Topside Electron Density Modeling Using Neural Network and Empirical Model Predictions by S. Dutta, M. B. Cohen

“…In prior work, an artificial neural network (NN) was developed and trained on two solar cycles worth of Defense Meteorological Satellite Program data (113 satellite‐years), along with global drivers and indices to predict topside electron density. …”
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Predicting GPR Signals from Concrete Structures Using Artificial Intelligence-Based Method by Wael Zatar, Tu T. Nguyen, Hai Nguyen

“…A total of 288 rebar picks were used for training, validation, and testing the proposed Artificial Neural Network (ANN) model. Multiple ANN model configurations with a variation in learning algorithms and the number of nodes in the hidden layer were explored to obtain the optimal model for the nondestructive data. …”
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Model of Multi-Algorithmic-Based Optimization of 4D Approach Trajectory under Thunderstorm Weather by Li Lu, Xin Lai

“…Firstly, the artificial neural network intelligent model was used to predict the thunderstorm movement track. …”
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Experimental and computational investigation of the effect of machining parameters on the turning process of C45 steel by Tien-Thinh Le, Hang Thi Pham, Hiep Khac Doan, Panagiotis G. Asteris

“…Finally, for practical purposes, an artificial neural network model based on machine learning is developed to predict the average temperature near the turning insert nose.…”
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The Co-Movement between International and Emerging Stock Markets Using ANN and Stepwise Models: Evidence from Selected Indices by Dania Al-Najjar

“…To validate the availability of the linkage between the indices, the author includes various tests of a correlation coefficient, stepwise regression analysis, and artificial neural network (ANN). Despite the results indicating that the ANN is more efficient than linear regression in investigating the availability of the relationship between ASEI and international indices, stepwise regression and neural network support this relationship. …”
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Tribological Behavior and Analysis on Surface Roughness of CNC Milled Dual Heat Treated Al6061 Composites by A. Bovas Herbert Bejaxhin, G.M. Balamurugan, S.M. Sivagami, K. Ramkumar, V. Vijayan, S. Rajkumar

“…The influencing factors are identified by the Taguchi, genetic algorithm (GA), and Artificial Neural Network (ANN) techniques and compared within it. …”
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Enhancing fingerprint identification using Fuzzy-ANN minutiae matching by S.P. Singh, Dinesh Kumar Nishad, Saifullah Khalid

“…The system's core lies in its ability to train an artificial neural network to learn an improved similarity function for minutiae matching. …”
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Surface Feature Prediction Modeling and Parameter Optimization for Turning TC17 Titanium Alloy by Zhibo Deng, Zhe Wang, Xuehong Shen

“…Data obtained from the Box-Behnken design experiments were used to develop the response surface methodology (RSM) and artificial neural network (ANN) models. Through analysis of variance (ANOVA), the relative effects of each cutting parameter on the responses have been determined. …”
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Impact of carbapenem-resistant infections on mortality in mechanically ventilated acute respiratory distress syndrome patients: A comparison with hypoxemia severity – An observatio... by Thejesh Srinivas, Shwethapriya R, Gagana Hanumaiah, Pratibha Todur, Souvik Chaudhuri, Ganesh Paramasivam, Prithvishree Ravindra, Vinutha R. Bhat, Sagar Shanmukhappa Maddani, Shobha U. Kamath, Danavath Nagendra, Vishwas P, Likith Hanumaiah, Pratik Paran Medhi, Prabha Prakash

“…Univariate and multivariable logistic regression analyses and artificial neural network model, were employed to analyze mortality outcomes. …”
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Prediksi Kesiapan Sekolah Menggunakan Machine Learning Berbasis Kombinasi Adam dan Nesterov Momentum by Indah Mustika Rahayu, Ahmad Yusuf, Mujib Ridwan

“…Penelitian menggunakan algoritma Artificial Neural Network dengan metode optimasi kombinasi Adam dan Nesterov Momentum. …”
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Combined interaction of fungicides binary mixtures: experimental study and machine learning-driven QSAR modeling by Mohsen Abbod, Ahmad Mohammad

“…QSAR modeling was conducted to assess their fungicidal activity through multiple linear regression (MLR), support vector machine (SVM), and artificial neural network (ANN). Most mixtures exhibited additive interaction, with the CA model proving more accurate than the IA model in predicting fungicidal activity. …”
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Analysis of the Correlation between Emerging Industry Development and University Students’ Entrepreneurship Based on Big Data by Yufeng Pan, Hui Huang

