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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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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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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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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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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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

“…Considering the perilesional area, Haralick feature differences, and the image of the gradient module can provide crucial parameters for accurate classification of US images. …”
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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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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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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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LightGBM-Based Human Action Recognition Using Sensors by Yinuo Liu, Ziwei Chen

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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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A human pose estimation network based on YOLOv8 framework with efficient multi-scale receptive field and expanded feature pyramid network by Shaobin Cai, Han Xu, Wanchen Cai, Yuchang Mo, Liansuo Wei

“…Therefore, EE-YOLOv8 achieves the highest accuracy while maintaining the lowest parameter count and computational complexity among all analyzed algorithms. …”
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Generalized Gaussian Distribution Improved Permutation Entropy: A New Measure for Complex Time Series Analysis by Kun Zheng, Hong-Seng Gan, Jun Kit Chaw, Sze-Hong Teh, Zhe Chen

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Neural network in an artificial intelligence model for realization of affective computing based on electroencephalogram analysis by A.G. Choban, D.G. Stadnikov, A.E. Sulavko

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OM-VST: A video action recognition model based on optimized downsampling module combined with multi-scale feature fusion. by Xiaozhong Geng, Cheng Chen, Ping Yu, Baijin Liu, Weixin Hu, Qipeng Liang, Xintong Zhang

“…Video classification, as an essential task in computer vision, aims to identify and label video content using computer technology automatically. …”
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Enhanced mastitis severity classification in dairy cows using DNN and RF: A study on PCA and correlation-based feature selection by Manar Lashin, Ayman Samir Farid, Abdullah T. Elgammal

“…Clinical measurements from 1,886 Holstein-Friesian dairy cows, aged 3-4 years, across 21 parameters were analyzed. Both Deep Neural Networks (DNN) and Random Forests (RF) were utilized, with dimensionality reduction applied through Principal Component Analysis (PCA) and correlation-based feature selection to retain essential features and enhance computational efficiency. …”
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MSFF-Net: Multi-Sensor Frequency-Domain Feature Fusion Network with Lightweight 1D CNN for Bearing Fault Diagnosis by Miao Dai, Hangyeol Jo, Moonsuk Kim, Sang-Woo Ban

“…With only about 70.3% of the parameters of the SOTA model, it offers faster inference and reduced computational cost. …”
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A Yarn Quality Prediction Method Based on M-ESTIMATOR Robust Broad Learning System With Tightly Cascaded Feature Layers by Baowei Zhang, Zhilin Guo, Yonghua Wang

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