Parameter Acquisition Study of Mining-Induced Surface Subsidence Probability Integral Method Based on RF-AGA-ENN Model by Jinman Zhang, Liangji Xu, Jiewei Li, Yueguan Yan, Ruirui Xu

“…To obtain more accurate PIM parameters in the absence of observational data, we propose a combined machine learning model (RF-AGA-ENN)—random forest (RF) extracts the best combination of features as the input layer of Elman neural network (ENN); ant colony algorithm (ACO) and genetic algorithm (GA) are combined (called AGA) for the weights and thresholds of ENN optimization. …”
Get full text

An Improved Deep Learning-Based Technique for Driver Detection and Driver Assistance in Electric Vehicles with Better Performance by Gunapriya Balan, Singaravelan Arumugam, Suresh Muthusamy, Hitesh Panchal, Hossam Kotb, Mohit Bajaj, Sherif S. M. Ghoneim, null Kitmo

“…The proposed model (RF-DNN) achieved 97.05% of accuracy and the PCA-DNN model achieved 95.55% of accuracy, whereas the artificial neural network as ANN with PCA and RF achieved nearly 92% of accuracy.…”
Get full text

An Ecolevel Estimation Method of Individual Driver Performance Based on Driving Simulator Experiment by Yiping Wu, Xiaohua Zhao, Ying Yao, Jian Rong

“…Taking a number of one hundred of data segments in vehicle starting process as training sample, the optimal structure, functions, and learning rate of a backpropagation neural network model with three layers were obtained, after repeated model simulation experiments. …”
Get full text

Advanced Soft Computing Techniques for Monthly Streamflow Prediction in Seasonal Rivers by Mohammed Achite, Okan Mert Katipoğlu, Veysi Kartal, Metin Sarıgöl, Muhammad Jehanzaib, Enes Gül

“…In this study, advanced soft computing techniques, including long short-term memory (LSTM), convolutional neural network–recurrent neural network (CNN-RNN), and group method of data handling (GMDH) algorithms, were employed to forecast monthly streamflow time series at two different stations in the Wadi Mina basin. …”
Get full text

A Deep Learning-Based Approach to Enable Action Recognition for Construction Equipment by Jinyue Zhang, Lijun Zi, Yuexian Hou, Mingen Wang, Wenting Jiang, Da Deng

“…The contributions of this research are as follows: (1) the development of a comprehensive video dataset of 2,064 clips with five action types for excavators and dump trucks; (2) a new deep learning-based CEAR approach (known as a simplified temporal convolutional network or STCN) that combines a convolutional neural network (CNN) with long short-term memory (LSTM, an artificial recurrent neural network), where CNN is used to extract image features and LSTM is used to extract temporal features from video frame sequences; and (3) the comparison between this proposed new approach and a similar CEAR method and two of the best-performing HAR approaches, namely, three-dimensional (3D) convolutional networks (ConvNets) and two-stream ConvNets, to evaluate the performance of STCN and investigate the possibility of directly transferring HAR approaches to the field of CEAR.…”
Get full text

Developing an Intelligent System for Efficient Botnet Detection in IoT Environment by Ramesh Singh Rawat, Manoj Diwakar, Umang Garg, Prakash Srivastava

“…This paper focused on analyzing botnet traffic in an IoT environment using machine learning and deep learning classifiers: Decision tree classifier, Naïve Bayes, K nearest neighbor, Convolution neural network, Recurrent neural network, and Random Forest. …”
Get full text

Assessing agritourism-integrated rural human settlement environment under the “dual-carbon” goal: evidence from Zhejiang, China by Shuaijun Lin, Hongfeng Zhang, Johnny F. I. Lam

“…Meanwhile, the BP neural network model was applied to predict the scores of 22 sample villages, and the prediction results were highly correlated with the actual ones. …”
Get full text

A Comprehensive Analysis of Supervised Learning Techniques for Electricity Theft Detection by Farah Aqilah Bohani, Azizah Suliman, Mulyana Saripuddin, Sera Syarmila Sameon, Nur Shakirah Md Salleh, Surizal Nazeri

“…In this paper, comparisons based on predictive accuracy, recall, precision, AUC, and F1-score of several supervised learning methods such as decision tree (DT), artificial neural network (ANN), deep artificial neural network (DANN), and AdaBoost are presented and their performances are analyzed. …”
Get full text

Enhancing breast cancer prediction through stacking ensemble and deep learning integration by Fatih Gurcan

