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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NEURAL NETWORKS INTEGRATION INTO LEGAL RESOURCES FOR ANTI-СORRUPTION MEASURES IN INTERNATIONAL ECONOMIC CO-OPERATION by Oleksii Makarenkov

“…The corrupt dimension of international communication is a constant variable, with a variable volume. The presence of virtuous individuals in top public positions within the world's most powerful nations has been demonstrated to reduce the level of global corruption-driven perversion and vice versa. …”
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Bathymetry Inversion Using a Deep‐Learning‐Based Surrogate for Shallow Water Equations Solvers by Xiaofeng Liu, Yalan Song, Chaopeng Shen

“…It encodes the input bathymetry and decodes to separate outputs for flow field variables. Utilizing the differentiability of the surrogate, a gradient‐based optimizer is used to perform bathymetry inversion. …”
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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

“…Secondly, utilizing four spectral bands from WFV images along with three effective vegetation indices as feature variables, a multi-feature fusion deep learning classification system was constructed. …”
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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

“…The algorithm leverages the strengths of CNNs in extracting discriminative features from images and the DCAMFL’s ability to handle uncertainties and overlapping linguistic variables. We evaluated the proposed algorithm on a comprehensive dataset containing images of cracked oil pipes, achieving remarkable results. …”
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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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Knowledge Distillation‐Based Zero‐Shot Learning for Process Fault Diagnosis by Yi Liu, Jiajun Huang, Mingwei Jia

“…When an unknown fault arises, there exist differences between the information extracted by the teacher model and the student model. Contributions of variables to faults are calculated by quantifying these differences through gradients, thereby isolating the unknown fault. …”
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Long sequence time-series forecasting method based on multi-scale segmentation by HE Shenglin, LONG Chen, ZHENG Jing, WANG Shuang, WEN Zhenkun, WU Huisi, NI Dong, HE Xiaorong, WU Xueqing

“…Experimental results on the real-world power transformer dataset, encompassing variables like electricity transformer temperature, electricity consumption load, and weather demonstrate that the proposed Transformer model based on the multi-scale segmentation approach outperforms traditional benchmark models such as Transformer, Informer, gated recurrent unit, temporal convolutional network and long short term memory in terms of mean absolute error (MAE) and mean squared error (MSE). …”
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Prediction of Sea Surface Current Around the Korean Peninsula Using Artificial Neural Networks by Jeong‐Yeob Chae, Hyunkeun Jin, Inseong Chang, Young Ho Kim, Young‐Gyu Park, Young Taeg Kim, Boonsoon Kang, Min‐su Kim, Ho‐Jeong Ju, Jae‐Hun Park

“…Here, we present a prediction framework applicable to surface current prediction in the seas around the Korean Peninsula using three‐dimensional (3‐D) convolutional neural networks. The network is based on a 3‐D U‐shaped network structure and is modified to predict sea surface currents using oceanic and atmospheric variables. …”
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High Perplexity Mountain Flood Level Forecasting in Small Watersheds Based on Compound Long Short-Term Memory Model and Multimodal Short Disaster-Causing Factors by Songsong Wang, Ouguan Xu

“…Mountain flood water levels exhibit high variability and complexity, making them challenging to predict, and gathering long-term data of disaster-causing factors is difficult in small watersheds, the available disaster-causing variables are short-term multimodal data. …”
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Contrasted Trends in Chlorophyll‐a Satellite Products by Etienne Pauthenet, Elodie Martinez, Thomas Gorgues, Joana Roussillon, Lucas Drumetz, Ronan Fablet, Maïlys Roux

“…To assess if these trends can be related to changes in the environment or to bias in radiometric products, a convolutional neural network is used to examine the relationship between physical ocean variables versus Schl. …”
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Accurate total consumer price index forecasting with data augmentation, multivariate features, and sentiment analysis: A case study in Korea. by Injae Seo, Minkyoung Kim, Jong Wook Kim, Beakcheol Jang

“…To address these challenges, we propose a novel framework consisting of four key components: (1) a hybrid Convolutional Neural Network-Long Short-Term Memory mechanism designed to capture complex patterns in CPI data, enhancing estimation accuracy; (2) multivariate inputs that incorporate CPI component indices alongside auxiliary variables for richer contextual information; (3) data augmentation through linear interpolation to convert monthly data into daily data, optimizing it for highly parametrized deep learning models; and (4) sentiment index derived from Korean CPI-related news articles, providing insights into external factors influencing CPI fluctuations. …”
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Deep learning in time series forecasting with transformer models and RNNs by Rogerio Pereira dos Santos, João P. Matos-Carvalho, Valderi R. Q. Leithardt

“…This study examined 14 neural network models applied to forecast weather variables, evaluated using metrics such as median absolute error (MedianAbsE), mean absolute error (MeanAbsE), maximum absolute error (MaxAbsE), root mean squared percent error (RMSPE), and root mean square error (RMSE). …”
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A novel ensemble model for fall detection: leveraging CNN and BiLSTM with channel and temporal attention by Sarita Sahni, Sweta Jain, Sri Khetwat Saritha

“…The channel attention module uncovers interrelationships between variables. Meanwhile, the temporal attention module captures associations within the sensor data’s temporal dimension, allowing the model to focus on critical features and enhance performance. …”
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Integrating Copula-Based Random Forest and Deep Learning Approaches for Analyzing Heterogeneous Treatment Effects in Survival Analysis by Jong-Min Kim

“…Using breast cancer data from the TCGA-BRCA dataset, which includes both clinical variables and gene expression profiles, we filter the data to focus on two racial groups: Black or African American and White. …”
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Recent Trends and Advances in Utilizing Digital Image Processing for Crop Nitrogen Management by Bhashitha Konara, Manokararajah Krishnapillai, Lakshman Galagedara

“…In addition, image data using more variables as model inputs, including agriculture sensors and meteorological data, have increased prediction accuracy. …”
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A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm by Jing Yang, Touseef Sadiq, Jiale Xiong, Muhammad Awais, Uzair Aslam Bhatti, Roohallah Alizadehsani, Juan Manuel Gorriz

“…However, the detection of myocarditis using CMR images can be challenging due to low contrast, variable noise, and the presence of multiple high CMR slices per patient. …”
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UAV-based estimation of post-sowing rice plant density using RGB imagery and deep learning across multiple altitudes by Trong Hieu Luu, Thanh Tam Nguyen, Quang Hieu Ngo, Huu Cuong Nguyen, Phan Nguyen Ky Phuc

“…The robust rice plant density estimation process incorporates two key innovations: first, a dynamic system of 12 adaptive segmentation thresholding blocks that effectively detects rice seed presence across diverse and variable background conditions. Second, a tailored three-layer convolutional neural network (CNN) accurately classifies vegetative situations. …”
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