Knowledge Distillation‐Based Zero‐Shot Learning for Process Fault Diagnosis by Yi Liu, Jiajun Huang, Mingwei Jia

“…Process data and image data are equivalent in their spatiotemporal dimensions, and convolutional neural networks are selected as the teacher model, pretrained on image data. …”
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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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Deep Learning and Methods Based on Large Language Models Applied to Stellar Light Curve Classification by Yu-Yang Li, Yu Bai, Cunshi Wang, Mengwei Qu, Ziteng Lu, Roberto Soria, Jifeng Liu

“…In this study, we present a comprehensive evaluation of models based on deep learning and large language models (LLMs) for the automatic classification of variable star light curves, using large datasets from the Kepler and K2 missions. …”
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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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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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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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Two-Mode Hereditary Model of Solar Dynamo by Evgeny Kazakov, Gleb Vodinchar, Dmitrii Tverdyi

“…The feedback is represented by an integral term of the type of convolution of a quadratic form of phase variables with a kernel of a fairly general form. …”
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Evaluating soil erosion zones in the Kangsabati River basin using a stacking framework and SHAP model: a comparative study of machine learning approaches by Javed Mallick, Saeed Alqadhi, Swapan Talukdar, Md Nawaj Sarif, Tania Nasrin, Hazem Ghassan Abdo

“…The Boruta algorithm assessed the importance of these variables. Random Forest (RF), (Deep Neural Networks) DNN, Convolution Neural Network (CNN), and stacking (Meta model) models were used to map soil erosion susceptibility based on the inventory map and controlling features. …”
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A multimodal functional structure-based graph neural network for fatigue detection by Dongrui Gao, Zhihong Zhou, Zongyao Peng, Haokai Zhang, Shihong Liu, Manqing Wang, Hongli Chang

“…An innovative intra- and inter-channel separable convolution module is designed to extract deep interaction patterns through parallel convolution operations within and across signal channels. …”
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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

“…In contrast, RNN models such as auto-temporal convolutional networks (TCN) and bidirectional TCN (BiTCN) were better suited to short-term forecasting, despite being more prone to significant errors. …”
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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

“…This paper presents deep learning models—specifically, Long Short-Term Memory (LSTM) networks and hybrid Convolutional Neural Network–LSTM (CNN-LSTM) with a Copula-Based Random Forest (CBRF) model to estimate Heterogeneous Treatment Effects (HTEs) in survival analysis. …”
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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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Deep learning classification of drainage crossings based on high-resolution DEM-derived geomorphological information by Michael Edidem, Bill Xu, Ruopu Li, Di Wu, Banafsheh Rekabdar, Guangxing Wang

“…At present, drainage crossing datasets are largely missing or available with variable quality. While previous studies have investigated basic convolutional neural network (CNN) models for drainage crossing characterization, it remains unclear if advanced deep learning models will improve the accuracy of drainage crossing classification. …”
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Deep Learning-Based Web Application for Automated Skin Lesion Classification and Analysis by Serra Aksoy, Pinar Demircioglu, Ismail Bogrekci

“…Background/Objectives: Skin lesions, ranging from benign to malignant diseases, are a difficult dermatological condition due to their great diversity and variable severity. Their detection at an early stage and proper classification, particularly between benign Nevus (NV), precancerous Actinic Keratosis (AK), and Squamous Cell Carcinoma (SCC), are crucial for improving the effectiveness of treatment and patient prognosis. …”
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geodl: An R package for geospatial deep learning semantic segmentation using torch and terra. by Aaron E Maxwell, Sarah Farhadpour, Srinjoy Das, Yalin Yang

“…Convolutional neural network (CNN)-based deep learning (DL) methods have transformed the analysis of geospatial, Earth observation, and geophysical data due to their ability to model spatial context information at multiple scales. …”
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