Ontology-Based Noise Source Identification and Key Feature Selection: A Case Study on Tractor Cab by Su Han, Yiqi Zhou, Yanzhao Chen, Chenglong Wei, Rui Li, Bo Zhu

“…A case study is conducted to demonstrate the effectiveness of the proposed method in resolving the problem of integrating multisource heterogeneous knowledge and exchanging noise diagnosis knowledge information in the field of NVH for agricultural machines. …”
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Dynamic Feature Extraction and Semi-Supervised Soft Sensor Model Based on SCINet for Industrial and Transportation Processes by Jun Wang, Changjian Qi, Xing Luo, Shihao Deng, Qi Lei

“…The dynamic features encoded by the dynamic feature extractor were transferred to the eXtreme Gradient Boosting (XGBoost) ensemble model with strong generalization ability. …”
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Toward Enhanced Adversarial Robustness Generalization in Object Detection: Feature Disentangled Domain Adaptation for Adversarial Training by Yoojin Jung, Byung Cheol Song

“…However, traditional AT is prone to overfitting to specific attack types and remains vulnerable to other kinds of attacks. To solve this problem, we propose Feature Disentangled Domain Adaptation (FDDA). …”
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Energy Services Demand Forecasting Combined with Feature Preferences and Bidirectional Long- and Short-Term Memory Networks by KANG Feng, TAN Huochao, SU Liwei, JIAN Donglin, WANG Shuai, QIN Hao, ZHANG Yongjun

“…Therefore, this paper proposes a user energy service demand prediction model based on feature selection. The methodology includes introducing a sampling algorithm to solve the class imbalance problem in the data on the basis of analysing the user energy service data, reducing the dimensionality of the data based on an autoencoder to ensure efficient clustering of the K-mean algorithm, constructing a feature selection algorithm based on a lightweight gradient lifting machine to filter the effective features and improve the training efficiency of the prediction model, and establishing a bidirectional long- and short-term memory neural network multi-label predicting model based on an attentional mechanism to refine the user’s energy service demand. …”
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ULTRASONIC SIGNAL FEATURE EXTRACTION METHOD BASED ON GENERAL CROSS-VALIDATION THRESHOLDING IN SYNCHROSQUEEZING WAVELET DOMAIN by XIAO ChangMing, XIAO Han, YI CanCan

“…Finally,the method is applied to the feature recognition of microcrack ultrasonic echo signals,and compared with the results of continuous wavelet transform. …”
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Research on axle-box bearing fault feature extraction algorithm based on simulation test and BOA-VMD by ZHANG Dongxing, YANG Gang, ZHOU Ao, QIN Limu, WEI Yuqian, YAN Lei

“…Aiming at the problem that axle-box bearing faults are difficult to find during the operation of urban rail trains, a bearing fault feature extraction based on variational mode decomposition (VMD) parameter optimization using butterfly optimization algorithm (BOA) was proposed. …”
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LOW FREQUENCY FAULT FEATURE EXTRACTION FOR GEARBOX BASED ON WAVELET TRANSFORM AND CONSTRAINED INDEPENDENT COMPONENT ANALYSIS by LENG JunFa, WANG ZhiYang, CHEN HuiTao, JING ShuangXi

“…Aiming at this problem,a method of gearbox low frequency fault feature extraction based on wavelet transform and c ICA is proposed. …”
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Brain Tumour Detection Using VGG-Based Feature Extraction With Modified DarkNet-53 Model by S. Trisheela, Roshan Fernandes, Anisha P. Rodrigues, S. Supreeth, B. J. Ambika, Piyush Kumar Pareek, Rakesh Kumar Godi, G. Shruthi

“…Artificial intelligence extends beyond pattern recognition, planning, and problem-solving, particularly in the realm of machine learning, where deep learning frameworks play a pivotal role. …”
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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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Network intrusion detection model using wrapper based feature selection and multi head attention transformers by Muhammad Umer, Muhammad Tahir, Muhammad Sardaraz, Muhammad Sharif, Hela Elmannai, Abeer D. Algarni

