Back Propagation Neural Network model for analysis of hyperspectral images to predict apple firmness by Shuiping Li, Yueyue Chen, Xiaobo Zhang, Junbo Wang, Xuanxiang Gao, Yunhong Jiang, Zhaojun Ban, Cunkun Chen

“…The coefficient of determination (R2) and root mean square error (RMSE) of Partial Least Squares (PLS) models are contrasted using various inputs. These results confirm that the Multiplicative Scatter Correction (MSC) preprocessing algorithm was the optimal choice (\begin{document}$ {R}_{p}^{2} $\end{document} = 0.7925, RMSEP = 0.6537), and the Competitive Adaptive Reweighted Sampling (CARS) feature selection algorithm demonstrated superior performance (\begin{document}$ {R}_{p}^{2} $\end{document} = 0.8325, RMSEP = 0.6257). …”
Get full text

Machine learning analysis of factors affecting college students’ academic performance by Jingzhao Lu, Yaju Liu, Shuo Liu, Zhuo Yan, Xiaoyu Zhao, Yi Zhang, Chongran Yang, Haoxin Zhang, Wei Su, Peihong Zhao

“…By employing the chi-square test to identify features closely related to academic performance, this paper discussed the main influencing factors and utilized machine learning models (such as LOG, SVC, RFC, XGBoost) for prediction. …”
Get full text

Advancing the accuracy of clathrin protein prediction through multi-source protein language models by Watshara Shoombuatong, Nalini Schaduangrat, Pakpoom Mookdarsanit, Jaru Nikom, Lawankorn Mookdarsanit

“…To enhance prediction performance, we utilized a feature selection method to optimize these fused feature embeddings. …”
Get full text

Spectral Entropic Radiomics Feature Extraction (SERFE): an adaptive approach for glioblastoma disease classification by V. L. Sowmya, A. Bharathi Malakreddy, Santhi Natarajan, N. Prathik, I. S. Rajesh

“…SERFE decomposes voxel intensity fluctuations into spectral signatures, employs entropy-based weighting to prioritize informative features, and preserves spatial topology through graph-based modeling. …”
Get full text

Research on early warning model of coal spontaneous combustion based on interpretability by Huimin Zhao, Xu Zhou, Jingjing Han, Yixuan Liu, Zhe Liu, Shishuo Wang

“…Finally, we used SHAP to provide global feature interaction interpretation and local interpretation for the model, analyzing the contributions of CH4, C2H6, C3H8, and CO to the model’s predictive outcomes. …”
Get full text

Forecasting Major Flares Using Magnetograms and Knowledge-informed Features: A Comparative Study of Deep Learning Models with Generalization to Multiple Data Products by Xuebao Li, Shunhuang Zhang, Yanfang Zheng, Ting Li, Rui Wang, Yingbo Liu, Hongwei Ye, Noraisyah Mohamed Shah, Pengchao Yan, Xuefeng Li, Xiaotian Wang, Yongshang Lv, Jinfang Wei, Honglei Jin, Changtian Xiang

“…The major results are as follows. (1) The R_VALUE feature consistently shows the best performance in both categorical and probabilistic forecasting for the knowledge-informed models. (2) The iTransformer yields the highest forecasting performance, with TSS and BSS scores of 0.768 ± 0.072 and 0.513 ± 0.063, respectively. …”
Get full text

Comparative Study of Machine Learning and Deep Learning Models for Early Prediction of Ovarian Cancer by Hardik Dhingra, Roopashri Shetty

“…The dataset undergoes comprehensive preprocessing, including handling missing values, outlier removal, normalization, and dimensionality reduction via PCA. Feature selection methods such as Feature Importance, Recursive Feature Elimination (RFE), and autoencoder-based techniques are employed to enhance model performance. …”
Get full text

Diabetes Detection Models in Mexican Patients by Combining Machine Learning Algorithms and Feature Selection Techniques for Clinical and Paraclinical Attributes: A Comparative Eval... by Antonio García-Domínguez, Carlos E. Galván-Tejada, Rafael Magallanes-Quintanar, Hamurabi Gamboa-Rosales, Irma González Curiel, Jesús Peralta-Romero, Miguel Cruz

“…The significance of feature selection is underscored in this study, showcasing its pivotal role in enhancing the performance of diabetes detection models. …”
Get full text

Toward an Accurate Liver Disease Prediction Based on Two-Level Ensemble Stacking Model by Marghany Hassan Mohamed, Botheina Hussein Ali, Ahmed Ibrahim Taloba, Ahmad O. Aseeri, Mohamed Abd Elaziz, Shaker El-Sappagah, Nora Mahmoud El-Rashidy

