Explainable artificial intelligence-machine learning models to estimate overall scores in tertiary preparatory general science course by Sujan Ghimire, Shahab Abdulla, Lionel P. Joseph, Salvin Prasad, Angela Murphy, Aruna Devi, Prabal Datta Barua, Ravinesh C. Deo, Rajendra Acharya, Zaher Mundher Yaseen

“…Educational data mining is valuable for uncovering latent relationships in educational settings, particularly for predicting students' academic performance. This study introduces an interpretable hybrid model, optimised through Tree-structured Parzen Estimation (TPE) and Support Vector Regression (SVR), to predict overall scores (OT) utilising five assignments and one examination mark as predictors. …”
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Development and validation of a novel AI-derived index for predicting COPD medical costs in clinical practice by Guan-Heng Liu, Chin-Ling Li, Chih-Yuan Yang, Shih-Feng Liu

“…Model performance was assessed with Mean Squared Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and R-squared (R²). …”
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A two stage grading approach for feature selection and classification of microarray data using Pareto based feature ranking techniques: A case study by Rasmita Dash

“…A grading method is used to rank the models and statistical test is performed to validate the findings. …”
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Integrated forecasting of monthly runoff considering the combined effects of teleconnection factors by Jianbo Chang, Baowei Yan, Mingbo Sun, Donglin Gu, Xuerui Zhou

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Mechanism-learning prediction model for pitting depth of buried pipeline based on HMOGWO-RF by Fulin SONG, Hong ZHAO, Xingyuan MIAO

“…Using a defined comprehensive evaluation index, a comparative analysis of the Pareto solution set was conducted to obtain the optimal combination of feature subsets and hyperparameters. The resulting feature subsets, which are both representative and optimized for performance, contribute to improvements in model stability and prediction accuracy. …”
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Surface anomaly detection on island-based PV panels using edge neural networks by ZHANG Yinxian, ZHANG Zhanyao, ZHANG Xiya

“…Additionally， a dual dynamic model compression technique is employed to reduce redundant channels and feature blocks， significantly lowering the model’s computational complexity and enabling rapid and accurate anomaly detection. …”
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Development of an XGBoost-based prediction model for wound recurrence risk in diabetic foot ulcer patients treated with antibiotic-loaded bone cement by Yi Zhang, Yi Zhang, Xingyu Sun, Cheng Cheng, Nianzong Hou, Nianzong Hou, Shiliang Han, Xin Tang

“…Artificial neural network, support vector machine, and XGBoost prediction models were built according to the selected optimal features. …”
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A Deep Learning Framework for the Classification of Brazilian Coins by Debabrata Swain, Viral Rupapara, Amro Nour, Santosh Satapathy, Biswaranjan Acharya, Shakti Mishra, Ali Bostani

“…We introduce a Repetitive Feature Extractor Convolution Neural Network (RFE-CNN) model to recognize the currency faster and accurately. …”
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Modeling the Analysis of the Financial Result of an Entity by Alexandra A. Derbicheva

“…Different approaches and models used for analytical study of financial results in Russia are tested. …”
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Research on Lithium-Ion Battery State of Health Prediction Based on XGBoost–ARIMA Joint Optimization by Chen Fei, Zhuo Lu, Weiwei Jiang, Liang Zhao, Fan Zhang

“…Subsequently, an XGBoost-based model is constructed to perform the initial SOH estimation. …”
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Fast Visual Tracking With Robustifying Kernelized Correlation Filters by Qianbo Liu, Guoqing Hu, Md Mojahidul Islam

“…The numerator and denominator of the filter model are updated separately instead of linearly interpolated only by storing the current model. …”
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The effect of resampling techniques on the performances of machine learning clinical risk prediction models in the setting of severe class imbalance: development and internal valid... by Janny Xue Chen Ke, Arunachalam DhakshinaMurthy, Ronald B. George, Paula Branco

“…Methods We previously developed and internally validated a multivariable logistic regression 30-day mortality prediction model in 30,619 patients using preoperative and intraoperative features. …”
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Development of a nomogram-based model incorporating radiomic features from follow-up longitudinal lung CT images to distinguish invasive adenocarcinoma from benign lesions: a retro... by Zhengming Wang, Fei Wang, Yan Yang, Weijie Fan, Li Wen, Dong Zhang

“…Logistic regression was used to build models based on clinicoradiological (CR), T0, T1, and delta radiomic features and compute signatures. …”
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Research on Sarcastic Emotion Recognition Based on Multiple Feature Fusion by Si Kaihao

“…Comparative assessments on standard datasets reveal that both models achieve superior performance over many existing approaches in the field. …”
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A Machine Learning Model Based on Radiomic Features as a Tool to Identify Active Giant Cell Arteritis on [¹⁸F]FDG-PET Images During Follow-Up by Hanne S. Vries, Gijs D. van Praagh, Pieter H. Nienhuis, Lejla Alic, Riemer H. J. A. Slart

“…For explainability, an occlusion map was created to illustrate the important regions of the aorta for the decision of the ML model. <b>Results</b>: The ten-feature model with ANOVA as the feature selector and random forest classifier demonstrated the highest performance (AUC = 0.92 ± 0.01). …”
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Comparative Analysis of Audio Features for Unsupervised Speaker Change Detection by Alymzhan Toleu, Gulmira Tolegen, Rustam Mussabayev, Alexander Krassovitskiy, Bagashar Zhumazhanov

“…This study examines how ten different audio features, including MFCC, mel-spectrogram, chroma, and spectral contrast etc., influence speaker change detection (SCD) performance. …”
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SLFCNet: an ultra-lightweight and efficient strawberry feature classification network by Wenchao Xu, Yangxu Wang, Jiahao Yang

“…While maintaining model compactness, this architecture significantly enhances its feature decoding capabilities. …”
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Enhanced Offline Writer Recognition System Employing Blended Multi-Input CNN and Bi-LSTM Model on Diverse Handwritten Texts by Naresh Purohit, Subhash Panwar

“…By integrating CNN and Bi-LSTMs, the proposed model combines spatial and temporal information, offering a comprehensive representation of handwriting. …”
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Joint feature selection and classification of low-resolution satellite images using the SAT-6 dataset by Rajalaxmi Padhy, Sanjit Kumar Dash, Jibitesh Mishra

“…This reduction in feature space is important because it reduces computational complexity and enhances the interpretability of the model. …”
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Improved Hierarchical Convolutional Features for Robust Visual Object Tracking by Jinping Sun

“…Thus, to improve the tracking performance and robustness, an improved hierarchical convolutional features model is proposed into a correlation filter framework for visual object tracking. …”
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