Long-term forecasting of shield tunnel position and attitude deviation using the 1DCNN-informer method by Jiajie Zhen, Ming Huang, Shuang Li, Kai Xu, Qianghu Zhao

“…Furthermore, the 1DCNN-Informer model was transferred to datasets from both similar and different geological conditions using the domain adversarial neural network (DANN) transfer learning method. …”
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Biological features and medical significance of the <i>Listeria</i> bacteria by I. A. Derevyanchenko, Lyudmila A. Kraeva

“…This review discusses current views on Listeria spp. prevalence and biological qualities, virulence and pathogenicity factors of L. monocytogenes, as well as methods for identifying different Listeria species.…”
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AI-Based Solar Panel Detection and Monitoring Using High-Resolution Drone Imagery by Raheleh Parsaeifar, Mojtaba Valinejadshoubi, Anthony Le Guen, Fernando Valdivieso

“…The model achieved, on average, an accuracy of 96.25% with an accuracy of detected solar panels in four consecutive images acquired on four different dates as follows: 0.97, 0.95, 0.97, and 0.96.". …”
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ZZ-YOLOv11: A Lightweight Vehicle Detection Model Based on Improved YOLOv11 by Zhe Zhang, Zhongyang Zhang, Gang Li, Chenxi Xia

“…For this reason, in this paper, we self-develop a module of GlobalEdgeInformationTransfer (GEIT), which can help us to transfer the edge information extracted from the shallow features to the whole network and fuse it with the features of different scales. Secondly, to reduce the number of parameters in the detection head and to fuse the extracted features better, a self-developed Lightweight Detail Convolutional Detection Head (LDCD) detection head is introduced. …”
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Improved Field Obstacle Detection Algorithm Based on YOLOv8 by Xinying Zhou, Wenming Chen, Xinhua Wei

“…This detection model was built upon the YOLOv8 architecture with three main improvements. First, to adapt to different tasks and complex environments in the field, improve the sensitivity of the detector to various target sizes and positions, and enhance detection accuracy, the CBAM (Convolutional Block Attention Module) was integrated into the backbone layer of the benchmark model. …”
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SAR Target Depression Angle Invariant Recognition of Few-Shot Learning Via Dense Graph Prototype Network by Xiangyu Zhou, Yuhui Zhang, Qianru Wei

“…Specifically, by leveraging the information propagation mechanism of a densely connected graph convolutional network (GCN), potential features are iteratively learned while retaining previous features, thereby clustering samples of the same class with different elevation angles and eliminating feature deviations. …”
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Adapter With Textual Knowledge Graph for Zero-Shot Sketch-Based Image Retrieval by Jie Zhang, Jiali Tang

“…Subsequently, a graph convolutional network (GCN) is used to mine the structural knowledge between nodes, further effectively learning relationships among different categories. …”
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SB‐YOLO‐V8: A Multilayered Deep Learning Approach for Real‐Time Human Detection by Prince Alvin Kwabena Ansah, Justice Kwame Appati, Ebenezer Owusu, Edward Kwadwo Boahen, Prince Boakye‐Sekyerehene, Abdullai Dwumfour

“…The versatility of SB‐YOLO‐V8 is underpinned by its robust handling of class imbalances and enhanced feature extraction through binary adaptive learning optimization (ALO) and Synthetic Minority Over‐sampling Technique (SMOTE), addressing common challenges like limited labeled data sets and varied object dynamics in different scenarios. The proposed method is trained using images and videos of human workers captured by autonomous farm equipment. …”
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Advances in the Automated Identification of Individual Tree Species: A Systematic Review of Drone- and AI-Based Methods in Forest Environments by Ricardo Abreu-Dias, Juan M. Santos-Gago, Fernando Martín-Rodríguez, Luis M. Álvarez-Sabucedo

“…Additionally, we discuss the challenges of model generalization across different forest ecosystems and propose future research directions, including the development of standardized datasets and improved model architectures for robust tree species classification. …”
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Physics-informed deep learning with Kalman filter mixture for traffic state prediction by Niharika Deshpande, Hyoshin (John) Park

