An explainable AI-based approach for predicting undergraduate students academic performance by Fatema Tuz Johora, Md Nahid Hasan, Aditya Rajbongshi, Md Ashrafuzzaman, Farzana Akter

“…This classifier outperformed the machine learning classifiers based on the four performance evaluation metrics. Two eXplainable Artificial Intelligence (XAI) algorithms, namely SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations), were integrated to provide a comprehensible prediction of the best model and determine the significant factors. …”
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A novel platelets-related gene signature for predicting prognosis, immune features and drug sensitivity in gastric cancer by Qun Li, Cheng Zhang, Yulin Ren, Lei Qiao, Shuning Xu, Ke Li, Ying Liu

“…A nomogram integrating the risk score and clinicopathological features was constructed. Functional enrichment and tumor microenvironment (TME) analyses were performed. …”
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Reliable prediction for TBM energy consumption during tunnel excavation: A novel technique balancing explainability and performance by Wenli Liu, Yafei Qi, Fenghua Liu

“…The XGB_MOFS model includes: (1) a causal inference framework to identify the causal relationships among influential factors, and (2) a MOFS approach to balance predictive performance and explainability. …”
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Evaluating E-Administration Features on User Satisfaction Using the Kano Model: A Melung Village-Owned Enterprise Case Study by Ilham Albana, Imamul Ihsan, Yerikho Putra Firnandri

“…This research evaluates user satisfaction with the e-administration system at the Village-Owned Enterprises (BUMDes) Melung, focusing on identifying features that influence user satisfaction using the Kano model. …”
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CNN-ViT: A multi-feature learning based approach for driver drowsiness detection by Madduri Venkateswarlu, Venkata Rami Reddy Chirra

“…This hybrid framework is designed to harness the complementary strengths of CNNs and transformers: CNNs excel at capturing fine-grained local features, while ViT effectively models global dependencies within images. …”
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AccFIT-IDS: accuracy-based feature inclusion technique for intrusion detection system by C. Rajathi, P. Rukmani

“…In stage 2, the Fintemediary is fed to a wrapper-based selection algorithm to derive an optimal subset Foptimal. Features are included or excluded based on their impact on model accuracy. …”
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Sustainable Development Model and Performance Assessment of Urban Metro Transit Infrastructure: A Post-Covid Case Study of the Magenta Line by Khursheed Salman, Paul Virendra Kumar, Kidwai Farhan Ahmad

“…The survey covers commuter perceptions of safety & security, financial & economic factors, infrastructure & comfort and functional & operational features. The Relative Importance Index approach is used to analyze the data and evaluate DM performance. …”
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Multilayer neural network model for unbalanced data by Xue ZHANG, Zhiguo SHI, Xuan LIU

“…Classification of unbalanced data often has low performance of the classifier because of the unbalance of data between classes.Using AUC (the area under the ROC curve) as evaluation index,combined with one class F-score feature selection and genetic algorithm,a multilayer neural network model was established,and a more favorable feature set for unbalanced data classification was selected,so as to establish a deeper model suitable for classification of unbalanced data.Based on Tensor Flow,a multilayer neural network model was established.Using four different UCI datasets for testing,and comparing with the traditional machine learning algorithms such as Naive Bayesian,KNN,neural networks,etc,the performance of the proposed model built on the unbalanced data classification is more excellent.…”
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Multilayer neural network model for unbalanced data by Xue ZHANG, Zhiguo SHI, Xuan LIU

“…Classification of unbalanced data often has low performance of the classifier because of the unbalance of data between classes.Using AUC (the area under the ROC curve) as evaluation index,combined with one class F-score feature selection and genetic algorithm,a multilayer neural network model was established,and a more favorable feature set for unbalanced data classification was selected,so as to establish a deeper model suitable for classification of unbalanced data.Based on Tensor Flow,a multilayer neural network model was established.Using four different UCI datasets for testing,and comparing with the traditional machine learning algorithms such as Naive Bayesian,KNN,neural networks,etc,the performance of the proposed model built on the unbalanced data classification is more excellent.…”
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Non-Exemplar Incremental ISAR Target Classification via Mix-Mamba Feature Adjustment Network by Ruihang Xue, Caipin Li, Wencan Peng, Xueru Bai, Feng Zhou

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A MFR Work Modes Recognition Method Based on Dual-Scale Feature Extraction by Zhiyuan Li, Xuan Fu, Chengjian Mo, Jianlong Tang, Ronghua Guo, Wenbo Li

