APPLICATION OF COMPRESSIVE SENSING AND IMPROVED DEEP WAVELET NEURAL NETWORK IN BEARING FAULT DIAGNOSIS by DU XiaoLei, CHEN ZhiGang, ZHANG Nan, XU Xu

“…The feature extraction ability and recognition ability of proposed method are superior than artificial neural network,deep belief network,deep sparse auto-encoder and so on.…”
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Prediction of thermal conductivity in CALF-20 with first-principles accuracy via machine learning interatomic potentials by Soham Mandal, Prabal K. Maiti

“…Here, we report the thermal transport study of CALF-20 using artificial neural network-based machine learning potentials. …”
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Forecasting financial distress for organizational sustainability: An empirical analysis by Soumya Ranjan Sethi, Dushyant Ashok Mahadik

“…This study aims to assess the predictive capabilities of Artificial Neural Network (ANN), Logistic Regression (LR), and Linear Discriminant Analysis (LDA) in predicting a company's bankruptcy. …”
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Machine learning-based analyzing earthquake-induced slope displacement. by Jiyu Wang, Niaz Muhammad Shahani, Xigui Zheng, Jiang Hongwei, Xin Wei

“…This study evaluates the capabilities of various machine learning models, including artificial neural network (ANN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) in analyzing earthquake-induced slope displacement. …”
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On Modelling and Comparative Study of LMS and RLS Algorithms for Synthesis of MSA by Ahmad Kamal Hassan, Adnan Affandi

“…This paper deals with analytical modelling of microstrip patch antenna (MSA) by means of artificial neural network (ANN) using least mean square (LMS) and recursive least square (RLS) algorithms. …”
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Evaluating Machine Learning Models for Prostate Cancer Classification Using Gene Expression Profiles from DNA Microarrays by Haddou Bouazza Sara, Haddou Bouazza Jihad

“…These methods were combined with classifiers such as K Nearest Neighbor (KNN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Decision Tree Classifier (DTC), Naïve Bayes (NB), and Artificial Neural Network (ANN). Our results demonstrated that the best combination was the Signal to Noise Ratio with Linear Discriminant Analysis, achieving a classification accuracy of 95% using only six genes. …”
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A Five-Level Wavelet Decomposition and Dimensional Reduction Approach for Feature Extraction and Classification of MR and CT Scan Images by Varun Srivastava, Ravindra Kumar Purwar

“…The reduced set of features is then fed into either K nearest neighbor algorithm or feed-forward artificial neural network, to classify images. The algorithm is compared with three other techniques in terms of accuracy. …”
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Wetland vegetation mapping improved by phenological leveraging of multitemporal nanosatellite images by Lucas T. Fromm, Laurence C. Smith, Ethan D. Kyzivat

“…Maximum Likelihood (MLC), Support Vector Machine (SVM), and Artificial Neural Network (ANN) classification algorithms are tested on individual, monthly- and multi-seasonal composite images. …”
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Predicting the Compressive Strength of High-Performance Concrete utilizing Radial Basis Function Model integrating with Metaheuristic Algorithms by LiWei Hu

“…For this reason, the usage of machine learning (ML) makes it easier to obtain the acceptable mix design saving time and money. The artificial neural network (ANN) model is the subset of ML, which the experimental tasks can replace. …”
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Fractional Analysis of MHD Boundary Layer Flow over a Stretching Sheet in Porous Medium: A New Stochastic Method by Imran Khan, Hakeem Ullah, Hussain AlSalman, Mehreen Fiza, Saeed Islam, Muhammad Shoaib, Muhammad Asif Zahoor Raja, Abdu Gumaei, Farkhanda Ikhlaq

“…In this article, an effective computing approach is presented by exploiting the power of Levenberg-Marquardt scheme (LMS) in a backpropagation learning task of artificial neural network (ANN). It is proposed for solving the magnetohydrodynamics (MHD) fractional flow of boundary layer over a porous stretching sheet (MHDFF BLPSS) problem. …”
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Comparison of numerical model, neural intelligent and GeoStatistical in estimating groundwater table by Maryam Bayatvarkeshi, Rojin Fasihi

“…In this study, MODFLOW numerical code in GMS software, artificial neural network (ANN) and neural – fuzzy (CANFIS) method in NeuroSolution software, wavelet-neural method in MATLAB software and geostatistical method in ArcGIS software were used. …”
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Hydraulic Fracturing Breakdown Pressure and Prediction of Maximum Horizontal In Situ Stress by Ali Lakirouhani, Somaie Jolfaei

“…Using the numerical model, 1,456 datasets were prepared to train an artificial neural network to predict the maximum horizontal stress. …”
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Image Reconstruction for High-Performance Electrical Capacitance Tomography System Using Deep Learning by Yanpeng Zhang, Deyun Chen

“…Therefore, an artificial neural network of the capacitance (ANNoC) system is introduced to estimate capacitance measurements.…”
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Labiodentals /r/ here to stay: Deep learning shows us why by Hannah King, Emmanuel Ferragne

“…Measurements of the lip area acquired using an artificial neural network suggest that /r/ indeed has a labiodental-like lip posture, thus providing a phonetic account for labiodentalisation. …”
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Multiparameter Logging Evaluation of Chang 73 Shale Oil in the Jiyuan Area, Ordos Basin by Bobiao Liu, Chengqian Tan, Yinchao Huai, Yuhan Tan, Han Zhang, Zhao Feng

“…The p-wave time difference curves calculated by the artificial neural network (ANN) method and the conventional logging curve fitting method were compared. …”
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Fault Diagnosis of Power Transformers With Membership Degree by Enwen Li, Linong Wang, Bin Song

“…Though a high correct rate is reported with intelligent methods as artificial neural network, support vector machine, and so on, these methods are usually too complicated to be implemented practically on a wide range. …”
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Application of Artificial Intelligence for the Estimation of Concrete and Reinforcement Consumption in the Construction of Integral Bridges by Željka Beljkaš, Miloš Knežević, Snežana Rutešić, Nenad Ivanišević

“…The estimation model was developed by using artificial neural networks. The best artificial neural network model showed high accuracy in material consumption estimation expressed as the mean absolute percentage error, 8.56% for concrete consumption estimate and 17.31% for reinforcement consumption estimate.…”
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Multimodel Modeling and Predictive Control for Direct-Drive Wind Turbine with Permanent Magnet Synchronous Generator by Lei Wang, Tao Shen, Chen Chen

“…In this strategy, wind turbine with direct-drive permanent magnet synchronous generator is modeled and a backpropagation artificial neural network is designed to estimate the wind speed loaded into the turbine model in real time through the estimated turbine shaft speed and mechanical power. …”
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Comparison of Neural Network Error Measures for Simulation of Slender Marine Structures by Niels H. Christiansen, Per Erlend Torbergsen Voie, Ole Winther, Jan Høgsberg

“…Training of an artificial neural network (ANN) adjusts the internal weights of the network in order to minimize a predefined error measure. …”
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Predictive Modeling of Tool Life in Turning Using ANN-Taguchi Hybridization by Shrishail Sollapur B., Shubham R. Suryawanshi, Mitali Mhatre S., Dipak K. Dond, Ganesh Chate, Abhijit Bhowmik

“…In this research, we delve into the complex relationship between tool lifespan and cutting speed through experiments guided by the Taguchi method and artificial neural network (ANN) models. Several case studies have been conducted to test the practicality and effectiveness of this method in representing complex tool lifespan-cutting speed relationships.…”
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