Experimental Investigation and Prediction of Compressive Strength of Ultra-High Performance Concrete Containing Supplementary Cementitious Materials by Jisong Zhang, Yinghua Zhao, Haijiang Li

“…Furthermore, to minimise the experimental workload of future studies, a prediction model is developed to predict the compressive strength of the UHPC using artificial neural networks (ANNs). The results indicate that the developed ANN model has high accuracy and can be used for the prediction of the compressive strength of UHPC with these SCMs.…”
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Forecasting Alisadr Cave Tourism Demand using Combination of Short-term and Long-term Forecasts by Elaheh Malekzadeh Hamedani, Marjan Kaedi, Zahra Zojaji

“…For this purpose, a method based on artificial neural networks is presented, in which the results of linear and non-linear methods and short-term and long-term forecasts are combined. …”
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Developing multifactorial dementia prediction models using clinical variables from cohorts in the US and Australia by Caitlin A. Finney, David A. Brown, Artur Shvetcov

“…Tree-based machine learning algorithms and artificial neural networks were used. APOE genotype was the best predictor of dementia cases and healthy controls. …”
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Artificial intelligence in the service of entrepreneurial finance: knowledge structure and the foundational algorithmic paradigm by Robert Kudelić, Tamara Šmaguc, Sherry Robinson

“…The results demonstrate a high representation of artificial neural networks, deep neural networks, and support vector machines across almost all identified topic niches. …”
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Developing a Suitable Model for Water Uptake for Biodegradable Polymers Using Small Training Sets by Loreto M. Valenzuela, Doyle D. Knight, Joachim Kohn

“…We first built semiempirical models using Artificial Neural Networks and all water uptake data, as individual input. …”
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A Mini-Review of Machine Learning in Big Data Analytics: Applications, Challenges, and Prospects by Isaac Kofi Nti, Juanita Ahia Quarcoo, Justice Aning, Godfred Kusi Fosu

“…The study outcome shows that deep neural networks (15%), support vector machines (15%), artificial neural networks (14%), decision trees (12%), and ensemble learning techniques (11%) are widely applied in BDA. …”
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Short‐Term and Long‐Term Memory Functionality of a Brain‐Like Device Built from Nanoparticle Atomic Switch Networks by Oradee Srikimkaew, Saman Azhari, Deep Banerjee, Yuki Usami, Hirofumi Tanaka

“…The findings provide insight into the the learning and memory abilities of atomic switch network memristors, facilitating the development of hardware‐implemented artificial neural networks.…”
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Multi-GPU Development of a Neural Networks Based Reconstructor for Adaptive Optics by Carlos González-Gutiérrez, María Luisa Sánchez-Rodríguez, José Luis Calvo-Rolle, Francisco Javier de Cos Juez

“…The Complex Atmospheric Reconstructor based on Machine Learning (CARMEN) is an algorithm based on artificial neural networks, designed to compensate the atmospheric turbulence. …”
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Optimal fixed-time sliding mode control for anti-lock braking systems based fuzzy logic and neural network by Najlae Jennan, El Mehdi Mellouli

“…The research introduces a novel control strategy that combines fixed-time sliding mode control (SMC), artificial neural networks (ANN), Takagi-Sugeno (T-S) fuzzy logic, and particle swarm optimization (PSO). …”
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Estimation of Costs and Durations of Construction of Urban Roads Using ANN and SVM by Igor Peško, Vladimir Mučenski, Miloš Šešlija, Nebojša Radović, Aleksandra Vujkov, Dragana Bibić, Milena Krklješ

“…The paper presents a research of precision that can be achieved while using artificial intelligence for estimation of cost and duration in construction projects. Both artificial neural networks (ANNs) and support vector machines (SVM) are analysed and compared. …”
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Financial Distress Warning: An Evaluation System including Ecological Efficiency by Shuang Wu, Hui Zhang, Yuan Tian, Liyuan Shi

