Dynamical System Modeling for Disruption in Supply Chain and Its Detection Using a Data-Driven Deep Learning-Based Architecture by Víctor Hugo de la Cruz Madrigal, Liliana Avelar Sosa, Jose-Manuel Mejía-Muñoz, Jorge Luis García Alcaraz, Emilio Jiménez Macías

“…In addition to modeling using system dynamics, a deep-learning-based network was proposed to detect disruptions using the data generated by the dynamic model. …”
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Estimating ocean currents from the joint reconstruction of absolute dynamic topography and sea surface temperature through deep learning algorithms by D. Ciani, C. Fanelli, B. Buongiorno Nardelli

“…<p>Our study focuses on absolute dynamic topography (ADT) and sea surface temperature (SST) mapping from satellite observations, with the primary objective of improving the satellite-derived ADT (and derived geostrophic currents) spatial resolution. …”
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Leveraging physics-informed neural networks for efficient modelling of coastal ecosystems dynamics: A case study of Sundarbans mangrove forest by Majdi Fanous, Jonathan M. Eden, Juntao Yang, Simon See, Vasile Palade, Alireza Daneshkhah

“…Modelling the hydro-morphodynamics of mangrove environments poses significant challenges due to their complex geometry, dynamic vegetation-water interactions, and limited in situ measurements. …”
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Resilience Analysis of Urban Road Networks Based on Adaptive Signal Controls: Day-to-Day Traffic Dynamics with Deep Reinforcement Learning by Wen-Long Shang, Yanyan Chen, Xingang Li, Washington Y. Ochieng

“…This study proposes a novel adaptive signal control strategy based on a doubly dynamic learning framework, which consists of deep reinforcement learning and day-to-day traffic dynamic learning, to improve the network performance by adjusting red/green time split. …”
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Dynamic atrous attention and dual branch context fusion for cross scale Building segmentation in high resolution remote sensing imagery by Yaohui Liu, Shuzhe Zhang, Xinkai Wang, Rui Zhai, Hu Jiang, Lingjia Kong

“…Furthermore, we fused triplet attention with depth-wise separable convolutions, reducing computational requirements and mitigating potential overfitting scenarios. …”
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imageseg: An R package for deep learning‐based image segmentation by Jürgen Niedballa, Jan Axtner, Timm Fabian Döbert, Andrew Tilker, An Nguyen, Seth T. Wong, Christian Fiderer, Marco Heurich, Andreas Wilting

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Forecasting Groundwater Level by Characterizing Multiple Spatial Dependencies of Environmental Factors Using Graph‐Based Deep Learning by Yinghan Wu, Gang Mei, Kaixuan Shao, Nengxiong Xu, Jianbing Peng

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PAMFPN: Position-Aware Multi-Kernel Feature Pyramid Network with Adaptive Sparse Attention for Robust Object Detection in Remote Sensing Imagery by Xiaofei Yang, Suihua Xue, Lin Li, Sihuan Li, Yudong Fang, Xiaofeng Zhang, Xiaohui Huang

“…RSA employs orthogonal large-kernel convolutions to capture anisotropic spatial features for arbitrarily oriented targets, while RCA dynamically adjusts the kernel scales based on content complexity, effectively addressing scale variations and intraclass diversity. …”
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Noise Reduction of Depth Cameras Images Based on Deep Neural Network by Alireza Ghasemi, Mohsen Ashourian

“…In this paper, the noise reduction of depth images using convolutional neural networks is considered. A convolutional neural network with a depth of 20 and three layers and a pre-trained neural network is used. …”
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FDM-RTDETR: A Multi-Scale Small Target Detection Algorithm by Hongya Wang, Yongtao Yu, Zhaoxia Tang

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Decoding laying hen behavior and physiological status through acoustic biomarkers: temporal patterns, rooster-hen vocalization identification in group housing and environmental ada... by Xuanting Lin, Wanjun Zhu, Longshen Liu, Zhenlei Zhou

“…In this study, continuous audio data were collected from commercial chicken houses over a period of 15 days, in addition to temperature and humidity index (THI) analysis, to develop a convolutional neural network (CNN)-based model for classifying chicken squawks. …”
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Analyzing the dynamics between crude oil spot prices and futures prices by maturity terms: Deep learning approaches to futures-based forecasting by Jeonghoe Lee, Bingjiang Xia

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Models and methods for analyzing complex networks and social network structures by Ju. P. Perova, V. P. Grigoriev, D. O. Zhukov

“…Dynamic models and stochastic dynamics analysis methods, which take into account the possibility of self-organization and the presence of memory, are used along with user deanonymization methods and recommendation systems, as well as statistical methods for analyzing profiles in social networks. …”
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Crowd anomaly estimation and detection: A review by A. Hussein, M.W. Raed, A. Al-Shaikhi, M. Mohandes, B. Liu

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Hare escape optimization algorithm with applications in engineering and deep learning by Doaa Alsamee, Reza Ramezani

“…Additionally, HEO was employed to optimize hyperparameters in convolutional neural networks (CNNs) for image classification tasks, significantly enhancing model accuracy and convergence speed. …”
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A Multiscale CNN-Based Intrinsic Permeability Prediction in Deformable Porous Media by Yousef Heider, Fadi Aldakheel, Wolfgang Ehlers

“…The primary goal is to develop an efficient, machine learning (ML)-based method that overcomes the limitations of traditional permeability estimation techniques, which often rely on time-consuming experiments or computationally expensive fluid dynamics simulations. The novelty of this work lies in leveraging convolutional neural networks (CNNs) to predict pore-fluid flow behavior under deformation and anisotropic flow conditions. …”
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Lightweight CNN-based seizure classification via leveraging chimera states in iEEG recordings by Fatemeh Azad, Saeed Bagheri Shouraki, Soheila Nazari, Mansun Chan

“…In this study, we propose the Chimera Synchronization Matrix-based Network (CSMNet), a novel algorithm that transforms iEEG signals into 2D images, capturing spatial-temporal dynamics with reduced computational complexity. These images are processed by a streamlined convolutional neural network (CNN) framework, which classifies iEEG recordings into pre-ictal, ictal, and post-ictal events with robust patient-independent performance. …”
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A Comparative Study of a Deep Reinforcement Learning Solution and Alternative Deep Learning Models for Wildfire Prediction by Cristian Vidal-Silva, Roberto Pizarro, Miguel Castillo-Soto, Ben Ingram, Claudia de la Fuente, Vannessa Duarte, Claudia Sangüesa, Alfredo Ibañez

“…This study compared three deep learning models for wildfire prediction: Deep Reinforcement Learning (DRL) with Actor–Critic architecture, Convolutional Neural Network (CNN), and Transformer-based models. …”
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Multi-Layer Modeling and Visualization of Functional Network Connectivity Shows High Performance for the Classification of Schizophrenia and Cognitive Performance via Resting fMRI by Duc My Vo, Anees Abrol, Zening Fu, Vince D. Calhoun

“…In the first, a deep convolutional neural network (DCNN) is trained to produce heatmaps from multiple convolution layers. …”
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Prediction of Time-evolving Radial Magnetic Fields on the Solar Surface Using Deep Learning by Hyun-Jin Jeong, Stefaan Poedts, Haopeng Wang, Ekatarina Dineva, Panagiotis Gonidakis, Francesco Carella, George Miloshevich, Luis Linan, Harim Lee

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