A Reduced Order Model Based on ANN-POD Algorithm for Steady-State Neutronics and Thermal-Hydraulics Coupling Problem by Hanxing Liu, Han Zhang

“…To solve this problem, this work develops a reduced order model based on the proper orthogonal decomposition (POD) and artificial neural networks (ANNs) to simulate the N/TH coupling system. …”
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Comparative Analysis of Machine Learning Techniques for Cryptocurrency Price Prediction by Sara Salehi

“…This study evaluates the efficacy of various machine learning models in predicting cryptocurrency prices, with a particular focus on Support Vector Machines for Regression (SVR), least-squares Boosting (LSBoost), and Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference System (ANFIS). …”
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Neural Network Technologies in Predicting the Operating Status of Agricultural Enterprises by Aleksandr V. Grachev

“…The data obtained were analyzed using artificial neural networks. The procedure included identifying a set of factors that described an agro-industrial complex or some of its properties that corresponded to a specific task. …”
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Double weighted combat data quality evaluation method based on CVF optimized FAHP by Jianwei Wang, Chengsheng Pan, Qing Zhang

“…Analysis of the experimental results indicates that the proposed method reduces the mean squared error to 5.35 when compared to results obtained using FAHP, interval intuitionistic fuzzy methods, and artificial neural networks, bringing it closer to actual standard values. …”
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Binary Classification of Customer’s Online Purchasing Behavior Using Machine Learning by Ahmad Aldelemy, Raed A. Abd-Alhameed

“…Our methodology includes data analysis, transformation, training, and testing machine learning classifiers such as Naïve Bayes, Decision Trees, Random Forests, Support Vector Machines, Logistic Regression, Artificial Neural Networks, AdaBoost, and Gradient Descent. …”
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Damage Identification of a Steel Frame Based on Integration of Time Series and Neural Network under Varying Temperatures by Minshui Huang, Wei Zhao, Jianfeng Gu, Yongzhi Lei

“…This study presents a methodology incorporating the autoregressive (AR) time series model with two-step artificial neural networks (ANNs) to identify damage under temperature variations. …”
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Implications of Spatiotemporal Data Aggregation on Short-Term Traffic Prediction Using Machine Learning Algorithms by Rivindu Weerasekera, Mohan Sridharan, Prakash Ranjitkar

“…As a step towards addressing these problems, this paper investigates the ability of Artificial Neural Networks, Random Forests, and Support Vector Regression algorithms to reliably model traffic flow at different data resolutions and respond to unexpected traffic incidents. …”
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A New Decomposition Ensemble Learning Approach with Intelligent Optimization for PM2.5 Concentration Forecasting by Guangyuan Xing, Shaolong Sun, Jue Guo

“…According to the principle of “divide and conquer,” we propose a novel decomposition ensemble learning approach by integrating ensemble empirical mode decomposition (EEMD), artificial neural networks (ANNs), and adaptive particle swarm optimization (APSO) for forecasting PM2.5 concentrations. …”
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Assessing the influence of bibliometric factors and organizational characteristics on the centrality degree of inter-university collaborative networks: a neural network approach by Juan David Reyes-Gómez, Efrén Romero-Riaño

“…This study used artificial neural networks, particularly a multilayer perceptron. …”
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Integrated Feature Selection of ARIMA with Computational Intelligence Approaches for Food Crop Price Prediction by Yuehjen E. Shao, Jun-Ting Dai

“…Other than the ARIMA, the components of the proposed integrated forecasting models include artificial neural networks (ANNs), support vector regression (SVR), and multivariate adaptive regression splines (MARS). …”
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Evaluation of daily based satellite rainfall estimates for flood monitoring in Gumera Watershed, Amhara region, Ethiopia by Gebrie Tsegaye Mersha, Asnake Mekuriaw

“…Three daily satellite rainfall estimates (Tropical Application of Meteorology using Satellite and ground-based observations (TA MSAT -V3.1), Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS-V2) and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks Climate Data Record (PERSIANN-CDR)) are evaluated against independent gauge data (2004–2019). …”
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Slot Parameter Optimization for Multiband Antenna Performance Improvement Using Intelligent Systems by Erdem Demircioglu, Ahmet Fazil Yagli, Senol Gulgonul, Haydar Ankishan, Emre Oner Tartan, Murat H. Sazli, Taha Imeci

