A Robust Fractional-Order Nonsingular Terminal Sliding Mode Control With Deep Learning-Based Lie Derivative Estimation for Maximum Power Point Tracking in Wind Turbine by Ahmed S. Alsafran, Safeer Ullah, Ameen Ullah, Ghulam Hafeez, Muhammad Zeeshan Babar, Baheej Alghamdi, Abdullah A. Algethami

“…This paper presents a Robust Fractional-Order Sliding Mode Control (FOSMC) with Nonsingular Integral Terminal Dynamics, integrated with Densely Connected Convolutional Networks (DenseNet) for Lie Derivatives Estimation, to achieve Maximum Power Point Tracking (MPPT) in Wind Energy Conversion Systems (WECS) based on Permanent Magnet Synchronous Generators (PMSG). …”
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Machine Learning and Deep Learning for Wildfire Spread Prediction: A Review by Henintsoa S. Andrianarivony, Moulay A. Akhloufi

“…However, these models often struggle with the dynamic nature of wildfires. In contrast, DL approaches, such as convolutional neural networks (CNNs) and convolutional recurrent networks (CRNs), excel at handling the spatiotemporal complexities of wildfire data. …”
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Integrating Semantic Zoning Information with the Prediction of Road Link Speed Based on Taxi GPS Data by He Bing, Xu Zhifeng, Xu Yangjie, Hu Jinxing, Ma Zhanwu

“…In order to incorporate the spatiotemporal dynamics and correlation characteristics of road links into speed prediction, this paper proposes a method based on LDA and GCN. …”
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Deep Learning for Automatic Detection of Volcanic and Earthquake-Related InSAR Deformation by Xu Liu, Yingfeng Zhang, Xinjian Shan, Zhenjie Wang, Wenyu Gong, Guohong Zhang

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Finger drawing on smartphone screens enables early Parkinson's disease detection through hybrid 1D-CNN and BiGRU deep learning architecture. by Zhaohui Zhu, E Wu, Pengfei Leng, Jiajun Sun, Mingming Ma, Zhigeng Pan

“…<h4>Conclusion</h4>This study establishes the technical feasibility of a hybrid deep learning framework for early PD detection using smartphone-captured finger motion dynamics. The developed model effectively combines one-dimensional convolutional neural networks with bidirectional GRUs to analyze drawing tasks. …”
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GNODEVAE: a graph-based ODE-VAE enhances clustering for single-cell data by Zeyu Fu, Chunlin Chen, Song Wang, Junping Wang, Shilei Chen

“…Through systematic evaluation across 10 graph convolutional layers, GAT demonstrated optimal performance, achieving average ARI advantages of 0.108 and 0.112 over alternative graph convolutional layers in VGAE and GNODEVAE architectures respectively, along with ASW advantages of 0.047 and 0.098. …”
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Confidence-Based Fusion of AC-LSTM and Kalman Filter for Accurate Space Target Trajectory Prediction by Caiyun Wang, Jirui Zhang, Jianing Wang, Yida Wu

“…The Attention-Based Convolutional Long Short-Term Memory (AC-LSTM) network is designed to capture nonlinear motion patterns by leveraging temporal attention mechanisms and convolutional layers while also estimating confidence levels via a signal-to-noise ratio (SNR)-based multitask learning approach. …”
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MST-AI: Skin Color Estimation in Skin Cancer Datasets by Vahid Khalkhali, Hayan Lee, Joseph Nguyen, Sergio Zamora-Erazo, Camille Ragin, Abhishek Aphale, Alfonso Bellacosa, Ellis P. Monk, Saroj K. Biswas

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Classification of Respiratory Diseases by Wichum Felix, Wiede Christian, Seidl Karsten

“…These findings provide indications that DPG might effectively classify respiratory conditions by analyzing respiratory motion dynamics.…”
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Prediction of Sea Surface Chlorophyll-a Concentrations by Remote Sensing and Deep Learning by Qingfeng Ruan, Delu Pan, Difeng Wang, Xianqiang He, Fang Gong, Qingjiu Tian

