Analysis of Deep Learning Techniques for Vehicle Detection and Reidentification Using Data from Multiple Drones and Public Datasets by FELIPE P.A. EUPHRÁSIO, RAFAEL M. DE ANDRADE, ELCIO H. SHIGUEMORI, LIANGRID L. SILVA, MOISÉS JOSÉ S. FREITAS, NATHAN AUGUSTO Z. XAVIER, ARGEMIRO S.S. SOBRINHO

“…Abstract The detection and re-identification of vehicles in dynamic environments, such as highways monitored by a swarm of drones, presents significant challenges, particularly due to the variability of images captured from different angles and under various conditions. …”
Get full text

ResWLI: a new method to retrieve water levels in coastal zones by integrating optical remote sensing and deep learning by Nan Xu, Huichao Xin, Jiarui Wu, Jiaqi Yao, He Ren, Han-Su Zhang, Hao Xu, Hong Luan, Dong Xu, Yongze Song

“…However, due to the high variability of tides and atmospheric forcings, acquiring precise water level data remains a large challenge. …”
Get full text

Explainable brain age prediction: a comparative evaluation of morphometric and deep learning pipelines by Maria Luigia Natalia De Bonis, Giuseppe Fasano, Angela Lombardi, Carmelo Ardito, Antonio Ferrara, Eugenio Di Sciascio, Tommaso Di Noia

“…SHAP provided the most consistent and interpretable results, while DeepSHAP exhibited greater variability. Further work is needed to assess the clinical utility of Grad-CAM. …”
Get full text

Deep Learning Models for Detection and Severity Assessment of Cercospora Leaf Spot (<i>Cercospora capsici</i>) in Chili Peppers Under Natural Conditions by Douglas Vieira Leite, Alisson Vasconcelos de Brito, Gregorio Guirada Faccioli, Gustavo Haddad Souza Vieira

Get full text

Deep fusion approach: Combining hyperspectral imaging and ground penetrating radar for accurate cornfield soil moisture mapping by Milad Vahidi, Sanaz Shafian, William Hunter Frame

“…To analyze the spectral signals from the canopy and the amplitude signals from the GPR, two separate one-dimensional convolutional neural network (1D-CNN) networks were developed. …”
Get full text

Abnormal traffic detection method based on LSTM and improved residual neural network optimization by Wengang MA, Yadong ZHANG, Jin GUO

Get full text

Bayesian optimization of hybrid quantum LSTM in a mixed model for precipitation forecasting by Yumin Dong, Huanxin Ding

“…However, the factors affecting precipitation are complex and nonlinear, and have spatiotemporal variability, making rainfall forecasting extremely challenging. …”
Get full text

Spectra of algebras of block-symmetric analytic functions of bounded type by A. Zagorodnyuk, V. V. Kravtsiv

Get full text

Hybrid Multi-Granularity Approach for Few-Shot Image Retrieval with Weak Features by Aiguo Lu, Zican Li, Yanwei Liu, Pandi Liu, Ke Wang

“…The Omni-Dimensional Dynamic Convolution module and Bi-Level Routing Attention mechanism are introduced to enhance the model’s adaptability to complex scenes and variable features, thereby improving its capability to capture details of small targets. …”
Get full text

CNN-based state prediction for a varying number of storage in economic dispatch by Xiang Pan, Wei Lin, Linze Yang, Yanfang Mo

“…However, the large-scale energy storage (ES) integration introduces numerous binary state variables into ED formulations. Although relaxation-based methods and machine learning techniques have been developed to alleviate the computational burden from ES binary variables, the former is restricted due to critical application conditions that may not hold in practice, and the latter cannot deal with a varying number of ES in the real-world deregulation of electricity markets. …”
Get full text

Spatiotemporal Multivariate Weather Prediction Network Based on CNN-Transformer by Ruowu Wu, Yandan Liang, Lianlei Lin, Zongwei Zhang

