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. …”
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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. …”
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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. …”
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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

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Restricted Boltzmann machine with Sobel filter dense adversarial noise secured layer framework for flower species recognition by Shyamala Devi M, Sundaravadivazhagan Balasubaramanian, Balasubramaniam S, Mohd Asif Shah

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

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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

“…Electroencephalography (EEG) is commonly used to analyze addiction mechanisms, but traditional feature extraction methods such as time-frequency analysis, Principal Component Analysis (PCA), and Independent Component Analysis (ICA) fail to capture complex relationships between variables.MethodsThis paper proposes DSCnet, a novel neural network model for addiction detection. …”
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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. …”
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International Natural Uranium Price Prediction Based on TF-CNN-BiLSTM Model by YANG Jingzhe, XUE Xiaogang

“…However, the model’s performance could be further enhanced by incorporating additional relevant features such as geopolitical indicators, economic indices, and policy variables. Future research should focus on expanding the model’s input space and refining its architecture to improve accuracy, especially in periods of market turbulence. …”

Research on foreign object intrusion detection in railway tracks based on MSL-YOLO by Hongxia Niu, Dingchao Feng, Tao Hou

“…Abstract Railway foreign object intrusion detection poses significant challenges due to complex backgrounds, variable lighting conditions, and the need for real-time, multi-scale object detection. …”
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Spatiotemporal Patterns of Intermittent Snow Cover From PlanetScope Imagery Using Deep Learning by Zhaocheng Wang, Jaya Venkatesh Jaya Baskar, Maneesh Sarma Sistla Naga Sai, Bohumil Svoma, Enrique R. Vivoni

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Ultrasound-based machine learning model to predict the risk of endometrial cancer among postmenopausal women by Yi-Xin Li, Yu Lu, Zhe-Ming Song, Yu-Ting Shen, Wen Lu, Min Ren

“…Abstract Background Current ultrasound-based screening for endometrial cancer (EC) primarily relies on endometrial thickness (ET) and morphological evaluation, which suffer from low specificity and high interobserver variability. This study aimed to develop and validate an artificial intelligence (AI)-driven diagnostic model to improve diagnostic accuracy and reduce variability. …”
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Deep Learning-Based Automatic Diagnosis System for Developmental Dysplasia of the Hip by Yang Li, Leo Yan Li-Han, Hua Tian

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Future variation and uncertainty source decomposition in deep learning bias-corrected CMIP6 global extreme precipitation historical simulation by Xiaohua Xiang, Yongxuan Li, Xiaoling Wu, Zhu Liu, Lei Wu, Biqiong Wu, Chuanxin Jin, Zhiqiang Zeng

“…In addition, this study endeavors to separate and quantify three different components of uncertainty (model uncertainty, scenario uncertainty, and internal variability) associated with ETCCDI extreme precipitation indices and evaluate the impact of bias correction on uncertainty variation. …”
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