“…However, conventional approaches often neglect epistemic uncertainty, which arises from incomplete knowledge across different spatiotemporal scales. This study addresses this challenge by introducing a novel methodology to establish dynamic spatiotemporal correlations that captures the unobserved heterogeneity in travel time through distinct peaks in probability density functions, guided by physics-based principles. …”
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Enhancing the Performance of YOLOv9t Through a Knowledge Distillation Approach for Real-Time Detection of Bloomed Damask Roses in the Field by Farhad Fatehi, Hossein Bagherpour, Jafar Amiri Parian

“…To facilitate this learning, two different techniques including online distillation (OD) and offline distillation (OFD) were explored. …”
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Discretization-independent surrogate modeling of physical fields around variable geometries using coordinate-based networks by James Duvall, Karthik Duraisamy

“…Both methods exhibit promising potential as viable options for surrogate modeling, being able to process snapshots of data that correspond to different mesh topologies.…”
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Enhanced YOLOv8 for Robust Pig Detection and Counting in Complex Agricultural Environments by Jian Li, Wenkai Ma, Yanan Wei, Tan Wang

“…These improvements include DCNv4 deformable convolutions for irregular pig postures, BiFPN bidirectional feature fusion for multi-scale information integration, EfficientViT linear attention for computational efficiency, and PIoU v2 loss for improved overlap handling. (2) A density-aware post-processing module with intelligent NMS strategies that adapt to different crowding scenarios. …”
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An Autism Spectrum Disorder Identification Method Based on 3D-CNN and Segmented Temporal Decision Network by Zhiling Liu, Ye Chen, Xinrui Dong, Jing Liu

“…This study aims to improve the ability to capture spatiotemporal dynamics of brain activity by proposing an advanced framework. (2) Methods: This study proposes an ASD recognition method that combines 3D Convolutional Neural Networks (3D-CNNs) and segmented temporal decision networks. …”
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Smart grid stability prediction model using two-way attention based hybrid deep learning and MPSO by Umesh Kumar Lilhore, Surjeet Dalal, Magdalena Radulescu, Marinela Barbulescu

“…As far as we know, it is the first work to suggest a dynamic short-term load prediction model that considers different significant features and enables precise predicting outcomes. …”
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An enhanced BERT model with improved local feature extraction and long-range dependency capture in promoter prediction for hearing loss by Jing Sun, Yangfan Huang, Jiale Fu, Li Teng, Xiao Liu, Xiaohua Luo

“…To address this challenge, we propose DNABERT-CBL (DNABERT-2_CNN_BiLSTM), an enhanced BERT-based architecture that fuses a convolutional neural network (CNN) and a bidirectional long and short-term memory (BiLSTM) layer. …”
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Artificial intelligence for children with attention deficit/hyperactivity disorder: a scoping review by Bo Sun, Bo Sun, Fei Cai, Huiman Huang, Bo Li, Bing Wei

“…The included articles reported the use of artificial intelligence for 3 different purposes. Of these included articles, artificial intelligence techniques were mostly used for the diagnosis of attention deficit/hyperactivity disorder (38/52, 79%). …”
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Ensemble Machine Learning, Deep Learning, and Time Series Forecasting: Improving Prediction Accuracy for Hourly Concentrations of Ambient Air Pollutants by Valentino Petrić, Hussain Hussain, Kristina Časni, Milana Vuckovic, Andreas Schopper, Željka Ujević Andrijić, Simonas Kecorius, Leizel Madueno, Roman Kern, Mario Lovrić

“…A diverse set of techniques was implemented to tackle this challenge, encompassing the utilisation of the prophet, random forest, and three different deep learning architectures: long short-term memory networks, convolutional neural networks, and multilayer perceptrons. …”
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Hierarchical Mixed-Precision Post-Training Quantization for SAR Ship Detection Networks by Hang Wei, Zulin Wang, Yuanhan Ni

“…Convolutional neural network (CNN)-based synthetic aperture radar (SAR) ship detection models operating directly on satellites can reduce transmission latency and improve real-time surveillance capabilities. …”
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CDFAN: Cross-Domain Fusion Attention Network for Pansharpening by Jinting Ding, Honghui Xu, Shengjun Zhou

“…Additionally, an Expert Feature Compensator is introduced to adaptively balance contributions from different scales, thereby optimizing the trade-off between local detail preservation and global contextual understanding. …”
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