Meta-Learning-Based Prediction of Different Corn Cultivars from Color Feature Extraction by Abdullah Beyaz, Dilara Gerdan

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A lightweight remote sensing image detection model with feature aggregation diffusion network by Xiaohui Cheng, Xukun Wang, Yun Deng, Qiu Lu, Yanping Kang, Jian Tang, Yuanyuan Shi, Junyu Zhao

“…A dilation-wise residual module further optimizes multi-scale feature extraction. Evaluated on benchmark datasets, LightFAD-YOLO achieves 1.7 % higher mAP0.5 and 6.4 % improved mAP0.5:0.95 over baseline models, with 9.9 % lower computational load. …”
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Bayesian-optimized stacking ensemble with multimodal feature selection for tobacco leaf maturity classification by Yunpeng Lu, Yang Wu, Lu Zhang, Jinguo Huang, Guangwei Sun, Jing Liu

“…In this study, we preprocess tobacco leaf images and extract features including color, shape, and texture, collectively forming a multimodal feature set. …”
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A Lightweight Network for UAV Multi-Scale Feature Fusion-Based Object Detection by Sheng Deng, Yaping Wan

“…This approach introduces a new module, C2f_SEPConv, which incorporates Partial Convolution (PConv) and channel attention mechanisms (Squeeze-and-Excitation, SE), effectively replacing the previous bottleneck and minimizing both the model’s parameter count and computational demands. Modifications to the detection head allow it to perform more effectively in scenarios with small targets in aerial images. …”
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Cross-domain topic transfer learning method based on multiple balance and feature fusion by Zhenshun Xu, Zhenbiao Wang, Wenhao Zhang, Zengjin Tang

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Deep multi-view feature fusion with data augmentation for improved diabetic retinopathy classification by Boualleg Yaakoub, Daouadi Kheir Eddine, Guehairia Oussama, Djeddi Chawki, Cheddad Abbas, Siddiqi Imran, Bouderah Brahim

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Dental bur detection system based on asymmetric double convolution and adaptive feature fusion by HongLing Hou, Ao Yang, Xiangyao Li, Kangkai Zhu, Yandi Zhao, Zhiqiang Wu

“…Moreover, to augment the efficiency of feature integration and diminish computational demands, a novel fusion network combining SlimNeck with BiFPN-Concat was introduced, effectively merging superficial spatial details with profound semantic features. …”
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MSFE-Net: Multi-Scale Feature Enhancement Network for Remote Sensing Object Detection by Kai Yuan, Xing Li, Yaoyao Ren, Lianpeng Zhang, Wei Liu, Erzhu Li

“…Compared to other models, MSFE-Net balances between parameter counts and computational demand, with Params slightly higher than YOLOv5s and YOLOv7-tiny and GFLOPs in a moderately high range. …”
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Target Detection Algorithm Based on Global Feature Fusion in Parallel Dual Path Backbone by QIU Yunfei, XIN Hao

“…The active downsampling of the backbone of conventional single path architecture often leads to insufficient feature extraction and information loss. At the same time, simply adding or splicing feature pyramids is not conducive to the integration of shallow to deep features. …”
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Local kernel renormalization as a mechanism for feature learning in overparametrized convolutional neural networks by R. Aiudi, R. Pacelli, P. Baglioni, A. Vezzani, R. Burioni, P. Rotondo

“…This finding highlights a simple mechanism for feature learning that can take place in overparametrized shallow convolutional neural networks, but not in shallow fully-connected architectures or in locally connected neural networks without weight sharing.…”
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Semi-Supervised Remote Sensing Building Change Detection with Joint Perturbation and Feature Complementation by Zhanlong Chen, Rui Wang, Yongyang Xu

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Rapid estimation method of lithium battery state of health based on novel health feature by DONG Xiaohong, DONG Jinbo, WANG Mingshen, ZENG Fei, PAN Yi

“…Therefore, a rapid estimation method of lithium battery SOH based on novel health feature is proposed in this paper. The charging data of the battery is firstly analyzed in the method, and based on the existing health characteristics of time interval of an equal charging voltage difference (TIECVD) in the constant current charging process of the battery, constructs a new health feature, that is, the health feature of charging voltage at the same starting point and charging time interval. …”
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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. …”
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Dynapenia and presarcopenia in patients with coronary atherosclerosis: clinical predictors and cardiac structural and functional features by A. G. Neeshpapa, V. N. Karetnikova, K. E. Krivoshapova, E. I. Karpova, A. N. Kokov, O. L. Barbarash

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Automation of Multi-Class Microscopy Image Classification Based on the Microorganisms Taxonomic Features Extraction by Aleksei Samarin, Alexander Savelev, Aleksei Toropov, Aleksandra Dozortseva, Egor Kotenko, Artem Nazarenko, Alexander Motyko, Galiya Narova, Elena Mikhailova, Valentin Malykh

“…The results (Precision = 0.910, Recall = 0.901, and F1-score = 0.905) confirm the effectiveness of the proposed method for biomedical diagnostic tasks, especially in settings with limited computational resources and a need for feature interpretability. …”
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Lateralised memory networks may explain the use of higher-order visual features in navigating insects. by Giulio Filippi, James Knight, Andrew Philippides, Paul Graham

“…Several experimental studies have suggested ants use "higher-order" visual information - that is features extracted across the whole extent of a visual scene - which raises the question as to how these features might be computed. …”
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BAHGRF3: Human gait recognition in the indoor environment using deep learning features fusion assisted framework and posterior probability moth flame optimisation by Muhammad Abrar Ahmad Khan, Muhammad Attique Khan, Ateeq Ur Rehman, Ahmed Ibrahim Alzahrani, Nasser Alalwan, Deepak Gupta, Saima Ahmed Rahin, Yudong Zhang

“…Both models are selected based on the top‐5 accuracy and less number of parameters. Later, both models are trained through deep transfer learning and extracted deep features fused using a voting scheme. …”
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Timeseries Fault Classification in Power Transmission Lines by Non-Intrusive Feature Extraction and Selection Using Supervised Machine Learning by Rab Nawaz, Hani A. Albalawi, Syed Basit Ali Bukhari, Khawaja Khalid Mehmood, Muhammad Sajid

“…Feature selection through dimensionality reduction techniques is used to improve model performance and more efficient use of computational resources. …”
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DynaOOD-Net: Dynamic Progressive Feature Fusion and Energy Balancing for Robust Out-of-Distribution Detection by Jiting Zhou, Zhihao Zhou, Pu Zhang

“…Additionally, Dynamic Progressive Feature Pyramid Network-Lite (DP-FPN-Lite) is proposed, which reduces computational and storage overhead by simplifying the network architecture while preserving the core concepts of DP-FPN. …”
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Computational complexity when constructing rational plans for program execution in a given field of parallel computers by V. M. Bakanov

“…Features of the internal properties of the algorithms that affect the efficiency of SPF transformations were identified during the course of computational experiments. …”
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