Leveraging data analytics for detection and impact evaluation of fake news and deepfakes in social networks by Tony Mathew Abraham, Tao Wen, Ting Wu, Yu-wang Chen

“…This paper begins with a review of the literature on the definitions of fake news and deepfakes, their different types and major differences, and the ways they spread. …”
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Use of Vision Transformer to Classify Sea Surface Phenomena in SAR Imagery by Junfei Xia, Roland Romeiser, Wei Zhang, Tamay Ozgokmen

“…In addition, our study is the first to apply a pretrained ViT model to a dataset with different polarizations and spatial resolutions—the AI4Arctic Sea Ice Challenge dataset—to rigorously assess model adaptability. …”
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Automated note annotation after bioacoustic classification: Unsupervised clustering of extracted acoustic features improves detection of a cryptic owl by Callan Alexander, Robert Clemens, Paul Roe, Susan Fuller

“…Adaptation of these methods to other species and vocalisations may facilitate improved detection and investigation of vocal characteristics across different populations or regions.…”
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A Comparative Study of Lesion-Centered and Severity-Based Approaches to Diabetic Retinopathy Classification: Improving Interpretability and Performance by Gang-Min Park, Ji-Hoon Moon, Ho-Gil Jung

“…Third, we analyze how various model architectures and classification strategies perform under different labeling schemes. Finally, we evaluate decision-making differences between labeling methods using visualization techniques. …”
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Multiphysics property prediction from hyperspectral drill core data by A. V. Kamath, S. T. Thiele, M. Kirsch, R. Gloaguen

“…Our results show that, with careful preprocessing and thorough data cleaning, differences in resolution can be overcome to learn the relationship between hyperspectral data and petrophysics. …”
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Artificial intelligence-based CT-free quantitative thyroid SPECT for thyrotoxicosis: study protocol of a multicentre, prospective, non-inferiority study by Jae Hoon Moon, Ji Hye Kim, Soyeon Ahn, Dongkyu Oh, Hyun Gee Ryoo, Hyun Woo Chung, Sang-Geon Cho, Kyounghyoun Kwon, Young So, Won Woo Lee

“…CT-free thyroid SPECT will be realised using only SPECT data by the trained convolutional neural networks. TcTU will be calculated by SPECT/CT and CT-free SPECT in each subject. …”
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Narrowband Radar Micromotion Targets Recognition Strategy Based on Graph Fusion Network Constructed by Cross-Modal Attention Mechanism by Yuanjie Zhang, Ting Gao, Hongtu Xie, Haozong Liu, Mengfan Ge, Bin Xu, Nannan Zhu, Zheng Lu

“…The network first adopts convolutional neural networks (CNNs) to extract unimodal features from RCSs, TF images, and CVDs independently. …”
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A Multi-Spatial Scale Ocean Sound Speed Prediction Method Based on Deep Learning by Yu Liu, Benjun Ma, Zhiliang Qin, Cheng Wang, Chao Guo, Siyu Yang, Jixiang Zhao, Yimeng Cai, Mingzhe Li

“…To investigate the interactions across multiple spatial scales and to achieve accurate predictions, we propose the STA-ConvLSTM framework that integrates spatiotemporal attention mechanisms with convolutional long short-term memory neural networks (ConvLSTM). …”
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A systematic review of deep learning methods for community detection in social networks by Mohamed El-Moussaoui, Mohamed Hanine, Ali Kartit, Monica Garcia Villar, Monica Garcia Villar, Monica Garcia Villar, Helena Garay, Helena Garay, Helena Garay, Isabel de la Torre Díez

“…This review investigates the employed methodologies, evaluates their effectiveness, and discusses the challenges identified in these works.ResultsOur review shows that models like graph neural networks (GNNs), autoencoders, and convolutional neural networks (CNNs) are some of the most commonly used approaches for community detection. …”
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Ultra-short-term Probabilistic Forecasting of Distributed Photovoltaic Power Generation Based on Hierarchical Correlation Modeling by Can CHEN, Zinuo SU, Yuan MA, Jialin LIU, Yuqing WANG, Fei WANG

“…On this basis, a hierarchical graph structure is constructed to simultaneously model the intra-subregion and inter-subregion spatio-temporal correlations, enabling effective utilization of correlation information across different hierarchical levels. Then, a probabilistic forecasting model based on hierarchical graph convolutional neural networks (GCNs) is proposed to mine deep spatio-temporal correlation features between PV power stations, thereby enhancing the accuracy of ultra-short-term probabilistic forecasting of regional distributed PV power. …”
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GU-Net3+: A Global-Local Feature Fusion Algorithm for Building Extraction in Remote Sensing Images by Yali Liu, Cui Ni, Peng Wang, Dongqing Yang, Hexin Yuan, Chao Ma

