INVESTIGATION OF THE CAPABILITIES OF ARTIFICIAL NEURAL NETWORKS WHEN CLASSIFYING OBJECTS DYNAMIC FEATURES by Nikita V. Laptev, Olga M. Gerget, Vladislav V. Laptev, Dmitriy Yu. Kolpashchikov

“…Unlike classical convolutional neural networks, the proposed network uses information about the sequence of images, thereby providing a higher classification accuracy of detected objects with dynamic features. …”
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Fish Body Pattern Style Transfer Based on Wavelet Transformation and Gated Attention by Hongchun Yuan, Yixuan Wang

“…This network innovatively integrates dynamic texture transfer with instance segmentation, adopting a two-stage processing architecture. …”
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Gesture-controlled reconfigurable metasurface system based on surface electromyography for real-time electromagnetic wave manipulation by Chen Junzai, Li Weiran, Gong Kailuo, Lu Xiaojie, Tong Mei Song, Wang Xiaoyi, Yang Guo-Min

“…By recognizing the sEMG signals of user gestures through a pre-trained convolutional neural network (CNN) model, the system dynamically modulates the metasurface, enabling precise control of the deflection direction and polarization state of electromagnetic waves. …”
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Environmental Sensitivity in AI Tree Bark Detection: Identifying Key Factors for Improving Classification Accuracy by Charles Warner, Fanyou Wu, Rado Gazo, Bedrich Benes, Songlin Fei

“…Enhanced AI tools will streamline forest inventories, support biodiversity monitoring, and bolster conservation in dynamic forest ecosystems.…”
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Performance of Fine-Tuning Techniques for Multilabel Classification of Surface Defects in Reinforced Concrete Bridges by Benyamin Pooraskarparast, Son N. Dang, Vikram Pakrashi, José C. Matos

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The medial prefrontal cortex encodes procedural rules as sequential neuronal activity dynamics by Shuntaro Ohno, Masanori Nomoto, Kaoru Inokuchi

“…In mice that had mastered the rule, the dynamics of neuronal sequences could predict success and failure of reward acquisition. …”
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Leveraging potential of limpid attention transformer with dynamic tokenization for hyperspectral image classification. by Dhirendra Prasad Yadav, Deepak Kumar, Anand Singh Jalal, Bhisham Sharma, Panos Liatsis

“…Due to this, it has less spatial and high spectral information. Convolutional neural networks (CNNs) emerge as a highly contextual information model for remote sensing applications. …”
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The Role of Time in Facial Dynamics and Challenges in Automatic Emotion Recognition (2019–2024) by Williams Contreras-Higuera, Lucrezia Crescenzi-Lanna

“…Based on a comprehensive literature review, this study highlights the critical role of the temporal dimension of facial dynamics in understanding facial expressions and improving the accuracy and robustness of automatic emotion recognition systems (machine-FER). …”
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Dynamic Cascade Detector for Storage Tanks and Ships in Optical Remote Sensing Images by Tong Wang, Bingxin Liu, Peng Chen

“…Regional Convolutional Neural Network (RCNN)−based detectors have played a crucial role in object detection in remote sensing images due to their exceptional detection capabilities. …”
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Machine learning-assisted decoding of temporal transcriptional dynamics via fluorescent timer by Nobuko Irie, Naoki Takeda, Yorifumi Satou, Kimi Araki, Masahiro Ono

“…Here, we introduce an integrative approach combining molecular biology and machine learning to elucidate Foxp3 transcriptional dynamics through flow cytometric Timer analysis. We have developed a convolutional neural network-based method that incorporates image conversion and class-specific feature visualisation for class-specific feature identification at the single-cell level. …”
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A Dynamic Branch Automatic Modulation Recognition Method for Heterogeneous Data-Driven by Yecai Guo, Mengjie Wang, Meiyu Liang

