Review of Research on Regenerative Braking Control Strategy of New Energy Vehicle by Fei Xie, Yongfa Qin

“…Aiming at the regenerative braking control problem of new energy vehicles， four basic regenerative braking control strategies， fuzzy control strategies， neural network control strategies， and the research status of comprehensive optimization control strategies based on the above control strategies are summarized. …”
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Fault Diagnosis of Planetary Gearbox based on 1-DCNN by Xuanyi Xue, Xinyu Pang

“…In order to improve the diagnosis efficiency of planetary gearboxes， a fault diagnosis method based on one-dimensional deep convolutional neural network （1-DCNN） is proposed， and the original signals are directly input to the network for diagnosis. …”
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Living body recognition method based on facial feature point motion by Yulong WANG, Kaiyuan LIU

“…A kind of living body recognition method was proposed,which was applied in mobile terminal and based on the deep learning.A facial movements LSTM network was trained using data sets.When users input a random sequence of video,user’s facial feature point data can be gained and whether the video forgery attacks happened will be determined by input user’s facial feature point data into circulation neural network.The test data shows that the proposed method can be protected effectively from photograph attack and video replay attack.…”
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Long-term forecasting of shield tunnel position and attitude deviation using the 1DCNN-informer method by Jiajie Zhen, Ming Huang, Shuang Li, Kai Xu, Qianghu Zhao

“…This study introduces a novel deep learning model, termed 1DCNN-Informer, which integrates the one-dimensional convolutional neural network (1DCNN) and the Informer model. The model was trained and validated using datasets from the Nanjing Metro shield tunnel project in China. …”
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Development of face image recognition algorithm using CNN in airport security checkpoints for terrorist early detection by Eca Indah Anggraini, Fachdy Nurdin, Mohammad Obie Restianto, Sudarti Dahsan, Andini Aprilia Ardhana, Asep Adang Supriyadi, Yahya Darmawan, Syachrul Arief, Agus Haryanto Ikhsanudin

“…This paper presents a novel approach to enhancing security measures at airport checkpoints by applying Convolutional Neural Network (CNN) and Artificial Neural Network (ANN) algorithms in face image recognition. …”
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Application of Extreme Gradient Boosting Based on Grey Relation Analysis for Prediction of Compressive Strength of Concrete by Liyun Cui, Peiyuan Chen, Liang Wang, Jin Li, Hao Ling

“…Another highlight is that its performance was compared with the frequently used artificial neural network (ANN) and genetic algorithm-artificial neural network (GA-ANN) by using random dataset and the same testing datasets. …”
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A Stock Closing Price Prediction Model Based on CNN-BiSLSTM by Haiyao Wang, Jianxuan Wang, Lihui Cao, Yifan Li, Qiuhong Sun, Jingyang Wang

“…CNN-BiSLSTM is compared with multilayer perceptron (MLP), recurrent neural network (RNN), long short-term memory (LSTM), BiLSTM, CNN-LSTM, and CNN-BiLSTM. …”
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EMD-GM-ARMA Model for Mining Safety Production Situation Prediction by Menglong Wu, Yicheng Ye, Nanyan Hu, Qihu Wang, Huimin Jiang, Wen Li

“…Finally, aiming to predict the mining safety production situation, the EMD-GM-ARMA model was constructed via superimposing the prediction results of each subsequence, thereby compared to the ARIMA model, wavelet neural network model, and PSO-SVM model. The results demonstrated that the EMD-GM-ARMA model and the PSO-SVM model hold the highest prediction accuracy in the short-term prediction, and the wavelet neural network has the lowest prediction accuracy. …”
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Machine learning-based estimation of crude oil-nitrogen interfacial tension by Safia Obaidur Rab, Subhash Chandra, Abhinav Kumar, Pinank Patel, Mohammed Al-Farouni, Soumya V. Menon, Bandar R. Alsehli, Mamata Chahar, Manmeet Singh, Mahmood Kiani

“…In this work, we aim to utilize eight machine learning methods of Decision Tree (DT), AdaBoost (AB), Random Forest (RF), K-nearest Neighbors (KNN), Ensemble Learning (EL), Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Multilayer Perceptron Artificial Neural Network (MLP-ANN) to construct data-driven intelligent models to predict crude oil – nitrogen IFT based upon experimental data of real crude oils samples encountered in underground oil reservoirs. …”
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Assimilation of Sparse Continuous Near‐Earth Weather Measurements by NECTAR Model Morphing by I. A. Galkin, B. W. Reinisch, A. M. Vesnin, D. Bilitza, S. Fridman, J. B. Habarulema, O. Veliz

