Fusion of MHSA and Boruta for key feature selection in power system transient angle stability by WANG Man, ZHOU Xiaoyu, CHEN Fan, LAI Yening, ZHU Ying

“…A transient power angle stability key feature selection method that seamlessly integrates multi-head self-attention (MHSA) and the Boruta algorithm. A deep neural network (DNN) with an MHSA model is initially constructed to execute transient stability assessments directly on the input grid features. …”
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Optimization of Jamming Type Selection for Countering Multifunction Radar Based on Generative Adversarial Imitation Learning by Tianjian Yang, You Chen, Siyi Cheng, Xing Wang, Xi Zhang

“…This difference is used as an internal reward to assist in updating the neural network parameters, effectively reducing the complexity of reward function design. …”
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Utilizing Machine Learning-based Classification Models for Tracking Air Pollution Sources: A Case Study in Korea by Yelim Choi, Bogyeong Kang, Daekeun Kim

“…Using 972 datasets consisting of five emission sources and 27 air pollutants, different classification models were implemented and subsequently compared: Random Forest (RF), Naïve Bayes Classifier (NBC), Support Vector Machine (SVM), Artificial Neural Network (ANN), and K-Nearest Neighbors (K-NN). …”
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Recent advances in journal bearings: wear fault diagnostics, condition monitoring and fault diagnosis methodologies by Nazik Jebur, Wafa Soud

“…Key findings indicate that ensemble models, such as the CNN and deep neural network (CNNEPDNN) model, significantly improve convergence speed, test accuracy, and F-Score in bearing fault diagnosis by 15-20% compared to individual models. …”
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Attention-enhanced corn disease diagnosis using few-shot learning and VGG16 by Ruchi Rani, Jayakrushna Sahoo, Sivaiah Bellamkonda, Sumit Kumar

“…The proposed work uses a pre-trained convolution neural network, VGG16, as the backbone, fine-tuned on the corn disease dataset. …”
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NuFold: end-to-end approach for RNA tertiary structure prediction with flexible nucleobase center representation by Yuki Kagaya, Zicong Zhang, Nabil Ibtehaz, Xiao Wang, Tsukasa Nakamura, Pranav Deep Punuru, Daisuke Kihara

“…NuFold is a deep neural network trained end-to-end for the output structure from the input sequence. …”
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Advanced Mineral Deposit Mapping via Deep Learning and SVM Integration With Remote Sensing Imaging Data by Nazir Jan, Nasru Minallah, Madiha Sher, Muhammad Wasim, Shahid Khan, Amal Al‐Rasheed, Hazrat Ali

“…Initially, we apply a deep convolutional neural network (CNN) to a specialized mineral dataset to map mineral deposits within the study area. …”
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Application of CNN-LSTM Model for Vehicle Acceleration Prediction Using Car-following Behavior Data by Shuning Tang, Yajie Zou, Hao Zhang, Yue Zhang, Xiaoqiang Kong

“…Then the convolutional neural network (CNN) and long short-term memory (LSTM) network are applied to predict vehicle acceleration. …”
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Critical Segments Identification for Link Travel Speed Prediction in Urban Road Network by Xiaolei Ru, Xiangdong Xu, Yang Zhou, Chao Yang

“…To identify these critical segments, we assume that the states of floating cars within different road segments are correlative and mutually representative and design a heuristic algorithm utilizing the attention mechanism embedding in the graph neural network (GNN). The results show that the designed model achieves a high accuracy compared to the conventional method using only two critical segments which account for 2.7% in the road networks. …”
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Optimization of dried garlic physicochemical properties using a self-organizing map and the development of an artificial intelligence prediction model by Hany S. El-Mesery, Mohamed Qenawy, Mona Ali, Merit Rostom, Ahmed Elbeltagi, Ali Salem, Abdallah Elshawadfy Elwakeel