“…Specifically, data preprocessing intensifies phase information in original I/Q data, while a sparse multi-scale convolutional module strengthens spatial feature extraction. …”
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Deep Temporal and Structural Embeddings for Robust Unsupervised Anomaly Detection in Dynamic Graphs by Samir Abdaljalil, Hasan Kurban, Rachad Atat, Erchin Serpedin, Khalid Qaraqe

“…We introduce Temporal Structural Graph Anomaly Detection (<sc>T-StructGAD</sc>), an unsupervised framework that leverages Graph Convolutional Gated Recurrent Units (<monospace>GConvGRU</monospace>s) and Long Short-Term Memory networks (<monospace>LSTM</monospace>s) to jointly model both structural and temporal dynamics in graph node embeddings. …”
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Deep learning architectures for influenza dynamics and treatment optimization: a comprehensive review by Adane Adugna, Desalegn Abebaw, Abtie Abebaw, Mohammed Jemal

“…As a major worldwide health concern, influenza still requires precise modeling of flu dynamics and efficient treatment approaches. Deep learning architectures are increasingly being applied to address the complexities of influenza dynamics and treatment optimization, which remain critical global health challenges. …”
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Leveraging Deep Learning and Internet of Things for Dynamic Construction Site Risk Management by Li-Wei Lung, Yu-Ren Wang, Yung-Sung Chen

“…Robust feature extraction is performed using convolutional neural networks (CNNs) and a fully connected approach for neural network training. …”
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Multiscale Graph Transformer Network With Dynamic Superpixel Pyramid for Hyperspectral Image Classification by Tingting Wang, Yao Sun, Yunfeng Hu

“…To address these limitations, we propose a multi-scale graph transformer network (MSGTN), which captures spatial features at different scales through multiscale graph convolutional networks (GCNs) with adaptive graph structures. …”
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EfficientTransformer: A Dynamic Anomaly Detection Model for Industrial Control Networks by Jinyang Liu, Guogang Wang, Xuejun Zong, Bowei Ning, Kan He

“…In this study, we propose a dynamic anomaly detection model for industrial control networks, named EfficientTransformer. …”
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Fine-Grained Aircraft Recognition Based on Dynamic Feature Synthesis and Contrastive Learning by Huiyao Wan, Pazlat Nurmamat, Jie Chen, Yice Cao, Shuai Wang, Yan Zhang, Zhixiang Huang

“…Second, to tackle the long-tail distribution problem, we design a dynamic feature hallucination module that synthesizes diverse hallucinated samples, thereby improving the feature diversity of tail categories. …”
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An Adaptive Spatio-Temporal Traffic Flow Prediction Using Self-Attention and Multi-Graph Networks by Basma Alsehaimi, Ohoud Alzamzami, Nahed Alowidi, Manar Ali

“…Capturing complex and dynamic spatio-temporal patterns within traffic data remains a significant challenge for traffic flow prediction. …”
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PCPE-YOLO with a lightweight dynamically reconfigurable backbone for small object detection by Weijia Chen, Jiaming Liu, Tong Liu, Yaoming Zhuang

“…Finally, we integrate an Efficient Up-Convolution Block to sharpen decoder feature maps, enhancing small object recall with minimal computational overhead. …”
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AGCN-T: A Traffic Flow Prediction Model for Spatial-Temporal Network Dynamics by Jian Feng, Lang Yu, Rui Ma

“…Traffic prediction is the key for Intelligent Transport Systems (ITS) to achieve traffic control and traffic guidance, and the key challenge is that traffic flow has complex spatial-temporal dependence and nonlinear dynamics. Aiming at the lack of the ability to model complex and dynamic spatial-temporal dependencies in current research, this paper proposes a traffic flow prediction model Attention based Graph Convolution Network (GCN) and Transformer (AGCN-T) to model spatial-temporal network dynamics of traffic flow, which can extract dynamic spatial dependence and long-distance temporal dependence to improve the accuracy of multistep traffic prediction. …”
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