Advancing shock prediction: leveraging prior knowledge and self-controlled data for enhanced model accuracy and generalizability by Cheng-Yu Tsai, Xiu-Rong Huang, Po-Tsun Kuo, Tzu-Tao Chen, Yun-Kai Yeh, Kuan-Yuan Chen, Arnab Majumdar, Chien-Hua Tseng

“…This study aims to develop an enhanced machine learning model that improves predictive performance by utilizing self-controlled data and applying feature engineering informed by medical knowledge to physiological waveforms, enabling the prediction of shock one hour in advance without relying on blood tests. …”
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Adaptive feature interaction enhancement network for text classification by Rui Su, Shangbing Gao, Kefan Zhao, Junqiang Zhang

“…Finally, the interactively enhanced features are re-input into the classifier to improve text classification performance. …”
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CatBoost Optimization Using Recursive Feature Elimination by Agus Hadianto, Wiranto Herry Utomo

“…The experiment is conducted by comparing the CatBoost regression model's performances with and without the use of RFE feature selection. …”
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Leveraging deep feature learning for handwriting biometric authentication by Parvaneh Afzali, Abdoreza Rezapour, Ahmad Rezaee Jordehi

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CT-Derived Features as Predictors of Clot Burden and Resolution by Quentin Auster, Omar Almetwali, Tong Yu, Alyssa Kelder, Seyed Mehdi Nouraie, Tamerlan Mustafaev, Belinda Rivera-Lebron, Michael G. Risbano, Jiantao Pu

“…Other multivariate models integrating demographic features showed comparable performance, while models solely based on body composition and baseline clot burden demonstrated inferior performance. …”
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A novel dynamic weighted prediction framework with stability-enhanced dynamic thresholding feature selection for neurodegenerative disease detection using gait features by Diksha Giri, Ranjit Panigrahi, Samrat Singh Bhandari, Moumita Pramanik, Akash Kumar Bhoi, Victor Hugo C. de Albuquerque

“…Unlike fixed threshold approaches, SEDT may adjust to different data subsets' feature relevance, preserving only the most important features for model training. …”
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GLClick: Interactive Segmentation Combining Global and Local Features by Jiaying Tang, Hongyuan Wang, Zongyuan Ding, Zihao Xin

“…We design an efficient global–local feature fusion module (GLFM) that integrates fine-grained features from various layers of ResNet50 with those from the Transformer feature pyramid. …”
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FEATURES OF MAGNETOVIBRATING TECHNOLOGIES OF SLIME SEPARATION IN GRINDING MANUFACTURE by Yury Mikhaylovich Vernigorov, Dmitry Mikhaylovich Plotnikov, Natalya Nikolayevna Frolova

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A Digital Twin Framework With Meta- and Transfer Learning for Scalable Multi-Machine Modeling and Optimization in Semiconductor Manufacturing by Chin-Yi Lin, Tzu-Liang Tseng, Tsung-Han Tsai

“…By harnessing a centralized meta-model repository and dynamically refining surrogate models, the proposed framework efficiently leverages limited data and transfers knowledge across heterogeneous manufacturing configurations. …”
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YOLO-AFR: An Improved YOLOv12-Based Model for Accurate and Real-Time Dangerous Driving Behavior Detection by Tianchen Ge, Bo Ning, Yiwu Xie

“…YOLO-AFR builds upon the YOLOv12 architecture and introduces three key innovations: (1) the redesign of the original A2C2f module by introducing a Feature-Refinement Feedback Network (FRFN), resulting in a new A2C2f-FRFN structure that adaptively refines multiscale features, (2) the integration of self-calibrated convolution (SC-Conv) modules in the backbone to enhance multiscale contextual modeling, and (3) the employment of a SEAM-based detection head to improve global contextual awareness and prediction accuracy. …”
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Enhancing Relationship Link Prediction With Hierarchical Feature Enhancement by Zhouying Xu, Huiyue Wang

“…In this paper, we propose a novel methodology by regarding users’ attributes as node-level features, extracting them from tweet text using GPT-style models, and considering the structure of the constructed network as network-level features. …”
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Adaptive Feature Selection of Unbalanced Data for Skiing Teaching by Tao Feng

“…For example, the sample size of some common movements (such as straight downhill) may be much larger than that of some difficult movements (such as aerial spins). If the features are not selected, the model may overly rely on the features of common actions and ignore the features of difficult actions. …”
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Shuffling Augmented Decoupled Features for Multimodal Emotion Recognition by Sunyoung Cho

“…Unlike existing unimodal augmentation approaches, our method preserves cross-modal semantic consistency by jointly augmenting the decoupled components. To enhance model generalization and stability, we propose a learning strategy that gradually incorporates more diverse information by using a combined set of original and augmented decoupled features. …”
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Detection of Pathogenic Microorganisms by Fusion of Recursive Feature Pyramid by HUANGZhitian, XIEYining, ZHAOJing, HEYongjun

“…The detection rate of the target is improved , and the depthwise separable convolution DSConv is used in the secondary feature extraction module to replace the ordinary convolution Conv , which reduces the number of parameters of the model and improves the performance. …”
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Features of Designing a Web-Service of Didactic Materials by Yulya Khritonenko, Sergey Kozlov

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Technological features of crankshaft hardening by vibration shock method by V. A. Lebedev, F. A. Pastukhov, M. M. Chaava, G. V. Serga

“…Introduction. The technological features of the processing crankshafts by the vibration shock method of surface plastic deformation (SPD), which is widely used in the technology of manufacturing machine parts, are considered. …”
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Explainable handcrafted features for mitotic event detection and classification by Panason Manorost, Thomas Deckers, Veerle Bloemen, Jean Marie Aerts

“…The applied machine learning approach not only allows high processing performance but also explains how selected features contribute to mitotic event detection. …”
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Health-related quality of life and family functioning in parents of children with Barth syndrome: an application of the Double ABCX model by Yoonjeong Lim, Ickpyo Hong, Areum Han

“…The parents completed a series of questionnaires. The Double ABCX model was applied to select measurement variables for this study. …”
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Artificial intelligence for herbicide recommendation: Case study for the use of clomazone in Brazilian soils by Hamurábi Anizio Lins, Matheus de Freitas Souza, Lucrecia Pacheco Batista, Luma Lorena Loureiro da Silva Rodrigues, Francisca Daniele da Silva, Bruno Caio Chaves Fernandes, Stefeson Bezerra de Melo, Paulo Sergio Fernandes das Chagas, Daniel Valadão Silva

“…Multilayer perceptron (MLP) ANN models were used to predict clomazone sorption. The variables were selected using the feature selection tool using the physical and chemical properties of the soils. …”
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Dual feature-based and example-based explanation methods by Andrei Konstantinov, Boris Kozlov, Stanislav Kirpichenko, Lev Utkin, Vladimir Muliukha

“…A dual linear surrogate model is trained on the dual dataset. The explanation feature importance values are computed by means of simple matrix calculations. …”
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