A Deep Learning Framework for Damage Assessment of Composite Sandwich Structures by Viviana Meruane, Diego Aichele, Rafael Ruiz, Enrique López Droguett

“…The proposed methodology is validated using numerical and experimental data from a composite sandwich panel with different damage configurations.…”
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Classification of Ship Type from Combination of HMM–DNN–CNN Models Based on Ship Trajectory Features by Dae-Woon Shin, Chan-Su Yang

“…This study proposes an enhanced ship-type classification model that employs a sequential processing methodology integrating hidden Markov model (HMM), deep neural network (DNN), and convolutional neural network (CNN) techniques. Four different ship types—fishing boat, passenger, container, and other ship—were classified using multiple ship trajectory features extracted from the automatic identification system (AIS) and small fishing vessel tracking system. …”
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Diagnosis of depression based on facial multimodal data by Nani Jin, Renjia Ye, Peng Li

“…Through the multi-modal feature fusion, the model can effectively capture different feature patterns related to depression.ResultsWe conduct extensive experiments on the publicly available clinical dataset, the Extended Distress Analysis Interview Corpus (E-DAIC). …”
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Structural Similarity-Guided Siamese U-Net Model for Detecting Changes in Snow Water Equivalent by Karim Malik, Colin Robertson

“…Aggregating snow measurements, however, can magnify the modifiable aerial unit problem, resulting in differing snow trends at different temporal resolutions. …”
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MSTCNet: Toward Generalization Improving for Multiframe Infrared Small Target Detection by Ruining Cui, Na Li, Junfu Liu, Huijie Zhao

“…These changes lead to differences between the data distribution in actual application scenarios and the training scenarios. …”
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CNN–Transformer Hybrid Architecture for Underwater Sonar Image Segmentation by Juan Lei, Huigang Wang, Zelin Lei, Jiayuan Li, Shaowei Rong

“…FLSSNet is built upon a CNN and Transformer backbone network, integrating four core submodules to address various technical challenges: (1) The asymmetric dual encoder–decoder (ADED) is capable of simultaneously extracting features from different modalities and systematically modeling both local contextual information and global spatial structure. (2) The Transformer feature converter (TFC) module optimizes the multimodal feature fusion process through feature transformation and channel compression. (3) The long-range correlation attention (LRCA) module enhances CNN’s ability to model long-range dependencies through the collaborative use of convolutional kernels, selective sequential scanning, and attention mechanisms, while effectively suppressing noise interference. (4) The recursive contour refinement (RCR) model refines edge contour information through a layer-by-layer recursive mechanism, achieving greater precision in boundary details. …”
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Deep Learning-Based Seedling Row Detection and Localization Using High-Resolution UAV Imagery for Rice Transplanter Operation Quality Evaluation by Yangfan Luo, Jiuxiang Dai, Shenye Shi, Yuanjun Xu, Wenqi Zou, Haojia Zhang, Xiaonan Yang, Zuoxi Zhao, Yuanhong Li

“…Different semantic segmentation models are trained and tested using low altitude high-resolution images of drones, and compared. …”
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A Motion‐Sensing Integrated Soft Robot with Triboelectric Nanogenerator for Pipeline Inspection by Rui Chen, Huigang Wang, Haoji Wang, Huijiang Wang, Li Bai, Xinpei Ai, Lifu Liu, Zhihao Hu, Zean Yuan

“…The T‐TENG‐based sensory system outputs distinct voltage signals upon exposed to different material and structural conditions, for which a 1D‐convolutional neutral network algorithm is exposed to process with the sequential signals. …”
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Enhancing LoRa-Based Outdoor Localization Accuracy Using Machine Learning by Nur Kelesoglu, Marzena Halama, Anna Strzoda

“…Additionally, we investigate the impact of different Feature Vector (FV) subsets on localization performance by analyzing the significance of LoRaWAN signal attributes. …”
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Image-Based Breast Cancer Histopathology Classification and Diagnosis Using Deep Learning Approaches by Lama A. Aldakhil, Haifa F. Alhasson, Shuaa S. Alharbi, Rehan Ullah Khan, Ali Mustafa Qamar

