Method to generate cyber deception traffic based on adversarial sample by Yongjin HU, Yuanbo GUO, Jun MA, Han ZHANG, Xiuqing MAO

“…In order to prevent attacker traffic classification attacks,a method for generating deception traffic based on adversarial samples from the perspective of the defender was proposed.By adding perturbation to the normal network traffic,an adversarial sample of deception traffic was formed,so that an attacker could make a misclassification when implementing a traffic analysis attack based on a deep learning model,achieving deception effect by causing the attacker to consume time and energy.Several different methods for crafting perturbation were used to generate adversarial samples of deception traffic,and the LeNet-5 deep convolutional neural network was selected as a traffic classification model for attackers to deceive.The effectiveness of the proposed method is verified by experiments,which provides a new method for network traffic obfuscation and deception.…”
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Enhanced pedestrian trajectory prediction via overlapping field-of-view domains and integrated Kolmogorov-Arnold networks. by Hongxia Wang, Yang Liu, Zhenkai Nie

“…By rigorously dividing monocular and binocular overlapping visual regions and utilizing different influence factors, the model pedestrian interactions more realistically. …”
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L’impérialisme humanitaire de Michael Ignatieff : le cas d’un « liberal hawk » by Pierre Guerlain

“…Though he often ends up in agreement with neoconservative or neoliberal imperialists his justifications are more intellectual and convoluted and therefore address a different audience.…”
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Identification of Pneumonia in Chest X-Ray Image Based on Transformer by Yongjun Ma, Wei Lv

“…After the comparison experiments on two different datasets, the experimental results show that the accuracy of the model in this paper improves from 76.3% to 87.3% and from 92.8% to 97.2%, respectively. …”
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Kidney Ensemble-Net: Enhancing Renal Carcinoma Detection Through Probabilistic Feature Selection and Ensemble Learning by Zaib Akram, Kashif Munir, Muhammad Usama Tanveer, Atiq Ur Rehman, Amine Bermak

“…Our approach begins by acquiring spatial features from contrast-enhanced images using a Convolutional Neural Network (CNN) effectively capturing intricate patterns and structures characteristic of different carcinoma subtypes. …”
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Review of Recent Advances in Remote Sensing and Machine Learning Methods for Lake Water Quality Management by Ying Deng, Yue Zhang, Daiwei Pan, Simon X. Yang, Bahram Gharabaghi

“…This review highlights the specific advantages of each satellite platform, considering factors like spatial and temporal resolution, spectral coverage, and the suitability of these platforms for different lake sizes and characteristics. In addition to remote sensing platforms, this paper explores the application of a wide range of machine learning models, from traditional linear and tree-based methods to more advanced deep learning techniques like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). …”
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A Multifrequency Brain Network-Based Deep Learning Framework for Motor Imagery Decoding by Juntao Xue, Feiyue Ren, Xinlin Sun, Miaomiao Yin, Jialing Wu, Chao Ma, Zhongke Gao

“…Further, a multilayer convolutional network model is designed to distinguish different MI tasks accurately, which allows extracting and exploiting the topology in the multifrequency brain network. …”
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Enhancing Learning-Based Cross-Modality Prediction for Lossless Medical Imaging Compression by Daniel S. Nicolau, Lucas A. Thomaz, Luis M. N. Tavora, Sergio M. M. Faria

“…Multimodal medical imaging, which involves the simultaneous acquisition of different modalities, enhances diagnostic accuracy and provides comprehensive visualization of anatomy and physiology. …”
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Enhancing Anti-Money Laundering Frameworks: An Application of Graph Neural Networks in Cryptocurrency Transaction Classification by Stefano Ferretti, Gabriele D'Angelo, Vittorio Ghini

“…Based on the dataset analysis, we experiment with different subsets of features. Our findings suggest that the use of Graph Neural Network convolutions, combined with a final linear layer and skip connections, allow for an improvement in the state-of-the-art results, especially when Chebyshev and GATv2 convolutions are used.…”
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Enhancing low‐light images with lightweight fused fixed‐directional filters network by Yang Li

