FRANet: A Feature Refinement Attention Network for SAR Image Denoising by Shuaiqi Liu, Yu Lei, Qi Hu, Ming Liu, Bing Li, Weiming Hu, Yu-Dong Zhang

“…Second, a feature attention encoder–decoder network is constructed for deep feature extraction. …”
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Impact of Parameters and Tree Stand Features on Accuracy of Watershed-Based Individual Tree Crown Detection Method Using ALS Data in Coniferous Forests from North-Eastern Poland by Marcin Kozniewski, Łukasz Kolendo, Szymon Chmur, Marek Ksepko

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Adolescents’ Digital activity and its Correlation with Cognitive-Emotional Features, School Performance, and Social and Age Factors: Cross-Sectional Study by George A. Karkashadze, Natalia E. Sergeeva, Leyla S. Namazova-Baranova, Elena A. Vishneva, Elena V. Kaytukova, Kamilla E. Efendieva, Tinatin Yu. Gogberashvili, Dmitriy S. Kratko, Safarbegim Kh. Sadilloeva, Marina A. Kurakina, Anastasiya I. Rykunova, Tatiana A. Konstantinidi, Nadezhda A. Ulkina, Daria A. Bushueva, Inessa A. Povalyeva, Leonid M. Yatsyk, Tatiana A. Salimgareeva, Yuliya V. Nesterova, Pavel A. Prudnikov, Natalia S. Sergienko, Margarita A. Soloshenko, Nikita S. Shilko, Yuliya E. Kazantzeva

“…The study of digital activity correlation with cognitive-emotional features, as well as with other parameters of adolescents’ life-activity in non-capital regions of Russian Federation remains relevant.Objective. …”
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Feature-Based Federated Transfer Learning: Communication Efficiency, Robustness and Privacy by Feng Wang, M. Cenk Gursoy, Senem Velipasalar

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The Bright Feature Transform for Prominent Point Scatterer Detection and Tone Mapping by Gregory D. Vetaw, Suren Jayasuriya

“…This paper introduces a fast image-processing method to visually identify and detect point scatterers in synthetic aperture imagery using the bright feature transform (BFT). The BFT is analytic, computationally inexpensive, and requires no thresholding or parameter tuning. …”
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Community evolution prediction based on feature change patterns in social networks by Jingyi Ding, Guojing Sun, Tiwen Wang, Licheng Jiao, Junzhao Du, Jianshe Wu, Hongfei Wang, Ruohui Cheng

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A Comparison Performance Analysis of QoS WLANs: Approaches with Enhanced Features by Ioannis Papapanagiotou, Georgios S. Paschos, Michael Devetsikiotis

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Pattern Recognition Methods and Features Selection for Speech Emotion Recognition System by Pavol Partila, Miroslav Voznak, Jaromir Tovarek

“…Selecting the correct parameters in combination with the classifier is an important part of reducing the complexity of system computing. …”
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Designing Macroscopic Surface Features Perceived as Soft During Finger Sliding by Qingyu Sun, Hongbo Wang, Shogo Okamoto

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Fusion of Deep and Time–Frequency Local Features for Melanoma Skin Cancer Detection by Hamidreza Eghtesaddoust, Morteza Valizadeh, Mehdi Chehel Amirani

“…The scale-invariant feature transform (SIFT) descriptors are handcrafted local features computed from the four subbands of one-level two-dimensional discrete wavelet transform (2D DWT). …”
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MW‐SAM:Mangrove wetland remote sensing image segmentation network based on segment anything model by Yu Zhang, Xin Wang, Jingye Cai, Qun Yang

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Electrosensory Midbrain Neurons Display Feature Invariant Responses to Natural Communication Stimuli. by Tristan Aumentado-Armstrong, Michael G Metzen, Michael K J Sproule, Maurice J Chacron

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Evaluating the value of machine learning models for predicting hematoma expansion in acute spontaneous intracerebral hemorrhage based on CT imaging features of hematomas and surrou... by Tianyu Yang, Tianyu Yang, Zhen Zhao, Yan Gu, Shengkai Yang, Yonggang Zhang, Lei Li, Ting Wang, Zhongchang Miao

“…Model performance was evaluated using receiver operating characteristic (ROC) curves and the area under the curve (AUC).ResultsEight feature parameters were extracted from the CT images. …”
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Survival Prediction of Esophageal Cancer Using 3D CT Imaging: A Context-Aware Approach With Non-Local Feature Aggregation and Graph-Based Spatial Interaction by Fuce Guo, Chen Huang, Shengmei Lin, Yongmei Dai, Qianshun Chen, Shu Zhang, Xunyu XU

“…In the current study, we aimed to develop an effective EC survival risk prediction using only 3D computed tomography (CT) images.The proposed model consists of two essential components: 1) non-local feature aggregation module(NFAM) that integrates visual features from tumor and lymph nodes at both local and global scales, 2) graph-based spatial interaction module(GSIM) that explores the latent contextual interactions between tumors and lymph nodes.The experimental results demonstrate that our model achieves superior performance compared to state-of-the-art survival prediction methods, emphasizing its robust predictive capability. …”
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Face Anti-spoofing Detection Based on Novel Encoder Convolutional Neural Network and Texture’s Grayscale Structural Information by Marwa Radad, Amira E. Enab, Salah S. Elagooz, Nawal A. El-Fishawy, Mohamed A. El-Rashidy

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REGRESSION MODELS OF INDIVIDUAL LINEAR DIMENSIONS NECESSARY FOR CONSTRUCTING THE CORRECT FORM OF DENTAL ARCH IN YOUNG WOMEN WITH A WIDE FACE, DEPENDING ON THE FEATURES OF ODONTOMET... by М. М. Shinkaruk-Dykovytska, T. P. Pozur, S. V. Koliadenko, V. O. Orlovskiy, G. M. Galunko

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Fish Detection in Fishways for Hydropower Stations Using Bidirectional Cross-Scale Feature Fusion by Junming Wang, Yuanfeng Gong, Wupeng Deng, Enshun Lu, Xinyu Hu, Daode Zhang

“…Finally, the performance of the fish detection model is demonstrated based on the Fish26 dataset, in which the detection accuracy, computational cost, and parameter count are significantly optimized by 1.7%, 23.4%, and 24%, respectively, compared to the state-of-the-art model. …”
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Rice disease detection method based on multi-scale dynamic feature fusion by Qian Fan, Runhao Chen, Bin Li

“…The model adopts the concept of ParameterNet to design the FlexiC3k2Net module, which replaces the neck feature extraction network, thereby bolstering the model's feature learning capabilities without significantly increasing computational complexity. …”
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Multi-Function Working Mode Recognition Based on Multi-Feature Joint Learning by Lei Liu, Minghua Wu, Dongyang Cheng, Wei Wang

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Lightweight Band-Adaptive Hyperspectral Image Compression With Feature Decouple and Recurrent Model by Jiahui Liu, Lili Zhang, Jingang Wang, Lele Qu

“…Furthermore, the implementation of current models in resource-limited settings is often impeded by their high parameter counts and computational demands. To address these challenges, we propose a lightweight band-adaptive hyperspectral image compression model (LBA-HIM) aimed at enhancing compression efficiency while ensuring low computational overhead. …”
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