Lithological classification using SDGSAT-1 TIS data and three-dimensional spectral feature space model by Qunjia Zhang, Zhenhua Guo, Lei Liu, Jiacheng Mei, Le Wang

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Natural Gas Consumption Forecasting Model Based on Feature Optimization and Incremental Long Short-Term Memory by Huilong Wang, Xianjun Gao, Ying Zhang, Yuanwei Yang

“…Subsequently, a weakly supervised cascade network for feature selection is designed to enable the model to adaptively select features based on prior knowledge. …”
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Effective feature selection based HOBS pruned- ELM model for tomato plant leaf disease classification. by M Amudha, K Brindha

“…The proposed classifier performance was compared to existing deep learning models, the experimental results show that the pruned DenseNet achieves an accuracy of 86.64% with a model size of 10.6 MB, while GhostNet reaches an accuracy of 92.15% at 10.9 MB. …”
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Enhancing ECG disease detection accuracy through deep learning models and P-QRS-T waveform features. by Rida Nayyab, Asim Waris, Iqra Zaheer, Muhammad Jawad Khan, Fawwaz Hazzazi, Muhammad Adeel Ijaz, Hassan Ashraf, Syed Omer Gilani

“…Performance metrics such as F1 score, recall, precision, and accuracy were evaluated for each model. …”
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Construction and Mathematical Application of Document-Level Relationship Extraction Model Combining R-GCN and Text Features by Zhiqin Chen

“…Therefore, a document-level relationship extraction model based on educational text features is proposed, which combines the graph convolutional neural network with text features to optimize the recognition ability of cross-sentence relationships. …”
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A Robust Hybrid CNN–LSTM Model for Predicting Student Academic Performance by Kuburat Oyeranti Adefemi, Murimo Bethel Mutanga

“…To improve the performance of the model, we incorporate feature selection techniques and optimization strategies to enhance reliability. …”
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A performance-driven hybrid text-image classification model for multimodal data by Swati Gupta, Bal Kishan

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Modeling the influence of streamwise flow field acceleration on the aerodynamic performance of an actuator disk by C. P. Zengler, N. Troldborg, M. Gaunaa

“…This variation can influence the induced velocity at the turbine and thus also the power performance. In the present work, a simple model is derived for the situation of an actuator disk (AD) operating in a background flow field featuring a constant streamwise velocity gradient. …”
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HARNESSING MACHINE LEARNING IN HPV DIAGNOSTICS: MODEL PERFORMANCE, EXPLAINABILITY, AND CLINICAL INTEGRATION by Bahar Senel

“…CNN-based models exhibited superior performance in cytology and histopathology-based HPV detection, achieving high accuracy in lesion classification. …”
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Evaluation of machine learning models for predicting performance metrics of aero-engine combustors by Huan Yang, Shu Guo, Haolin Xie, Jian Wen, Jiarui Wang

“…The Extra Tree model exhibits superior predictive accuracy for various combustor performance metrics. …”
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Enhancing Performance of Credit Card Model by Utilizing LSTM Networks and XGBoost Algorithms by Kianeh Kandi, Antonio García-Dopico

“…LSTM, a recurrent neural network renowned for its ability to capture temporal dependencies within sequences of transactions, is compared with XGBoost, a formidable ensemble learning algorithm that enhances feature-based classification. By meticulously carrying out preprocessing tasks, constructing competent training models, and implementing ensemble techniques, our proposed framework demonstrates unwavering performance in accurately identifying fraudulent transactions. …”
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Model Input-Output Configuration Search With Embedded Feature Selection for Sensor Time-Series and Image Classification by Anh Tuan Hoang, Zsolt Janos Viharos

“…This paper proposes MICS-EFS, a Model Input-Output Configuration Search with Embedded Feature Selection. …”
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A precise machine learning model: Detecting cervical cancer using feature selection and explainable AI by Rashiduzzaman Shakil, Sadia Islam, Bonna Akter

“…Furthermore, Chi-square and Least Absolute Shrinkage and Selection Operator are two distinct feature selection processes that have been applied to evaluate the feature rank, which are mostly correlated to identify the particular disease, and also integrate an explainable artificial intelligence technique, namely Shapley Additive Explanations, for clarifying the model outcome. …”
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A Reinforcement Learning Model for Optimal Treatment Strategies in Intensive Care: Assessment of the Role of Cardiorespiratory Features by Cristian Drudi, Maximiliano Mollura, Li-wei H. Lehman, Riccardo Barbieri

“…We then compared this model with other RL models trained with a different set of features. …”
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PM2.5 Prediction Using Genetic Algorithm-Based Feature Selection and Encoder-Decoder Model by Minh Hieu Nguyen, Phi Le Nguyen, Kien Nguyen, Van An Le, Thanh-Hung Nguyen, Yusheng Ji

“…Moreover, the GA in our model has the advantage of obtaining the optimal feature combination for predicting the PM2.5 concentration. …”
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Diagnostic model for COPD patients with nocardia infection: a study based on clinical features and risk factors by Kai Zhang, Kangli Yang, Hongmin Wang

“…Multivariate logistic regression identified independent predictors, which informed a nomogram model. Model performance was assessed via concordance index (C-index), calibration curves, and ROC analysis. …”
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Fall recognition using a three stream spatio temporal GCN model with adaptive feature aggregation by Jungpil Shin, Abu Saleh Musa Miah, Rei Egawa, Koki Hirooka, Md. Al Mehedi Hasan, Yoichi Tomioka, Yong Seok Hwang

“…Each stream employs adaptive graph-based feature aggregation and consecutive separable convolutional neural networks (Sep-TCN), significantly reducing the computational complexity and the number of parameters of the model compared to prior systems. …”
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Novel model for medium to long term photovoltaic power prediction using interactive feature trend transformer by Xiang Liu, Qingyu Liu, Shuai Feng, Yangyang Ge, Haoran Chen, Chunling Chen

“…In order to improve the accuracy of medium and long-term photovoltaic power prediction, a unique hybrid deep learning model named interactive feature trend transformer (IFTformer) has been designed. …”
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A Study on Interpretable Electric Load Forecasting Model with Spatiotemporal Feature Fusion Based on Attention Mechanism by Shuaishuai Li, Weizhen Chen

“…Combined with the SHAP quantitative feature contribution and feature deletion experiment, a complete chain of “feature extraction time series modeling weight allocation interpretation and verification” is constructed. …”
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Predicting Freeway Traffic Crash Severity Using XGBoost-Bayesian Network Model with Consideration of Features Interaction by Yang Yang, Kun Wang, Zhenzhou Yuan, Dan Liu

“…Furthermore, the XGBoost (eXtreme Gradient Boosting) model was established, and the SHAP (SHapley Additive exPlanation) value was introduced to interpret the XGBoost model; the importance ranking of the influence degree of each feature towards the target variables and the visualization of the global influence of each feature towards the target variables were both obtained. …”
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