“…In addition to ensemble methods, deep learning models including convolutional neural network (CNN), recurrent neural network (RNN), gated recurrent unit (GRU), bidirectional long short-term memory (BILSTM), long short-term memory (LSTM) were analyzed as meta predictors. …”
Get full text

Localization of mobile robot in prior 3D LiDAR maps using stereo image sequence by I.V. Belkin, A.A. Abramenko, V.D. Bezuglyi, D.A. Yudin

“…A novel localization approach for mobile ground robot, which successfully combines conventional computer vision techniques, neural network based image analysis and numerical optimization, is proposed. …”
Get full text

Research on Optimization of Injection Molding Process Parameters of Automobile Plastic Front-End Frame by Kai Yang, Lingfeng Tang, Peng Wu

“…Finally, the optimized neural network model was used to predict the combination of process parameters with the minimum volume shrinkage and warpage amount. …”
Get full text

Balancing central control and sensory feedback produces adaptable and robust locomotor patterns in a spiking, neuromechanical model of the salamander spinal cord. by Alessandro Pazzaglia, Andrej Bicanski, Andrea Ferrario, Jonathan Arreguit, Dimitri Ryczko, Auke Ijspeert

“…This study introduces a novel neuromechanical model employing a detailed spiking neural network to explore the role of axial proprioceptive sensory feedback, namely stretch feedback, in salamander locomotion. …”
Get full text

Cyberattack Monitoring Architectures for Resilient Operation of Connected and Automated Vehicles by Zulqarnain H. Khattak, Brian L. Smith, Michael D. Fontaine

“…The proposed algorithm was also compared to convolutional neural network (CNN) and other classical algorithms. The monitoring system detected three different emulated cyberattacks with high accuracy. …”
Get full text

Nonlinear Adaptive Neural Control of Power Converter‐Driven DC Motor System: Design and Experimental Validation by Tousif Khan Nizami, Sasank Das Gangula, Ramanjaneya Reddy Udumula, Arghya Chakravarty, Fareed Ahmad, Alireza Hosseinpour

“…The proposed technique integrates an adaptive polynomial‐neural network with a backstepping strategy to yield a robust control system for output tracking in DC motor. …”
Get full text

Articulatory-to-Acoustic Conversion Using BiLSTM-CNN Word-Attention-Based Method by Guofeng Ren, Guicheng Shao, Jianmei Fu

“…By considering the graphical representation of the articulators’ motion, this study combined Bidirectional Long Short-Term Memory (BiLSTM) with convolution neural network (CNN) and adopted the idea of word attention in Mandarin to extract semantic features. …”
Get full text

Multiscale Time-Frequency Sparse Transformer Based on Partly Interpretable Method for Bearing Fault Diagnosis by Shouquan Che, Jianfeng Lu, Congwang Bao, Caihong Zhang, Yongzhi Liu

“…Transformer model is being gradually studied and applied in bearing fault diagnosis tasks, which can overcome the feature extraction defects caused by long-term dependencies in convolution neural network (CNN) and recurrent neural network (RNN). …”
Get full text

Research on Airport Target Recognition under Low-Visibility Condition Based on Transfer Learning by Jiajun Li, Yongzhong Wang, Yuexin Qian, Tianyi Xu, Kaiwen Wang, Liancheng Wan

“…According to the results, the dark channel algorithm has the best image defogging enhancement effect, and the GoogLeNet deep neural network has the highest target recognition rate.…”
Get full text

Prediction of end-point phosphorus content of molten steel in BOF with machine learning models by Kang Y., Ren M.-M., Zhao J.-X., Yang L.-B., Zhang Z.-K., Wang Z., Cao G.

“…Four machine learning regression models (Lasso, Random Forest, Xgboost, and Neural Network) were established to predict the end-point phosphorus content of molten steel in the BOF based on raw and auxiliary material data, process parameters, and production quality data. …”
Get full text

Network Embedding-Aware Point-of-Interest Recommendation in Location-Based Social Networks by Lei Guo, Haoran Jiang, Xiyu Liu, Changming Xing

“…On the other hand, neural network-based embedding methods have shown its power in many recommendation tasks with its ability to extract high-level representations from raw data. …”
Get full text

Designing Channel Attention Fully Convolutional Networks with Neural Architecture Search for Customer Socio-Demographic Information Identification Using Smart Meter Data by Zhirui Luo, Qingqing Li, Ruobin Qi, Jun Zheng

“…Our results show that the deep neural network architectures designed automatically by our proposed method significantly outperform all baseline methods in addressing the socio-demographic questions investigated in our study.…”
Get full text