A Projective-Geometry-Aware Network for 3D Vertebra Localization in Calibrated Biplanar X-Ray Images by Kangqing Ye, Wenyuan Sun, Rong Tao, Guoyan Zheng

“…The network design of ProVLNet features three components: a Siamese 2D feature extractor to extract local appearance features from the biplanar X-ray images, a spatial alignment fusion module to incorporate the projective geometry in fusing the extracted 2D features in 3D space, and a 3D landmark regression module to regress the 3D coordinates of the vertebrae from the 3D fused features. …”
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Link Aggregation for Skip Connection–Mamba: Remote Sensing Image Segmentation Network Based on Link Aggregation Mamba by Qi Zhang, Guohua Geng, Pengbo Zhou, Qinglin Liu, Yong Wang, Kang Li

“…However, the intricate geographical features and varied land cover boundary interferences in remote sensing imagery still challenge conventional segmentation networks’ spatial representation and long-range dependency capabilities. …”
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SSC-Net: A multi-task joint learning network for tongue image segmentation and multi-label classification by Xiaopeng Sha, Zheng Guan, Ying Wang, Jinglu Han, Yi Wang, Zhaojun Chen

“…Methods Firstly, the shared feature encoder extracts features for both segmentation and classification tasks, where the segmentation result is utilized to mask redundant features that may impede classification accuracy. …”
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MDGCN: Multiple Graph Convolutional Network Based on the Differential Calculation for Passenger Flow Forecasting in Urban Rail Transit by Chenxi Wang, Huizhen Zhang, Shuilin Yao, Wenlong Yu, Ming Ye

“…Secondly, we designed the Diff-graph convolutional layer to identify the changing trend of heterogeneous features and used the attention mechanism unit with the LSTM unit to achieve adaptive fusion of multiple features and modeling of temporal correlation. …”
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Insulator discharge severity assessment algorithm based on RDIDSNet by Cheng Chi, Li Keyu, Yanhui Meng, Yang Yang, JiNing Zhao, Shaotong Pei, Haosen Sun

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Investigating the Effects of Spectroscopic Method in Estimating Soluble Solid Content Values and Firmness of Cherries from an Environmental Point of View: Prediction of Environment... by Shirzad Naim, Shahgholi Gholamhossein, Ardabili Sina, Szymanek Mariusz

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Evaluating Land use Mixed-ness on Street Level through Spatial Analyses and Gini Method by Hamid Motieyan, Mohammad Azmoodeh

“…Compared to previous studies, the distinguishing feature of this study is considering the distance of land uses in calculating the amount of dispersion, which will lead to a proper evaluation of the results obtained. 2-Materials and Methods The present study intends to provide a way to spatially analyze the characteristics of streets and land use in an area, through the Gini method, to discuss justice in the distribution of land uses along the streets and at the regional scale. …”
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Intelligent Fault Diagnosis of Hydraulic System Based on Multiscale One-Dimensional Convolutional Neural Networks with Multiattention Mechanism by Jiacheng Sun, Hua Ding, Ning Li, Xiaochun Sun, Xiaoxin Dong

“…Finally, the proposed method is evaluated and experimentally compared using the UCI hydraulic system dataset. …”
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Predicting correlation relationships of entities between attack patterns and techniques based on word embedding and graph convolutional network by Weicheng QIU, Xiuzhen CHEN, Yinghua MA, Jin MA, Zhihong ZHOU

“…Threat analysis relies on knowledge bases that contain a large number of security entities.The scope and impact of security threats and risks are evaluated by modeling threat sources, attack capabilities, attack motivations, and threat paths, taking into consideration the vulnerability of assets in the system and the security measures implemented.However, the lack of entity relations between these knowledge bases hinders the security event tracking and attack path generation.To complement entity relations between CAPEC and ATT＆amp;CK techniques and enrich threat paths, an entity correlation prediction method called WGS was proposed, in which entity descriptions were analyzed based on word embedding and a graph convolution network.A Word2Vec model was trained in the proposed method for security domain to extract domain-specific semantic features and a GCN model to capture the co-occurrence between words and sentences in entity descriptions.The relationship between entities was predicted by a Siamese network that combines these two features.The inclusion of external semantic information helped address the few-shot learning problem caused by limited entity relations in the existing knowledge base.Additionally, dynamic negative sampling and regularization was applied in model training.Experiments conducted on CAPEC and ATT＆amp;CK database provided by MITRE demonstrate that WGS effectively separates related entity pairs from irrelevant ones in the sample space and accurately predicts new entity relations.The proposed method achieves higher prediction accuracy in few-shot learning and requires shorter training time and less computing resources compared to the Bert-based text similarity prediction models.It proves that word embedding and graph convolutional network based entity relation prediction method can extract new entity correlation relationships between attack patterns and techniques.This helps to abstract attack techniques and tactics from low-level vulnerabilities and weaknesses in security threat analysis.…”
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Combined L-Band Polarimetric SAR and GPR Data to Develop Models for Leak Detection in the Water Pipeline Networks by Yuyao Zhang, Hongliang Guan, Fuzhou Duan