“…First, frequency domain transformation and a Convolutional Block Attention Module (CBAM) were applied to preprocess the remote sensing images, enhancing building details while suppressing irrelevant noise interference. …”
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Fine-Scale Small Water Body Uncovered by GF-2 Remote Sensing and Multifeature Deep Learning Model by Yixin Jiang, Chunlin Wang, Zhaji Huang, Dandan Li, Biao Wang, Yanlan Wu, Hui Liu, Zihan Liu

“…Spatial characteristics of small water bodies in different urban zones and their relationship with overall urban water resources are then analyzed. …”
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Breast Tumor Detection and Diagnosis Using an Improved Faster R-CNN in DCE-MRI by Haitian Gui, Han Jiao, Li Li, Xinhua Jiang, Tao Su, Zhiyong Pang

“…We adopted Faster RCNN as the architecture, introduced ROI aligning to minimize quantization errors and feature pyramid network (FPN) to extract different resolution features, added a bounding box quadratic regression feature map extraction network and three convolutional layers to reduce interference from tumor surrounding information, and extracted more accurate and deeper feature maps. …”
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Validation of a Swine Cough Monitoring System Under Field Conditions by Luís F. C. Garrido, Gabriel S. T. Rodrigues, Leandro B. Costa, Diego J. Kurtz, Ruan R. Daros

“…A hybrid deep learning model combining Convolutional Neural Networks and Recurrent Neural Networks was trained and evaluated using these labels. …”
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Novel Neural Networks for Camera Calibration in Underwater Environments by Cristian H. Sanchez-Saquin, Leonardo Barriga-Rodriguez, Leonardo A. Baldenegro-Perez, Guillermo Ronquillo-Lomeli, Noe A. Rodriguez-Olivares

“…A novel method for camera calibration in underwater environments using convolutional networks is presented. Two modified neural network architectures, ZCalibAquaNet and BCalibAquaNet, were developed for calibration matrix estimation from chessboard images in underwater environments. …”
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Reverse design of solid propellant grain based on deep learning: Imaging internal ballistic data by Lin Sun, Xiangyu Peng, Yang Liu, Shu Long, Weihua Hui, Ran Wei, Futing Bao

“…To achieve rapid and accurate matching between the targeted ballistic curve and complex grain shape, this paper proposes a novel reverse design method for SRM propellant grain based on time-series data imaging and convolutional neural network (CNN). First, a finocyl grain shape-internal ballistic curve dataset is created using parametric modeling techniques to comprehensively cover the design space. …”
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Multimodal sleep staging network based on obstructive sleep apnea by Jingxin Fan, Jingxin Fan, Jingxin Fan, Mingfu Zhao, Li Huang, Li Huang, Bin Tang, Bin Tang, Lurui Wang, Zhong He, Zhong He, Xiaoling Peng

“…The Multi-Scale Feature Extraction Module (MFEM) employs convolutional layers with varying dilation rates to capture spatial patterns from fine to coarse granularity. …”
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Advancing arabic dialect detection with hybrid stacked transformer models by Hager Saleh, Hager Saleh, Hager Saleh, Abdulaziz AlMohimeed, Rasha Hassan, Mandour M. Ibrahim, Saeed Hamood Alsamhi, Moatamad Refaat Hassan, Sherif Mostafa

“…The stacking model compares various models, including long-short-term memory (LSTM), gated recurrent units (GRU), convolutional neural network (CNN), and two transformer models using different word embedding. …”
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MS-YOLOv8: multi-scale adaptive recognition and counting model for peanut seedlings under salt-alkali stress from remote sensing by Fan Zhang, Longgang Zhao, Longgang Zhao, Dongwei Wang, Jiasheng Wang, Igor Smirnov, Juan Li

“…First, a lightweight adaptive feature fusion module (called MSModule) is constructed, which groups the channels of input feature maps and feeds them into different convolutional layers for multi-scale feature extraction. …”
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Coupling Deep Learning and Physically Based Hydrological Models for Monthly Streamflow Predictions by Wenxin Xu, Jie Chen, Gerald Corzo, Chong‐Yu Xu, Xunchang John Zhang, Lihua Xiong, Dedi Liu, Jun Xia

“…It also saves decision‐makers the time and effort of trying different combinations of predictors, which is indispensable when building DL models. …”
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