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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A joint data and knowledge‐driven method for power system disturbance localisation by Zikang Li, Jiyang Tian, Hao Liu

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

“…However, due to the intricate interplay of market supply-demand dynamics, geopolitical events, and economic policies, traditional econometric and statistical models fall short in capturing the temporal dependencies and complex patterns in uranium price data. …”

User preference modeling for movie recommendations based on deep learning by Yang Gao, Hong Zheng, Haonan Cui

“…Abstract Current movie recommendation systems often struggle to capture complex user preferences and dynamics, primarily relying on content-based or collaborative filtering techniques. …”
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A simple yet effective approach for predicting disease spread using mathematically-inspired diffusion-informed neural networks by ByeongChang Jeong, Yeon Ju Lee, Cheol E. Han

“…Abstract The COVID-19 outbreak has highlighted the importance of mathematical epidemic models like the Susceptible-Infected-Recovered (SIR) model, for understanding disease spread dynamics. However, enhancing their predictive accuracy complicates parameter estimation. …”
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Speech Emotion Recognition Using Multi-Scale Global–Local Representation Learning with Feature Pyramid Network by Yuhua Wang, Jianxing Huang, Zhengdao Zhao, Haiyan Lan, Xinjia Zhang

“…In speech sequence modeling, a vital challenge is to learn context-aware sentence expression and temporal dynamics of paralinguistic features to achieve unambiguous emotional semantic understanding. …”
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On the Synergy of Optimizers and Activation Functions: A CNN Benchmarking Study by Khuraman Aziz Sayın, Necla Kırcalı Gürsoy, Türkay Yolcu, Arif Gürsoy

“…In this study, we present a comparative analysis of gradient descent-based optimizers frequently used in Convolutional Neural Networks (CNNs), including SGD, mSGD, RMSprop, Adadelta, Nadam, Adamax, Adam, and the recent EVE optimizer. …”
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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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Machine Learning Approach for Estimating Magnetic Field Strength in Galaxy Clusters from Synchrotron Emission by Jiyao Zhang, Yue Hu, Alex Lazarian

“…To address the challenge, we propose a novel method that employs Convolutional Neural Networks (CNNs) alongside synchrotron emission observations to estimate magnetic field strengths in galaxy clusters. …”
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Joint Three-Task Optical Performance Monitoring with High Performance and Superior Generalizability Using a Meta-Learning-Based Convolutional Neural Network-Attention Algorithm and... by Di Zhang, Junyao Shi, Yameng Cao, Yan Ling Xue

“…Nonlinear noise power (NLNP) estimation, optical signal-to-noise ratio (OSNR) monitoring, and modulation format identification (MFI) are crucial for optical performance monitoring (OPM) in future dynamic WDM optical networks. This paper proposes an OPM scheme to simultaneously implement these three tasks in both single-channel and WDM systems by combining amplitude-differential phase histograms (ADPH) with the MAML-CNN-ATT algorithm that integrates model-agnostic meta-learning (MAML), the convolutional neural network (CNN), and the attention mechanism (ATT). …”
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A Crowd Density Detection Algorithm for Tourist Attractions Based on Monitoring Video Dynamic Information Analysis by Lina Li

“…In this paper, we analyze and calculate the crowd density in a tourist area utilizing video surveillance dynamic information analysis and divide the crowd counting and density estimation task into three stages. …”
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Innovative Lightweight Detection for Airborne Remote Sensing: Integrating G-Shuffle and Dynamic Multiscale Pyramid Networks by Ruofei Liang, Yigang Cen, Linna Zhang, Fugui Zhang, Yansen Huang, Fei Gan

“…Second, the G-Shuffle module is designed to significantly enhance feature extraction efficiency and interchannel information interaction, balancing computational complexity and detection accuracy. Lastly, a dynamic multiscale pyramid network is introduced, employing pyramid convolution to effectively extract and fuse multiscale features. …”
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Dynamic focusing of an ultrasonic be am by means of a phased annular array using a pulse technique by Tamara KUJAWSKA

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Advanced Hyena Hierarchy Architectures for Predictive Modeling of Interest Rate Dynamics from Central Bank Communications by Tao Song, Shijie Yuan, Rui Zhong

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DSAT: a dynamic sparse attention transformer for steel surface defect detection with hierarchical feature fusion by Shouluan Wu, Hui Yang, Liefa Liao, Chao Song, Yating Fang, Jianglong Fu, Tan Li

“…To address these limitations, we propose the Dynamic Sparse Attention Transformer (DSAT), a novel architecture that integrates two key innovations: (1) a Dynamic Sparse Attention (DSA) mechanism, which adaptively focuses on defect-salient regions while minimizing computational overhead; (2) an enhanced SPPF-GhostConv module, which combines Spatial Pyramid Pooling Fast with Ghost Convolution to achieve efficient hierarchical feature fusion. …”
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DMSF-YOLO: Cow Behavior Recognition Algorithm Based on Dynamic Mechanism and Multi-Scale Feature Fusion by Changfeng Wu, Jiandong Fang, Xiuling Wang, Yudong Zhao

“…For the problem in multi-scale behavior changes of dairy cows, a multi-scale convolution module (MSFConv) is designed, and some C3k2 modules of the backbone network and neck network are replaced with MSFConv, which can extract cow behavior information of different scales and perform multi-scale feature fusion. …”
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BAM-SLDK: biologically inspired attention mechanism with spiking learnable delayed kernel synapses by Mario Chacón-Falcón, Alberto Patiño-Saucedo, Luis Camuñas-Mesa, Teresa Serrano-Gotarredona, Bernabé Linares-Barranco

“…The proposed model increases temporal learning ability, attending simultaneously to spatial and temporal dynamics with few parameters required. More precisely, our main technical contributions are: (1) we add kernels to the temporal dimension to enlarge the receptive field of the convolution; (2) we time kernels activations to mimic multiple delayed times; and (3) we introduce three different pruning techniques to optimize the number of delays and parameters used. …”
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The Algorithm for Preparing a Set of Data for Teaching Neural Networks on the Example of the Task to Analyze the Radiological Images of Lungs by A. A. Kosareva

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Optimization of TCN-BiLSTM for dissolved oxygen prediction based on improved sparrow search algorithm by Pei Shi, Mingjie Tang, Quan Wang, Xiaofei Ma

“…In addition, the traditional Temporal Convolutional Network (TCN) often fails to capture the dynamic fluctuations present in DO data, resulting in suboptimal prediction performance. …”
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Harnessing conversion bridge strategy by organic semiconductor in polymer matrix memristors for high‐performance multi‐modal neuromorphic signal processing by Weijia Dong, Xuan Ji, Chuanbin An, Chenhui Xu, Xuwen Zhang, Bin Zhao, Yuqian Liu, Shiyu Wang, Xi Yu, Xinjun Liu, Yang Han, Yanhou Geng

“…This advancement enables multi‐modal signal processing at distinct operational mechanisms—non‐volatile mode for image recognition in convolutional neural networks (CNNs) and volatile mode for dynamic classification and prediction in reservoir computing (RC). …”
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