A Distributed Detection Method for Quality-related Faults in Complex Non-stationary Industrial Processes by Jie DONG, Daye LI, Yanmei WEI, Kaixiang PENG, Hui YANG

“…This classical method identifies a feature subset that maximizes the correlation between features and targets and minimizes redundancy between features based on mutual information. …”
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NavBLIP: a visual-language model for enhancing unmanned aerial vehicles navigation and object detection by Ye Li, Li Yang, Meifang Yang, Fei Yan, Tonghua Liu, Chensi Guo, Rufeng Chen

“…Furthermore, NavBLIP employs a multimodal control strategy that dynamically selects context-specific features to optimize real-time performance, ensuring efficiency in high-stakes operations.Results and discussionExtensive experiments on benchmark datasets such as RefCOCO, CC12M, and Openlmages reveal that NavBLIP outperforms existing state-of-the-art models in terms of accuracy, recall, and computational efficiency. …”
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Plantention: A general-purpose, lightweight and attention-based model for multi-crop leaf disease classification by Brindha Subburaj, Rohan M, Samhruth Ananthanarayanan, Daehan Won

“…The model performance was assessed using comprehensive performance metrics, and the model achieved an accuracy of 98.34 %, precision of 98.74 %, recall of 99.19 %, and F1 Score of 99.02 %. …”
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Analysis of the exercise intention-behavior gap among college students using explainable machine learning by Cui Cui, Cui Cui, Jixin Yin

“…The performance of these models was evaluated, and SHapley Additive exPlanations (SHAP) was applied to the best-performing model to interpret feature importance.ResultsAmong the models tested, SHAP analysis revealed that perceived barriers were the most influential factor contributing to the intention-behavior gap. …”
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Distilling knowledge from graph neural networks trained on cell graphs to non-neural student models by Vasundhara Acharya, Bülent Yener, Gillian Beamer

“…Our experiments revealed that the teacher’s guidance was effective when distribution shifts existed in the data. The teacher model demonstrated decent performance due to its higher complexity and ability to use cell graph structures and features. …”
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Advancing Aviation Safety Through Predictive Maintenance: A Machine Learning Approach for Carbon Brake Wear Severity Classification by Patsy Jammal, Olivia Pinon Fischer, Dimitri N. Mavris, Gregory Wagner

“…The inclusion of environmental variables substantially improves model performance, with relative humidity and wind direction identified as key predictors. …”
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A CT-based radiomics model for predicting progression-free survival in patients with epithelial ovarian cancer by Yinping Leng, Jingjing Zhou, Wenjie Liu, Fengyuan Luo, Fei Peng, Lianggeng Gong

“…Results Multivariate Cox regression analysis revealed that the International Federation of Obstetrics and Gynecology stage and residual tumor are significant predictors of PFS. Twelve radiomic features were selected by LASSO Cox regression. The combined model demonstrated superior predictive performance, with a C-index of 0.78 (95% CI: 0.689–0.889) in the training set, and 0.73 (95% CI: 0.572–0.886) in the test set. …”
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Real-Time Bus Departure Prediction Using Neural Networks for Smart IoT Public Bus Transit by Narges Rashvand, Sanaz Sadat Hosseini, Mona Azarbayjani, Hamed Tabkhi

“…We leverage AI-driven models to enhance the accuracy of bus schedules by preprocessing data, engineering relevant features, and implementing a fully connected neural network that utilizes historical departure data to predict departure times at subsequent stops. …”
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Wind Turbine Blade Fault Detection Method Based on TROA-SVM by Zhuo Lei, Haijun Lin, Xudong Tang, Yong Xiong, He Wen

“…Rigorous analysis and feature selection are subsequently conducted to eliminate redundant data, thereby focusing on retaining the most significant features for classification. …”
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Predictive Modeling of Long-Term Care Needs in Traumatic Brain Injury Patients Using Machine Learning by Tee-Tau Eric Nyam, Kuan-Chi Tu, Nai-Ching Chen, Che-Chuan Wang, Chung-Feng Liu, Ching-Lung Kuo, Jen-Chieh Liao

