TADNet: A Time and Attention-Based Point Cloud Denoising Network for Autonomous Driving in Adverse Weather by Y. Zhang, H. Huang, X. Yan, Y. Liang, Y. Li, J. Yang

“…The method is based on the 3D-OutDet network with the addition of Convolutional Block Attention Module (CBAM), which highlights important features and suppresses minor ones. …”
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GLAI-Net: Global–Local Awareness Integrated Network for Semantic Change Detection in Remote Sensing Images by Qing Ding, Fengyan Wang, Mingchang Wang, Ying Zhang, Gui Cheng

“…We design a parallel encoding structure and utilize convolutional neural networks and transformer to achieve multi-scale modeling of images and enhance feature expression ability. …”
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Fault Diagnosis Based On Improved Information Entropy And 1dcnn For Marine Turbocharger Rotor With Variable Speed by Hu Lei, Hu Haoran, Hu Nao, Liu Luyuan, Dong Fei, Yang Jianguo, Zhong Jiahong

“…Faults in the turbocharger rotor at the different speeds are classified using a one-dimensional convolutional neural network (1DCNN), and the arithmetic ability of the diagnostic algorithm is evaluated. …”
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Soil moisture retrieval over agricultural region through machine learning and sentinel 1 observations by Deepanshu Lakra, Deepanshu Lakra, Shobhit Pipil, Prashant K. Srivastava, Suraj Kumar Singh, Manika Gupta, Rajendra Prasad

“…The backscattering coefficients were taken as the input variables and SM as the output variable for the training and testing of different models. The performance analysis of RMSE, R-squared, and correlation coefficients revealed that the Random Forest (RF) and Convolutional Neural Network (CNN) models demonstrated superior performance for SM estimation over the wheat field. …”
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Visible-infrared person re-identification with region-based augmentation and cross modality attention by Yuwei Guo, Wenhao Zhang, Licheng Jiao, Shuang Wang, Shuo Wang, Fang Liu

“…Specifically, we propose a region-based data augmentation module PedMix to enhance pedestrian region coherence by mixing the corresponding regions from different modalities, thus generating more natural images. …”
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Identification of Low‐Value Defects in Infrared Images of Porcelain Insulators Based on STCE‐YOLO Algorithm by Shaotong Pei, Weiqi Wang, Chenlong Hu, Keyu Li, Haichao Sun, Mianxiao Wu, Bo Lan

“…And the multiple attention mechanism improved to the third generation of variability convolution is used to detect the head to improve the accuracy of the algorithm's target localization. …”
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AI-enhanced patient-specific dosimetry in I-131 planar imaging with a single oblique view by Mostafa Jalilifar, Mahdi Sadeghi, Alireza Emami-Ardekani, Ahmad Bitarafan-Rajabi, Kouhyar Geravand, Parham Geramifar

“…Notably, MLP-based dose estimations closely matched ground truth data with < 15% differences in most tissues. The MLP-estimated dose values present a robust patient-specific dosimetry approach capable of swiftly predicting absorbed doses in different organs using WB planar images and a single oblique view. …”
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An ensemble-based 3D residual network for the classification of Alzheimer's disease. by Xiaoli Yang, Jiayi Zhou, Chenchen Wang, Xiao Li, Jiawen Wang, Angchao Duan, Nuan Du

“…Early MCI diagnosis is crucial for slowing AD progression, but distinguishing MCI from normal controls (NC) is challenging due to subtle imaging differences. Furthermore, differentiating early MCI (EMCI) from late MCI (LMCI) is also important for interventions. …”
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Evaluation of YOLO Efficiency in Automatic Orange Detection in Multi-Exposure Images by M. R. Oviedo Espinosa, L. R. Porto, V. S. W. Orlando, A. M. G. Tommaselli, A. P. Dal Poz, N. N. Imai

“…In this sense, this paper focus on the detection of oranges in multispectral images, with different spectral bands and exposures, using a convolutional neural network (CNN) known as YOU ONLY LOOK ONCE (YOLO). …”
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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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Ensemble Deep Learning Object Detection Fusion for Cell Tracking, Mitosis, and Lineage by Imad Eddine Toubal, Noor Al-Shakarji, D. D. W. Cornelison, Kannappan Palaniappan

“…Building upon our previous work, we propose a new deep learning-based method, EDNet, for cell detection, tracking, and motility analysis that is more robust to shape across different cell lines, and models cell lineage and proliferation. …”
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Enhancing LoRa-Based Outdoor Localization Accuracy Using Machine Learning by Nur Kelesoglu, Marzena Halama, Anna Strzoda

“…We further propose a Hybrid Model that combines convolutional feature extraction with gradient-boosted regression. …”
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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

“…In our study, we focus on using histopathology slide images and assess the current state of breast cancer classification, particularly with artificial intelligence, specifically deep learning and convolutional neural networks. The histopathology images are key tools in the diagnosis process, allowing pathologists to visually assess tissue samples for signs of cancer. …”
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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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Unsupervised Domain Adaptation via Contrastive Learning and Complementary Region-Class Mixing by Xiaojing Li, Wei Zhou, Mingjian Jiang

“…In semantic segmentation, current deep convolutional neural networks rely heavily on extensive data to achieve superior segmentation results. …”
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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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