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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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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Modeling the Relationship between Rice Yield and Climate Variables Using Statistical and Machine Learning Techniques by Lasini Wickramasinghe, Rukmal Weliwatta, Piyal Ekanayake, Jeevani Jayasinghe

“…Rice harvest and yield data over the last three decades and monthly climatic data were used to develop the prediction model by applying artificial neural networks (ANNs), support vector machine regression (SVMR), multiple linear regression (MLR), Gaussian process regression (GPR), power regression (PR), and robust regression (RR). …”
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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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PharmacoNER Tagger: a deep learning-based tool for automatically finding chemicals and drugs in Spanish medical texts by Jordi Armengol-Estapé, Felipe Soares, Montserrat Marimon, Martin Krallinger

“…In the recent years, the field of natural language processing has mainly pivoted to deep learning and state-of-the-art results for most tasks involving natural language are usually obtained with artificial neural networks. Competitive resources for drug name recognition in English medical texts are already available and heavily used, while for other languages such as Spanish these tools, although clearly needed were missing. …”
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Forecasting basal area increment in forest ecosystems using deep learning: A multi-species analysis in the Himalayas by P. Casas-Gómez, J.F. Torres, J.C. Linares, A. Troncoso, F. Martínez-Álvarez

“…Traditional forecasting techniques, such as Linear Mixed Models, Random Forest and standard Artificial Neural Networks, often fail to account for the time-dependent nature of tree growth and utilize simple architectures. …”
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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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Investigation of boiler energy consumption in the gas refinery units using RSM ANN and Aspen HYSYS by Erfan Gholamzadeh, Ahad Ghaemi, Abolfazl Shokri, Bahman Heydari

“…Using Aspen HYSYS simulations and modeling approaches like Artificial Neural Networks (ANNs) and Response Surface Methodology (RSM), data from 579 days of boiler operation was gathered and examined. …”
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Machine learning models for predicting the bearing capacity of shallow foundations: A Comparative study and sensitivity analysis by Hamid Mohammadnezhad, Seyedmohammad Eslami

“…In this study, classic machine learning regression methods such as KNN, SVM and Decision Tree based models alongside the utilization of Artificial Neural Networks (ANN) regression are examined and compelling results are demonstrated. …”
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A Hybrid Prognostic Approach for Remaining Useful Life Prediction of Lithium-Ion Batteries by Wen-An Yang, Maohua Xiao, Wei Zhou, Yu Guo, Wenhe Liao

“…Empirical comparisons show that the proposed hybrid prognostic approach using the selective kernel ensemble-based RVM learning algorithm performs better than the hybrid prognostic approaches using the popular learning algorithms of feedforward artificial neural networks (ANNs) like the conventional backpropagation (BP) algorithm and support vector machines (SVMs). …”
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Comparative use of different AI methods for the prediction of concrete compressive strength by Mouhamadou Amar

“…The simulations used artificial neural networks or deep learning, generalized linear, decision tree, random forest, support vector machine, and gradient-boosted tree models to predict the compressive strength of 8 concrete mix designs containing different SCMs. …”
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Towards the implementation of automated scoring in international large-scale assessments: Scalability and quality control by Ji Yoon Jung, Lillian Tyack, Matthias von Davier

“…The results showed that the supervised learning approach, particularly combining multiple machine translations with artificial neural networks (MMT_ANNs), showed comparable performance to human scoring. …”
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On the assessment and reliability of political and ideological education in colleges using deep learning methods by Yongsheng Ma, Xianhui Sun, Aiqun Ma

“…Sophisticated deep learning techniques including artificial neural networks (ANN), convolutional neural networks (CNN), and support vector machines (SVM) were utilized to enhance the reliability of these evaluations. …”
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Evaluation of Three Satellite Precipitation Products TRMM 3B42, CMORPH, and PERSIANN over a Subtropical Watershed in China by Junzhi Liu, Zheng Duan, Jingchao Jiang, A-Xing Zhu

“…This study conducted a comprehensive evaluation of three satellite precipitation products (TRMM (Tropical Rainfall Measuring Mission) 3B42, CMORPH (the Climate Prediction Center (CPC) Morphing algorithm), and PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks)) using data from 52 rain gauge stations over the Meichuan watershed, which is a representative watershed of the Poyang Lake Basin in China. …”
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An intrusion detection model based on Convolutional Kolmogorov-Arnold Networks by Zhen Wang, Anazida Zainal, Maheyzah Md Siraj, Fuad A. Ghaleb, Xue Hao, Shaoyong Han

“…Abstract The application of artificial neural networks (ANNs) can be found in numerous fields, including image and speech recognition, natural language processing, and autonomous vehicles. …”
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Advanced Ai Tools for Predicting Mechanical Properties of Self-Compacting Concrete by AGRAWAL Achal, CHANDAK Narayan

“…The present study utilizes advanced numerical evaluation techniques like Artificial Intelligence (AI), including Support Vector Machines (SVM), Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference Systems with Genetic Algorithms (ANFIS-GA), Gene Expression Programming (GEP), and Multiple Linear Regression (MLR) to develop and compare the predictive models for determination of compressive and tensile strength. …”
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A Novel Hybrid Die Design for Enhanced Grain Refinement: Vortex Extrusion–Equal-Channel Angular Pressing (Vo-CAP) by Hüseyin Beytüt, Kerim Özbeyaz, Şemsettin Temiz

“…The optimization process utilized an integrated approach combining Finite Element Analysis (FEA), artificial neural networks (ANNs), and the non-dominated sorting genetic algorithm II (NSGA-II). …”
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A Novel Ionospheric Inversion Model: PINN‐SAMI3 (Physics Informed Neural Network Based on SAMI3) by Jiayu Ma, Haiyang Fu, J. D. Huba, Yaqiu Jin

“…The PINN‐SAMI3 achieves good inversion results even using sparse data in comparison to the traditional artificial neural networks (ANN). The framework will contribute to advance the future space weather prediction capability with artificial intelligence (AI).…”
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