An Overview of Pavement Degradation Prediction Models by Amir Shtayat, Sara Moridpour, Berthold Best, Shahriar Rumi

“…The findings show that most previous studies preferred machine learning approaches and artificial neural networks forecasting and estimating the road pavement conditions because of their ability to deal with massive data, their higher accuracy, and them being worthwhile in solving time-series problems.…”
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Neural Network-Based Analysis of Flame States in Pulverised Coal and Biomass Co-Combustion by Żaklin Grądz, Waldemar Wójcik, Baglan Imanbek, Bakhyt Yeraliyeva

“…The measurement data after preprocessing were classified using artificial neural networks to determine the conditions for flame stability. …”
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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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Performance Sensitivity of a Wind Farm Power Curve Model to Different Signals of the Input Layer of ANNs: Case Studies in the Canary Islands by Sergio Velázquez Medina, José A. Carta, Ulises Portero Ajenjo

“…A wind farm power curve model is proposed in this paper which is developed using artificial neural networks, and a study is undertaken of the influence on model performance when parameters such as the meteorological conditions (wind speed and direction) of areas other than the wind farm location are added as signals of the input layer of the neural network. …”
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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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BIM-Based Machine Learning Application for Parametric Assessment of Building Energy Performance by Panagiotis Tsikas, Athanasios Chassiakos, Vasileios Papadimitropoulos, Antonios Papamanolis

“…They include statistical regression modeling (SRM), decision trees (DTs), random forests (RFs), and artificial neural networks (ANNs). The analysis reveals the contribution of each factor and highlights the ANN as the best performing model. …”
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A scientometric review of the relationship between learning agility and work engagement in modern management context by Farira Nareswari, Rini Juni Astuti

“…Machine learning, artificial neural networks, and predictive analytics can improve learning agility and work engagement. …”
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Fuel Cell Output Current Prediction with a Hybrid Intelligent System by José-Luis Casteleiro-Roca, Antonio Javier Barragán, Francisca Segura, José Luis Calvo-Rolle, José Manuel Andújar

“…This hybrid model uses artificial neural networks to predict the output current of the fuel cell in a very precise way. …”
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Three-dimensional design, simulation and optimization of a centrifugal compressor impeller with double-splitter blades by Mohammadjavad Tasharrofi, Mojtaba Heidarian Shahri, Ali Madadi

“…Since the optimization process only using genetic algorithms is very time-consuming and has high computational costs, artificial neural networks were used to reduce costs. The objective function in this optimization process was to increase efficiency while maintaining the flow rate and pressure ratio at the design point. …”
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Shannon Entropy Computations in Navier–Stokes Flow Problems Using the Stochastic Finite Volume Method by Marcin Kamiński, Rafał Leszek Ossowski

“…Further numerical extension of this technique is seen in an application of the artificial neural networks, where polynomial approximation may be replaced automatically by some optimal, and not necessarily polynomial, bases.…”
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Prediction of the Impact of Bank Failure Risk on Micro-Credit in Iran: An Artificial Intelligence Approach by Reza Taheri Haftasiabi, Yusef Mohammadzadeh, Ameneh Naderi

“…Machine learning tools, including artificial neural networks (ANN) and support vector machine (SVM), were used to analyze macroeconomic indicators such as GDP, inflation, exchange rate, interest rate, and financial variables of banks such as investment volume, amount of loans granted, total deposits, and bankruptcy risk indicators. …”
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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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Enhancing the mechanical properties’ performances coconut fiber and CDW composite in paver block: multiple AI techniques with a Performance analysis by G. Uday Kiran, G. Nakkeeran, Dipankar Roy, Sumant Nivarutti Shinde, George Uwadiegwu Alaneme

“…In this study, Response Surface Methodology (RSM), Support Vector Machine (SVM), Gradient Boosting (GB), Artificial Neural Networks (ANN), and Random Forest (RF) machine learning method for optimization and predicting the mechanical properties of natural fiber addition incorporated with construction and demolition waste (CDW) as replacement of Fine Aggregate in Paver blocks. …”
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Automatic Adaptive Algorithm for Delineation of Cerebral-Spinal Fluid Regions for Non-contrast Magnetic Resonance Imaging Volumetry and Cisternography in Mice by Ryszard Gomolka

“…Despite the increasing use of artificial neural networks in image analysis, this analytical approach provides robustness, especially when the dataset is insufficiently small and limited for training the network. …”
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Improving catalysts and operating conditions using machine learning in Fischer-Tropsch synthesis of jet fuels (C8-C16) by Parisa Shafiee, Bogdan Dorneanu, Harvey Arellano-Garcia

“…Moreover, various machine-learning models (Random Forest (RF), Gradient Boosted, CatBoost, and artificial neural networks (ANN)) were evaluated to predict CO conversion and C8-C16 selectivity. …”
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A Step Towards Neuroplasticity: Capsule Networks with Self-Building Skip Connections by Nikolai A. K. Steur, Friedhelm Schwenker

“…<b>Background:</b> Integrating nonlinear behavior into the architecture of artificial neural networks is regarded as essential requirement to constitute their effectual learning capacity for solving complex tasks. …”
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Intelligent model and optimization of ultrasound-assisted extraction of antioxidants and amylase enzyme from Gnaphalium affine D. Don by Naphatrapi Luangsakul, Kannika Kunyanee, Sandra Kusumawardani, Tai Van Ngo

“…The study involves two statistical methods: artificial neural networks (ANN) and response surface methodology (RSM) to model and optimize extraction procedure for improving the yield of antioxidant and amylase enzyme activity (AEA). …”
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Modelling and Optimization of Fluid Frictional Torque in a Single Stage Centrifugal Pump with a Vaned Diffuser Based on RSM, ANN and Desirability Function by K. Singh, A. Singh, D. K. Singh

“…Response Surface Methodology (RSM) and Artificial Neural Networks (ANNs) are utilized to capture complex parameter interactions, with optimization performed using a Desirability Function (DF). …”
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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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Enhancing Fault Detection and Classification in MMC-HVDC Systems: Integrating Harris Hawks Optimization Algorithm with Machine Learning Methods by Omar Hazim Hameed Hameed, Uğurhan Kutbay, Javad Rahebi, Fırat Hardalaç, Ibrahim Mahariq

“…Leveraging machine learning (ML) and artificial neural networks (ANN), this technique demonstrates its effectiveness in generating a fault locator with exceptional accuracy. …”
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