Forecasting basal area increment in forest ecosystems using deep learning: A multi-species analysis in the Himalayas by P. Casas-Gómez, J.F. Torres, J.C. Linares, A. Troncoso, F. Martínez-Álvarez

“…To overcome these limitations, we introduce the use of two different Deep Learning models: the Long Short-Term Memory network and the Temporal Convolutional Neural Network, which capture the temporal dependencies of growth by incorporating lagged Basal Area Increment values. …”
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An adaptive assessment method of power system transient stability considering PMU data loss by Binyu Ma, Jun Yang, Xiaotao Peng, Kezheng Jiang, Dan Liu, Kan Cao

“…Secondly, a weighted integrated assessment model based on PMU clusters is constructed by using an improved eXplainable Convolutional neural network for Multivariate time series classification (XCM) as a TSA classifier. …”
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Deep learning with ensemble-based hybrid AI model for bipolar and unipolar depression detection using demographic and behavioral based on time-series data by Naga Raju Kanchapogu, Sachi Nandan Mohanty

“…Demographic data is modeled using an XGBoost ensemble, while temporal data is analyzed through a deep convolutional neural network (CNN). …”
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Deep learning enhanced light sheet fluorescence microscopy for in vivo 4D imaging of zebrafish heart beating by Meng Zhang, Renjian Li, Songnian Fu, Sunil Kumar, James Mcginty, Yuwen Qin, Lingling Chen

“…We demonstrate that the convolutional neural network (CNN)-transformer network developed here, namely U-net integrated transformer (UI-Trans), successfully achieves the mitigation of complex noise-scattering-coupled degradation and outperforms state-of-the-art deep learning networks, due to its capability of faithfully learning fine details while comprehending complex global features. …”
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Video-Based Detection Infrastructure Enhancement for Automated Ship Recognition and Behavior Analysis by Xinqiang Chen, Lei Qi, Yongsheng Yang, Qiang Luo, Octavian Postolache, Jinjun Tang, Huafeng Wu

“…First, we employed the convolutional neural network in the YOLO model to extract multi-scaled ship features from the input ship images. …”
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Improving High-Precision BDS-3 Satellite Orbit Prediction Using a Self-Attention-Enhanced Deep Learning Model by Shengda Xie, Jianwen Li, Jiawei Cai

“…This study introduces a novel data-driven methodology, Sample Convolution and Interaction Network with Self-Attention (SCINet-SA), to augment dynamic methods and improve BDS-3 ultra-rapid orbit prediction. …”
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Towards Transparent AI in Medicine: ECG-Based Arrhythmia Detection with Explainable Deep Learning by Oleksii Kovalchuk, Oleksandr Barmak, Pavlo Radiuk, Liliana Klymenko, Iurii Krak

“…Second, we proposed an arrhythmia classification method utilizing a modified convolutional neural network (CNN) architecture with additional convolutional and batch normalization layers. …”
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Demand prediction for shared bicycles around metro stations incorporating STAGCN. by Xue Xing, Le Wan, Fahui Luo

“…A predictive model integrating a Spatiotemporal Attention Graph Convolutional Network (STAGCN), Long Short-Term Memory (LSTM) network, and Informer is developed to forecast shared bike demand for metro connectivity. …”
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An intelligent fault diagnosis model for bearings with adaptive hyperparameter tuning in multi-condition and limited sample scenarios by Jianqiao Li, Zhihao Huang, Liang Jiang, Yonghong Zhang

“…To address these issues, this paper presents an advanced diagnosis method using a hybrid Grey Wolf Algorithm (HGWA)-optimized convolutional neural network (CNN) and Bidirectional long short-term memory (BiLSTM) architecture. …”
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Water Level Time Series Forecasting Using TCN Study Case in Surabaya by Deni Saepudin, Egi Shidqi Rabbani, Dio Navialdy, Didit Adytia

“…In this study, we employ the Temporal Convolutional Network (TCN) machine learning model for water level forecasting using data from a sea level station monitoring facility in Surabaya. …”
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PatchDetect: Breast Cancer Detection combining Unet-ResNet-50 and Patch Embedding LSTM by Hadj Ahmed Bouarara, kadda benyahia