“…Based on the data of listed companies, Data Envelopment Analysis (DEA) is applied to evaluating the business efficiency, financial efficiency, financing efficiency, human capital efficiency, and ecological efficiency, and the accuracy of the evaluation system that includes ecological efficiency is measured by artificial neural networks (ANNs). Besides, the logit model is applied to test the results. …”
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Applying Adaptive Neural Fuzzy Inference System to Improve Concrete Strength Estimation in Ultrasonic Pulse Velocity Tests by Loan T. Q. Ngo, Yu-Ren Wang, Yi-Ming Chen

“…To improve the result of nondestructive testing methods, this research applies artificial neural networks and adaptive neural fuzzy inference system in predicting the concrete strength estimation using nondestructive testing method, the ultrasonic pulse velocity test. …”
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Mathematical Modeling of Properties and Structures of Crystals: From Quantum Approach to Machine Learning by Grzegorz Matyszczak, Christopher Jasiak, Gabriela Rusinkiewicz, Kinga Domian, Michał Brzozowski, Krzysztof Krawczyk

“…., cellular automata, CA), to machine learning (e.g., artificial neural networks, ANNs).…”
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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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Application of Soft Computing Paradigm to Large Deformation Analysis of Cantilever Beam under Point Load by Yanmei Cui, Yong Hong, Naveed Ahmad Khan, Muhammad Sulaiman

“…Since finding an exact solution to such nonlinear models is difficult task, this paper focuses on developing soft computing technique based on artificial neural networks (ANNs), generalized normal distribution optimization (GNDO) algorithm, and sequential quadratic programming (SQP). …”
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Nonintrusive Method Based on Neural Networks for Video Quality of Experience Assessment by Diego José Luis Botia Valderrama, Natalia Gaviria Gómez

“…In this paper, a model based on artificial neural networks such as BPNNs (Backpropagation Neural Networks) and the RNNs (Random Neural Networks) is applied to evaluate the subjective quality metrics MOS (Mean Opinion Score) and the PSNR (Peak Signal Noise Ratio), SSIM (Structural Similarity Index Metric), VQM (Video Quality Metric), and QIBF (Quality Index Based Frame). …”
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Combination linear lines of position and neural network for mobile station location estimation by Chien-Sheng Chen

“…It is much easier to solve two linear line equations rather than nonlinear circular ones. Artificial neural networks are widely used techniques in various areas due to overcoming the problem of exclusive and nonlinear relationships. …”
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Machine Learning-based Water Quality Forecasting for Shenzhen Bay by XIONG Jianzhi, XIONG Rui, LU Haiyan, ZHENG Yi

“…Based on high-frequency monitoring data collected by the buoy online monitoring system in Shenzhen Bay, machine learning methods including artificial neural networks (ANN), support vector regression (SVR), and random forest (RF) are employed to conduct short-term forecasting of water quality parameters such as dissolved oxygen (DO), chlorophyll-a (Chl.a), total nitrogen (TN), and total phosphorus (TP). …”
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Application,Challenges,and Prospect of Machine Learning in Dam Seepage Prediction by WANG Wei, LIAO Jielin, ZHU Shaokun

“…The adverse effects of seepage will increase the dam failure risk, and applying machine learning to accurate seepage prediction is crucial to dam safety and stability.This paper reviews the application, challenges, and solutions of machine learning in dam seepage prediction.Machine learning can not only predict the seepage behavior of dams but also identify some key parameters such as dam permeability coefficient and groundwater level in seepage prediction.Artificial neural networks, support vector machines, and decision trees have been widely employed in seepage prediction of dams.Integrated algorithms greatly improve prediction accuracy by integrating the advantages of multiple algorithms.Machine learning models still have many shortcomings in data quantity and quality, model interpretability, complexity, scalability, deployment, and implementation.Future research directions include developing advanced machine learning algorithms, creating physics data dual-drive models and interpretable models, and enhancing experimental testing and validation.The relevant achievements can provide references for studying dam seepage prediction based on machine learning models.…”
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Forecasting the Cell Temperature of PV Modules with an Adaptive System by Giuseppina Ciulla, Valerio Lo Brano, Edoardo Moreci

“…In this work an alternative method, based on the employment of artificial neural networks (ANNs), was proposed to predict the operating temperature of a PV module. …”
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