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

“…To address this issue, we propose SB‐YOLO‐V8 (Scene‐Based—You Only Look Once—Version 8), an optimized YOLO‐based convolutional neural network (CNN) designed specifically for real‐time human detection, not just limited to citrus farms but applicable to various agricultural and urban environments. …”
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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

“…Central to our approach is the development of a novel deep learning (DL) model called the physics informed-graph convolutional gated recurrent neural network (PI-GRNN). …”
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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

“…Existing frameworks based on convolutional neural networks and snapshot-matrix decomposition often rely on lossy pixelization and data-preprocessing, limiting their effectiveness in realistic engineering 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

“…Complementing this, the SMCE module integrates multi-scale convolutional pathways to reinforce spatial detail extraction at varying receptive fields. …”
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Deep Learning-Based Daily Streamflow Prediction Model for the Hanjiang River Basin by Jianze Huang, Jialang Chen, Haijun Huang, Xitian Cai

“…The proposed model integrates self-attention (SA), a one-dimensional convolutional neural network (1D-CNN), and bidirectional long short-term memory (BiLSTM). …”
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Challenges in AI-driven multi-omics data analysis for Oncology: Addressing dimensionality, sparsity, transparency and ethical considerations by Maryem Ouhmouk, Shakuntala Baichoo, Mounia Abik

“…By effectively combining different types of omics data, AI techniques can unveil hidden connections, detect biomarkers, and improve disease prediction through the integration of multi-omics layers and modalities, which can lead to significant advancements in precision medicine. …”
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Hybrid transformer-CNN and LSTM model for lung disease segmentation and classification by Syed Mohammed Shafi, Sathiya Kumar Chinnappan

“…Consequently, an improved Transformer-based convolutional neural network (CNN) model (ITCNN) is proposed to segment the affected region in the segmentation process. …”
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Temporal Dynamics in Short Text Classification: Enhancing Semantic Understanding Through Time-Aware Model by Khaled Abdalgader, Atheer A. Matroud, Ghaleb Al-Doboni

“…The model employs a hybrid architecture combining Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (BiLSTM) networks, enriched with attention mechanisms to capture both local and global dependencies. …”
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