A New Hybrid PSO-HHO Wrapper Based Optimization for Feature Selection by Sumbul Azeem, Shazia Javed, Iftikhar Naseer, Oualid Ali, Taher M. Ghazal

“…Therefore, an effective preprocessing feature selection procedure is essential to identify the relevant features and eliminate unnecessary ones. …”
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An Intelligent Diagnosis System for English Writing Based on Data Feature Extraction and Fusion by Yizhou He

“…Secondly, the features of English lexical data are extracted and fused to provide better input for the diagnostic model, which effectively solves the problems of complex vocabulary and feature redundancy in English writing. …”
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Adaptive Enhancement Algorithm of High-Resolution Satellite Image Based on Feature Fusion by Ruizhe Wang, Wang Xiao

“…Since the traditional adaptive enhancement algorithm of high-resolution satellite images has the problems of poor enhancement effect and long enhancement time, an adaptive enhancement algorithm of high-resolution satellite images based on feature fusion is proposed. …”
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A Moving Object Detection Method Based on Conditional Information and Feature Deepening by Hongrui Zhang, Luxia Yang

“…Next, a multiscale feature refinement module is devised to further explore the rich semantic information in the deep feature, thereby enhancing the feature representation capability of the network. …”
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Technical and scientific terms in poetry translation: The tensions of an 'anti-poetical' textual feature by Feng Wang, Kelly Washbourne

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UFM: Unified feature matching pre-training with multi-modal image assistants. by Yide Di, Yun Liao, Hao Zhou, Kaijun Zhu, Qing Duan, Junhui Liu, Mingyu Lu

“…We present Multimodal Image Assistant (MIA) transformers, finely tunable structures adept at handling diverse feature matching problems. UFM exhibits versatility in addressing both feature matching tasks within the same modal and those across different modals. …”
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Deliberation network-based feature fusion for Chinese- English neural machine translation by Renshui Fan

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Transformer-based multi-task learning for table tennis motion feature recognition by Tianfang Ma

“…In order to solve the above-mentioned problems, this paper proposes a novel table tennis motion feature recognition method based on Transformer-based multi-task learning. …”
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FDTransUnet: An aluminum surface defect segmentation model based on feature differentiation. by Mingzhu Tang, Wencheng Wang

“…First, the limited defective samples are effectively expanded by the feature differentiation data augmentation strategy to alleviate the overfitting problem caused by the insufficient sample. …”
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Adaptive Particle Swarm Optimization with Landscape Learning for Global Optimization and Feature Selection by Khalil Abbal, Mohammed El-Amrani, Oussama Aoun, Youssef Benadada

“…We tested them on global optimization functions and the feature selection problem. The proposed method gives encouraging results, outperforming native PSO in almost all instances and remaining competitive with state-of-the-art methods.…”
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Multi-stage detection method for APT attack based on sample feature reinforcement by Lixia XIE, Xueou LI, Hongyu YANG, Liang ZHANG, Xiang CHENG

“…Given the problems that the current APT attack detection methods were difficult to perceive the diversity of stage flow features and generally hard to detect the long duration APT attack sequences and potential APT attacks with different attack stages, a multi-stage detection method for APT attack based on sample feature reinforcement was proposed.Firstly, the malicious flow was divided into different attack stages and the APT attack identification sequences were constructed by analyzing the characteristics of the APT attack.In addition, sequence generative adversarial network was used to simulate the generation of identification sequences in the multi-stage of APT attacks.Sample feature reinforcement was achieved by increasing the number of sequence samples in different stages, which improved the diversity of multi-stage sample features.Finally, a multi-stage detection network was proposed.Based on the multi-stage perceptual attention mechanism, the extracted multi-stage flow features and identification sequences were calculated by attention to obtain the stage feature vectors.The feature vectors were used as auxiliary information to splice with the identification sequences.The detection model’s perception ability in different stages was enhanced and the detection accuracy was improved.The experimental results show that the proposed method has remarkable detection effects on two benchmark datasets and has better effects on multi-class potential APT attacks than other models.…”
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Multi-stage detection method for APT attack based on sample feature reinforcement by Lixia XIE, Xueou LI, Hongyu YANG, Liang ZHANG, Xiang CHENG