“…These wavelet transform branches capture the multi‐scale information of the image by combining different directions and convolutional kernels and utilize a trainable custom gamma mapping layer for non‐linear modulation to enhance specific regions of the image. …”
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Visual automatic detection method for hydraulic support loss state at the working face by TENG Daiyu, NAN Bingfei

“…The location information of different hydraulic support bases and push rods was analyzed to determine the number of each hydraulic support in the monitoring video. …”
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A hybrid adversarial autoencoder-graph network model with dynamic fusion for robust scRNA-seq clustering by Binhua Tang, Yingying Feng, Xinyu Gao

“…Abstract Background Single-cell RNA sequencing (scRNA-seq) allows the exploration of biological heterogeneity among different cell types within tissues at a single-cell resolution. …”
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Prediction of Temperature Distribution with Deep Learning Approaches for SM1 Flame Configuration by Gökhan Deveci, Özgün Yücel, Ali Bahadır Olcay

“…In addition, a comparison was made with different deep learning networks, namely Res-Net, EfficientNetB0, and Inception Net V3, to better understand the performance of the model. …”
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Harnessing Multi-Source Data and Deep Learning for High-Resolution Land Surface Temperature Gap-Filling Supporting Climate Change Adaptation Activities by Katja Kustura, David Conti, Matthias Sammer, Michael Riffler

“…Land surface temperature (LST) is a widely used proxy for investigating climate-change-induced phenomena, providing insights into the surface radiative properties of different land cover types and the impact of urbanization on local climate characteristics. …”
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1D CNN-Based Intracranial Aneurysms Detection in 3D TOF-MRA by Wenguang Hou, Shaojie Mei, Qiuling Gui, Yingcheng Zou, Yifan Wang, Xianbo Deng, Qimin Cheng

“…It transfers 3D classification into 2D case by projecting the 3D patch into 2D planes along different directions on the basis of voxel’s intensity. …”
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Precision in practice: exploring the impact of ai and machine learning on ultrasound guided regional anaesthesia by Noor Ul Huda Bhatti, Syed Ghazi Ali Kirmani, Maryam Butt

“…In one experiment, Alkhatib et al. used Convolutional neural network (CNN) based deep trackers to track the median and sciatic nerve with a surprising accuracy of 0.87.2 Another study employed the same CNN model to locate and discriminate accurate images of sacrum, vertebral levels and intervertebral gaps during percutaneous spinal needle insertion.3 Another study used a different AI model called SVM (support vector machine) classification, image processing, and template matching to locate lumbar level L3-L4 and the ideal puncture site for epidural anaesthesia in real-time. …”
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Compressive Sensing Network Deeply Induced by Visual Mechanism by Mingkun Feng, Xiaole Han, Kai Zheng

“…Specifically, we introduce a discrete wavelet transform-based visual weighting module and an inverse discrete wavelet reconstruction fusion module to adjust the weights and fuse different frequency sub-bands, which not only enhances image reconstruction quality but also significantly reduces computational complexity. …”
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Accurate bladder cancer diagnosis using ensemble deep leaning by Rana A. El-Atier, M. S. Saraya, Ahmed I. Saleh, Asmaa H. Rabie

“…In fact, the used voting method depends on using majority voting based on two different scenarios according to the results of CNN, GAN, and XDL. …”
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EEG-based neurodegenerative disease diagnosis: comparative analysis of conventional methods and deep learning models by B. R. Nayana, M. N. Pavithra, S. Chaitra, T. N. Bhuvana Mohini, Thompson Stephan, Vijay Mohan, Neha Agarwal

“…The implementation is carried out under three different verticals. Firstly, a conventional machine learning model was developed post-pre-processing, and feature extraction from the power spectral density was done using a Random Forest classifier. …”
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Comparison of Deep Learning Sentiment Analysis Methods, Including LSTM and Machine Learning by Jean Max T. Habib, A. A. Poguda

“…We present a comparison of several deep learning models, including convolutional neural networks, recurrent neural networks, and long-term and shortterm bidirectional memory, evaluated using different approaches to word integration, including Bidirectional Encoder Representations from Transformers (BERT) and its variants, FastText and Word2Vec. …”
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