Using Machine Learning for Aerostructure Surface Damage Digital Reconstruction by Yijia Wu, Hon Ping Tang, Anthony Mannion, Robert Voyle, Ying Xin

“…Then, we use the prediction result of a feedforward neural network (FNN) to simulate the damage structure and the mapping relationship to explore its reconstructive possibility. …”
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DTI-MHAPR: optimized drug-target interaction prediction via PCA-enhanced features and heterogeneous graph attention networks by Guang Yang, Yinbo Liu, Sijian Wen, Wenxi Chen, Xiaolei Zhu, Yongmei Wang

“…Our approach initiates with the construction of a heterogeneous graph from various similarity metrics, which is then encoded via a graph neural network. We concatenate and integrate the resultant representation vectors to merge multi-level information. …”
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Feature Extraction of Broken Glass Cracks in Road Traffic Accident Site Based on Deep Learning by Shuai Liang

“…This paper studies the feature extraction and middle-level expression of Convolutional Neural Network (CNN) convolutional layer glass broken and cracked at the scene of road traffic accident. …”
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A digital twin framework with MobileNetV2 for damage detection in slab structures by Huong Duong Nguyen, Huan Nguyen, Xiaohong Gao

“… In this study, a digital twin framework is proposed for damage detection in a civil structure, which consists of a finite element model, neural networks, model updating methods, and signal processing. …”
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Predicting the heat capacity of strontium-praseodymium oxysilicate SrPr4(SiO4)3O using machine learning, deep learning, and hybrid models by Amir Hossein Sheikhshoaei, Ali Khoshsima, Davood Zabihzadeh

“…In this study, the capability of five advanced machine learning models, including Random Forest (RF), Gradient Boosting (GBoost), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Decision Tree (DT) models, and three deep learning models, TabNet, Deep Belief Network (DBN), and Deep Neural Network (DNN) was investigated. Our analysis indicates that the Random Forest and Deep Belief Network models outperform all other competing models. …”
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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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Real-world pharmacovigilance analysis unveils the toxicity profile of amivantamab targeting EGFR exon 20 insertion mutations in non-small cell lung cancer by Jing Zhang, Wenjie Li

“…A comprehensive disproportionality analysis was performed, employing the reporting odds ratio (ROR), proportional reporting ratio (PRR), Empirical Bayes Geometric Mean (EBGM), and the Bayesian confidence propagation neural network to calculate information components (ICs), to identify statistically significant adverse events. …”
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State of health estimation of individual batteries through incremental curve analysis under parameter uncertainty by Yue Zhao, Qian Li, Xiaohui Li, Ge Zhang, Hang Shi, Qinghua Li

“…Subsequently, a method is proposed to fuse these HIs using an artificial neural network to achieve precise SOH estimation. The effectiveness of the proposed method is validated through extensive long‐term degradation experiments on Lithium Cobalt Oxide batteries. …”
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Geographical origin discrimination of Chenpi using machine learning and enhanced mid-level data fusion by Xin Kang Li, Li Jun Tang, Ze Ying Li, Dian Qiu, Zhuo Ling Yang, Xiao Yi Zhang, Xiang-Zhi Zhang, Jing Jing Guo, Bao Qiong Li

“…The K-nearest neighbors and artificial neural network models, using modified mid-level data fusion, provide the best performance, misclassified only one sample. …”
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Intelligent Early Warning System for Construction Safety of Excavations Adjacent to Existing Metro Tunnels by Wei Tian, Jiang Meng, Xing-Ju Zhong, Xiao Tan

“…This study uses a backpropagation neural network to predict the real-time deformation of the tunnel based on monitoring data from the adjacent construction site. …”
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Application of IoT-Based Drones in Precision Agriculture for Pest Control by Mohamad Reda. A. Refaai, Vinjamuri SNCH Dattu, N. Gireesh, Ekta Dixit, CH. Sandeep, David Christopher

“…These deep neural networks are adapted to the immediate situation using transfer learning and deep extraction of features approaches. …”
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Assessment model of ozone pollution based on SHAP-IPSO-CNN and its application by Xiaolei Zhou, Xingyue Wang, Ruifeng Guo

“…To address this problem, a convolutional neural network (CNN) model combining the improved particle swarm optimization (IPSO) algorithm and SHAP analysis, called SHAP-IPSO-CNN, is developed in this study, aiming to reveal the key factors affecting ground-level ozone pollution and their interaction mechanisms. …”
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Fuzzy Comprehensive Evaluation Model of Project Investment Risk Based on Computer Vision Technology by Hongjian Wang

“…Then, this paper establishes a model of fuzzy comprehensive evaluation of project investment risk through computer vision technology, real-time embedded systems, and neural network models in big data and artificial intelligence technology to realize the analysis and prediction of project investment risk. …”
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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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Comparative Analysis of Shear Strength Prediction Models for Reinforced Concrete Slab–Column Connections by Sarmed Wahab, Nasim Shakouri Mahmoudabadi, Sarmad Waqas, Nouman Herl, Muhammad Iqbal, Khurshid Alam, Afaq Ahmad

“…Compared with the design codes and other machine learning models, the particle swarm optimization-based feedforward neural network (PSOFNN) performed the best predictions. …”
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Innovative Framework for Historical Architectural Recognition in China: Integrating Swin Transformer and Global Channel–Spatial Attention Mechanism by Jiade Wu, Yang Ying, Yigao Tan, Zhuliang Liu

“…Through extensive experiments on a constructed historical building dataset, our model achieves an outstanding performance of over 97.8% in key metrics including accuracy, precision, recall, and F1 score (harmonic mean of the precision and recall), surpassing traditional CNN (convolutional neural network) architectures and contemporary deep learning models. …”
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Transformer-Based Optimization for Text-to-Gloss in Low-Resource Neural Machine Translation by Younes Ouargani, Noussaim El Khattabi

“…With a 55.18 Recall-Oriented Understudy for Gisting Evaluation (ROUGE) score, and a 63.6 BiLingual Evaluation Understudy 1 (BLEU1) score, our proposed model not only outperforms state-of-the-art models on the Phoenix14T dataset but also outperforms some of the best alternative architectures, specifically Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU). …”
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Prediction Model of Cutting Parameters for Turning High Strength Steel Grade-H: Comparative Study of Regression Model versus ANFIS by Adel T. Abbas, Mohanad Alata, Adham E. Ragab, Magdy M. El Rayes, Ehab A. El Danaf

“…In this paper the artificial neural network was used for predicting the surface roughness for different cutting parameters in CNC turning operations. …”
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Accelerating Multilingual Cryptocurrency Forensics: An NLP-Driven Approach for Efficient Mnemonic Identification by Hsin-Hsiung Kao

“…Our analysis reveals that the Text Convolutional Neural Network (TextCNN) model exhibits superior performance, achieving a 99.9993% accuracy rate, nearly matching the 100% accuracy of the Mnemonic Library Matching Method. …”
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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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