Evaluating the Performance of Machine Learning Classifiers for Network Intrusion Detection: A Comparative Study Using the UNSW-NB15 Dataset by Iwan Handoyo Putro

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Accurate Modeling and Analysis of Isolation Performance in Multiport Amplifiers by Marinella Aloisio, Piero Angeletti, Salvatore D'Addio

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Comparative Feature-Guided Regression Network with a Model-Eye Pretrained Model for Online Refractive Error Screening by Jiayi Wang, Tianyou Zheng, Yang Zhang, Tianli Zheng, Weiwei Fu

“…The pretrained model helps achieve better performance with small samples. …”
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New Features and Enhancements in Community Land Model (CLM5) Snow Albedo Modeling: Description, Sensitivity, and Evaluation by Cenlin He, Mark Flanner, David M. Lawrence, Yu Gu

“…Abstract We enhance the Community Land Model (CLM) snow albedo modeling by implementing several new features with more realistic and physical representations of snow‐aerosol‐radiation interactions. …”
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A Model for Automated Business Writing Assessment by Daniil Dmitrievich Zafievsky, Nadezhda Stanislavona Lagutina, Oksana Andreyevna Melnikova, Anatoliy Yurievich Poletaev

“…The algorithms are based on a syntactic analysis with the use of dictionaries. The model performance was subsequently evaluated on a corpus of 20 teacher-assessed business letters. …”
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Features of the use of specialized languages for the construction of geometric models of cellular objects in fi element modeling and optimization calculations by V. V. Naprasnikov, Yu. V. Polozkov, A. V. Borodulya, D. P. Kunkevich

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Cross-Domain Feature Fusion Network: A Lightweight Road Extraction Model Based on Multi-Scale Spatial-Frequency Feature Fusion by Lin Gao, Tianyang Shi, Lincong Zhang

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The Design and Development of Online Course Materials: Some Features and Recommendations by Liliana Cuesta

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Employee Turnover Prediction Model Based on Feature Selection and Imbalanced Data Handling by Yuan Fang, Zhongqiu Zhang

“…Results showed that the RF model, trained on GAN-balanced data and using RFE-selected only five features, achieved the best performance with an accuracy of 0.98, precision of 0.98, recall of 0.92, F1-score of 0.95, and Cohen’s Kappa of 0.94. …”
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Evaluation of Feature Transformation and Machine Learning Models on Early Detection of Diabetes Mellitus by Ahmed Ali Linkon, Inshad Rahman Noman, Md Rashedul Islam, Joy Chakra Bortty, Kanchon Kumar Bishnu, Araf Islam, Rakibul Hasan, Masuk Abdullah

“…We explore three feature transformation techniques, no transformation, normalization, and min-max scaling, to assess their influence on the performance of various ML models. …”
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Transformer-Based Multi-Scale Feature Remote Sensing Image Classification Model by Ting Sun, Jun Li, Xiangrui Zhou, Zan Chen

“…Thus we design a down-sampling module in the model to obtain multi-scale features. Finally, we adopt the polynomial form of cross entropy for the trained loss function to improve the generalization performance and robustness of the model. …”
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Distributed Photovoltaic Communication Anomaly Detection Based on Spatiotemporal Feature Collaborative Modeling by Li Di, Zhuo Lv, Hao Chang, Junfei Cai

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Multiple-Stream Models for a Single-Modality Dataset with Fractal Dimension Features by Yen-Ching Chang

“…Multiple-stream deep learning (DL) models are typically used for multiple-modality datasets, with each model extracting favorable features from its own modality dataset. …”
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Exploration of physiological sensors, features, and machine learning models for pain intensity estimation. by Fatemeh Pouromran, Srinivasan Radhakrishnan, Sagar Kamarthi

“…Then we identified the physiological sensor, feature set and machine learning model that give the best predictive performance. …”
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Explicit and Implicit Feature Contrastive Learning Model for Knowledge Graph Link Prediction by Xu Yuan, Weihe Wang, Buyun Gao, Liang Zhao, Ruixin Ma, Feng Ding

“…Recently, graph neural networks and contrastive learning have demonstrated superior performance compared with traditional translation-based models; they successfully extracted common features through explicit linking between entities. …”
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CFP-AL: Combining Model Features and Prediction for Active Learning in Sentence Classification by Keuntae Kim, Yong Suk Choi

“…In this study, we propose a new insight, CFP-AL (Combining model Features and Prediction for Active Learning), from the perspective of feature space by analyzing and diagnosing methods that have shown good performance in NLP (Natural Language Processing) sentence classification. …”
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Intelligent Feature Selection Ensemble Model for Price Prediction in Real Estate Markets by Daniel Cristóbal Andrade-Girón, William Joel Marin-Rodriguez, Marcelo Gumercindo Zuñiga-Rojas

“…This study examines the effects of dimensionality reduction through Recursive Feature Elimination (RFE), Random Forest (RF), and Boruta on real estate price prediction, assessing ensemble models like Bagging, Random Forest, Gradient Boosting, AdaBoost, Stacking, Voting, and Extra Trees. …”
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A pedestrian group crossing intention prediction model integrating spatiotemporal features by Hai Zou, Yongqing Guo, Fulu Wei, Dong Guo, Qingyin Li, Jahongir Pirov

“…The model combines spatiotemporal features, including pedestrian pose key points, 2D positional trajectories, and group information. …”
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FDTransUnet: An aluminum surface defect segmentation model based on feature differentiation. by Mingzhu Tang, Wencheng Wang

“…Aiming at the current problems in the field of industrial defect segmentation, such as difficulty of obtaining a large number of defect samples, low recognition accuracy and lack of segmentation accuracy, a surface defect segmentation model for aluminum based on feature differentiation is proposed: FDTransUnet. …”
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Combining handcrafted and learned features using deep learning to improve protein-protein interaction prediction performance by Tran Hoai Nhan, Nguyen Phuc Xuan Quynh, Le Anh Phuong

“…Improving protein representation quality can significantly impact PPI model performance. This study aims to propose a deep neural network-based model, in which both handcrafted and learned features are fused to improve PPI prediction. …”
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