“…The slot parameters on MMPA are modeled using soft computing technique of artificial neural networks (ANN). To achieve the best ANN performance, Particle Swarm Optimization (PSO) and Differential Evolution (DE) are applied with ANN’s conventional training algorithm in optimization of the modeling performance. …”
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Performance Evaluation of Various Z-Source Inverter Topologies for PV Applications Using AI-Based MPPT Techniques by N. Kalaiarasi, A. Sivapriya, Pradeep Vishnuram, Mukesh Pushkarna, Mohit Bajaj, Hossam Kotb, Sadam Alphonse

“…This paper discusses the performance of various topologies of ZSI, such as traditional Z-source inverters (XZSIs); for integrating a PV source into a load, switched inductor Z-source inverters (SIZSIs) and transient Z-source inverters (TZSIs) are used. Also, artificial neural networks (ANNs), fuzzy logic controller (FLC), and adaptive neuro-fuzzy inference system (ANFIS)-based MPPT techniques are discussed for obtaining maximum power from PV panels. …”
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Daily Prediction Model of Photovoltaic Power Generation Using a Hybrid Architecture of Recurrent Neural Networks and Shallow Neural Networks by Wilson Castillo-Rojas, Juan Bekios-Calfa, César Hernández

“…For the implementation of these models, a hybrid architecture based on recurrent neural networks (RNN) with long short-term memory (LSTM) or gated recurrent units (GRU) structure, combined with shallow artificial neural networks (ANN) with multilayer perceptron (MLP) structure, is established. …”
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Modelling Laser Milling of Microcavities for the Manufacturing of DES with Ensembles by Pedro Santos, Daniel Teixidor, Jesus Maudes, Joaquim Ciurana

“…In total, 162 different conditions are tested in a process that is modeled with the following state-of-the-art data-mining regression techniques: Support Vector Regression, Ensembles, Artificial Neural Networks, Linear Regression, and Nearest Neighbor Regression. …”
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Forward and Reverse Process Models for the Squeeze Casting Process Using Neural Network Based Approaches by Manjunath Patel Gowdru Chandrashekarappa, Prasad Krishna, Mahesh B. Parappagoudar

“…The present research work is focussed to develop an intelligent system to establish the input-output relationship utilizing forward and reverse mappings of artificial neural networks. Forward mapping aims at predicting the density and secondary dendrite arm spacing (SDAS) from the known set of squeeze cast process parameters such as time delay, pressure duration, squeezes pressure, pouring temperature, and die temperature. …”
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Attribute Selection Impact on Linear and Nonlinear Regression Models for Crop Yield Prediction by Alberto Gonzalez-Sanchez, Juan Frausto-Solis, Waldo Ojeda-Bustamante

“…Multiple linear regression, stepwise linear regression, M5′ regression trees, and artificial neural networks (ANN) were ranked. The models were built using real data of eight crops sowed in an irrigation module of Mexico. …”
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Layer ensemble averaging for fault tolerance in memristive neural networks by Osama Yousuf, Brian D. Hoskins, Karthick Ramu, Mitchell Fream, William A. Borders, Advait Madhavan, Matthew W. Daniels, Andrew Dienstfrey, Jabez J. McClelland, Martin Lueker-Boden, Gina C. Adam

“…Abstract Artificial neural networks have advanced due to scaling dimensions, but conventional computing struggles with inefficiencies due to memory bottlenecks. …”
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Automated CATS system for distance learning by Yu. B. Popova

“…As mathematical methods, it is proposed to use the analysis of expert systems, as well as artificial neural networks. These mathematical methods made it possible to develop adaptability algorithms, their software implementation and testing in the educational process. …”
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Bio‐Inspired Neuromorphic Sensory Systems from Intelligent Perception to Nervetronics by Elvis K. Boahen, Hyukmin Kweon, Hayoung Oh, Ji Hong Kim, Hayoung Lim, Do Hwan Kim

“…This study explores the latest advancements in artificial synaptic properties triggered by various stimuli, including optical, auditory, mechanical, and chemical inputs, and their subsequent processing through artificial neural networks for applications in image recognition and multimodal pattern recognition. …”
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