Flood Classification and Improved Loss Function by Combining Deep Learning Models to Improve Water Level Prediction in a Small Mountain Watershed by Rukai Wang, Ximin Yuan, Fuchang Tian, Minghui Liu, Xiujie Wang, Xiaobin Li, Minrui Wu

“…Results show that the hierarchical prediction method is an effective means of extracting flood features by addressing the variability of prediction parameters for different flood magnitudes. The integration of Graph Convolutional and Time Aware models enables the model to recognize the spatiotemporal flood characteristics, overcoming limitations of prevailing methods and ensuring long‐term forecast accuracy. …”
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ScarNet: Development and Validation of a Novel Deep CNN Model for Acne Scar Classification With a New Dataset by Masum Shah Junayed, Md Baharul Islam, Afsana Ahsan Jeny, Arezoo Sadeghzadeh, Topu Biswas, A. F. M. Shahen Shah

“…In this paper, a novel automated acne scar classification system is proposed based on a deep Convolutional Neural Network (CNN) model. First, a dataset of 250 images from five different classes is collected and labeled by four well-experienced dermatologists. …”
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Exploring deep learning for landslide mapping: A comprehensive review by Zhi-qiang Yang, Wen-wen Qi, Chong Xu, Xiao-yi Shao

“…This study analyzed the structures of different DL networks, discussed five main application scenarios, and assessed both the advancements and limitations of DL in geological hazard analysis. …”
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Deep Learning-Based Object Detection Strategies for Disease Detection and Localization in Chest X-Ray Images by Yi-Ching Cheng, Yi-Chieh Hung, Guan-Hua Huang, Tai-Been Chen, Nan-Han Lu, Kuo-Ying Liu, Kuo-Hsuan Lin

“…Given the prevalence of normal images over diseased ones in clinical settings, we created various training datasets and approaches to assess how different proportions of background images impact model performance. …”
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Multi-Scale Analysis of Knee Joint Acoustic Signals for Cartilage Degeneration Assessment by Anna Machrowska, Robert Karpiński, Marcin Maciejewski, Józef Jonak, Przemysław Krakowski, Arkadiusz Syta

“…The CNN model is trained on features extracted from these signals to accurately classify different stages of cartilage degeneration. The proposed method demonstrates the potential for early detection of knee joint pathology, providing a valuable tool for preventive healthcare and reducing the need for invasive diagnostic procedures. …”
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Forecasting basal area increment in forest ecosystems using deep learning: A multi-species analysis in the Himalayas by P. Casas-Gómez, J.F. Torres, J.C. Linares, A. Troncoso, F. Martínez-Álvarez

“…To overcome these limitations, we introduce the use of two different Deep Learning models: the Long Short-Term Memory network and the Temporal Convolutional Neural Network, which capture the temporal dependencies of growth by incorporating lagged Basal Area Increment values. …”
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Myocardial Iron Overload Assessment with Automatic Segmentation of Cardiac MR Images based on Deep Neural Networks by Mohamad Amin Bakhshali, Maryam Gholizadeh, Parvaneh Layegh, Saeid Eslami

“…Automatic LV segmentation was implemented with U-Net, an automatically adapted deep convolutional neural network based on U-Net. With the signal intensity of the LV segmented area, T2* value can be calculated at different echo times, a widely used and approved method to assess myocardial iron overload. …”
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Open-Circuit Fault Diagnosis Method of Energy Storage Converter Based on MFCC Feature Set by Bin YU, Xingrong SONG, Ting ZHOU, Linbo LUO, Hui LI, Liang CHE

“…Firstly, the three-phase current on the alternating current (AC) side is taken as the input signal, and an MFCC fault feature data set is constructed by analyzing the signal spectrum energy distribution and envelope characteristics in different frequency intervals. Then, through kernel principal component analysis (KPCA), the dimension reduction screening of nonlinear fault features under charge and discharge conditions is realized. …”
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Ribosome phenotypes for rapid classification of antibiotic-susceptible and resistant strains of Escherichia coli by Alison Farrar, Piers Turner, Hafez El Sayyed, Conor Feehily, Stelios Chatzimichail, Sammi Ta, Derrick Crook, Monique Andersson, Sarah Oakley, Lucinda Barrett, Christoffer Nellåker, Nicole Stoesser, Achillefs Kapanidis

“…Using 60,382 cells from an antibiotic-susceptible laboratory strain of E. coli, we showed that antibiotics with different mechanisms of action result in distinct ribosome phenotypes, which can be identified by a CNN with high accuracy (99%, 98%, 95%, and 99% for ciprofloxacin, gentamicin, chloramphenicol, and carbenicillin). …”
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Multi-View Contrastive Fusion POI Recommendation Based on Hypergraph Neural Network by Luyao Hu, Guangpu Han, Shichang Liu, Yuqing Ren, Xu Wang, Ya Liu, Junhao Wen, Zhengyi Yang