“…The DeLong test revealed no significant differences among the best predictive models under various feature combinations. SHAP analysis illustrated a similar distribution of feature importance for the top 11 features in XGBoost, with 27 features, and Random Forest with 11 features. …”
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A crack detection model fusing local details and global context for nuclear cladding coating surfaces by Wei Quan, Xiwen Li, Jie Yang, Zhaoyu Chen

“…This model effectively captures both local detailed features and global context in crack images. …”
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Distributional properties of the entropy transformed Weibull distribution and applications to various scientific fields by Tabassum Naz Sindhu, Anum Shafiq, Showkat Ahmad Lone, Qasem M. Al-Mdallal, Tahani A. Abushal

“…Some of its core characteristics, such as its statistical and computational features, are simply and clearly presented. To examine the ultimate performance of maximum likelihood estimators during the process of estimating model parameters, a comprehensive simulation analysis has been conducted. …”
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Vit-Traj: A Spatial–Temporal Coupling Vehicle Trajectory Prediction Model Based on Vision Transformer by Rongjun Cheng, Xudong An, Yuanzi Xu

“…Experimental results on the NGSIM and HighD datasets indicate that, compared to State-of-the-Art models, the proposed model exhibits better performance. …”
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Semantic Fusion-Oriented Bi-Typed Multi-Relational Heterogeneous Graph Neural Network by Yifan Sun, Jing Yan, Lilei Lu, Hongbo Zhang, Yanhong Shang

“…Ultimately, SF-BMHG combines intra-type relationship features and inter-type semantic features to produce rich feature embeddings. …”
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A Lightweight Neural Network for Denoising Wrapped-Phase Images Generated with Full-Field Optical Interferometry by Muhammad Awais, Younggue Kim, Taeil Yoon, Wonshik Choi, Byeongha Lee

“…We evaluate the model’s performance on both synthetic and experimentally acquired datasets and compare it with other state-of-the-art deep learning-based denoising methods. …”
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EnGCI: enhancing GPCR-compound interaction prediction via large molecular models and KAN network by Weihao Liu, Xiaoli Li, Bo Hang, Pu Wang

“…Consequently, exploring the performance of these models in GCI prediction, as well as evaluating their effectiveness when integrated with other deep learning models, has emerged as a compelling research area. …”
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A novel method for distracted driving behaviors recognition with hybrid CNN-BiLSTM-AM model by Dengfeng Zhao, Haojie Li, Zhijun Fu, Bao Ma, Fang Zhou, Chaohui Liu, Wenbin He

“…A fully connected neural network layer is applied to establish a nonlinear mapping relationship between the extracted features and driving behavior categories. Bayesian optimization algorithm is adopted to automatically optimize hyperparameters of the network so as to improve training efficiency and performance of the proposed model. …”
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Lithological Mapping in High-Vegetation Areas Using Sentinel-2, Sentinel-1, and Digital Elevation Models by Yansi Chen, Genyuan Liu, Zhihong Song, Ming Li, Minhua Wang, Shuang Wang

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Development of cardiovascular and all-cause mortality risk prediction models for maintenance hemodialysis patients based on metabolomics by Lian-Lian You, Cui Dong, Zhi-Hong Wang, Shuang Zhang, Yu Zhang, Ting-Ting Kuai, Jia Xiao, Shu-Xin Liu, Qing-Cheng Zeng

“…Multivariate Cox proportional regression risk models were constructed using clinical, metabolomics, and combined features. …”
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Tachyon: Enhancing stacked models using Bayesian optimization for intrusion detection using different sampling approaches by T. Anitha Kumari, Sanket Mishra

“…An ablation study of ensemble models was also conducted to assess the performance of the proposed model in various scenarios.…”
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