A Multimodal Bone Stick Matching Approach Based on Large-Scale Pre-Trained Models and Dynamic Cross-Modal Feature Fusion by Tao Fan, Huiqin Wang, Ke Wang, Rui Liu, Zhan Wang

“…Unlike traditional methods that rely solely on image data, our method leverages large-scale pre-trained models, namely Vision-RWKV for visual feature extraction, RWKV for inscription analysis, and BERT for archeological metadata encoding. …”
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Evolutionary polynomial modeling for interpretable drought prediction and resilient resource management by Tulio J. Francisco, Bruno da Silva Macêdo, Zaher Mundher Yaseen, Nikolay O. Nikitin, Matteo Bodini, Angela Gorgoglione, Camila M. Saporetti, L. Goliatt

“…This study proposes an innovative approach to predicting drought use, the Evolutionary Polynomial Expansion with Feature Selection (EPEFS) model, a hybrid method that integrates polynomial regression with feature selection to increase accuracy and interpretability. …”
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DCANet: A Dual-Branch Cross-Scale Feature Aggregation Network for Remote Sensing Image Semantic Segmentation by Yanhong Yang, Fei Wang, Haozheng Zhang, Yushan Xue, Guodao Zhang, Shengyong Chen

“…Although existing dual-branch based methods enable feature complementarity, information redundancy during feature extraction and fusion hinders the full and effective utilization of multiscale features, thus limiting model performance. …”
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Performance Prediction of the Gearbox Elastic Support Structure Based on Multi-Task Learning by Chengshun Zhu, Zhizhou Lu, Jie Qi, Meng Xiang, Shilong Yuan, Hui Zhang

“…This can lead to task conflicts or insufficient feature modeling, which in turn affects the learning efficiency of inter-task correlations. …”
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Predicting vessel recanalization in extracranial internal carotid artery dissection: a nomogram based on ultrasonography and clinical features by Xinchun Xu, Xinchun Xu, Xinchun Xu, Yanhong Yan, Yanhong Yan, Yafeng Qu, Lianlian Zhang, Lianlian Zhang, Pinjing Hui, Pinjing Hui

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Developing a hybrid feature selection method to detect botnet attacks in IoT devices by Alshaeaa H.Y., Ghadhban Z.M., Ministry of Education, Iraq

“…The AdaBoost model achieved an accuracy of 99.28% with binary classification by using 18 features, and the RF model achieved an accuracy of 86.62% with multi-classification by using 22 features. …”
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Noise Immunity Radio Link Model in Dynamic Intentional Exposure by S. V. Dvornikov, A. V. Pshenichnikov

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Comparative Performance Analysis of Optimization Algorithms in Artificial Neural Networks for Stock Price Prediction by Ekaprana Wijaya, Moch. Arief Soeleman, Pulung Nurtantio Andono

“…The research employs a systematic approach involving the design, training, and validation of ANN models optimized by these techniques. Performance metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and R Square are utilized to evaluate the effectiveness of each method. …”
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Evaluating the Impact of Feature Engineering in Phishing URL Detection: A Comparative Study of URL, HTML, and Derived Features by Yanche Ari Kustiawan, Khairil Imran Ghauth

“…Moreover, URL features like URLLength and NoOfSubDomain consistently rank high in importance, while derived features such as SuspiciousCharRatio and URLComplexityScore notably enhance detection performance in specific models.…”
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Automated lung cancer detection using novel genetic TPOT feature optimization with deep learning techniques by Mohamed Hammad, Mohammed ElAffendi, Muhammad Asim, Ahmed A. Abd El-Latif, Radwa Hashiesh

“…However, previous deep learning models for lung cancer detection have faced challenges such as limited data, inadequate feature extraction, interpretability issues, and susceptibility to data variability. …”
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Comparative analysis of multi-zone peritumoral radiomics in breast cancer for predicting NAC response using ABVS-based deep learning models by Minfang Wang, Wanjun Chen, Ruiping Ren, Yuanwei Lin, Jiawen Tang, Meng Wu

“…The radiomics features of the best-performing model were ranked by importance, with subsequent ablation studies validating the predictive contribution of high-ranking features.ResultsAmong the study population, 138 patients (34.3%) were classified as NAC non-responders. …”
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Machine learning-based prediction of Sasang constitution types using comprehensive clinical information and identification of key features for diagnosis by Sa-Yoon Park, Musun Park, Won-Yung Lee, Choong-Yeol Lee, Ji-Hwan Kim, Siwoo Lee, Chang-Eop Kim

“…We investigate a data-driven integrative diagnostic model by applying machine learning to a multicenter clinical dataset with comprehensive features. …”
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Consistency and Stability in Feature Selection for High-Dimensional Microarray Survival Data in Diffuse Large B-Cell Lymphoma Cancer by Kazeem A. Dauda, Rasheed K. Lamidi

“…High-dimensional survival data, such as microarray datasets, present significant challenges in variable selection and model performance due to their complexity and dimensionality. …”
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A multi-dimensional student performance prediction model (MSPP): An advanced framework for accurate academic classification and analysis by V. Balachandar, K. Venkatesh

“…To address these challenges of the existing system, in this research we propose a new model Multi-dimensional Student Performance Prediction Model (MSPP) that is inspired by advanced data preprocessing and feature engineering techniques using deep learning. …”
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Feature-based ensemble modeling for addressing diabetes data imbalance using the SMOTE, RUS, and random forest methods: a prediction study by Younseo Jang

“…Purpose This study developed and evaluated a feature-based ensemble model integrating the synthetic minority oversampling technique (SMOTE) and random undersampling (RUS) methods with a random forest approach to address class imbalance in machine learning for early diabetes detection, aiming to improve predictive performance. …”
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NEW ORGANIZATION PROCESS OF FEATURE SELECTION BY FILTER WITH CORRELATION-BASED FEATURES SELECTION METHOD by Olga Solovei

“… The subject of the article is feature selection techniques that are used on data preprocessing step before building machine learning models. …”
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A radiomics-based interpretable model integrating delayed-phase CT and clinical features for predicting the pathological grade of appendiceal pseudomyxoma peritonei by Dong Bai, Guanjun Shi, Yuanzi Liang, Fang Li, Zhuozhao Zheng, Zhiqun Wang

“…Abstract Objective This study aimed to develop an interpretable machine learning model integrating delayed-phase contrast-enhanced CT radiomics with clinical features for noninvasive prediction of pathological grading in appendiceal pseudomyxoma peritonei (PMP), using Shapley Additive Explanations (SHAP) for model interpretation. …”
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Recursive feature elimination for summer wheat leaf area index using ensemble algorithm-based modeling: The case of central Highland of Ethiopia by Dereje Biru, Berhan Gessesse, Gebeyehu Abebe

“…However, building a high-performance predictive model faces challenges in selecting suitable machine learning algorithms and identifying important variables. …”
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Median U-Turn Intersection Critical Parameter Research and Operational Performance Evaluation by Changxiang Zhao, Xuewen Liu, Tianhao Wu, Weiwei Zhang

“…By appropriately improving intersection features and conducting reasonable evaluations, the overall performance and sustainability of the MUT intersections in Xi’an city can be enhanced. …”
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A deep learning model for prediction of lysine crotonylation sites by fusing multi-features based on multi-head self-attention mechanism by Yunyun Liang, Minwei Li

“…In the past few years, some calculation methods have been developed, but there is room for improvement in prediction performance. In this paper, we propose an effective model named DeepMM-Kcr, which is based on multiple features and an innovative deep learning framework. …”
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