DualCMNet: a lightweight dual-branch network for maize variety identification based on multi-modal feature fusion by Xinhua Bi, Hao Xie, Ziyi Song, Jinge Li, Chang Liu, Xiaozhu Zhou, Helong Yu, Chunguang Bi, Ming Zhao

“…Additionally, existing multimodal methods face high computational complexity, making it difficult to balance accuracy and efficiency.MethodsBased on multi-modal data from 11 maize varieties, this paper presents DualCMNet, a novel dual-branch deep learning framework that utilizes a one-dimensional convolutional neural network (1D-CNN) for hyperspectral data processing and a MobileNetV3 network for spatial feature extraction from images. …”
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SFSIN: A Lightweight Model for Remote Sensing Image Super-Resolution with Strip-like Feature Superpixel Interaction Network by Yanxia Lyu, Yuhang Liu, Qianqian Zhao, Ziwen Hao, Xin Song

“…However, existing super-resolution methods are not applicable to resource-constrained edge devices because they are hampered by a large number of parameters and significant computational complexity. …”
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MSF-ACA: Low-Light Image Enhancement Network Based on Multi-Scale Feature Fusion and Adaptive Contrast Adjustment by Zhesheng Cheng, Yingdan Wu, Fang Tian, Zaiwen Feng, Yan Li

“…To address the issues of loss of important detailed features, insufficient contrast enhancement, and high computational complexity in existing low-light image enhancing methodologies, this paper presents a low-light image enhancement network (MSF-ACA), which uses multi-scale feature fusion and adaptive contrast adjustment. …”
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Ghost-Attention-YOLOv8: Enhancing Rice Leaf Disease Detection with Lightweight Feature Extraction and Advanced Attention Mechanisms by Thanh Dang Bui, Tra My Do Le

“…The Ghost model optimizes feature extraction by reducing computational complexity, while the attention modules enable the model to focus on relevant regions, improving detection performance. …”
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An object detection model AAPW-YOLO for UAV remote sensing images based on adaptive convolution and reconstructed feature fusion by Yiming Wu, Xiaofang Mu, Hong Shi, Mingxing Hou

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Advancing Glaucoma Diagnosis Through Multi‐Scale Feature Extraction and Cross‐Attention Mechanisms in Optical Coherence Tomography Images by Hamid Reza Khajeha, Mansoor Fateh, Vahid Abolghasemi, Amir Reza Fateh, Mohammad Hassan Emamian, Hassan Hashemi, Akbar Fotouhi

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Enhanced framework for credit card fraud detection using robust feature selection and a stacking ensemble model approach by Rahul Kumar Gupta, Asmaul Hassan, Samir Kumar Majhi, Nikhat Parveen, Abu Taha Zamani, Raju Anitha, Binayak Ojha, Abhinav Kumar Singh, Debendra Muduli

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A multi-model feature fusion based transfer learning with heuristic search for copy-move video forgery detection by Hessa Alfraihi, Muhammad Swaileh A. Alzaidi, Hamed Alqahtani, Abdulbasit A. Darem, Ali M. Al-Sharafi, Ahmad A. Alzahrani, Menwa Alshammeri, Abdulwhab Alkharashi

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Hybrid feature-time series neural network for predicting ACL forces in martial artists with resistive braces after reconstruction by Dongyue Li, Haojie Li, Yang Hang

“…The goal was to leverage time-series biomechanical parameters and static clinical features to optimize postoperative recovery strategies.MethodsA prospective cohort of 44 martial artists post-ACL reconstruction was randomized into an experimental group (EG, n = 22) using a resistive brace and a control group (CG, n = 22) using a traditional brace. …”
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DuSAFNet: A Multi-Path Feature Fusion and Spectral–Temporal Attention-Based Model for Bird Audio Classification by Zhengyang Lu, Huan Li, Min Liu, Yibin Lin, Yao Qin, Xuanyu Wu, Nanbo Xu, Haibo Pu