“…The experimental results show that the proposed method’s performance is advantageous in recognizing work modes under the comprehensive MFR signal model.…”
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Spectroscopic, Thermally Induced, and Theoretical Features of Neonicotinoids’ Competition for Adsorption Sites on Y Zeolite by Bojana Nedić Vasiljević, Maja Milojević-Rakić, Maja Ranković, Anka Jevremović, Ljubiša Ignjatović, Nemanja Gavrilov, Snežana Uskoković-Marković, Aleksandra Janošević Ležaić, Hong Wang, Danica Bajuk-Bogdanović

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Interpretable machine learning models for prolonged Emergency Department wait time prediction by Hao Wang, Nethra Sambamoorthi, Devin Sandlin, Usha Sambamoorthi

“…Utilizing machine learning (ML) to predict patient wait times could aid in ED operational management. Our aim is to perform a comprehensive analysis of ML models for ED wait time prediction, identify key feature importance and associations with prolonged wait times, and interpret prediction model clinical relevance among ED patients. …”
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SwinNowcast: A Swin Transformer-Based Model for Radar-Based Precipitation Nowcasting by Zhuang Li, Zhenyu Lu, Yizhe Li, Xuan Liu

“…Through the novel design of a multi-scale feature balancing module (M-FBM), the model dynamically integrates local-scale features with global spatiotemporal dependencies. …”
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Learning High-Order Features for Fine-Grained Visual Categorization with Causal Inference by Yuhang Zhang, Yuan Wan, Jiahui Hao, Zaili Yang, Huanhuan Li

“…Causal interventions are applied by severing specific causal links, effectively reducing confounding effects and enhancing model robustness. The framework combines high-order feature fusion with interventional fine-grained learning by performing causal interventions on both classifiers and categories. …”
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Comparison of machine learning models for mucopolysaccharidosis early diagnosis using UAE medical records by Aamna AlShehhi, Hiba Alblooshi, Ruba Fadul, Natnael Tumzghi, Amal Al Tenaiji, Mariam Al Harbi, Fatma Al-Jasmi

“…Finally, the best-performing model was further interpreted using feature contributions analysis methods such as Shapley additive explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). …”
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Morphological features of the simulated gunshot wounds of rabbits’ soft tissues at different temperatures of injuring object by R.N. Mikhaylusov, V.V. Negoduyko, G.I. Gubina-Vakulik, S.B. Pavlov, G.B. Pavlovа, A.M. Veligotsky, O.M. Khvysiuk

“…The article presents the results of experimental modeling of super­ficial fragment gunshot wounds of soft tissues, obtained in low-energy gunshot wounds. …”
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A Study on Canopy Volume Measurement Model for Fruit Tree Application Based on LiDAR Point Cloud by Na Guo, Ning Xu, Jianming Kang, Guohai Zhang, Qingshan Meng, Mengmeng Niu, Wenxuan Wu, Xingguo Zhang

“…Experimental results indicate that the PLSR model outperformed other approaches, achieving optimal performance with three principal components. …”
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A Novel Supervoxel-Based NE-PC Model for Separating Wood and Leaf Components from Terrestrial Laser Scanning Data by Shengqin Gong, Xin Shen, Lin Cao

“…However, previous studies on wood–leaf separation exhibited limitations in unsupervised adaptability and robustness to complex tree architectures, while demonstrating inadequate performance in fine branch detection. This study proposes a novel unsupervised model (NE-PC) that synergizes geometric features with graph-based path analysis to achieve accurate wood–leaf classification without training samples or empirical parameter tuning. …”
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ST-MSRN: An enhanced spatio-temporal super-resolution model for complex meteorological data reconstruction by Ping Mei, Zhi Yang, Changzheng Liu, Lei Wang, Zixin Yin

“…The framework employs parallel multi-scale convolutions to hierarchically extract meteorological patterns, while the integrated Efficient Multi-scale Attention (EMA) module adaptively weights features based on spatio-temporal heterogeneity. Experimental results demonstrate: (1) Successful upscaling from 1.5° spatial/3-day temporal to 0.25°/daily resolution; (2) Superior performance over traditional methods (spline/nearest-neighbor interpolation) and mainstream deep learning methods, with marked improvements in key indicators such as structural similarity (SSIM) and peak signal-to-noise ratio (PSNR) for temperature and precipitation data, while the mean absolute error (MAE) and mean squared error (MSE) have been significantly reduced. …”
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