Prediction of antipsychotic drug efficacy for schizophrenia treatment based on neural features of the resting-state functional connectome by Song Liu, Meng Wang, Weiyi Han, Anran Chen, Xuzhen Liu, Kang Liu, Xue Li, Yi Chen, Luwen Zhang, Qing Liu, Xiaoge Guo, Xiujuan Wang, Ning Kang, Yong Han, Yuanbo Li, Xi Su, Luxian Lv, Bing Liu, Wenqiang Li, Yongfeng Yang

“…Among these positive features, the specific connections within the parietal lobe played a crucial role in the model’s predictive performance. …”
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Ship Target Detection in SAR Images Based on Multiple Attention Mechanism and Cross-Scale Feature Fusion by Yuwu Wang, Tieming Wu, Limin Guo, Yuhan Mo

“…This fusion mechanism not only improves the model’s performance in recognizing complex scenarios, such as small ship targets, fuzzy targets, and dense targets in ports, but also significantly enhances the overall accuracy, robustness, and multiscale perception capability of the model. …”
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Fine-grained crop pest classification based on multi-scale feature fusion and mixed attention mechanisms by Yiheng Qian, Zhiyong Xiao, Zhaohong Deng

“…FFM focuses on extracting key fine-grained features and fusing them across multiple scales, while MAM leverages an attention mechanism to model long-range dependencies within the channel domain, further enhancing feature representation. …”
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A combined feature selection approach for malicious email detection based on a comprehensive email dataset by Han Zhang, Yong Shi, Ming Liu, Libo Chen, Songyang Wu, Zhi Xue

“…We extract 79 static features from both the header and body parts of email samples, perform textual feature extraction on the pre-processed body parts, and combine various machine learning algorithms for detection model construction and experimental comparison. …”
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Efficient deep learning-based tomato leaf disease detection through global and local feature fusion by Hao Sun, Rui Fu, Xuewei Wang, Yongtang Wu, Mohammed Abdulhakim Al-Absi, Zhenqi Cheng, Qian Chen, Yumei Sun

“…Abstract In the context of intelligent agriculture, tomato cultivation involves complex environments, where leaf occlusion and small disease areas significantly impede the performance of tomato leaf disease detection models. …”
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Speech Emotion Recognition Using Multi-Scale Global–Local Representation Learning with Feature Pyramid Network by Yuhua Wang, Jianxing Huang, Zhengdao Zhao, Haiyan Lan, Xinjia Zhang

“…In previous studies, the SER method based on the single-scale cascade feature extraction module could not effectively preserve the temporal structure of speech signals in the deep layer, downgrading the sequence modeling performance. …”
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Machine learning-driven prediction of medical expenses in triple-vessel PCI patients using feature selection by Kuan-Yu Chen, Yen-Chun Huang, Chih-Kuang Liu, Shao-Jung Li, Mingchih Chen

“…Among these, the eXGB model exhibited outstanding performance, with the following metrics: MSE (0.02419), RMSE (0.15552), and MAPE (0.00755). …”
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Predicting local control of brain metastases after stereotactic radiotherapy with clinical, radiomics and deep learning features by Hemalatha Kanakarajan, Wouter De Baene, Patrick Hanssens, Margriet Sitskoorn

“…We examined whether a model using a combination of radiomics, DL and clinical features achieves better accuracy than models using only a subset of these features. …”
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Research on Blueberry Maturity Detection Based on Receptive Field Attention Convolution and Adaptive Spatial Feature Fusion by Bingqiang Huang, Zongyi Xie, Hanno Homann, Zhengshun Fei, Xinjian Xiang, Yongping Zheng, Guolong Zhang, Siqi Sun

“…Specifically, the C2F-RFAConv module is introduced to enhance spatial receptive field learning and a P2-level detection layer is introduced for small and distant targets and fused by a four-head adaptive spatial feature fusion detection head (Detect-FASFF). Additionally, the Focaler-CIoU loss is chosen to mitigate sample imbalance, accelerate convergence, and improve overall model performance. …”
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An efficient approach for diagnosing faults in photovoltaic array using 1D-CNN and feature selection Techniques by Yousif Mahmoud Ali, Lei Ding, Shiyao Qin

