STDNet: Improved lip reading via short-term temporal dependency modeling by Xiaoer Wu, Zhenhua Tan, Ziwei Cheng, Yuran Ru

“…Conclusions: The proposed model effectively addresses short-term temporal dependency limitations in lip reading, and improves the temporal robustness of the model against variable-length sequences. These advancements validate the importance of explicit short-term dynamics modeling for practical lip-reading systems.…”
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Lubricating Grease Thickness Classification of Steel Wire Rope Surface Based on GEMR-MobileViT by Ruqing Gong, Yuemin Wang, Fan Zhou, Binghui Tang

“…To achieve automated lubrication quality control and address challenges like variable lighting and motion blur that degrade recognition accuracy in practical settings, this paper proposes an improved lightweight GEMR-MobileViT. …”
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Comparison of Machine Learning Methods for Menstrual Cycle Analysis and Prediction by Mutiara Khairunisa, Desak Made Sidantya Amanda Putri, I Gusti Ngurah Lanang Wijayakusuma

“…This study compares three machine learning methods—Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Decision Tree—for analyzing and predicting menstrual cycles. …”
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Evaluation of Flavor Type of Tobacco Blending Module: A Prediction Model Based on Near-Infrared Spectrum by Lin Wang, Yuhan Guan, Yaohua Zhang

“…Combining the power of XGBoost and deep learning, we constructed a flavor prediction model based on feature variables. The XGBoost model was utilized to extract essential information from the high-dimensional near-infrared spectra, while a convolutional neural network with an attention mechanism was employed to predict the flavor type of the modules. …”
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Predicting per capita expenditure using satellite imagery and transfer learning: A case study of east Java province, Indonesia by Heri Kuswanto, Wahidatul Wardah Al Maulidiyah, Widhianingsih Tintrim Dwi Ary, Yudistira Ashadi

“…The satellite images are processed using a transfer learning approach that employs a pretrained Convolutional Neural Network (CNN) model with VGG-16 architecture as a feature extractor. …”
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Solar Wind Speed Prediction via Graph Attention Network by Yanru Sun, Zongxia Xie, Haocheng Wang, Xin Huang, Qinghua Hu

“…Second, our approach employs the dilated causal convolution to extend the receptive field and prolong the prediction time. …”
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MATHEMATICAL MODELS OF CREATION OF A SUBSYSTEM OF ENSURING SAFETY OF INFORMATION IN THE DISTRIBUTED INFORMATION SYSTEMS by D. O. Esikov, R. N. Akinshin, P. I. Abramov, L. E. Loutina

“…The optimal solution of the problem by optimizing a linear convolution is obtained.…”
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Development of Continuous AMSR-E/2 Soil Moisture Time Series by Hybrid Deep Learning Model (ConvLSTM2D and Conv2D) and Transfer Learning for Reanalyses by Visakh Sivaprasad, Mehdi Rahmati, Anne Springer, Harry Vereecken, Carsten Montzka

“…Surface soil moisture (SSM) is a crucial climate variable of the Earth system that regulates water and energy exchanges between the land and atmosphere, directly influencing hydrological, biogeochemical, and energy cycles. …”
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The Elitist Non-Dominated Sorting Crisscross Algorithm (Elitist NSCA): Crisscross-Based Multi-Objective Neural Architecture Search by Zhihui Chen, Ting Lan, Dan He, Zhanchuan Cai

“…In recent years, neural architecture search (NAS) has been proposed for automatically designing neural network architectures, which searches for network architectures that outperform novel human-designed convolutional neural network (CNN) architectures. Related research has always been a hot topic. …”
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A bearing fault diagnosis method for hydrodynamic transmissions integrating few-shot learning and transfer learning by Dong Sun, Xudong Yang, Hai Yang

“…First, a Siamese Wide Convolutional Neural Network (Siamese-WDCNN) is constructed based on public bearing datasets to extract essential features of vibration signals through few-shot contrastive learning. …”
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Forecasting Shifts in Europe's Renewable and Fossil Fuel Markets Using Deep Learning Methods by Yonghong Liu, Muhammad S. Saleem, Javed Rashid, Sajjad Ahmad, Muhammad Faheem