“…The relationships between the input process factors and response factors’ physicochemical properties of dried garlic were optimized by a self-organizing map (SOM), and the model was developed using machine learning. Artificial Neural Network (ANN) with 99% predicting accuracy and Self-Organizing Maps (SOM) with 97% clustering accuracy were used to determine the quality characteristics of garlic. …”
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A disproportionality analysis of FDA adverse event reporting system events for misoprostol by Li Yang, Wenting Xu

“…This study used proportional disequilibrium methods such as reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN), and empirical Bayes geometric mean (EBGM) to detect AEs. …”
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A Modified Time Reversal Method for Guided Wave Detection of Bolt Loosening in Simulated Thermal Protection System Panels by Guan-nan Wu, Chao Xu, Fei Du, Wei-dong Zhu

“…To analyze a large number of tightness indices, a principle component analysis method is introduced, and a neural network-based loosening detection method is proposed. …”
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Automated Pavement Crack Damage Detection Using Deep Multiscale Convolutional Features by Weidong Song, Guohui Jia, Hong Zhu, Di Jia, Lin Gao

“…To address these challenges, we propose the CrackSeg—an end-to-end trainable deep convolutional neural network for pavement crack detection, which is effective in achieving pixel-level, and automated detection via high-level features. …”
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Application of Improved Deep Learning Method in Intelligent Power System by HuiJie Liu, Yang Liu, ChengWen Xu

“…The method uses the convolutional neural network to establish the energy prediction calculation model, uses CNN adaptive data features to mine characteristics, quantifies power uncertainty, uses drop regularization to optimize the deep network structure, uses the deep forest to learn the extracted data features, and builds a prediction model, in order to achieve accurate prediction of power load and solve the problem that the accuracy of existing forecasting methods decreases due to random fluctuations of power. …”
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Artificial Intelligence Scribe and Large Language Model Technology in Healthcare Documentation: Advantages, Limitations, and Recommendations by Sarah A. Mess, MD, Alison J. Mackey, PhD, David E. Yarowsky, PhD

“…Insidious and potentially significant errors of omission, fabrication, or substitution may occur. The neural network algorithms of LLMs have unpredictable sensitivity to user input and inherent variability in their output. …”
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Benchmarking human face similarity using identical twins by Shoaib Meraj Sami, John McCauley, Sobhan Soleymani, Nasser Nasrabadi, Jeremy Dawson

“…The facial similarity measure is determined via a deep convolutional neural network. This network is trained on a tailored verification task designed to encourage the network to group together highly similar face pairs in the embedding space and achieves a test AUC of 0.9799. …”
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A Denoising Based Autoassociative Model for Robust Sensor Monitoring in Nuclear Power Plants by Ahmad Shaheryar, Xu-Cheng Yin, Hong-Wei Hao, Hazrat Ali, Khalid Iqbal

“…Sensors health monitoring is essentially important for reliable functioning of safety-critical chemical and nuclear power plants. Autoassociative neural network (AANN) based empirical sensor models have widely been reported for sensor calibration monitoring. …”
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Estimating traffic flow at urban intersections using low occupancy floating vehicle data by Lili Zhang, Kang Yang, Ke Zhang, Wei Wei, Jing Li, Hongxin Tan

“…These estimated flow rates are then refined using the proposed Radial Basis Function (RBF) neural network approximation method to achieve higher accuracy. …”
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When Remote Sensing Meets Foundation Model: A Survey and Beyond by Chunlei Huo, Keming Chen, Shuaihao Zhang, Zeyu Wang, Heyu Yan, Jing Shen, Yuyang Hong, Geqi Qi, Hongmei Fang, Zihan Wang

“…Most deep-learning-based vision tasks rely heavily on crowd-labeled data, and a deep neural network (DNN) is usually impacted by the laborious and time-consuming labeling paradigm. …”
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Intelligent On/Off Dynamic Link Management for On-Chip Networks by Andreas G. Savva, Theocharis Theocharides, Vassos Soteriou

“…., expected future utilization link levels), where links are turned off and on via the use of a small and scalable neural network. Simulation results with various synthetic traffic models over various network topologies show that the proposed work can reach up to 13% power savings when compared to a trivial threshold computation, at very low (<4%) hardware overheads.…”
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