“… This study presents a novel framework for breast cancer detection, combining patch embedding, feature extraction using a pre-trained Convolutional Neural Network (CNN) model (ResNet50), Long Short-Term Memory (LSTM) networks for image sequence analysis, and Fully Connected Layers for final classification. …”
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A novel approach for joint indoor localization and activity recognition using a hybrid CNN-GRU and MRF framework. by Sarmad Sohaib, Syed Mohsin Bokhari, Muhammad Shafi, Anas Alhashmi

“…This work proposes a new hybrid model for joint indoor localization and activity recognition by combining a Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) model with a Markov Random Field (MRF) for better classification. …”
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Overview of Machine Learning Applications at the Pierre Auger Observatory by Rodriguez Ezequiel

“…Both transformers and convolutional networks perform better at the reconstruction of mass-composition sensitive observables like Xmax and the muon number, demonstrating the benefits of the Observatory’s upgrade.…”
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A System for Real-Time Detection of Abandoned Luggage by Ivan Vrsalovic, Jonatan Lerga, Marina Ivasic-Kos

“…We used several popular deep convolutional neural network architectures for object detection, e.g., Yolov8, Yolov11, and DETR encoder–decoder transformer with a ResNet-50 deep convolutional backbone, we fine-tuned them on our dataset, and compared their performance in detecting people and luggage in surveillance scenes recorded by an airport surveillance camera. …”
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Lower Limb Movement Recognition Based on a Hybrid Deep Learning Model Using Surface Electromyography by Yonglin Han, Qing Tao

“…By integrating the advantages of three advanced architectures—Temporal Convolutional Network (TCN), Long Short-Term Memory (LSTM), and Transformer—the model constructs an efficient framework for lower limb motion recognition. …”
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Attention-Driven Hybrid Ensemble Approach With Bayesian Optimization for Accurate Energy Forecasting in Jeju Island’s Renewable Energy System by Muhammad Ali Iqbal, Joon-Min Gil, Soo Kyun Kim

“…ABHEF integrates state-of-the-art models—ConvBiLSTM (Convolutional Bidirectional Long Short-Term Memory), ETCN (Enhanced Temporal Convolutional Network), TFT (Temporal Fusion Transformer), and DAT (Dual Attention Transformer)—to capture both short-term fluctuations and long-term trends in energy data. …”
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A Novel Swin-Transformer with Multi-Source Information Fusion for Online Cross-Domain Bearing RUL Prediction by Zaimi Xie, Chunmei Mo, Baozhu Jia

“…The method uses a Bidirectional Long Short-Term Memory (Bi-LSTM) network to capture temporal features, which are transformed into 2D images using Gramian Angular Fields (GAF) for spatial feature extraction by a 2D Convolutional Neural Network (CNN). …”
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XCF-LSTMSATNet: A Classification Approach for EEG Signals Evoked by Dynamic Random Dot Stereograms by Tingting Zhang, Xu Yan, Xin Chen, Yi Mao

“…Following this, EEGNet employs deep convolutional layers to extract spatial features, while separable convolutions are subsequently used to derive high-dimensional spatial-temporal features. …”
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Multi-anchor adaptive fusion and bi-focus attention for enhanced gait-based emotion recognition by Jincheng Li, Xuejing Dai, Ruiao Yan, Chengqing Tang, Yunpeng Li

“…To address these issues, we propose a novel temporal graph convolutional network (MDT-GCN) that integrates multi-anchor (MAAF) and bi-focus attention (BFA) mechanisms. …”
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Spatiotemporal evaluation of irrigation groundwater quality in Hungarian agricultural sites using hydrochemical and machine learning approaches by Musaab A. A. Mohammed, Norbert P. Szabó, Viktória Mikita, Péter Szűcs

“…Groundwater samples from the Debrecen area (2019–2024) were analyzed using Hierarchical Cluster Analysis (HCA), irrigation indices (SAR, Na%, IWQI), and Self-Organizing Maps (SOMs) to understand spatial and temporal patterns. A Convolutional Neural Network (CNN) model was developed to predict IWQI from key water quality parameters, aiming to reduce manual calculation errors. …”
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