Traffic environment perception algorithm based on multi-task feature fusion and orthogonal attention by Zhengfeng LI, Mingen ZHONG, Yihong ZHANG, Kang FAN, Zhiying DENG, Jiawei TAN

“…This mechanism minimizes computational load while amplifying significant spatial features within the input images. By selectively focusing on critical areas of interest, HWAttention significantly boosts the performance of the model across various environments, ensuring that it remains efficient even under real-time constraints. …”
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A Framework for Breast Cancer Classification with Deep Features and Modified Grey Wolf Optimization by Fathimathul Rajeena P.P, Sara Tehsin

“…Due to low-contrast images and irrelevant information in publicly available breast cancer datasets, existing models generally perform poorly. Pre-trained convolutional neural network models trained on generic datasets tend to extract irrelevant features when applied to domain-specific classification tasks, highlighting the need for a feature selection mechanism to transform high-dimensional data into a more discriminative feature space. …”
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Improved RT-DETR for Infrared Ship Detection Based on Multi-Attention and Feature Fusion by Chun Liu, Yuanliang Zhang, Jingfu Shen, Feiyue Liu

“…The model’s target detection performance on resource-constrained devices is further enhanced by incorporating advanced techniques such as group convolution and ShuffleNetV2. …”
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Generation of Seismocardiography Heartbeats Using a Wasserstein Generative Adversarial Network With Feature Control by James Skoric, Yannick D'Mello, David V. Plant

“…Additionally, the model demonstrated strong performance in practical applications, with the synthetic data achieving an accuracy of 88% in lung volume classification as compared to 89% achieved with real data. …”
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Spatial feature recognition and layout method based on improved CenterNet and LSTM frameworks by Yuxuan Gu, Fengyu Liu, Xiaodi Yi, Lewei Yang, Yunshu Wang

“…Experiments verified that compared with the CenterNet model, the recognition performance of the proposed HCenterNet-DIoU model improved by 7.44%. …”
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Enhanced particle swarm optimization for feature selection in SVM-based Alzheimer’s disease diagnosis by Qian Zhang, Jinhua Sheng, Rougang Zhou, Qiao Zhang, Binbing Wang, Rong Zhang

“…AD, respectively. Through optimized feature selection, the OLDPSO-SVM model enhances diagnostic performance and provides valuable insights for developing MRI-based multimodal diagnostic tools for AD.…”
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ScaleFormer architecture for scale invariant human pose estimation with enhanced mixed features by Congying Ge, Wei Fu Qin

“…We design an adaptive feature representation mechanism that enables the model to maintain consistent performance across different scales. …”
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PLGNN: graph neural networks via adaptive feature perturbation and high-way links by Meixia He, Peican Zhu, Yang Liu, Keke Tang

“…Subsequently, an adaptive feature perturbation strategy is proposed to reduce model’s overfitting and also improve robustness of PLGNN. …”
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Spatial features of CO2 for occupancy detection in a naturally ventilated school building by Qirui Huang, Marc Syndicus, Jérôme Frisch, Christoph van Treeck

“…Additional ventilation information further enhanced the performance to 61.8 % (RMSE 9.02 occupants). By incorporating spatial features, the model using only CO2-related features revealed similar performance as the model containing additional ventilation information, resulting in a better low-cost occupancy detection method for naturally ventilated buildings.…”
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Feature Selection for Hypertension Risk Prediction Using XGBoost on Single Nucleotide Polymorphism Data by Lailil Muflikhah, Tirana Noor Fatyanosa, Nashi Widodo, Rizal Setya Perdana, Solimun, Hana Ratnawati

“…This study aims to develop a feature selection model using the XGBoost algorithm to identify specific single nucleotide polymorphisms (SNPs) as biomarkers for detecting hypertension risk. …”
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An Interior Point Method for L1/2-SVM and Application to Feature Selection in Classification by Lan Yao, Xiongji Zhang, Dong-Hui Li, Feng Zeng, Haowen Chen

“…Our experiments with artificial data and real data demonstrate that the L1/2-SVM model works well and the proposed algorithm is more effective than some popular methods in selecting relevant features and improving classification performance.…”
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Hyperspectral Image Classification Method Based on Morphological Features and Hybrid Convolutional Neural Networks by Tonghuan Ran, Guangfeng Shi, Zhuo Zhang, Yuhao Pan, Haiyang Zhu

