Multi-Branch Feature Alignment Network for Misaligned and Occluded Person Re-Identification by Chunyan Lyu, Hai Huang, Lixi Zhang, Wenting Zhu, Zhengyang Wang, Kejun Wang, Caidong Jiao

“…To mitigate these challenges, we introduce a Multi-Branch Feature Alignment Network (MBFA) comprising three distinct deep neural network branches. …”
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Fault diagnosis algorithm based on multi-channel neighbor feature convolutional network by Huang Xiao, Hanqing Jian

“…First, to mitigate the covariate shift problem in the data, inverted mel-scale frequency cepstral coefficients are introduced to obtain domain-invariant features with high recognition accuracy. …”
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Activity Feature Solving Based on TF-IDF for Activity Recognition in Smart Homes by Jinghuan Guo, Yong Mu, Mudi Xiong, Yaqing Liu, Jingxuan Gu

“…To improve the common employed solving strategy, an activity feature solving strategy based on TF-IDF is proposed in this paper. …”
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Multi-source image feature extraction and segmentation techniques for karst collapse monitoring by Wenbo Lin, Xiao Li, Tingting Li

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Visual Place Recognition Based on Dynamic Difference and Dual-Path Feature Enhancement by Guogang Wang, Yizhen Lv, Lijie Zhao, Yunpeng Liu

“…Aiming at the problem of appearance drift and susceptibility to noise interference in visual place recognition (VPR), we propose DD–DPFE: a Dynamic Difference and Dual-Path Feature Enhancement method. Embedding differential attention mechanisms in the DINOv2 model to mitigate the effects of process interference and adding serial-parallel adapters allows efficient model parameter migration and task adaptation. …”
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Core features of positive mental health in adolescents and their protective role against psychopathology by Desiree Y. Phua, Chermaine S. M. Chew

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Integrated Neural Network for Ordering Optimization with Intertemporal-Dependent Demand and External Features by Minxia Chen, Ke Fu

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Domain Alignment Dynamic Spectral and Spatial Feature Fusion for Hyperspectral Change Detection by Xuexiang Qin, Yuxiang Zhang, Yanni Dong

“…To mitigate these challenges, we propose a novel approach called domain alignment dynamic spectral and spatial feature fusion (DADSSFF) for hyperspectral change detection. …”
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StomachNet: Optimal Deep Learning Features Fusion for Stomach Abnormalities Classification by Muhammad Attique Khan, Muhammad Shahzad Sarfraz, Majed Alhaisoni, Abdulaziz A. Albesher, Shuihua Wang, Imran Ashraf

“…A fully automated design is proposed in this work employing optimal deep learning features for classifying gastrointestinal infections. …”
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Feature-Based Dataset Fingerprinting for Clustered Federated Learning on Medical Image Data by Daniel Scheliga, Patrick Mäder, Marco Seeland

“…To alleviate these problems, we propose a Feature-based dataset FingerPrinting mechanism (FFP). …”
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PFFNet: A pyramid feature fusion network for microaneurysm segmentation in fundus images by Jiaxin Lu, Beiji Zou, Xiaoxia Xiao, Qinghua Peng, Junfeng Yan, Wensheng Zhang, Kejuan Yue

“…This rich contextual information will help to identify MAs from low‐contrast background. Furthermore, to mitigate issue related to category imbalance, a combo loss function is introduced. …”
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Similar and different features of soils in boreal deserts of Eastern and Western Central Asia by E. I. Pankova, M. P. Lebedeva

“…Gerasimov in 1954 entitled ―Similarity and difference in the nature of deserts‖. Among common features, the preservation of the traits of former elevated moistening should be mentioned, which seems to be mitigated or deleted by current active aeolian processes. …”
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Detecting Lameness in Dairy Cows Based on Gait Feature Mapping and Attention Mechanisms by Xi Kang, Junjie Liang, Qian Li, Gang Liu

“…The proposed system comprises (1) a Cow Lameness Feature Map (CLFM) model extracting holistic gait kinematics (hoof trajectories and dorsal contour) from walking sequences, and (2) a DenseNet-Integrated Convolutional Attention Module (DCAM) that mitigates inter-individual variability through multi-feature fusion. …”
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Extracting Optimal Number of Features for Machine Learning Models in Multilayer IoT Attacks by Badeea Al Sukhni, Soumya K. Manna, Jugal M. Dave, Leishi Zhang

“…Therefore, this research aims to develop a Semi-Automated Intrusion Detection System (SAIDS) that integrates efficient feature selection, feature weighting, normalisation, visualisation, and human–machine interaction to detect and identify multilayer attacks, enhancing mitigation strategies. …”
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Comprehensive Review of Hybrid Feature Selection Methods for Microarray-Based Cancer Detection by Azka Khoirunnisa, Didit Adytia, Mustafa Mat Deris, Adiwijaya

“…More recently, the focus of research has shifted to hybrid approaches that merge several feature selection techniques to mitigate the weaknesses of one method while maximizing the strengths of others. …”
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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

“…The increasing prevalence of diabetes necessitates the development of effective early detection methods to mitigate its health impacts. This paper investigates the impact of feature transformation and machine learning (ML) models on the early detection of diabetes using a binary tabular classification dataset. …”
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Exploring the potentialities and challenges of deep learning for simulation and prediction of urban sprawl features by Ange Gabriel Belinga, Stéphane Cedric Tékouabou Koumetio, Mohamed El Haziti

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Rock fracture type recognition based on deep feature learning of microseismic signals by LI Dianze, XU Huajie, ZHANG Bo

“…It offers valuable insights for rockburst hazard monitoring and mitigation in mining and geotechnical engineering.…”
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Underutilized Feature Extraction Methods for Burn Severity Mapping: A Comprehensive Evaluation by Linh Nguyen Van, Giha Lee

“…This study aims to enhance ML classifier accuracy in BSM by evaluating various FE techniques that mitigate multicollinearity among vegetation indices. …”
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Adversarial Threats to Cloud IDS: Robust Defense With Adversarial Training and Feature Selection by Hariprasad Holla, Shashidhar Reddy Polepalli, Arun Ambika Sasikumar

“…Additionally, our explicit evaluation using the black-box Square Attack demonstrates a significant accuracy reduction to 63%, clearly underscoring IDS susceptibility to practical adversarial threats. To mitigate these vulnerabilities, we explicitly propose a dual-layered defense strategy: (i) adversarial training, explicitly incorporating adversarial examples into model training to improve robustness, and (ii) SHAP-based robust feature selection, explicitly enhancing interpretability and resilience by identifying stable, attack-resistant features. …”
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