“…Firstly, a PVA modeling method using MATLAB/Simulink is employed to simulate I-V curves and extract their features. …”
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Intelligent System for Student Performance Prediction Using Machine Learning by Mustafa S. Ibrahim Alsumaidaie, Ahmed Adil Nafea, Abdulrahman Abbas Mukhlif, Ruqaiya D. Jalal, Mohammed M AL-Ani

“…The steps of research methodology contained (data collection, preprocessing, feature identification, model construction, and evaluation). …”
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A Lightweight Intrusion Detection System with Dynamic Feature Fusion Federated Learning for Vehicular Network Security by Junjun Li, Yanyan Ma, Jiahui Bai, Congming Chen, Tingting Xu, Chi Ding

“…The proposed framework employs a two-stream architecture, including a transformer-augmented autoencoder for abstract feature extraction and a lightweight CNN-LSTM–Attention model for preserving temporal and local patterns. …”
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Enhancing Heart Attack Prediction: Feature Identification from Multiparametric Cardiac Data Using Explainable AI by Muhammad Waqar, Muhammad Bilal Shahnawaz, Sajid Saleem, Hassan Dawood, Usman Muhammad, Hussain Dawood

“…These techniques enable healthcare practitioners to understand the model’s decisions, identify key predictive features, and enhance clinical judgment. …”
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Improving Generalization of Genetic Programming for High-Dimensional Symbolic Regression with Shapley Value Based Feature Selection by Chunyu Wang, Qi Chen, Bing Xue, Mengjie Zhang

“…Abstract Symbolic Regression (SR) on high-dimensional datasets often encounters significant challenges, resulting in models with poor generalization capabilities. While feature selection has the potential to enhance the generalization and learning performance in general, its application in Genetic Programming (GP) for high-dimensional SR remains a complex problem. …”
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RHCrackNet: Refined Hierarchical Feature Fusion and Enhancement Network for Pixel-Level Pavement Anomaly Detection by Wenjing Liu, Zhenhua Li, Ji Wang, Qingjie Lu

“…To address these limitations, we propose a novel pavement anomaly detection network called RHCrackNet. This model incorporates feature fusion modules and feature enhancement modules to dynamically aggregate high-level semantic features with low-level detail features and enhance them through attention mechanisms. …”
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Random forest-based frame work for multi-distress prediction in CRCP: a feature importance approach by Ali Alnaqbi, Ghazi G. Al-Khateeb, Waleed Zeiada, Muamer Abuzwidah

“…Punchouts showed moderate predictability, influenced primarily by freeze index and heavy truck traffic. Sequential feature addition analysis confirmed that model performance improved significantly with the top-ranked variables, with diminishing returns thereafter. …”
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Development of a Human Performance Baseline of Lay Error in Targeting by Thirimachos Bourlai, Parker Ensing, Alexia Toma, Victor Philippe, Jennifer Forsythe, Cody L. Lundberg, Nicholas R. Gans

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CAD-ViT: Coordinate Attention-Enhanced Vision Transformer With Dilated Feature Fusion for Diabetic Retinopathy Staging by Ye Wang, Xiaofang Gou, Wenman Li

“…Furthermore, transformers are utilized to extract global features, enhancing model performance. Experiments conducted on the APTOS-2019 and DDR datasets demonstrate that our method achieves a diagnostic accuracy of 99.2% and grading accuracy of 88.6% on the APTOS-2019 dataset, and a grading accuracy of 84.8% on the DDR dataset. …”
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Wood Species Identification Based on Gray Level Co-Occurrence Matrix (GLCM) Features on Macroscopic Images by Muhammad Ghiffaari Ilham Ramadhan, Bambang Sugiarto, Okta Dwi Mulya, Defti Septian Chairulsyah, Adyanto Syahrizal, Gunawan Gunawan, Riffa Haviani Laluma, Rini Nuraini Sukmana, Teguh Wiharko

“…The result showed that the Model C (90/10) training data ratio demonstrates good performance in classifying wood species from the macroscopic images. …”
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Hybrid Methods Random Forest and FOX-Inspired Optimization Algorithm for Selecting Features in Cervical Cancer Data by Afidatul Masbakhah, Umu Sa'adah, Mohamad Muslikh

“…Therefore, the hybrid RF-FOX method allows the performance of the model to be more optimized, thus helping in the identification of patients at risk of cervical cancer more precisely.…”
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