Synergistic 2D/3D Convolutional Neural Network for hyperspectral image classification by Yang, Xiaofei, Zhang, Xiaofeng, Ye, Yunming, Lau, Raymond Y. K., Lu, Shijian, Li, Xutao, Huang, Xiaohui

“…The SyCNN consists of a hybrid module that combines 2D and 3D CNNs in feature learning and a data interaction module that fuses spectral and spatial hyperspectral information. …”
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Learning disentangled representation implicitly via transformer for occluded person re-identification by Jia, Mengxi, Cheng, Xinhua, Lu, Shijian, Zhang, Jian

“…To better eliminate interference from occlusions, we design a contrast feature learning technique (CFL) for better separation of occlusion features and discriminative ID features. …”
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Fingerprint-enhanced graph attention network (FinGAT) model for antibiotic discovery by Choo, Hou Yee, Wee, Junjie, Shen, Cong, Xia, Kelin

“…In this paper, we propose a fingerprint-enhanced graph attention network (FinGAT) model by the combination of sequence-based 2D fingerprints and structure-based graph representation. In our feature learning process, sequence information is transformed into a fingerprint vector, and structural information is encoded through a GAT module into another vector. …”
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Knowledge-based BERT word embedding fine-tuning for emotion recognition by Zhu, Zixiao, Mao, Kezhi

“…By combining the emotionally discriminative fine-tuned embedding with contextual information-rich embedding from pre-trained BERT model, the emotional features underlying the texts could be more effectively captured in the subsequent feature learning module, which in turn leads to improved emotion recognition performance. …”
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BELMKN : Bayesian extreme learning machines Kohonen Network by Simha C, Sumanth, G, Nagaraj, Thapa, Meenakumari, M, Indiramma, Senthilnath, Jayavelu

“…In the first level, the Extreme Learning Machine (ELM)-based feature learning approach captures the nonlinearity in the data distribution by mapping it onto a d-dimensional space. …”
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Reconstructing 3D cardiac anatomies from misaligned multi-view magnetic resonance images with Mesh Deformation U-Nets by Beetz, M, Banerjee, A, Grau, V

“…Its architecture combines spectral graph convolutions and mesh sampling operations in a hierarchical encoder-decoder structure to enable efficient multi-scale feature learning directly on mesh data. A targeted preprocessing step approximately fits a template mesh to the sparse MRI contours, before the Mesh Deformation U-Net corrects for motion-induced slice misalignment by simultaneously utilising information from multiple MRI views and the template-induced anatomical shape prior. …”

Domain adaptation : methods and applications by Wei, Pengfei

“…First of all, we develop methods, specifically, one deep feature learning method and one subspace-based method, for homogeneous problem settings. …”
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Noise Eliminated Ensemble Empirical Mode Decomposition Scalogram Analysis for Rotating Machinery Fault Diagnosis by Faysal, Atik, Ngui, Wai Keng, Lim, Meng Hee, Leong, Mohd Salman

“…A convolution neural network (CNN) classifier was applied for classification because of its feature learning ability. A generalized CNN architecture was proposed to reduce the model training time. …”
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3D LiDAR Vehicle Perception and Classification Using 3D Machine Learning Algorithm by Yong, Ericsson, Muhammad Aizzat, Zakaria, Mohamad Heerwan, Peeie, M. Izhar, Ishak

“…With its improved accessibility in the recent years, the advent of deep learning had allowed feature learning from sparse 3D point clouds. Hence, this leads a plethora of methods in object detection for 3D sparse point clouds. …”
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Distributed denial of service attack Detection in IoT networks using deep learning and feature fusion : A review by Nuhu Ahmad, Abdul hafiz, Anis Farihan, Mat Raffei, Mohd Faizal, Ab Razak, Ahmad Syafadhli, Abu Bakar

“…The deep learning technique shows great promise because automatic feature learning capabilities are well suited for the complex and high-dimensional data of IoT systems. …”
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Distributed Denial of Service Attack Detection in IoT Networks using Deep Learning and Feature Fusion: A Review by Nuhu Ahmad, Abdulhafiz, Anis Farihan, Mat Raffei, Mohd Faizal, Ab Razak, Ahmad, Abubakar