“…Energy sources like the sun and wind are variable, making forecasting difficult. Changes in weather, demand, and energy policy exacerbate this unpredictability. …”
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Impurity rates detection for pepper harvesting based on YOLOv8n-Seg-ASB and random forest by Lijian Lu, Jin Lei, Chenming Cheng, Shiguo Wang, Chengfu Wang, Xinyan Qin

“…To address the inaccuracies and inefficiencies of pepper impurity rates detection caused by complex material compositions and variable harvesting environments, this paper proposes a detection technique based on deep and machine learning algorithms. …”
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A deep-learning algorithm using real-time collected intraoperative vital sign signals for predicting acute kidney injury after major non-cardiac surgeries: A modelling study. by Sehoon Park, Soomin Chung, Yisak Kim, Sun-Ah Yang, Soie Kwon, Jeong Min Cho, Min Jae Lee, Eunbyeol Cho, Jiwon Ryu, Sejoong Kim, Jeonghwan Lee, Hyung Jin Yoon, Edward Choi, Kwangsoo Kim, Hajeong Lee

“…Using data from three hospitals, we constructed a convolutional neural network-based EfficientNet framework to analyze intraoperative data and created an ensemble model incorporating 103 baseline variables of demographics, medication use, comorbidities, and surgery-related characteristics. …”
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RNN and GNN based prediction of agricultural prices with multivariate time series and its short-term fluctuations smoothing effect by Youngho Min, Young Rock Kim, YunKyong Hyon, Taeyoung Ha, Sunju Lee, Jinwoo Hyun, Mi Ra Lee

“…Since environmental factors including weather affect price fluctuations of agricultural commodities, we constructed a multivariate time series dataset combining wholesale prices of four agricultural commodities in South Korea, six weather variables, and week numbers. We adopted two prominent prediction methods based on recurrent neural networks (RNN) and graph neural networks (GNN): one is the stacked long short-term memory, and the other consists of two GNN-based methods, the spectral temporal graph neural network (StemGNN) and the temporal graph convolutional network. …”
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Smoothing Estimation of Parameters in Censored Quantile Linear Regression Model by Mingquan Wang, Xiaohua Ma, Xinrui Wang, Jun Wang, Xiuqing Zhou, Qibing Gao

“…The method associates the convolutional smoothing estimation with the loss function, which is quadratically derivable and globally convex by using a non-negative kernel function. …”
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Role of Artificial Intelligence and Deep Learning in Easier Skin Cancer Detection through Antioxidants Present in Food by Sreevidya R. C., Jalaja G, Sajitha N, D. Lakshmi Padmaja, S. Nagaprasad, Kumud Pant, Yekula Prasanna Kumar

“…These factors have been considered independent variables, and accuracy, sensitivity, and specificity have been considered the dependent variables. …”
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Identification of Subtypes of Post-Stroke and Neurotypical Gait Behaviors Using Neural Network Analysis of Gait Cycle Kinematics by Andrian Kuch, Nicolas Schweighofer, James M. Finley, Alison McKenzie, Yuxin Wen, Natalia Sanchez

“…We first trained a Convolutional Neural Network and a Temporal Convolutional Network to extract features that distinguish impaired from neurotypical gait. …”
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Image data-driven intelligent recognition of permafrost strength and feature visualization based analysis by Zhaoming YAO, Xun WANG, Hang WEI, Xiaolong WANG

“…It was found that the model effectively recognized these three variables, demonstrating the scientific validity and reliability of the model in identifying frozen soil strength. …”
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Modeling the Relationship between Financial Stability and Banking Risks: Artificial Intelligence Approach by Hakeem Faraj Gumar, Parviz Piri, Mehdi Heydari

“…Deep, convolutional, and recurrent neural network models also showed similar performance with coefficients of determination of about 0.94. …”
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CDSE-UNet: Enhancing COVID-19 CT Image Segmentation With Canny Edge Detection and Dual-Path SENet Feature Fusion by Jiao Ding, Jie Chang, Renrui Han, Li Yang

“…Moreover, we have developed a Multiscale Convolution Block (MSCovBlock), replacing the standard convolution in UNet, to adapt to the varied lesion sizes and shapes. …”
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