Comparative Validation and Misclassification Diagnosis of 30-Meter Land Cover Datasets in China by Xiaolin Xu, Dan Li, Hongxi Liu, Guang Zhao, Baoshan Cui, Yujun Yi, Wei Yang, Jizeng Du

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Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models by Benedictor Alexander Nguchu, Benedictor Alexander Nguchu, Yifei Han, Yanming Wang, Peter Shaw

“…We use machine learning (ML) algorithms, including Random Forest, Logistic Regression, and Support Vector Machine, to evaluate the diagnostic power of the brain features and network patterns in differentiating the PD subtypes and distinguishing PD from HC. …”
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DepthFormer: Depth‐Enhanced Transformer Network for Semantic Segmentation of the Martian Surface From Rover Images by Yuan Ma, Zhaojin Li, Bo Wu, Ran Duan

“…The stereo images acquired by the Zhurong rover along its traverse are used for training and testing the DepthFormer network. Different from regular deep‐learning networks only dealing with three bands (red, green and blue) of images, the DepthFormer incorporates the depth information available from the stereo images as the fourth band in the network to enable more accurate segmentation of various surface features. …”
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Remaining Useful Life Prediction for Rolling Bearings Based on TCN–Transformer Networks Using Vibration Signals by Xiaochao Jin, Yaping Ji, Shiteng Li, Kailang Lv, Jianzheng Xu, Haonan Jiang, Shengnan Fu

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Gaitformer: a spatial-temporal attention-enhanced network without softmax for Parkinson’s disease early detection by Shupei Jiao, Hua Huo, Wei Liu, Changwei Zhao, Lan Ma, Jinxuan Wang, Ningya Xu, Chen Zhang, Dongfang Li

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GRU2-Net: Global response double U-shaped network for lesion segmentation in ultrasound images by Xiaokai Jiang, Xuewen Ding, Jinying Ma, Chunyu Liu, Xinyi Li

“…To address these issues, we propose a Global Response Double U-shaped Network, a hybrid CNN-Transformer architecture designed for lesion segmentation in ultrasound images. …”
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Distilling knowledge from graph neural networks trained on cell graphs to non-neural student models by Vasundhara Acharya, Bülent Yener, Gillian Beamer

“…These cell graphs encapsulate the local spatial arrangement of cells in histopathology images, a factor proven to have significant prognostic value. Graph Neural Networks (GNNs) can effectively utilize these spatial feature representations and other features, demonstrating promising performance across classification tasks of varying complexities. …”
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Translational approach for dementia subtype classification using convolutional neural network based on EEG connectome dynamics by Thawirasm Jungrungrueang, Sawrawit Chairat, Kasidach Rasitanon, Praopim Limsakul, Krit Charupanit

“…We also employed a convolutional neural network model, enhanced with these dynamic features, to differentiate between dementia subtypes. …”
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Prediction of foreign currency exchange rates using an attention-based long short-term memory network by Shahram Ghahremani, Uyen Trang Nguyen

“…We conducted comprehensive experiments to evaluate and compare the performance of ALFA against several models used in previous work and against state-of-the-art deep learning models such as temporal convolutional networks (TCN) and Transformer. …”
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TGF-Net: Transformer and gist CNN fusion network for multi-modal remote sensing image classification. by Huiqing Wang, Huajun Wang, Linfen Wu

“…To minimize the duplication of information in multimodal data, the TGF-Net network incorporates a feature reconstruction module (FRM) that employs matrix factorization and self-attention mechanism for decomposing and evaluating the similarity of multimodal features. …”
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Cross-dataset person re-identification method based on multi-pool fusion and background elimination network by Yanfeng LI, Bin ZHANG, Jia SUN, Houjin CHEN, Jinlei ZHU

