment and multi-scale feature representation, thereby significantly improving feature extraction performance. …”
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Optimising Insider Threat Prediction: Exploring BiLSTM Networks and Sequential Features by Phavithra Manoharan, Wei Hong, Jiao Yin, Hua Wang, Yanchun Zhang, Wenjie Ye

“…In addition, we investigate the performance of different RNN models, such as RNN, LSTM, and BiLSTM, in incorporating these features. …”
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Thermal Load Predictions in Low-Energy Buildings: A Hybrid AI-Based Approach Integrating Integral Feature Selection and Machine Learning Models by Youness El Mghouchi, Mihaela Tinca Udristioiu

“…In this phase, an Integral Feature Selection (IFS) method was employed in conjunction with the best ML models. …”
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Application and feasibility analysis of knowledge-based machine learning in predicting fatigue performance of stainless steel by Jia Wang, Dongkui Fan, C.S. Cai

“…Based on the best-performing model, corresponding S-N curves were constructed. …”
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Prediction of axillary lymph node metastasis in triple negative breast cancer using MRI radiomics and clinical features by Yunyun Shen, Renjun Huang, Yinghui Zhang, Jianguo Zhu, Yonggang Li

“…We analyzed clinical characteristics and radiomic features from T2-weighted MRI. Using LASSO regression for feature selection, we applied Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) to build prediction models. …”
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Leakage Detection in Subway Tunnels Using 3D Point Cloud Data: Integrating Intensity and Geometric Features with XGBoost Classifier by Anyin Zhang, Junjun Huang, Zexin Sun, Juju Duan, Yuanai Zhang, Yueqian Shen

“…Moreover, compared to deep learning models, including PointNet, PointNet++, and DGCNN, the proposed method demonstrates superior performance in both detection accuracy and computational efficiency.…”
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YOLOv11-HRS: An Improved Model for Strawberry Ripeness Detection by Jianhua Liu, Jing Guo, Suxin Zhang

“…To address these problems, an efficient strawberry ripeness detection model, YOLOv11-HRS, is proposed. This model incorporates a hybrid channel–space attention mechanism to enhance its attention to key features and to reduce interference from complex backgrounds. …”
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Predicting the immune therapy response of advanced non-small cell lung cancer based on primary tumor and lymph node radiomics features by Dong Xie, Jinna Yu, Cong He, Han Jiang, Yonggang Qiu, Linfeng Fu, Lingting Kong, Hongwei Xu

“…Finally, the nodal model exhibited a higher classification performance than the tumor model.…”
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Granger Causality-Based Forecasting Model for Rainfall at Ratnapura Area, Sri Lanka: A Deep Learning Approach by Shanthi Saubhagya, Chandima Tilakaratne, Pemantha Lakraj, Musa Mammadov

“…The identification of the optimal set of features for the LSTM model was conducted using Random Forest and Granger Causality tests. …”
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Methodology for predicting material performance by context-based modeling: A case study on solid amine CO2 adsorbents by Shuangjun Li, Zhixin Huang, Yuanming Li, Shuai Deng, Xiangkun Elvis Cao

“…In this work, we proposed a novel methodology for predicting material performance through context-based modeling using large language models (LLMs). …”
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Research on a Target Detection Algorithm for Common Pests Based on an Improved YOLOv7-Tiny Model by He Gong, Xiaodan Ma, Ying Guo

“…Activation Function Optimization: The traditional ReLU activation function is replaced with the Gaussian Error Linear Unit (GELU) to improve nonlinear expression and classification performance. Experimental results demonstrate that the improved model surpasses YOLOv7-tiny in accuracy, inference speed, and model size, achieving a MAP@0.5 of 92.8%, reducing inference time to 4.0 milliseconds, and minimizing model size to just 4.8 MB. …”
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Building Information Modeling and AI Algorithms for Optimizing Energy Performance in Hot Climates: A Comparative Study of Riyadh and Dubai by Mohammad H. Mehraban, Aljawharah A. Alnaser, Samad M. E. Sepasgozar

“…For this reason, this study aimed to enhance the energy performance of residential buildings in the hot climates of Dubai and Riyadh by integrating Building Information Modeling (BIM) and Machine Learning (ML). …”
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Postpartum Haemorrhage Risk Prediction Model Developed by Machine Learning Algorithms: A Single-Centre Retrospective Analysis of Clinical Data by Wenhuan Wang, Chanchan Liao, Hongping Zhang, Yanjun Hu

“…This study used machine learning algorithms and new feature selection methods to build an efficient PPH risk prediction model and provided new ideas and reference methods for PPH risk management. …”
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Data-centric automated approach to predict autism spectrum disorder based on selective features and explainable artificial intelligence by Asma Aldrees, Stephen Ojo, James Wanliss, Muhammad Umer, Muhammad Attique Khan, Bayan Alabdullah, Shtwai Alsubai, Nisreen Innab

“…In the first set of experiments, authors utilized three feature engineering techniques (Chi-square, backward feature elimination, and PCA) with multiple machine learning models for autism presence prediction in toddlers. …”
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Estimation of Leaf Chlorophyll Content of Maize from Hyperspectral Data Using E2D-COS Feature Selection, Deep Neural Network, and Transfer Learning by Riqiang Chen, Lipeng Ren, Guijun Yang, Zhida Cheng, Dan Zhao, Chengjian Zhang, Haikuan Feng, Haitang Hu, Hao Yang

“…Combining the E2D-COS feature selection with TL and DNN significantly improves the estimation accuracy: the R² of the proposed Maize-LCNet model is improved by 0.06–0.11 and the RMSE is reduced by 0.57–1.06 g/cm compared with LCNet-field. …”
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DOA Estimation by Feature Extraction Based on Parallel Deep Neural Networks and MRMR Feature Selection Algorithm by Ashwaq Neaman Hassan Al-Tameemi, Mahmood Mohassel Feghhi, Behzad Mozaffari Tazehkand

“…Then, a minimum redundancy and maximum relevance (MRMR) algorithm is proposed to select the distinct features. The performance of the proposed model is evaluated by conducting a wide range of experiments on uncorrelated narrowband data with different scenarios and based on the root mean square error (RMSE) coefficient. …”
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Features of morphometric parameters of vessels in the human portal venous system identified by multislice computed tomography by A. N. Russkikh, A. D. Shabokha, N. V. Tyumentsev, S. N. Derevtsova

“…The average age of the patients was 54.9 ± 1.7 years (36–71 years). Measurements were performed on 3D models of the vascular bed in the portal venous system (GE Advantage Workstation and Siemens singo.via workstations). …”
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