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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Rules-Based Energy Management System for an EV Charging Station Nanogrid: A Stochastic Analysis by Gabriel Henrique Danielsson, Leonardo Nogueira Fontoura da Silva, Joelson Lopes da Paixão, Alzenira da Rosa Abaide, Nelson Knak Neto

“…The methodology includes forecasting models based on an Artificial Neural Network for photovoltaic generation, a parametric estimation for wind generation, and a Monte Carlo simulation to predict the energy consumption of electric vehicles. …”
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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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Optimized Prediction of Weapon Effectiveness in BVR Air Combat Scenarios Using Enhanced Regression Models by Andre R. Kuroswiski, Annie S. Wu, Angelo Passaro

“…For instance, Lasso regression, a PR method with regularization, achieves results that are 33% better and 2.1 times faster than the best artificial neural network-based solution. Our results challenge common assumptions in the literature about the complexity and feasibility of higher-order PR solutions, suggesting that they can be a compelling alternative for various challenges across domains. …”
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A Tuning Method for the Supplementary Voltage Controller of Dual-Side Grid Forming Converters in Distributed Storage Systems by Angel Perez-Basante, Asier Gildemuro, Ander Ordono, Salvador Ceballos, Eneko Unamuno, Jon Andoni Barrena

“…Real-time estimation of the optimum controller gains by making use of an artificial neural network is proposed. Simulation and experimental results are presented to validate the method.…”
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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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Predicting the Adsorption Efficiency Using Machine Learning Framework on a Carbon-Activated Nanomaterial by Kalapala Prasad, V. Ravi Kumar, R. Suresh Kumar, A. S. Rajesh, Anjani Kumar Rai, Essam A. Al-Ammar, Saikh Mohammad Wabaidur, Amjad Iqbal, Dawit Kefyalew

“…The present study implements that the effectiveness of PCM adsorption on a carbon-activated nanomaterial was predicted using an artificial neural network, a machine learning technology. As a factor of adsorbent particle size, adsorbent dosage, training time, and starting concentrations, the adsorption capacity for each medicinal ingredient was examined. …”
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An Approach to Integrating Tactical Decision-Making in Industrial Maintenance Balance Scorecards Using Principal Components Analysis and Machine Learning by Néstor Rodríguez-Padial, Marta Marín, Rosario Domingo

“…In the proposed Custom Balance Scorecard design, an exploratory data phase is integrated with another analysis and prediction phase using Principal Component Analysis algorithms and Machine Learning that uses Artificial Neural Network algorithms. This new extension allows better control over the maintenance function of an industrial plant in the medium-term with a yearly horizon taken over monthly intervals which allows the measurement of the indicators of strategic productive areas and the discovery of hidden behavior patterns in work orders. …”
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Image-Based Face Recognition Techniques Used in Disease Detection Approaches: A Survey by Ghalib Ahmed Salman, Inas Jawad Kadhim, Ahmed Husham Al-Badri

“…FR applications relying on Artificial Neural Network (ANN) techniques have recorded higher accuracy rates in diagnosing facial diseases. …”
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Displacement-Based Back-Analysis of the Model Parameters of the Nuozhadu High Earth-Rockfill Dam by Yongkang Wu, Huina Yuan, Bingyin Zhang, Zongliang Zhang, Yuzhen Yu

“…In this method, an artificial neural network is used as a substitute for time-consuming finite element analysis, and an evolutionary algorithm is applied for both network training and parameter optimization. …”
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Development of a Medical Information System with Data Storage and Intelligent Image Analysis by D. A. Yakimov, N. V. Vygovskaya, I. V. Drozdov

“…Conditions have been prepared for the implementation of the second stage – integration of an automated workstation of medical information systems into the existing technological process in a medical institution and detection of pathology using an artificial neural network.…”
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Predicting and evaluating the engineering properties of civic garbage torched bottom ash and sisal fibre-reinforced earth blocks by Abinaya Thennarasan Latha, Balasubramanian Murugesan

“…The properties are predicted using artificial neural network (ANN) analysis and response surface methodology (RSM). …”
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Lung Cancer Prediction Using Neural Network Ensemble with Histogram of Oriented Gradient Genomic Features by Emmanuel Adetiba, Oludayo O. Olugbara

“…This paper reports an experimental comparison of artificial neural network (ANN) and support vector machine (SVM) ensembles and their “nonensemble” variants for lung cancer prediction. …”
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Intelligent Classification of Stable and Unstable Slope Conditions Based on Landslide Movement by Long Tsang, Ali Ghorbani, Seyed Mohammad Hossein Khatami, Dmitrii Ulrikh

“…Three models of Tree, Adaboost and artificial neural network (ANN) were developed for classification into two categories, stable and unstable. …”
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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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Perfusion MRI in automatic classification of multiple sclerosis lesion subtypes by Ehsan Homayouny, Rasoul Mahdavifar Khayati, Seyed Massood Nabavi, Vania Karami

“…Therefore, a Bayesian classifier based on the adaptive mixture method was used to segment all lesions, and an artificial neural network (ANN) employed a multi‐layer Perceptron as a subtype classifier. …”
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Automatic Tracking and Characterization of Cumulonimbus Clouds from FY-2C Geostationary Meteorological Satellite Images by Yu Liu, Du-Gang Xi, Zhao-Liang Li, Chun-Xiang Shi

“…First, a seeded region growing (SRG) algorithm is used with artificial neural network (ANN) cloud classification as preprocessing to identify consistent homogeneous Cb patches from infrared images. …”
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Confirmation of Thermal Images and Vibration Signals for Intelligent Machine Fault Diagnostics by Achmad Widodo, Djoeli Satrijo, Toni Prahasto, Gang-Min Lim, Byeong-Keun Choi

“…This paper deals with the maintenance technique for industrial machinery using the artificial neural network so-called self-organizing map (SOM). …”
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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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Applying in machine learning and deep learning in finance industry: A case study on repayment prediction by Nguyễn Phát Đạt, Hồ Mai Minh Nhật, Trương Công Vinh, Lê Quang Chấn Phong, Lê Hoành Sử

“…Employed techniques encompass Logistic Regression (LR), K-Nearest Neighbor (KNN), Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), in conjunction with deep learning architectures such as Long Short-Term Memory (LSTM) and Artificial Neural Network (ANN). Following methodological refinement, it becomes apparent that ensemble learning approaches, exemplified by XGB and LGBM, exhibit markedly superior predictive performance, surpassing conventional models with an accuracy rate exceeding 85%. …”
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