“…Residual connections and an attention mechanism are added to the CNN structure to prevent gradient vanishing, and the scale of the control parameters of the model structure is optimized to guarantee the model’s feature extraction ability. …”
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MRI-based deep learning with clinical and imaging features to differentiate medulloblastoma and ependymoma in children by Yasen Yimit, Yasen Yimit, Parhat Yasin, Yue Hao, Abudouresuli Tuersun, Abudouresuli Tuersun, Chencui Huang, Xiaoguang Zou, Xiaoguang Zou, Ya Qiu, Ya Qiu, Yunling Wang, Mayidili Nijiati, Mayidili Nijiati

“…The model performance was assessed using a 7:3 random split of the dataset for training and validation, respectively. …”
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Cross-Modal Object Detection Based on Content-Guided Feature Fusion and Self-Calibration by Liyang Ning, Xuxun Liu, Luoyu Zhou, Xueyu Zou

“…Our model outperforms baseline models and other state-of-the-art methods in detection accuracy, demonstrating robust performance for cross-modal object detection tasks across various environments.…”
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Bearing Fault Diagnosis Based on Spatial Features of 2.5 Dimensional Sound Field by Junjian Hou, Jun Ma, Zhanpeng Fang, Wuyi Ming, Wenbin He

“…Different from the 2D technique with only one source image, the 2.5D acoustic field model consists of source image, holographic sound image, and the differences between them, and its effective feature model is constructed by Gabor wavelet feature extraction and random forest feature reduction algorithm. …”
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Dynamic Bidirectional Feature Enhancement Network for Thin Cloud Removal in Remote Sensing Images by Yu Wang, Hao Chen, Ye Zhang, Guozheng Li

“…Next, an adaptive local feature enhancement block is constructed using cross-fusion and adjacent feature propagation between dynamic convolutions, aimed at enhancing the ability of model to recover details. …”
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Block-chain abnormal transaction detection method based on adaptive multi-feature fusion by Huijuan ZHU, Jinfu CHEN, Zhiyuan LI, Shangnan YIN

“…Aiming at the problem that the performance of intelligent detection models was limited by the representation ability of original data (features), a residual network structure ResNet-32 was designed to automatically mine the intricate association relationship between original features, so as to actively learn the high-level abstract features with rich semantic information.Low-level features were more transaction content descriptive, although their distinguishing ability was weaker than that of the high-level features.How to integrate them together to obtain complementary advantages was the key to improve the detection performance.Therefore, multi feature fusion methods were proposed to bridge the gap between the two kinds of features.Moreover, these fusion methods can automatically remove the noise and redundant information from the integrated features and further absorb the cross information, to acquire the most distinctive features.Finally, block-chain abnormal transaction detection model (BATDet) was proposed based on the above presented methods, and its effectiveness in the abnormal transaction detection is verified.…”
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Chain hybrid feature selection algorithm based on improved Grey Wolf Optimization algorithm. by Xiaotong Bai, Yuefeng Zheng, Yang Lu, Yongtao Shi

“…Hybrid feature selection algorithm is a strategy that combines different feature selection methods aiming to overcome the limitations of a single feature selection method and improve the effectiveness and performance of feature selection. …”
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Feature selection method for software defect number prediction based on maximum information coefficient by Guoqing LIU, Xingqi WANG, Dan WEI, Jinglong FANG, Yanli SHAO

“…The traditional feature selection method only considers the linear correlation between variables and ignores the nonlinear correlation, so it is difficult to select effective feature subsets to build the effective model to predict the number of faults in software modules.Considering the linear and nonlinear relationship, a feature selection method based on maximum information coefficient (MIC) was proposed.The proposed method separated the redundancy analysis and correlation analysis into two phases.In the previous phase, the cluster algorithm, which was based on the correlation between features, was used to divide the redundant features into the same cluster.In the later phase, the features in each cluster were sorted in descending order according to the correlation between features and the number of software defects, and then the top features were selected to form the feature subset.The experimental results show that the proposed method can improve the prediction performance of software defect number prediction model by effectively removing redundant and irrelevant features.…”
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Feature selection method for software defect number prediction based on maximum information coefficient by Guoqing LIU, Xingqi WANG, Dan WEI, Jinglong FANG, Yanli SHAO

“…The traditional feature selection method only considers the linear correlation between variables and ignores the nonlinear correlation, so it is difficult to select effective feature subsets to build the effective model to predict the number of faults in software modules.Considering the linear and nonlinear relationship, a feature selection method based on maximum information coefficient (MIC) was proposed.The proposed method separated the redundancy analysis and correlation analysis into two phases.In the previous phase, the cluster algorithm, which was based on the correlation between features, was used to divide the redundant features into the same cluster.In the later phase, the features in each cluster were sorted in descending order according to the correlation between features and the number of software defects, and then the top features were selected to form the feature subset.The experimental results show that the proposed method can improve the prediction performance of software defect number prediction model by effectively removing redundant and irrelevant features.…”
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