“…The existing cross-dataset person re-identification methods were generally aimed at reducing the difference of data distribution between two datasets,which ignored the influence of background information on recognition performance.In order to solve this problem,a cross-dataset person re-ID method based on multi-pool fusion and background elimination network was proposed.To describe both global and local features and implement multiple fine-grained representations,a multi-pool fusion network was constructed.To supervise the network to extract useful foreground features,a feature-level supervised background elimination network was constructed.The final network loss function was defined as a multi-task loss,which combined both person classification loss and feature activation loss.Three person re-ID benchmarks were employed to evaluate the proposed method.Using MSMT17 as the training set,the cross-dataset mAP for Market-1501 was 35.53%,which was 9.24% higher than ResNet50.Using MSMT17 as the training set,the cross-dataset mAP for DukeMTMC-reID was 41.45%,which was 10.72% higher than ResNet50.Compared with existing methods,the proposed method shows better cross-dataset person re-ID performance.…”
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Environmental risk assessment based on multiscale spatial recurrent neural network algorithm for IoT agriculture area by M. Sofiya, M. Arulmozhi

“…The Exhaustive Traffic Information Rate (ETIR) method evaluates the marginal rate of each feature, and the AntLion Behavior Optimization (ALBO) algorithm selects the most significant features, reducing dimensionality. …”
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DHCT-GAN: Improving EEG Signal Quality with a Dual-Branch Hybrid CNN–Transformer Network by Yinan Cai, Zhao Meng, Dian Huang

“…In this study, we developed DHCT-GAN, a new EEG denoising model, using a dual-branch hybrid network architecture. This model independently learns features from both clean EEG signals and artifact signals, then fuses this information through an adaptive gating network to generate denoised EEG signals that accurately preserve EEG signal features while effectively removing artifacts. …”
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Individual-level cortical morphological network analysis in idiopathic normal pressure hydrocephalus: diagnostic and prognostic insights by Yifeng Yang, Meijing Yan, Lianxi Sun, Xiao Liu, Xuhao Fang, Shihong Li, Guangwu Lin

“…Cortical morphological similarity networks were constructed using a morphometric inverse divergence network (MIND) framework, integrating five key cortical features: cortical thickness, mean curvature, sulcal depth, surface area, and cortical volume. …”
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A Lightweight Dual-Branch Complex-Valued Neural Network for Automatic Modulation Classification of Communication Signals by Zhaojing Xu, Youchen Fan, Shengliang Fang, You Fu, Liu Yi

“…Currently, deep learning has become a mainstream approach for automatic modulation classification (AMC) with its powerful feature extraction capability. Complex-valued neural networks (CVNNs) show unique advantages in the field of communication signal processing because of their ability to directly process complex data and obtain signal amplitude and phase information. …”
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Modeling of Exhaust Gas Temperature at the Turbine Outlet Using Neural Networks and a Physical Expansion Model by Alessandro Brusa, Alice Grossi, Mirco Lenzi, Fenil Panalal Shethia, Nicolò Cavina, Ioannis Kitsopanidis

“…The models are calibrated with steady-state data and then evaluated based on accuracy and robustness under transient operating conditions on six driving cycles with different features. …”
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Leveraging graph neural networks and gate recurrent units for accurate and transparent prediction of baseball pitching speed by Chen Yang, Pengfei Jin, Yan Chen

“…Combining graph neural networks (GNN) with gate recurrent units (GRU) may offer a better solution. …”
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Optimizing soybean variety selection for the Pan-African Trial network using factor analytic models and envirotyping by Maurício S. Araújo, João P. S. Pavan, André A. Stella, Bruno F. Fregonezi, Natally F. Lima, Erica P. Leles, Michelle F. Santos, Peter Goldsmith, Godfree Chigeza, Brian W. Diers, José B. Pinheiro

“…Data for 37 environmental features were obtained from NASA POWER and SoilGrids. …”
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Multilevel Assessment of Exercise Fatigue Utilizing Multiple Attention and Convolution Network (MACNet) Based on Surface Electromyography by Guofu Zhang, Banghua Yang, Peng Zan, Dingguo Zhang

“…However, the currently available research primarily distinguishes between fatigue and non-fatigue states, offering limited and less robust findings in multilevel evaluations. Methods: This study proposes a multiple attention and convolution network (MACNet) for a three-level assessment of muscle fatigue based on sEMG. …”
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An Autism Spectrum Disorder Identification Method Based on 3D-CNN and Segmented Temporal Decision Network by Zhiling Liu, Ye Chen, Xinrui Dong, Jing Liu

“…The method first uses the 3D-CNN to automatically extract high-dimensional spatial features directly from the raw 4D fMRI data. It then captures temporal dynamic properties through a designed segmented Long Short-Term Memory (LSTM) network. …”
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