“…The model uses a wrapper-based feature selection technique using machine learning algorithms to select the best features, which are then combined and fed into a Multi-Head Attention-based transformer for getting the predictions. …”
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Multi-objective: hybrid particle swarm optimization with firefly algorithm for feature selection with Leaky ReLU by Ashish Kumar Singh, Anoj Kumar

“…Abstract High-dimensional datasets often pose challenges due to the presence of numerous irrelevant and redundant features, which can compromise the performance of machine learning models. …”
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Hierarchical Multi-Scale Patch Attention and Global Feature-Adaptive Fusion for Robust Occluded Face Recognition by Elhamsadat Hejazi, Majid Ahmadi, Arash Ahmadi

“…Occluded face recognition remains a challenging problem in biometric identification, where real-world obstructions such as masks, sunglasses, scarves, and hands obscure key facial features. …”
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Single-Character-Based Embedding Feature Aggregation Using Cross-Attention for Scene Text Super-Resolution by Meng Wang, Qianqian Li, Haipeng Liu

“…In this paper, we propose single-character-based embedding feature aggregation using cross-attention for scene text super-resolution (SCE-STISR) to solve this problem. …”
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Networking of Smart Meters Based on Time-Varying Feature of Low-Voltage Power Line Channel in Microgrid by Ya-Xin Huang, Xiao-Di Zhang, Fei Yu, Yong-Qing Wei, Hai-Long Zhang

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Comprehensive Feature-Driven PCOS Predictor: A Reinforcement Learning-Based Binary Equilibrium Optimization Approach by S. Reka, T. Suriya Praba, Krishna Kumar Manchala, Anna Venkateswarlu

“…Then, Reinforcement Learning-based Binary Equilibrium Optimizer is used to find the reduced optimal features. Here, RL uses SARSA (State–Action–Reward–State–Action) to increase the population diversity in the search space, to avoid the problem of local optima, finally balances the exploration capability during the search. …”
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MSF-SLAM: Enhancing Dynamic Visual SLAM with Multi-Scale Feature Integration and Dynamic Object Filtering by Yongjia Duan, Jing Luo, Xiong Zhou

“…At the core of our approach lies a novel Multi-Scale Feature Consolidation (MSFConv) module, which we have developed to significantly boost the feature extraction capabilities of the YOLOv8 network. …”
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Improving Gaussian Naive Bayes classification on imbalanced data through coordinate-based minority feature mining by Wei Wang, Li Yan, Fen Liu, Yanxi Li

“…The algorithm transforms the dataset from absolute coordinates to RLDC-relative coordinates, revealing latent local relative density change features. Due to the imbalanced distribution, sparse feature space, and class overlap, minority class samples can exhibit distinct patterns in these transformed features. …”
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A novel multimodal image feature fusion mechanism: Application to rabbit liveweight estimation in commercial farms by Daoyi Song, Zhenhao Lai, Shuqi Yang, Dongyu Liu, Jinxia (Fiona) Yao, Hongying Wang, Liangju Wang

“…Specifically, a dual-stream feature fusion mechanism was proposed to effectively integrate information from both near-infrared and depth imaging. …”
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Robust Registration of Multimodal Remote Sensing Images Using Self-Similar Adjacent Self-Convolutional Feature by Tian Gao, Chaozhen Lan, Liang Lv, Qunshan Shi, Wenjun Huang, Yiqiao Wang, Zhizhen Mu

“…., optical, infrared, SAR), image registration remains a challenging problem. This article proposes a robust matching method based on self-similarity adjacent self-convolution features (SASF), consisting of two key steps. …”
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Azimuth-Guided Feature Embedding Network With Dual Inference Mechanism for Few-Shot SAR Target Recognition by Yan Peng, Xuelian Yu, Haohao Ren, Lei Miao, Lin Zou, Yun Zhou

“…To be specific, the feature extraction model, i.e., AGFEN is composed of the azimuth embedding module (AEM) and the dynamic feature embedding network (DFEN). …”
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