Multi‐data classification detection in smart grid under false data injection attack based on Inception network by H. Pan, H. Yang, C. N. Na, J. Y. Jin

“…Abstract During operation, the smart grid is subject to different false data injection attacks (FDIA). If the different kinds of FDIAs and typical failures have been detected, the system operator can develop various defenses to protect the smart grid in multiple categories. …”
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Time series image coding classification theory based on Lagrange multiplier method by Wentao Jiang, Ming Zhao, Hongbo Li

“…To address this gap, our paper integrates time series image encoding with algebraic techniques, utilizing Gramian Angular Summation Fields (GASF) and Gramian Angle Difference Fields (GADF) as effective encoding methods. …”
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Towards Automatic Detection of Pneumothorax in Emergency Care with Deep Learning Using Multi-Source Chest X-ray Data by Santiago Ibañez Caturla, Juan de Dios Berná Mestre, Oscar Martinez Mozos

“…In particular, we use datasets which contain chest X-ray images corresponding to different conditions (including pneumothorax). A convolutional neural network (CNN) with an EfficientNet architecture is trained and optimized to identify radiographic signs of pneumothorax using those public datasets. …”
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Terrain and Atmosphere Classification Framework on Satellite Data Through Attentional Feature Fusion Network by Antoni Jaszcz, Dawid Połap

“…The neural architecture model takes into account different types of features by extracting them by focusing on spatial, local patterns and multi-scale representation. …”
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Research on the Construction of Crossborder e-Commerce Logistics Service System Based on Machine Learning Algorithms by Jinbo Xu, Shibiao Mu

“…At the same time, it also compared the effect of the time series model, random forest model, GBDT, single Xgboost model, and the model used in this topic and analyzed the reasons for this difference and the application of each model.…”
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On the performance of non‐profiled side channel attacks based on deep learning techniques by Ngoc‐Tuan Do, Van‐Phuc Hoang, Van Sang Doan, Cong‐Kha Pham

“…This paper proposes and evaluates the applications of different DL techniques including the Convolutional Neural Network and the multilayer perceptron models for non‐profiled attacks on the AES‐128 encryption implementation. …”
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Exploring latent weight factors and global information for food-oriented cross-modal retrieval by Wenyu Zhao, Dong Zhou, Buqing Cao, Wei Liang, Nitin Sukhija

“…Though several studies are introduced to bridge this gap, they still suffer from two major limitations: 1) The simple embedding concatenation only can capture the simple interactions rather than complex interactions between different recipe components. 2) The image feature extraction based on convolutional neural networks only considers the local features and ignores the global features of an image, as well as the interactions between different extracted features. …”
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Automated liver segmentation from CT images using modified ResUNet by R.V. Manjunath, Yashaswini Gowda N, H.M. Manu

“…In this study we proposed an automatic system that utilizes convolutional layers to efficiently extract features while maintaining spatial information. …”
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Ultrasound Localization of Nitinol Wire of Sub-Wavelength Dimension by D. R. DeVries, L. J. Olafsen, J. S. Olafsen, H. H. Nguyen, K. E. Schubert, S. Dayawansa, J. H. Huang

“…<italic>Results:</italic> For the full range of diameters traversing the phantom, the wires were localized successfully via visual inspection of both regular and difference ultrasound images. Similarly, two convolutional neural networks were trained, and both achieved an accuracy of over 95&#x0025;. …”
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Advances in soil salinity diagnosis for mangrove swamp rice production in Guinea Bissau, West Africa by Gabriel Garbanzo, Jesus Céspedes, Marina Temudo, Maria do Rosário Cameira, Paula Paredes, Tiago Ramos

“…Silt raster, normalized salinity index (NDSI), and normalized difference water index (NDWI) were the main contributors in the predicted data for soil electrical conductivity of the saturation paste extract (ECe, dS m−1). …”
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A Novel Parallel Multi-Scale Attention Residual Network for the Fault Diagnosis of a Train Transmission System by Yong Chang, Tengfei Gao, Juanhua Yang, Zongyao Liu, Biao Wang

“…Firstly, multi-scale learning modules (MLMods) with different structures and convolutional kernel sizes are designed by combining a residual neural network (ResNet) and an Inception network, which can automatically learn multi-scale fault information from vibration signals. …”
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Research on unsupervised domain adaptive bearing fault diagnosis method by WU ShengKai, SHAO Xing, WANG CuiXiang, GAO Jun

“…Aiming at the problem that the bearing fault diagnosis algorithm based on deep learning has poor diagnosis performance when the fault samples are lack of labels in different working conditions and real environmentsly, an unsupervised domain adaptive bearing fault diagnosis method was proposed to realize the unsupervised fault diagnosis of bearings under different working conditions. …”
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Research on Classification and Identification of Crack Faults in Steam Turbine Blades Based on Supervised Contrastive Learning by Qinglei Zhang, Laifeng Tang, Jiyun Qin, Jianguo Duan, Ying Zhou

“…This method combines a one-dimensional convolutional neural network (1DCNN) and a channel attention mechanism (CAM). 1DCNN can effectively extract local features of time series data, while CAM assigns different weights to each channel to highlight key features. …”
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Investigating Brain Responses to Transcutaneous Electroacupuncture Stimulation: A Deep Learning Approach by Tahereh Vasei, Harshil Gediya, Maryam Ravan, Anand Santhanakrishnan, David Mayor, Tony Steffert

“…Additionally, the responsiveness of different EEG frequency bands to TEAS was investigated. …”
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PointNet++SAKS: A Point Cloud Model Based on KANs and Attention Mechanism for Objects Classification and Semantic Segmentation by Xiaofeng Lu, Zhiwei Guan, Dangfeng Pang, Rupeng Dou, Xiaolong Zheng

“…The introduced SegNext combines Transformer and Convolutional Neural Network (CNN) to extract features from different scales using convolutional kernels of different sizes or convolutional operations with different step sizes. …”
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Synergizing BRDF correction and deep learning for enhanced crop classification in GF-1 WFV imagery by Yuanwei Chen, Yang Li, Runze Li, Chongzheng Guo, Jilin Li

“…First, a BRDF correction method based on normalized difference vegetation index (NDVI) and anisotropy flat index (AFX) is developed to normalize radiometric discrepancies. …”
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Machine Learning Prediction of Storm‐Time High‐Latitude Ionospheric Irregularities From GNSS‐Derived ROTI Maps by Lei Liu, Y. Jade Morton, Yunxiang Liu

“…Abstract This study presents an image‐based convolutional long short‐term memory (convLSTM) machine learning algorithm to predict storm‐time ionospheric irregularities. …”
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KSC-Net: a biologically inspired spatio-temporal correlation network for video-based human action recognition by Hui Ma, Xuelian Ma

“…First, a Dynamic Feature Filter (DF) is introduced to enhance sensitivity to salient motion by suppressing redundant visual signals through second-order temporal difference and Laplacian-based spatial filtering. This module mimics the edge-enhancing and motion-focusing mechanisms of human vision. …”
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Algorithm for Extraction of Reflection Waves in Single-Well Imaging Based on MC-ConvTasNet by Wanting Lin, Jiaqi Xu, Hengshan Hu

“…In the signal channels of the common-source gather, there exists a notable arrival time difference between direct waves and reflected waves. …”
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WDM C-band four channel demultiplexer using cascaded multimode interference on SiN strip waveguide structure by Malka Dror

“…Our design was optimized using AI-enhanced RSoft-CAD simulations that combined the Beam Propagation Method (BPM) and Finite-Difference Time-Domain (FDTD) techniques, integrated with convolutional neural network (CNN) machine learning algorithms. …”
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