“…We evaluate multiple linear regression (MLR), random forest (RF), and multi-layer perceptron neural network (MLPNN) models for their ability to predict the SSRDC values using the selected features. …”
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Using the antibody-antigen binding interface to train image-based deep neural networks for antibody-epitope classification. by Daniel R Ripoll, Sidhartha Chaudhury, Anders Wallqvist

“…We combined large-scale sequence-based protein-structure predictions to generate ensembles of 3-D Ab models, reduced the Ab binding interface to a 2-D image (fingerprint), used pre-trained convolutional neural networks to extract features, and trained deep neural networks (DNNs) to classify Abs. …”
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DPA-HairNet: A Dual Encoder Attention Based Network for Hair Artifact Removal in Dermoscopic Images by F M Javed Mehedi Shamrat, Mohd Yamani Idna Idris, Chowdhury Forhadul Karim, Xujuan Zhou, Raj Gururajan

“…To address this challenge, we introduce DPA-HairNet, a novel Dual Encoder Attention-Based Network designed specifically for effective hair artifact removal while preserving lesion integrity. …”
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Prediction of Omicron Virus Using Combined Extended Convolutional and Recurrent Neural Networks Technique on CT-Scan Images by Anand Kumar Gupta, Asadi Srinivasulu, Kamal Kant Hiran, Goddindla Sreenivasulu, Sivaram Rajeyyagari, Madhusudhana Subramanyam

“…This research article aims to introduce a combined ML and DL technique based on the combination of an Extended Convolutional Neural Network (ECNN) and an Extended Recurrent Neural Network (ERNN) to diagnose and predict Omicron virus-infected cases automatically using chest CT-scan images. …”
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Traffic signal optimization control method based on attention mechanism updated weights double deep Q network by Huizhen Zhang, Zhenwei Fang, Youqing Chen, Haotian Dai, Qi Jiang, Xinyan Zeng

“…In this paper, for the feature extraction defects of deep double Q network and the problem of underestimating the evaluation value of actions, we propose an Attention Mechanism Updated Weights Double Deep Q Network (AMUW–DDQN) based on the attention mechanism for the optimal control of traffic signals. …”
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Classification evaluation and improvement of airborne PolSAR images for land use mapping using deep learning by Jiafeng Wang, Yongjiu Feng, Xiaohua Tong, Zhenkun Lei, Mengrong Xi, Yi Zhou, Panli Tang

“…Polarimetric synthetic aperture radar (PolSAR) images have been widely used in many fields due to its advantage in obtaining full polarization information, especially in land use classification. To evaluate the performance of airborne PolSAR images in land use classification, this paper systematically evaluated the potential value of PolSAR images in land use classification by using machine learning and deep learning algorithms, and improved the classification performance of airborne PolSAR images by constructing a multi-structural feature aware attention network (MSFA-Net). …”
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A Real-Time Polygonal Wheel-Rail Force Identification Method Based on Convolutional Neural Networks (CNN) by Zeteng Zhang, Jinhai Wang, Jianwei Yang, Dechen Yao

“…Finally, the data are input into the designed real-time polygonal wheel-rail force identification network for learning. Simulation data are used for network learning and comparison. …”
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HyDA-Net: A Hybrid Dense Attention Network for Remote Sensing Multi-Image Super-Resolution by Mohamed Ramzy Ibrahim, Robert Benavente, Daniel Ponsa, Felipe Lumbreras

“…In this article, a novel hybrid dense attention network (HyDA-Net) is proposed that highlights the idea of multi-image SR to address SISR problems. …”
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Copper Stress Levels Classification in Oilseed Rape Using Deep Residual Networks and Hyperspectral False-Color Images by Yifei Peng, Jun Sun, Zhentao Cai, Lei Shi, Xiaohong Wu, Chunxia Dai, Yubin Xie

“…For spatial image data, deep residual networks were employed to evaluate the effectiveness of visible-light and false-color images. …”
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Diagnosis Model for Refrigerant Charge Fault under Heating Conditions based on Multi-layer Convolution Neural Network by Cheng Hengda, Chen Huanxin, Li Zhengfei, Cheng Xiangdong

“…With 20 chosen input features, the accuracy of the 9 level refrigerant charge fault diagnosis reached 91%,surpassing the performance of traditional back propagation neural networks(BPNN).This is the first time to achieve VRF system refrigerant charge fault diagnosis by using a convolutional network, laying a foundation for the expansion of related research.…”
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MEF-CAAN: Multi-Exposure Image Fusion Based on a Low-Resolution Context Aggregation Attention Network by Wenxiang Zhang, Chunmeng Wang, Jun Zhu

“…Finally, the high-resolution fused image is generated by a weighted summation operation. Our proposed network is unsupervised and adaptively adjusts the weights of channels to achieve better feature extraction. …”
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