“…This limitation reduces the accuracy of diagnostic results, mainly when applied to different clinical environments. Furthermore, class imbalances within these datasets, where certain cancer types or stages are underrepresented, lead to biased diagnoses, with more common cases being easily identified while rarer cases are frequently missed. …”
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Class-weighted Dempster–Shafer in dual-level fusion for multimodal fake real estate listings detection by Maifuza Mohd Amin, Nor Samsiah Sani, Mohammad Faidzul Nasrudin

“…Single-level fusion models whether at the feature, decision, or intermediate level struggle with balancing the contributions of different modalities leading to suboptimal decision-making. …”
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Deep Learning-Based Denoising for Optical Coherence Tomography: Evaluating Self-Supervised and Generative Models Across Retinal Datasets by Diogen BABUC, Alesia LOBONŢ, Alexandru FARCAŞ, Todor IVAŞCU, Sebastian-Aurelian ŞTEFĂNIGĂ

“…We used OCT scans with different retinal diseases datasets, such as diabetic retinopathy, age-related macular degeneration, macular hole, central serous retinopathy, and normal retinas. …”
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A Novel Audio Copy Move Forgery Detection Method With Classification of Graph-Based Representations by Beste Ustubioglu, Gul Tahaoglu, Arda Ustubioglu, Guzin Ulutas, Muhammed Kilic

“…This paper presents a novel method to detect audio copy-move forgery, a type of manipulation where segments of an audio file are duplicated and moved to different locations within the same file. The proposed method consists of two main stages. …”
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Unsupervised Domain Adaptation via Contrastive Learning and Complementary Region-Class Mixing by Xiaojing Li, Wei Zhou, Mingjian Jiang

“…However, these deep models have poor generalization ability across different domain datasets. To alleviate the degradation of the model’s performance in different domains, unsupervised domain adaptation attempts to transfer the knowledge from a labeled source domain to an unlabeled target domain. …”
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Deep Learning Ensemble Approach for Predicting Expected and Confidence Levels of Signal Phase and Timing Information at Actuated Traffic Signals by Seifeldeen Eteifa, Amr Shafik, Hoda Eldardiry, Hesham A. Rakha

“…This architecture is combined with different deep learning methods, including Multilayer Perceptrons (MLP), Long-Short-Term Memory neural networks (LSTM), and Convolutional Long-Short-Term Memory neural networks (CNNLSTM) to form an ensemble of predictors. …”
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GraphGIM: rethinking molecular graph contrastive learning via geometry image modeling by Chaoyi Li, Hongxin Xiang, Wenjie Du, Tengfei Ma, Haowen Chen, Xiangxiang Zeng, Lei Xu

“…Furthermore, we find that as the convolutional layers process the image becomes deeper, the information of feature maps gradually changes from global molecular-level information (molecular scaffolds) to local atomic-level information (molecular atoms and functional groups), which provides chemical information at different scales. …”
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Information-Guided Diffusion Model for Downscaling Land Surface Temperature from SDGSAT-1 Remote Sensing Images by Jianxin Wang, Zhitao Fu, Bohui Tang, Jianhui Xu

“…Additionally, the study reveals the spatial feature impact of different auxiliary information in LST downscaling and the variations in features across different regions and temperature ranges.…”
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Predicting the thermal conductivity of polymer composites with one-dimensional oriented fillers using the combination of deep learning and ensemble learning by Yinzhou Liu, Weidong Zheng, Haoqiang Ai, Lin Cheng, Ruiqiang Guo, Xiaohan Song

“…However, for composites consist of different matrices and fillers, the CNN model needs to be retrained, which greatly wastes computing resources. …”
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Interpretable multi-instance heterogeneous graph network learning modelling CircRNA-drug sensitivity association prediction by Mengting Niu, Chunyu Wang, Yaojia Chen, Quan Zou, Ximei Luo

“…Abstract Background Different expression levels of circular RNAs (circRNAs) affect the sensitivity of human cells to drugs, thus producing different responses to the therapeutic effects of drugs. …”
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SET: A Shared-Encoder Transformer Scheme for Multi-Sensor, Multi-Class Fault Classification in Industrial IoT by Kamran Sattar Awaisi, Qiang Ye, Srinivas Sampalli

“…Our experimental results indicate that SET consistently outperforms baseline methods, including Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN)-LSTM, and Multilayer Perceptron (MLP), as well as the proposed comparative variant of SET, Multi-Encoder Transformer (MET), in terms of accuracy, precision, recall, and F1-score across different fault intensities. …”
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