“…Changes in weather involve both strongly correlated spatial and temporal continuation relationships, and at the same time, the variables interact with each other, so capturing the dynamic correlations among space, time, and variables is particularly important for accurate weather prediction. …”
Get full text

Zebrafish identification with deep CNN and ViT architectures using a rolling training window by Jason Puchalla, Aaron Serianni, Bo Deng

“…Abstract Zebrafish are widely used in vertebrate studies, yet minimally invasive individual tracking and identification in the lab setting remain challenging due to complex and time-variable conditions. Advancements in machine learning, particularly neural networks, offer new possibilities for developing simple and robust identification protocols that adapt to changing conditions. …”
Get full text

UCSwin‐UNet model for medical image segmentation based on cardiac haemangioma by Jian‐Ting Shi, Gui‐Xu Qu, Zhi‐Jun Li

“…Abstract Cardiac hemangioma is a rare benign tumour that presents diagnostic challenges due to its variable clinical symptoms, imaging features, and locations. …”
Get full text

Attention-Guided Sample-Based Feature Enhancement Network for Crowded Pedestrian Detection Using Vision Sensors by Shuyuan Tang, Yiqing Zhou, Jintao Li, Chang Liu, Jinglin Shi

“…This challenge includes both inter-class occlusion caused by environmental objects obscuring pedestrians, and intra-class occlusion resulting from interactions between pedestrians. In complex and variable urban settings, these compounded occlusion patterns critically limit the efficacy of both one-stage and two-stage pedestrian detectors, leading to suboptimal detection performance. …”
Get full text

A metaheuristic optimization-based approach for accurate prediction and classification of knee osteoarthritis by Amal G. Diab, El-Sayed M. El-Kenawy, Nihal F. F. Areed, Hanan M. Amer, Mervat El-Seddek

“…The prevailing method for knee joint analysis involves manual diagnosis, segmentation, and annotation to diagnose osteoarthritis (OA) in clinical practice while being highly laborious and a susceptible variable among users. To address the constraints of this method, several deep learning techniques, particularly the deep convolutional neural networks (CNNs), were applied to increase the efficiency of the proposed workflow. …”
Get full text

Deep learning modeling of manufacturing and build variations on multistage axial compressors aerodynamics by Giuseppe Bruni, Sepehr Maleki, Senthil K. Krishnababu

Get full text

DSCnet: detection of drug and alcohol addiction mechanisms based on multi-angle feature learning from the hybrid representation of EEG by Jing Wu, Nan Zhang, Qilei Ye, Xiaorui Zheng, Minmin Shao, Xian Chen, Hui Huang

“…DSCnet combines embedding layers, skip connections, depthwise separable convolution, and our self-designed Directional Adaptive Feature Modulation (DAFM) module. …”
Get full text

Fidex and FidexGlo: From Local Explanations to Global Explanations of Deep Models by Guido Bologna, Jean-Marc Boutay, Damian Boquete, Quentin Leblanc, Deniz Köprülü, Ludovic Pfeiffer

“…In our framework, the discriminative boundaries are parallel to the input variables and their location is precisely determined. …”
Get full text

International Natural Uranium Price Prediction Based on TF-CNN-BiLSTM Model by YANG Jingzhe, XUE Xiaogang

“…To address these challenges, this study proposed a novel TF-CNN-BiLSTM model, which synergistically combines the self-attention mechanism of Transformer, the local feature extraction capability of convolutional neural network (CNN), and the bidirectional temporal dependency modeling of bidirectional long short-term memory (BiLSTM). …”

PGHDR: Dynamic HDR reconstruction with progressive feature alignment and quality-guided fusion by Ying Qi, Qiushi Li, Zhaoyuan Huang, Jian Li, Chenyang Wang, Teng Wan, Qiang Zhang

“…Existing methods typically adopt an align-then-fuse strategy, often overlooking the spatial variability of alignment quality, which makes it difficult to balance ghosting suppression and detail preservation when handling complex motion. …”
Get full text