Chinese Word Sense Disambiguation Based on Word translation and Part of speech by ZHANG Chunxiang, ZHAO Lingyun, GAO Xueyao

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On the generalisation capabilities of Fisher vector‐based face presentation attack detection by Lázaro J. González‐Soler, Marta Gomez‐Barrero, Christoph Busch

“…In contrast, for more realistic scenarios, existing algorithms face difficulties in detecting unknown PAI species which are only included in the test set. A feature space based on Fisher Vectors computed from compact binarised statistical image features histograms, which allows discovering semantic feature subsets from known samples to enhance the detection of unknown attacks is presented. …”
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MED-AGNeT: An attention-guided network of customized augmentation of samples based on conditional diffusion for textile defect detection by Jun Liu, Haolin Li, Hao Liu, Jiuzhen Liang

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Estimating Canopy Chlorophyll Content of Potato Using Machine Learning and Remote Sensing by Xiaofei Yang, Hao Zhou, Qiao Li, Xueliang Fu, Honghui Li

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A Texture-Enhanced Deep Learning Network for Cloud Detection of GaoFen/WFV by Integrating an Object-Oriented Dynamic Threshold Labeling Method and Texture-Feature-Enhanced Attentio... by Bo Zhong, Xiao Tang, Xiaobo Luo, Shanlong Wu, Kai Ao

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HGCS-Det: A Deep Learning-Based Solution for Localizing and Recognizing Household Garbage in Complex Scenarios by Houkui Zhou, Chang Chen, Zhongyi Xia, Qifeng Ding, Qinqin Liao, Qun Wang, Huimin Yu, Haoji Hu, Guangqun Zhang, Junguo Hu, Tao He

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DMCF-Net: Dilated Multiscale Context Fusion Network for SAR Flood Detection by Zhimin Wang, Lingli Zhao, Nan Jiang, Weidong Sun, Jie Yang, Lei Shi, Hongtao Shi, Pingxiang Li

“…Experimental results show that DMCF-Net outperforms other deep learning models, achieving an F1 score of 81.6% and an intersection over union of 68.9%, while also having lower computational cost (97.4G) and fewer parameters (16.4M).…”
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Generative AI for Bayesian Computation by Nick Polson, Vadim Sokolov

“…Generative quantile methods have a number of advantages over traditional approaches such as approximate Bayesian computation (ABC) or GANs. Primarily, quantile architectures are density-free and exploit feature selection using dimensionality reducing summary statistics. …”
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Leveraging assistive technology for visually impaired people through optimal deep transfer learning based object detection model by Mahir Mohammed Sharif Adam, Nojood O. Aljehane, Mohammed Yahya Alzahrani, Samah Al Zanin

“…In recent times, deep learning (DL) techniques have become a powerful approach for extracting feature representations from data, leading to significant advancements in the field of object detection. …”
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Distributed denial of service (DDoS) classification based on random forest model with backward elimination algorithm and grid search algorithm by Mohamed S. Sawah, Hela Elmannai, Alaa A. El-Bary, Kh. Lotfy, Osama E. Sheta

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Evaluation of Mechanical Characteristics of Tungsten Inert Gas (TIG) Welded Butt Joint of Inconel 600 by Arash Moradi, Fatemeh Marashi Najafi, Yong Chen, Mahmoud Chizari

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BGLE-YOLO: A Lightweight Model for Underwater Bio-Detection by Hua Zhao, Chao Xu, Jiaxing Chen, Zhexian Zhang, Xiang Wang

“…The model has small parameters and low computational effort and is suitable for edge devices. …”
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LFN-YOLO: precision underwater small object detection via a lightweight reparameterized approach by Mingxin Liu, Mingxin Liu, Yujie Wu, Ruixin Li, Cong Lin, Cong Lin

“…Experiments conducted on public datasets, including URPC, Brackish, and TrashCan, showed that the mAP@0.5 reached 74.1%, 97.5%, and 66.2%, respectively, with parameter sizes and computational complexities of 2.7M and 7.2 GFLOPs, and the model size is only 5.9 Mb. …”
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HSTCN-NuSVC: A Homogeneous Stacked Deep Ensemble Learner for Classifying Human Actions Using Smartphones by Sarmela Raja Sekaran, Ying Han Pang, Ooi Shih Yin, Lim Zheng You

“…Existing HAR models face challenges such as tedious manual feature extraction/selection techniques, limited model generalisation, high computational cost, and inability to retain longer-term dependencies. …”
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An improved lightweight tongue segmentation model with self-attention parallel network and progressive upsampling by Xuan Wang, Yifang Cao, Yijia Chen, Huixia Li, Aiqing Han, Yan Tang

“…The improved model not only has fewer parameters but also exhibits a notably lower computational complexity compared to classical models. …”
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Filamentary Convolution for SLI: A Brain-Inspired Approach with High Efficiency by Boyuan Zhang, Xibang Yang, Tong Xie, Shuyuan Zhu, Bing Zeng

“…We propose filamentary convolution to replace rectangular kernels, reducing the parameters while preserving inter-frame features by focusing solely on frequency patterns. …”
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An Efficient Fine-Grained Recognition Method Enhanced by Res2Net Based on Dynamic Sparse Attention by Qifeng Niu, Hui Wang, Feng Xu

“…Furthermore, strategic architectural optimizations are applied throughout to minimize computational complexity, resulting in a model that demands significantly fewer parameters and exhibits faster inference times. …”
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Hyperspectral Imaging for Non-Destructive Moisture Prediction in Oat Seeds by Peng Zhang, Jiangping Liu

“…Among them, SNV combined with FD was identified as the optimal preprocessing scheme, effectively enhancing spectral feature expression. To further refine the predictive model, three feature selection methods—successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and principal component analysis (PCA)—were assessed. …”
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Multi-Domain Controversial Text Detection Based on a Machine Learning and Deep Learning Stacked Ensemble by Jiadi Liu, Zhuodong Liu, Qiaoqi Li, Weihao Kong, Xiangyu Li

“…Firstly, considering the multidimensional complexity of textual features, we integrate comprehensive feature engineering, i.e., encompassing word frequency, statistical metrics, sentiment analysis, and comment tree structure features, as well as advanced feature selection methodologies, particularly lassonet, i.e., a neural network with feature sparsity, to effectively address dimensionality challenges while enhancing model interpretability and computational efficiency. …”
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An analytics-driven model for identifying autism spectrum disorder using eye tracking by Deblina Mazumder Setu

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