Energy Consumption Prediction for Drilling Pumps Based on a Long Short-Term Memory Attention Method by Chengcheng Wang, Zhi Yan, Qifeng Li, Zhaopeng Zhu, Chengkai Zhang

“…However, due to the complex and variable geological conditions, diverse operational parameters, and inherent nonlinear relationships in the drilling process, accurately predicting energy consumption presents considerable challenges. …”
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Multimode Fiber Specklegram Sensor for Multi-Position Loads Recognition Using Traversal Occlusion by Bohao Shen, Jianzhi Li, Zhe Ji

“…Our study introduces a construction method for a multi-variable, multi-class, one-shot specklegram dataset, significantly enhancing the sample diversity for more perturbation positions and intensities in an MMF-distributed sensor recognition model. …”
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From Tables to Computer Vision: Transforming HPDC Process Data into Images for CNN-Based Deep Learning by A. Burzyńska

“…This paper proposes a methodology for leveraging convolutional neural networks (CNNs) in conjunction with advanced data preprocessing to facilitate optimal quality control decision-making in high pressure casting (HPDC) processes. …”
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Dual-Stream Enhanced Deep Network for Transmission Near-Infrared Dorsal Hand Vein Age Estimation with Attention Mechanisms by Zhenghua Shu, Zhihua Xie, Xiaowei Zou

“…To this end, this paper proposes an efficient dorsal hand vein age estimation model using a deep neural network with attention mechanisms. Specifically, a convolutional neural network (CNN) is developed to extract the expressive features for age estimation. …”
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How to Handle Data Imbalance and Feature Selection Problems in CNN-Based Stock Price Forecasting by Zinnet Duygu Aksehir, Erdal Kilic

“…In literature, the convolutional neural networks (CNN) models were used for stock market forecasting and gave successful results. …”
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Machine learning–enhanced screening funnel for clinical trials in Alzheimer's disease by Scott Gladstein, Liuqing Yang, Dustin Wooten, Xin Huang, Robert Comley, Qi Guo, the Alzheimer's Disease Neuroimaging Initiative

“…METHODS A traditional screening funnel is enhanced using machine learning models, including 3D convolutional neural networks and ensemble models, which integrate neuroimaging, demographic, genetic, and clinical data. …”
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Chess Position Evaluation Using Radial Basis Function Neural Networks by Dimitrios Kagkas, Despina Karamichailidou, Alex Alexandridis

“…Various networks were trained and tested as we considered different variations of each method regarding input variable configurations and dataset filtering. Ultimately, the results indicated that the proposed approach was the best in performance. …”
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Detecting shallow subsurface anomalies with airborne and spaceborne remote sensing: A review by Adam M. Morley, Tamsin A. Mather, David M. Pyle, J-Michael Kendall

“…To close, we take a brief look at future research opportunities with very high resolution (VHR) datasets, multi-branch convolutional neural networks (CNNs) and active remote sensing in variable potential fields.…”
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Studying Forgetting in Faster R-CNN for Online Object Detection: Analysis Scenarios, Localization in the Architecture, and Mitigation by Baptiste Wagner, Denis Pellerin, Sylvain Huet

“…In this context, the widely used architecture Faster R-CNN (Region Convolutional Neural Network) faces catastrophic forgetting: the acquisition of new knowledge leads to the loss of previously learned information. …”
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Mapping herbaceous wetlands using combined phenological and hydrological features from time-series Sentinel-1/2 imagery by Zhaolong Yang, Xiaodong Na

“…A deep learning algorithm (temporal convolutional neural network (TempCNN)) and the CVHIs dataset were used to map wetlands in the Zhalong National Nature Reserve in China. …”
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Hybrid CNN–LSTM Model With Soft Attention Mechanism for Short‐Term Load Forecasting in Smart Grid by Syed Muhammad Hasanat, Muhammad Haris, Kaleem Ullah, Syed Zarak Shah, Usama Abid, Zahid Ullah

“…These methods optimize smart grid performance under variable conditions by leveraging the synergistic integration of multiple architectures. …”
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Synthesizing field plot and airborne remote sensing data to enhance national forest inventory mapping in the boreal forest of Interior Alaska by Pratima Khatri-Chhetri, Hans-Erik Andersen, Bruce Cook, Sean M. Hendryx, Liz van Wagtendonk, Van R. Kane

“…To achieve this goal, we compared the performance of two advanced modeling approaches, the convolutional neural network (CNN) and the XGBoost model. …”
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Multi-Time Scale Scenario Generation for Source–Load Modeling Through Temporal Generative Adversarial Networks by Liang Ma, Shigong Jiang, Yi Song, Chenyi Si, Xiaohan Li

“…However, traditional scenario generation methods struggle with high-dimensional variables and complex spatiotemporal characteristics, posing severe challenges for distribution network planning. …”
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SFMHANet: Surface Fitting Constrained Multidimensional Hybrid Attention Network for Aero-Optics Thermal Radiation Effect Correction by Yu Shi, ShanLin Niu, Lei Wang, Liang Ye, YaoZong Zhang, HanYu Hong

“…Finally, to achieve cross-dimensional information interaction of features, we propose a multidimensional hybrid attention module, a second-order pooling channel attention block, and a cross-convolution spatial attention block in the correction network. …”
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Bathymetry Inversion Using a Deep‐Learning‐Based Surrogate for Shallow Water Equations Solvers by Xiaofeng Liu, Yalan Song, Chaopeng Shen

“…The surrogate uses the convolutional autoencoder with a shared‐encoder, separate‐decoder architecture. …”
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Synergizing BRDF correction and deep learning for enhanced crop classification in GF-1 WFV imagery by Yuanwei Chen, Yang Li, Runze Li, Chongzheng Guo, Jilin Li

“…Three typical deep learning architectures—Feature Pyramid Network (FPN), Fully Convolutional Network (FCN), and UNet, are employed to perform classification experiments. …”
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Enhancing Traffic Accident Severity Prediction Using ResNet and SHAP for Interpretability by Ilyass Benfaress, Afaf Bouhoute, Ahmed Zinedine

“…The proposed model leverages residual learning to effectively model intricate relationships between numerical and categorical variables, resulting in a notable increase in prediction accuracy. …”
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Semantic ECG hash similarity graph by Yixian Fang, Shilin Zhang, Yuwei Ren

“…Abstract Graph-based methods have made significant progress in addressing the dependent correlations among ECG time series variables. However, most existing graph structures primarily focus on local similarity while overlooking global semantic correlation. …”
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Improving Oil Pipeline Surveillance with a Novel 3D Drone Simulation Using Dynamically Constrained Accumulative Membership Fuzzy Logic Algorithm (DCAMFL) for Crack Detection by Omar Saber Muhi, Hameed Mutlag Farhan, Sefer Kurnaz

“…In this paper, we propose a novel approach for crack detection in oil pipes using a combination of 3D drone simulation, convolutional neural network (CNN) feature extraction, and the dynamically constrained accumulative membership fuzzy logic algorithm (DCAMFL). …”
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A Deep Learning Model with Conv-LSTM Networks for Subway Passenger Congestion Delay Prediction by Wei Chen, Zongping Li, Can Liu, Yi Ai

“…The spatiotemporal variables include inbound passenger flow, outbound passenger flow, number of passengers delayed, and average delay time. …”
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