“…Experiments show that the big data integration system established by GM correlation analysis and ant colony Elman regression artificial neural network has high accuracy and can well identify the priority relevance of the industrial direction of strategic emerging industries to college students’ entrepreneurship. …”
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ANALYZING THE CRITERIA AFFECTING TRANSITION TO AIRPLANE BY COMPARING DIFFERENT METHODS by Dilaver Tengilimoğlu, İzay Reyhanoğlu

“…Additionally, it was observed that the Artificial Neural Networks (ANN) model made more accurate predictions compared to others. …”
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Multifractal Analysis and Compressive Strength Prediction for Concrete through Acoustic Emission Parameters by Zhiqiang Lv, Annan Jiang, Jiaxu Jin, Xiangfeng Lv

“…SVM prediction results using AE parameters perform higher precision than the artificial neural network (ANN). Furthermore, a significant reduction in sample size uses AE parameters to predict concrete strength.…”
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Compressive Strength Evaluation of Fiber-Reinforced High-Strength Self-Compacting Concrete with Artificial Intelligence by Tu T. Nguyen, Hoa Pham Duy, Tung Pham Thanh, Hoang Hiep Vu

“…It is shown that the performances of the artificial neural network (ANN) were better than that of the adaptive neurofuzzy inference system (ANFIS) model. …”
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Fault Diagnosis of Batch Reactor Using Machine Learning Methods by Sujatha Subramanian, Fathima Ghouse, Pappa Natarajan

“…Appropriate statistical and geometric features are extracted from the residual signature and the total numbers of features are reduced using SVM attribute selection filter and principle component analysis (PCA) techniques. artificial neural network (ANN) classifiers like multilayer perceptron (MLP), radial basis function (RBF), and Bayes net are used to classify the different types of faults from the reduced features. …”
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ANN Model-Based Simulation of the Runoff Variation in Response to Climate Change on the Qinghai-Tibet Plateau, China by Chang Juan, Wang Genxu, Mao Tianxu, Sun Xiangyang

“…To identify the impacts of climate change in the runoff process in the Three-River Headwater Region (TRHR) on the Qinghai-Tibet Plateau, two artificial neural network (ANN) models, one with three input variables (previous runoff, air temperature, and precipitation) and another with two input variables (air temperature and precipitation only), were developed to simulate and predict the runoff variation in the TRHR. …”
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Research on Monthly Runoff Forecast in Beijiang River Basin Based on Multi-model Ensemble Method by ZHONG Yixuan, LIAO Xiaolong, QUAN Xujian, YI Ling, CHEN Yan, LI Yuanyuan, XUE Jiao

“…The accuracy of the monthly runoff forecast plays a fairly important role in aspects such as optimal allocation of water resources,flood control and drought relief in a basin,water dispatching,and power generation optimization of reservoir groups.The commonly used methods for the monthly runoff forecast mainly include water balance models,mathematical statistics models,and artificial neural networks.Studies have shown that any single model cannot achieve the optimal monthly runoff forecast.Therefore,the multi-model ensemble method provides an effective way to eliminate model uncertainty and improve the accuracy of the monthly runoff forecast.Specifically,the research takes Pingshi,Lishi,Hengshi,and Shijiao stations in the Beijiang River Basin as the research object to analyze and compare the effects of the seasonal auto-regressive (SAR) model,two-parameter monthly water balance (TPMWB) model,and artificial neural network (ANN) model.Then,the multi-model ensemble method for the above-mentioned stations is proposed on the basis of the Bayesian model averaging (BMA) method.The research results reveal that compared with any of the three models,the multi-model ensemble method has significantly improved the accuracy of the monthly runoff forecast with a higher determination coefficient (DC) and a lower mean absolute percentage error (MAPE),and thus it can provide better support for decisions in dispatching in the basin.…”
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APPLICATION OF BOX-BEHNKEN, ANN, AND ANFIS TECHNIQUES FOR IDENTIFICATION OF THE OPTIMUM PROCESSING PARAMETERS FOR FDM 3D PRINTING PARTS by Nguyen Huu Tho

“…The research methodology of the RSM Box-Behnken DOE method, ANN (Artificial neural network), and ANFIS (Adaptive neuro-fuzzy inference systems) has been used to determine the optimum process 3D printing parameters. …”
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Smart Shift Decision Method Based on Stacked Autoencoders by Zengcai Wang, Yazhou Qi, Guoxin Zhang, Lei Zhao

“…The stacked autoencoder (SAE) algorithm, a type of artificial neural network, is used in this study to predict shifting gear timing on the basis of throttle percentage, vehicle velocity, and acceleration. …”
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