“…Given the problems that the current APT attack detection methods were difficult to perceive the diversity of stage flow features and generally hard to detect the long duration APT attack sequences and potential APT attacks with different attack stages, a multi-stage detection method for APT attack based on sample feature reinforcement was proposed.Firstly, the malicious flow was divided into different attack stages and the APT attack identification sequences were constructed by analyzing the characteristics of the APT attack.In addition, sequence generative adversarial network was used to simulate the generation of identification sequences in the multi-stage of APT attacks.Sample feature reinforcement was achieved by increasing the number of sequence samples in different stages, which improved the diversity of multi-stage sample features.Finally, a multi-stage detection network was proposed.Based on the multi-stage perceptual attention mechanism, the extracted multi-stage flow features and identification sequences were calculated by attention to obtain the stage feature vectors.The feature vectors were used as auxiliary information to splice with the identification sequences.The detection model’s perception ability in different stages was enhanced and the detection accuracy was improved.The experimental results show that the proposed method has remarkable detection effects on two benchmark datasets and has better effects on multi-class potential APT attacks than other models.…”
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Automatic feature selection and weighting in molecular systems using Differentiable Information Imbalance by Romina Wild, Felix Wodaczek, Vittorio Del Tatto, Bingqing Cheng, Alessandro Laio

“…DII can also produce sparse solutions and determine the optimal size of the reduced feature space. We demonstrate the usefulness of this approach on two benchmark molecular problems: (1) identifying collective variables that describe conformations of a biomolecule, and (2) selecting features for training a machine-learning force field. …”
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Automatic Feature Engineering-Based Optimization Method for Car Loan Fraud Detection by Jian Yang, Zixin Tang, Zhenkai Guan, Wenjia Hua, Mingyu Wei, Chunjie Wang, Chenglong Gu

“…Problems like feature dimension explosion, low interpretability, long training time, and low detection accuracy are solved by compressing abstract and uninterpretable features to limit the depth of DFS algorithm. …”
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Improving unsupervised pedestrian re‐identification with enhanced feature representation and robust clustering by Jiang Luo, Lingjun Liu

“…The above structures are connected through a clustering contrastive learning framework, which not only improves the discriminative power of features and the accuracy of clustering, but also solves the problem of inconsistent clustering updates. …”
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An Unbiased Feature Estimation Network for Few-Shot Fine-Grained Image Classification by Jiale Wang, Jin Lu, Junpo Yang, Meijia Wang, Weichuan Zhang

“…To address the problems mentioned, we propose an unbiased feature estimation network. …”
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Feature-Based Dataset Fingerprinting for Clustered Federated Learning on Medical Image Data by Daniel Scheliga, Patrick Mäder, Marco Seeland

“…Additionally, shared raw data fingerprints can directly leak sensitive visual information, in certain cases even resembling the original client training data. To alleviate these problems, we propose a Feature-based dataset FingerPrinting mechanism (FFP). …”
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Multiscale feature cross‐layer fusion remote sensing target detection method by Yuting Lin, Jianxun Zhang, Jiaming Huang

“…To address the above problems, this article proposes an improved multiscale feature cross‐layer fusion remote sensing target detector based on YOLOv5. …”
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Fan Blade Crack Detection Algorithm Based on Multi-Scale Feature Fusion by Yongjun Qi, Hailin Tang, Altangerel Khuder

“…In order to quickly and accurately detect and maintain the fan blades, based on the intelligent big data from the environment, we propose the convolutional neural network model to solve the problem of low recognition rate due to the lack of feature extraction in the fan blade crack image, and the long short-term memory network (Long Short-Term Memory, LSTM) convolutional neural network model, and the dimensionality reduction of the captured image data, which is beneficial to improve the recognition rate of the picture and reduce the loss rate of the picture through the detection model’s suitable recognition of complex background problems such as target occlusion and overlap. …”
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Improvement and Optimization of Feature Selection Algorithm in Swarm Intelligence Algorithm Based on Complexity by Bingsheng Chen, Huijie Chen, Mengshan Li

“…The swarm intelligence algorithm simulates the behavior of animal populations in nature and is a new type of intelligent solution that is different from traditional artificial intelligence. Feature selection is a very common data dimensionality reduction method, which requires us to select the feature subset with the best evaluation criteria from the original feature set. …”
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