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Deep graph representation learning: methods, applications, and challenges by ZHANG Xulong, QU Xiaoyang, XIAO Chunguang, WANG Jianzong

“…We discuss various techniques within these categories, including matrix factorization, random walks, graph convolutional networks, and graph Transformers. Furthermore, we delve into the specific applications of GNN in heterogeneous graph embedding, encompassing both static and dynamic aspects. …”
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Exploring deep learning for landslide mapping: A comprehensive review by Zhi-qiang Yang, Wen-wen Qi, Chong Xu, Xiao-yi Shao

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Classification of ROI-based fMRI data in short-term memory tasks using discriminant analysis and neural networks by Magdalena Fafrowicz, Marcin Tutajewski, Igor Sieradzki, Jeremi K. Ochab, Jeremi K. Ochab, Anna Ceglarek-Sroka, Koryna Lewandowska, Tadeusz Marek, Barbara Sikora-Wachowicz, Igor T. Podolak, Paweł Oświęcimka, Paweł Oświęcimka, Paweł Oświęcimka

“…The presented results reveal the benefits of applying machine learning algorithms to investigate working memory dynamics.…”
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Noise Pollution Prediction in a Densely Populated City Using a Spatio-Temporal Deep Learning Approach by Marc Semper, Manuel Curado, Jose Luis Oliver, Jose F. Vicent

“…Several complementary approaches are compared: Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Graph Convolutional Networks (GCNs). …”
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Scalable 3D reconstruction for X-ray single particle imaging with online machine learning by Jay Shenoy, Axel Levy, Kartik Ayyer, Frédéric Poitevin, Gordon Wetzstein

“…Abstract X-ray free-electron lasers offer unique capabilities for measuring the structure and dynamics of biomolecules, helping us understand the basic building blocks of life. …”
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Automatic Quantification of Atmospheric Turbulence Intensity in Space-Time Domain by Damián Gulich, Myrian Tebaldi, Daniel Sierra-Sosa

“…We capture videos of a static image under controlled air turbulence intensities using an inexpensive camera, and then, by slicing these videos in the space-time domain, we extract spatio-temporal representations of the turbulence dynamics. These representations are then fed into a Convolutional Neural Network for classification. …”
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Magnetic soliton-based LIF neurons for spiking neural networks (SNNs) in multilayer spintronic devices by Kishan K. Mishra

“…By incorporating the non-volatile properties of skyrmions and adding a chiral Dzyaloshinskii–Moriya interaction term, we further explored LIF dynamics, yielding encouraging results. Our proposed neuron model, implemented in fully connected and convolutional layers, achieves over 95% classification accuracy on the MNIST and Fashion MNIST datasets using a modified spike-based backpropagation method. …”
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Toward long-range ENSO prediction with an explainable deep learning model by Qi Chen, Yinghao Cui, Guobin Hong, Karumuri Ashok, Yuchun Pu, Xiaogu Zheng, Xuanze Zhang, Wei Zhong, Peng Zhan, Zhonglei Wang

“…In this study, we introduce CTEFNet, a multivariate deep learning model that synergizes convolutional neural networks and transformers to enhance ENSO forecasting. …”
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Automated violence monitoring system for real-time fistfight detection using deep learning-based temporal action localization by Baolong Qi, Baoyuan Wu, Bailing Sun

“…The proposed framework leverages both Context-Aware Encoded Transformer (CAET) for modeling interactions between individuals and their environment and Spatial–Temporal Graph Convolutional Networks (ST-GCN) for capturing intra-person and inter-person dynamics from skeletal data. …”
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Spatio-Temporal Graph Neural Networks for Streamflow Prediction in the Upper Colorado Basin by Akhila Akkala, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi, Pouya Hosseinzadeh, Ayman Nassar

“…This study presents a spatio-temporal graph neural network (STGNN) model for streamflow prediction in the Upper Colorado River Basin (UCRB), integrating graph convolutional networks (GCNs) to model spatial connectivity and long short-term memory (LSTM) networks to capture temporal dynamics. …”
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