“…The two-level ensemble stacking model achieved the highest performance with the metrics values: accuracy (94.01%), Precision (94.44%), Recall (94.25%), F1-score (94.01%), and area under the ROC curve (94.25%) when trained with feature selection technique. …”
Get full text

Machine Learning-Based Prediction Performance Comparison of Marshall Stability and Flow in Asphalt Mixtures by Muhammad Farhan Zahoor, Arshad Hussain, Afaq Khattak

“…Robust statistical measures such as MSE, MAE, R², and RMSE were employed to evaluate each model’s performance. Our results indicate that the RF algorithm had the best performance for both MS and MF parameter prediction, followed by ANN and DT. …”
Get full text

Novel approach for Arabic fake news classification using embedding from large language features with CNN-LSTM ensemble model and explainable AI by Omar Ibrahim Aboulola, Muhammad Umer

“…By generating higher performance metrics and displaying comparable results, this work opens the way for more reliable and interpretable text classification solutions.…”
Get full text

A quantitatively interpretable model for Alzheimer’s disease prediction using deep counterfactuals by Kwanseok Oh, Da-Woon Heo, Ahmad Wisnu Mulyadi, Wonsik Jung, Eunsong Kang, Kun Ho Lee, Heung-Il Suk

“…Deep learning (DL) for predicting Alzheimer’s disease (AD) has provided timely intervention in disease progression yet still demands attentive interpretability to explain how their DL models make definitive decisions. Counterfactual reasoning has recently gained increasing attention in medical research because of its ability to provide a refined visual explanatory map. …”
Get full text

EEG-based neurodegenerative disease diagnosis: comparative analysis of conventional methods and deep learning models by B. R. Nayana, M. N. Pavithra, S. Chaitra, T. N. Bhuvana Mohini, Thompson Stephan, Vijay Mohan, Neha Agarwal

“…Firstly, a conventional machine learning model was developed post-pre-processing, and feature extraction from the power spectral density was done using a Random Forest classifier. …”
Get full text

Combining multi-parametric MRI radiomics features with tumor abnormal protein to construct a machine learning-based predictive model for prostate cancer by Chi Zhang, Zewen Wang, Peicheng Shang, Yibin Zhou, Jin Zhu, Lijun Xu, Zeyu Chen, Mengqi Yu, Yachen Zang

“…Feature selection was performed using t-tests and the Least Absolute Shrinkage and Selection Operator (LASSO) regression, followed by model construction using the random forest algorithm. …”
Get full text

Machine learning model for predicting Amyloid-β positivity and cognitive status using early-phase 18F-Florbetaben PET and clinical features by Dong Hyeok Choi, So Hyun Ahn, Yujin Chung, Jin Sung Kim, Jee Hyang Jeong, Hai-Jeon Yoon

“…To develop a machine learning model for predicting Aβ positivity, we utilized early-phase PET and clinical features. …”
Get full text

A Novel Fault Diagnosis Model for Bearing of Railway Vehicles Using Vibration Signals Based on Symmetric Alpha-Stable Distribution Feature Extraction by Yongjian Li, Weihua Zhang, Qing Xiong, Tianwei Lu, Guiming Mei

“…In this paper, a novel fault diagnosis model for axle box bearing based on symmetric alpha-stable distribution feature extraction and least squares support vector machines (LS-SVM) using vibration signals is proposed which is conducted in three main steps. …”
Get full text

Analyzing feature importance for older pedestrian crash severity: A comparative study of DNN models, emphasizing road and vehicle types with SHAP interpretation by Rocksana Akter, Susilawati Susilawati, Hamza Zubair, Wai Tong Chor

“…Compared to XGBoost, the DNN models demonstrated superior performance in predicting severe outcomes. …”
Get full text

Wavelet Multiresolution Analysis-Based Takagi–Sugeno–Kang Model, with a Projection Step and Surrogate Feature Selection for Spectral Wave Height Prediction by Panagiotis Korkidis, Anastasios Dounis

“…The multiresolution analysis emerges via wavelets, since they are prominent models characterised by their inherent multiresolution nature. …”
Get full text

Interpretable machine learning models for predicting skip metastasis in cN0 papillary thyroid cancer based on clinicopathological and elastography radiomics features by Xiaohua Yao, Mingming Tang, Min Lu, Jie Zhou, Debin Yang

“…We collected preoperative clinicopathological data and extracted, standardized radiomics features from elastography imaging to develop various ML models. …”
Get full text

River total dissolved gas prediction using a hybrid greedy-stepwise feature selection and bidirectional long short-term memory model by Khabat Khosravi, Salim Heddam, Changhyun Jun, Sayed M. Bateni, Dongkyun Kim, Essam Heggy

“…A greedy stepwise feature selection technique (GSFST) is employed to identify the optimal model inputs. …”
Get full text