“…When applied to the sparse spatial data, such a neural network becomes a nonlinear multiscale interpolator of missing information. …”
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A Novel AI-Based Integrated Cybersecurity Risk Assessment Framework and Resilience of National Critical Infrastructure by Sardar Muhammad Ali, Abdul Razzaque, Muhammad Yousaf, Sardar Sadaqat Ali

“…We trained three ML classifiers: Support Vector Machine (SVM), Naïve Bayes (NB), and K-Nearest Neighbors (KNN), along with three DL models: Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN). …”
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An Examination of SuperDARN Backscatter Modes Using Machine Learning Guided by Ray‐Tracing by B. S. R. Kunduri, J. B. H. Baker, J. M. Ruohoniemi, E. G. Thomas, S. G. Shepherd

“…In the second stage, the output probabilities from the neural network and actual SuperDARN data are clustered together to determine the category of the backscatter. …”
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A convergent Deep Learning algorithm for approximation of polynomials by Després, Bruno

“…We start from the contractive functional equation proposed in [4], where it was shown that the polynomial solution of functional equation can be used to initialize a Neural Network structure, with a controlled accuracy. …”
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Research on network analysis method for development ability of big data industry in underdeveloped area by Jun-xin SHEN, Ying-qian CHEN

“…Traditional evaluation methods of industrial development ability were mostly lack of objectivity.An evaluation model was proposed by using a BP neural network based on entropy weight.Evaluation index system of big data industry development ability in underdeveloped areas was established.Taking Guizhou industrial development data as samples,entropy weight method was used to determine expected output and compared with the actual output .The experimental results show that the proposed entropy weight-BP evaluation model can optimize error of using single BP network and improve the accuracy and objectivity of evaluation.…”
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Adaptive Integral Observer-Based Synchronization for Chaotic Systems with Unknown Parameters and Disturbances by Xiuchun Li, Jianhua Gu, Wei Xu

“…Numerical simulations are performed in the end and the results show that the proposed method is not only suitable to the representative chaotic systems but also applied to some neural network chaotic systems.…”
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Recommendation model combining review’s feature and rating graph convolutional representation by Hailin FENG, Xiao ZHANG, Tongcun LIU

“…In order to fully exploit the effective information of the ratings and further investigate the importance of the review, a recommendation model combining review’s feature and rating graph convolutional representation was proposed.Graph convolutional neural network was used to learn the representation of user and item from the ratings data.Combining with text convolutional features, attention mechanism was utilized to distinguish the importance of the review.Finally, the representation learned from the review and the rating data was fused by the hidden factor model.The experimental results on Amazon’s public data showed that the proposed model significantly outperformed the traditional approaches, proving the effectiveness of the proposed model.…”
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Simultaneous estimation of multiple soil properties from vis-NIR spectra using a multi-gate mixture-of-experts with data augmentation by Xiaoqing Wang, Mei-Wei Zhang, Ya-Nan Zhou, Lingli Wang, Ling-Tao Zeng, Yu-Pei Cui, Xiao-Lin Sun

“…Previous studies have utilized multi-task convolutional neural network (multi-CNN) with share-bottom structures based on the hard parameter sharing. …”
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Seasonal Tree Height Dynamic Estimation Using Multi-source Remotely Sensed Data in Shenzhen by Hang Song, Xuemei Zhang, Ting Hu, Jinglei Liu, Bing Xu

“…It was found that (a) the seasonal tree height neural network demonstrated the highest prediction accuracy in tree height estimation (R2 = 0.72, mean absolute error = 1.89 m), and the optimization process of Shapley additive explanations reduced 23 features, which improved the prediction accuracy (R2 = 0.80, mean absolute error = 1.58 m) and saved computational resources; (b) the seasonal tree height neural network has a strong generalizability for estimating tree height across seasons and regions; and (c) during 2018 to 2023, tree heights in Shenzhen were mainly concentrated in 6 to 14 m, and the spatial distribution has a strong autocorrelation. …”
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Smart grid stability prediction model using two-way attention based hybrid deep learning and MPSO by Umesh Kumar Lilhore, Surjeet Dalal, Magdalena Radulescu, Marinela Barbulescu

“…This research presents a hybrid deep learning model (Convolutional Neural Network [CNN] with Bi-LSTM) with a two-way attention method and a multi-objective particle swarm optimization method (MPSO) for short-term load prediction from a smart grid. …”
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Investigating the Working Efficiency of Typical Work in High-Altitude Alpine Metal Mining Areas Based on a SeqGAN-GABP Mixed Algorithm by Ning Hua, He Huang, Xinhong Zhang

“…Finally, three high-altitude alpine metal mines in Xinjiang were selected as representative examples to verify the proposed framework by comparing it with other state-of the art models (multiple linear regression prediction model, backpropagation (BP) neural network model, and genetic algorithm back propagation (GA-BP) neural network model). …”
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