“…The model first constructs three distinct hypergraphs, representing interaction, trajectory, and geographical location, capturing the complex relationships and high-order dependencies between users and POIs from different perspectives. Subsequently, a targeted hypergraph convolutional network is designed for aggregation and propagation, learning the latent factors within each view. …”
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Advancement in Graph Neural Networks for EEG Signal Analysis and Application: A Review by S. M. Atoar Rahman, Md Ibrahim Khalil, Hui Zhou, Yu Guo, Ziyun Ding, Xin Gao, Dingguo Zhang

“…Electroencephalography (EEG) can non-invasively measure neuronal events and reflect brain activity at different locations on the scalp. Early studies for EEG signal processing have focused more on extracting EEG temporal features and considered the topology of EEG channels less due to the limitation of rich spatial information. …”
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Robust Automated Mouse Micro-CT Segmentation Using Swin UNEt TRansformers by Lu Jiang, Di Xu, Qifan Xu, Arion Chatziioannou, Keisuke S. Iwamoto, Susanta Hui, Ke Sheng

“…Further evaluation on an external mouse dataset acquired on a different micro-CT with lower kVp and higher imaging noise was also employed to assess model robustness and generalizability. …”
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FetalMovNet: A Novel Deep Learning Model Based on Attention Mechanism for Fetal Movement Classification in US by Musa Turkan, Emre Dandil, Furkan Erturk Urfali, Mehmet Korkmaz

“…To evaluate FetalMovNet, we construct a new dataset containing fetal movements in US across seven different anatomical structures-head, body, arm, hand, heart, leg, and foot. …”
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Multi-scale extreme climate disaster prediction model integrated with ConvLSTM: taking rainstorm and flood disaster as an example by Lei He, Yunhao He, Yongqiang Xia, Yuxia Li, Bin Liu, Siqi Zhang, Cunjie Zhang

“…Firstly, four disaster indicators (population disaster index, housing disaster index, agricultural disaster index and economic disaster index) were introduced to reflect different losses, which could form a comprehensive disaster index to quantify the overall loss degree; Second, with raster data and VGGNet, a lightweight regression convolutional neural network model VGG-Light was proposed to solve these problem; Third, focused on impact of precipitation on disaster situations, the ConvLSTM module was used to capture the spatiotemporal characteristics of precipitation data, and then the TSVGG-Light model was presented for feature fusion. …”
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ViViT-Prob: A Radar Echo Extrapolation Model Based on Video Vision Transformer and Spatiotemporal Sparse Attention by Yunan Qiu, Bingjian Lu, Wenrui Xiong, Zhenyu Lu, Le Sun, Yingjie Cui

“…The model takes historical sequences as input and initially maps them into a fixed-dimensional vector space through 3D convolutional patch encoding. Subsequently, a multi-head spatiotemporal fusion module with sparse attention encodes these vectors, effectively capturing spatiotemporal relationships between different regions in the sequences. …”
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Multiscale Spatial-Spectral CNN-Transformer Network for Hyperspectral Image Super-Resolution by Jiayang Zhang, Hongjia Qu, Junhao Jia, Yaowei Li, Bo Jiang, Xiaoxuan Chen, Jinye Peng

“…This module processes the previously obtained local spatial-spectral features, thoroughly exploring the elaborate global interrelationship and long-range dependencies among different spectral bands through a coarse-to-fine strategy. …”
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Spectral Super-Resolution Reconstruction of Multispectral Remote Sensing Images via Clustering-Based Spectral Feature by Wang Benlin, Yu Qinglin, Wang Zuo, Li Weitao, Wang Yong, Liu Huan, Gu Shuangxi, Zhang Lingling, Lv Dong

“…To address this, we proposed a jointly fused convolutional neural network for spectral super-resolution (JF-CNNSSR), leveraging spectral reflectance variations across different land cover types. …”
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Tree semantic segmentation from aerial image time series by Venkatesh Ramesh, Arthur Ouaknine, David Rolnick

“…Effective monitoring of different tree species is essential to understanding and improving the health and biodiversity of forests. …”
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RNN and GNN based prediction of agricultural prices with multivariate time series and its short-term fluctuations smoothing effect by Youngho Min, Young Rock Kim, YunKyong Hyon, Taeyoung Ha, Sunju Lee, Jinwoo Hyun, Mi Ra Lee

“…In this investigation, we applied five different smoothing time window lengths to evaluate the effect of mitigating short-term fluctuations on the predictive performance of the models. …”
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APO-CViT: A Non-Destructive Estrus Detection Method for Breeding Pigs Based on Multimodal Feature Fusion by Jinghan Cai, Wenzheng Liu, Tonghai Liu, Fanzhen Wang, Zhihan Li, Xue Wang, Hua Li

“…By integrating the Vision Transformer and convolutional neural networks, the model extracted and fused features from multimodal data. …”
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