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Burned Area Detection in the Eastern Canadian Boreal Forest Using a Multi-Layer Perceptron and MODIS-Derived Features by Hadi Mahmoudi Meimand, Jiaxin Chen, Daniel Kneeshaw, Mohammadreza Bakhtyari, Changhui Peng

“…This study develops, compares, and optimizes machine learning (ML)-based models for burned area classification in the eastern Canadian boreal forest from 2000 to 2023 using MODIS-derived features extracted from Google Earth Engine (GEE), and the feature extraction includes maximum, minimum, mean, and median values per feature to enhance spectral representation and reduce noise. …”
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Golden eagle optimized CONV-LSTM and non-negativity-constrained autoencoder to support spatial and temporal features in cancer drug response prediction by Wesam Ibrahim Hajim, Suhaila Zainudin, Kauthar Mohd Daud, Khattab Alheeti

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Cross-Modality Object Detection Based on DETR by Xinyi Huang, Guochun Ma

“…In this paper, we propose a novel lightweight Cross-Modality Hybrid Encoder (CHE) that maintains low computational consumption while enhancing the performance of the detection model, which includes two modules: the Attention-based Cross-Modality Feature Interaction (ACFI) module for feature interaction within and between modalities, and the Res-CNN-based Cross-Modality Feature Fusion (RCFF) module for feature association and enhancement. …”
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Detection of Potentially Anomalous Cosmic Particle Tracks Acquired with CMOS Sensors: Validation of Rough k–Means Clustering with PCA Feature Extraction by Hachaj Tomasz, Piekarczyk Marcin, Wąs Jarosław

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Classification of benign and malignant solid breast lesions on the ultrasound images based on the textural features: the importance of the perifocal lesion area by А.А. Kolchev, D.V. Pasynkov, I.A. Egoshin, I.V. Kliouchkin, О.О. Pasynkova

“…Subsequently, eight gray-level co-occurrence matrices (GLCM) were constructed for each lesion, and 13 Haralick textural features were calculated for each GLCM. Additionally, we computed the differences in feature values at different spatial shifts and the differences in feature values between the inner and outer areas of the lesion. …”
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Machine Learning-Enhanced Discrimination of Gamma-Ray and Hadron Events Using Temporal Features: An ASTRI Mini-Array Analysis by Valentina La Parola, Giancarlo Cusumano, Saverio Lombardi, Antonio Alessio Compagnino, Antonino La Barbera, Antonio Tutone, Antonio Pagliaro

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Estimating Winter Canola Aboveground Biomass from Hyperspectral Images Using Narrowband Spectra-Texture Features and Machine Learning by Xia Liu, Ruiqi Du, Youzhen Xiang, Junying Chen, Fucang Zhang, Hongzhao Shi, Zijun Tang, Xin Wang

“…The Gray Level Co-occurrence Matrix (GLCM) method was employed to compute texture indices. Correlation analysis and autocorrelation analysis were utilized to determine the final spectral feature scheme, texture feature scheme, and spectral-texture feature scheme. …”
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Modeling the dynamics of charged drop of one liquid in another under the action of an electric field by Н.Ж. Джайчибеков, Б.С. Шалабаева, В.Н. Киреев

“… The work is devoted to the study of the features of the behavior of a group of droplets of one viscous liquid in another under the influence of various physical fields. …”
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Laryngeal cancer diagnosis based on improved YOLOv8 algorithm by Xin Nie, Xueyan Zhang, Di Wang, Yuankun Liu, Lumin Xing, Wenjian Liu

“…Additionally, a tiny fully convolutional network architecture has been employed, reducing the number of model parameters and computational costs while maintaining or enhancing performance, which is crucial for real-time medical imaging analysis. …”
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Fusion of transfer learning with nature-inspired dandelion algorithm for autism spectrum disorder detection and classification using facial features by G. Elangovan, N. Jagadish Kumar, J. Shobana, M. Ramprasath, Gyanendra Prasad Joshi, Woong Cho

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