“…The deep learning technique shows great promise because automatic feature learning capabilities are well suited for the complex and high-dimensional data of IoT systems. …”
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A novel medical image segmentation approach by using multi-branch segmentation network based on local and global information synchronous learning by Shangzhu Jin, Sheng Yu, Jun Peng, Hongyi Wang, Yan Zhao

“…Abstract In recent years, there have been several solutions to medical image segmentation, such as U-shaped structure, transformer-based network, and multi-scale feature learning method. However, their network parameters and real-time performance are often neglected and cannot segment boundary regions well. …”
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A neural network approach for short-term water demand forecasting based on a sparse autoencoder by Haidong Huang, Zhenliang Lin, Shitong Liu, Zhixiong Zhang

“…In this method, the SAE is used as a feature learning method to extract useful information from hourly water demand data in an unsupervised manner. …”
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V2PNet: Voxel-to-Point Feature Propagation and Fusion That Improves Feature Representation for Point Cloud Registration by Han Hu, Yongkuo Hou, Yulin Ding, Guoqiang Pan, Min Chen, Xuming Ge

“…However, although point-based methods are geometrically precise, the discrete nature of point clouds negatively affects feature learning performance. Moreover, although voxel-based methods can exploit the learning power of convolutional neural networks, their resolution and detail extraction may be inadequate. …”
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Customized 2D CNN Model for the Automatic Emotion Recognition Based on EEG Signals by Farzad Baradaran, Ali Farzan, Sebelan Danishvar, Sobhan Sheykhivand

“…In this research, different feature learning and hand-crafted feature selection/extraction algorithms were investigated and compared with each other in order to classify emotions. …”
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Vehicular Network Intrusion Detection Using a Cascaded Deep Learning Approach with Multi-Variant Metaheuristic by Ankit Manderna, Sushil Kumar, Upasana Dohare, Mohammad Aljaidi, Omprakash Kaiwartya, Jaime Lloret

“…Additionally, information gained using MV-GBO-based feature extraction is employed to enhance feature learning. The effectiveness of the proposed model is evaluated on reliable datasets such as KDD-CUP99, ToN-IoT, and VeReMi, which are utilized on the MATLAB platform. …”
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Smoking behavior detection algorithm based on YOLOv8-MNC by Zhong Wang, Zhong Wang, Lanfang Lei, Peibei Shi

“…The YOLOv8-MNC algorithm employs three key strategies: (1) It utilizes NWD Loss to mitigate the effects of minor deviations in object positions on IoU, thereby enhancing training accuracy; (2) It incorporates the Multi-head Self-Attention Mechanism (MHSA) to bolster the network’s global feature learning capacity; and (3) It implements the lightweight general up-sampling operator CARAFE, in place of conventional nearest-neighbor interpolation up-sampling modules, minimizing feature information loss during the up-sampling process.ResultsExperimental results from a customized smoking behavior dataset demonstrate significant improvement in detection accuracy. …”
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MSGNN-DTA: Multi-Scale Topological Feature Fusion Based on Graph Neural Networks for Drug–Target Binding Affinity Prediction by Shudong Wang, Xuanmo Song, Yuanyuan Zhang, Kuijie Zhang, Yingye Liu, Chuanru Ren, Shanchen Pang

“…To address the challenge of accurately extracting drug and target protein features, we introduce a gated skip-connection mechanism during the feature learning process to fuse multi-scale topological features, resulting in information-rich representations of drugs and proteins. …”
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Vehicle Classification Using Deep Feature Fusion and Genetic Algorithms by Ahmed S. Alghamdi, Ammar Saeed, Muhammad Kamran, Khalid T. Mursi, Wafa Sulaiman Almukadi

“…After performing appropriate data preparation and preprocessing steps, feature learning and extraction is carried out using pre-trained VGG16 first that learns and extracts deep features from the set of input images. …”
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Effectiveness of Human–Artificial Intelligence Collaboration in Cephalometric Landmark Detection by Van Nhat Thang Le, Junhyeok Kang, Il-Seok Oh, Jae-Gon Kim, Yeon-Mi Yang, Dae-Woo Lee

“…We selected 1193 cephalograms and used them to train the deep anatomical context feature learning (DACFL) model. The number of target landmarks was 41. …”
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