Automatic diagnosis of selected retinal diseases based on OCT B-scan by Tomasz Marciniak, Agnieszka Stankiewicz

“…The prepared software allows the reader to familiarize themselves with the topic of current artificial neural network solutions.…”
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ANN-Enhanced Energy Reference Models for Industrial Buildings: Multinational Company Case Study by Younes Ouaomar, Said Benkechcha, Mourad Kaddiri

“…This paper established a novel approach for developing simplified yet accurate models using artificial neural networks (ANNs) in industrial environments. …”
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Data-Driven Bearing Fault Diagnosis for Induction Motor by Aqib Raqeeb, Fahim Shah, Zaheer Alam, Subhashree Choudhury, Bilal Khan, R. Palanisamy

“…Our convolutional neural networks-based approach is compared to traditional methods such as support vector machine, nearest neighbors, and artificial neural networks. Results demonstrate the superior performance of our data-driven fault diagnosis using convolutional neural networks.…”
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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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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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Machine Learning vs. Econometric Models to Forecast Inflation Rate in Romania? The Role of Sentiment Analysis by Mihaela Simionescu

“…The ARDL model, utilizing inflation and sentiment index data from the previous period, outperformed the proposed seasonal autoregressive integrated moving average (SARIMA) model and the ML techniques (support vector machine and artificial neural networks). The forecasts based on the ARDL model predicted correctly all the changes in inflation, while accuracy measures (mean error, mean absolute error, root squared mean error) in the short-run 2023: Q1–2024: Q3 indicated the most accurate predictions. …”
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Comparison of Machine Learning Algorithms for the Prediction of Mechanical Stress in Three-Phase Power Transformer Winding Conductors by Fausto Valencia, Hugo Arcos, Franklin Quilumba

“…This research compares four machine learning techniques: linear regression, support vector regression, random forests, and artificial neural networks, with regard to the determination of mechanical stress in power transformer winding conductors due to three-phase electrical faults. …”
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Identification of discrete chaotic maps with singular points by P. G. Akishin, P. Akritas, I. Antoniou, V. V. Ivanov

“…We investigate the ability of artificial neural networks to reconstruct discrete chaotic maps with singular points. …”
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Detection and diagnosis of fault bearing using wavelet packet transform and neural network by Said DJABALLAH, Kamel Meftah, Khaled Khelil, Mohsein Tedjini, Lakhdar Sedira

“…This work is part of the diagnosis and classification of bearing defects by vibration analysis of signals from defective bearings using time domain and frequency analysis and wavelet packet transformations (Wavelet Packet Transform WPT) with Artificial Neural Networks (ANN). WPT is used for extracting defect indicators to train the neural classifier. …”
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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-driven insights into ctDNA for oral cancer: Applications, models, and future prospects by Dheeraj Kumar, Saraswati Patel

“…We highlight the integration of advanced machine learning (ML) models—Support Vector Machines (SVM), Random Forests (RF), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN)—in ctDNA detection and analysis. …”
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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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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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Quality Control of Olive Oils Using Machine Learning and Electronic Nose by Emre Ordukaya, Bekir Karlik

“…Different machine learning classifiers such as Naïve Bayesian, K-Nearest Neighbors (k-NN), Linear Discriminate Analysis (LDA), Decision Tree, Artificial Neural Networks (ANN), and Support Vector Machine (SVM) were used. …”
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Development of Pedestrian Level of Service (PLOS) for Jaywalking Pedestrians at Unsignalised Intersections on Urban Arterials Using Ann &Amp; Clustering: A Case Study in India by Ampereza Ankunda, Malaya Mohanty, Satya Ranjan Samal

“…The pedestrian crossing speeds ranged from 0.6 to 1.4 m/s. Artificial Neural Networks (ANN) are used to determine the pedestrian crossing speeds from vehicular and pedestrian counts whose outputs are close to the field data. …”
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ANN and RSM Modeling for the Synthesis of Avocado Seed Starch Combined Orange Peel Extract Antimicrobial Packaging Film by Yasin Ahmed Waday, Ermias Girma Aklilu

“…The results showed that both models performed reasonably well, but trained artificial neural networks have more modeling capability rather than the response surface method. …”
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Prevention and Detection Research of Intelligent Sports Rehabilitation under the Background of Artificial Intelligence by Qiong Huang, Fubin Wang

“…This paper is aimed at studying the prevention and detection of sports rehabilitation in the context of artificial intelligence and proposing a compliance control method for lower limb rehabilitation robots based on artificial neural networks. In this paper, a double closed-loop control system is designed: the outer loop is an adaptive impedance control model based on sEMG feedback, and the purpose is to adjust the predicted desired joint trajectories. …”
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Study of the Permanent Deformation of Soil Used in Flexible Pavement Design by Wendel S. Cabral, Suelly H. A. Barroso, Samuel A. Torquato

“…This is a study of the permanent deformation (PD) of soil used in pavement layers, obtaining prediction models through the technique of artificial neural networks, in addition to the design of pavement structures using mechanistic-empirical and empirical methods. …”
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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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