Neural Network Based Vibration Analysis with Novelty in Data Detection for a Large Steam Turbine by K. P. Kumar, K.V.N.S. Rao, K.R. Krishna, B. Theja

“…This paper describes about normal and abnormal vibration data detection procedure for a large steam turbine (210 MW) using artificial neural networks (ANN). Self-organization map is trained with the normal data obtained from a thermal power station, and simulated with abnormal condition data from a test rig developed at laboratory. …”
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Influence of proteinoids on calcium carbonate polymorphs precipitation in supersaturated solutions by Panagiotis Mougkogiannis, Andrew Adamatzky

“…We discuss the implications and applications of our work in the fields of bio-inspired computing, artificial neural networks, and origin of life research.…”
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Application of machine learning in fracture analysis of edge crack semi-infinite elastic plate by Saeed H. Moghtaderi, Alias Jedi, Ahmad Kamal Ariffin, Prakash Thamburaja

“…This paper discusses the application of machine learning techniques, notably artificial neural networks (ANN), in the fracture analysis of semi-infinite elastic plates with edge cracks. …”
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Discriminative training of spiking neural networks organised in columns for stream‐based biometric authentication by Enrique Argones Rúa, Tim Vanhamme, Davy Preuveneers, Wouter Joosen

“…One of the challenges when using SNNs is the discriminative training of the network since it is not straightforward to apply the well‐known error backpropagation (EBP), massively used in traditional artificial neural networks (ANNs). A network structure based on neuron columns is proposed, resembling cortical columns in the human cortex, and a new derivation of error backpropagation for the spiking neural networks that integrate the lateral inhibition in these structures. …”
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Modelling and optimization of well hole cleaning using artificial intelligence techniques by Nageswara Rao Lakkimsetty, Hassan Rashid Ali Al Araimi, G. Kavitha

“…This study aims to improve the accuracy and practicality of hole cleaning assessment by applying Artificial Intelligence (AI) techniques, specifically Artificial Neural Networks (ANN) and Genetic Algorithms (GA), to predict downhole parameters and optimize drilling processes. …”
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Evaluation of Induced Settlements of Piled Rafts in the Coupled Static-Dynamic Loads Using Neural Networks and Evolutionary Polynomial Regression by Ali Ghorbani, Mostafa Firouzi Niavol

“…Based on the results of well-validated three-dimensional finite difference modeling, artificial neural networks and evolutionary polynomial regressions are then applied and introduced as capable methods to accurately present both static and coupled static-dynamic settlements. …”
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ANALYZING THE CRITERIA AFFECTING TRANSITION TO AIRPLANE BY COMPARING DIFFERENT METHODS by Dilaver Tengilimoğlu, İzay Reyhanoğlu

“…Additionally, it was observed that the Artificial Neural Networks (ANN) model made more accurate predictions compared to others. …”
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Development of Comprehensive Predictive Models for Evaluating Böhme Abrasion Value (BAV) of Dimension Stones Using Non-Destructive Testing Methods by Ekin Köken

“…Three predictive models were established using multivariate adaptive regression spline (MARS), M5P, and artificial neural networks (ANN) methodologies. The performance of the models was assessed through scatter plots and statistical indicators, showing that the ANN-based model outperforms those based on M5P and MARS. …”
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Hybrid neural networks for continual learning inspired by corticohippocampal circuits by Qianqian Shi, Faqiang Liu, Hongyi Li, Guangyu Li, Luping Shi, Rong Zhao

“…Our CH-HNNs incorporate artificial neural networks and spiking neural networks, leveraging prior knowledge to facilitate new concept learning through episode inference, and offering insights into the neural functions of both feedforward and feedback loops within corticohippocampal circuits. …”
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Research on Monthly Runoff Forecast in Beijiang River Basin Based on Multi-model Ensemble Method by ZHONG Yixuan, LIAO Xiaolong, QUAN Xujian, YI Ling, CHEN Yan, LI Yuanyuan, XUE Jiao

“…The accuracy of the monthly runoff forecast plays a fairly important role in aspects such as optimal allocation of water resources,flood control and drought relief in a basin,water dispatching,and power generation optimization of reservoir groups.The commonly used methods for the monthly runoff forecast mainly include water balance models,mathematical statistics models,and artificial neural networks.Studies have shown that any single model cannot achieve the optimal monthly runoff forecast.Therefore,the multi-model ensemble method provides an effective way to eliminate model uncertainty and improve the accuracy of the monthly runoff forecast.Specifically,the research takes Pingshi,Lishi,Hengshi,and Shijiao stations in the Beijiang River Basin as the research object to analyze and compare the effects of the seasonal auto-regressive (SAR) model,two-parameter monthly water balance (TPMWB) model,and artificial neural network (ANN) model.Then,the multi-model ensemble method for the above-mentioned stations is proposed on the basis of the Bayesian model averaging (BMA) method.The research results reveal that compared with any of the three models,the multi-model ensemble method has significantly improved the accuracy of the monthly runoff forecast with a higher determination coefficient (DC) and a lower mean absolute percentage error (MAPE),and thus it can provide better support for decisions in dispatching in the basin.…”
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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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Neural and Hybrid Modeling: An Alternative Route to Efficiently Predict the Behavior of Biotechnological Processes Aimed at Biofuels Obtainment by Stefano Curcio, Alessandra Saraceno, Vincenza Calabrò, Gabriele Iorio

“…The present paper was aimed at showing that advanced modeling techniques, based either on artificial neural networks or on hybrid systems, might efficiently predict the behavior of two biotechnological processes designed for the obtainment of second-generation biofuels from waste biomasses. …”
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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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Influence of Caprock Morphology on Solubility Trapping during CO2 Geological Sequestration by Pradeep Reddy Punnam, Balaji Krishnamurthy, Vikranth Kumar Surasani

“…In the future, the simulation data using Artificial Neural Networks can be applied to predict the structural and solubility trapping of geological formations. …”
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Social exclusion as a side effect of machine learning mechanisms by A. G. Tertyshnikova, U. O. Pavlova, M. V. Cimbal

“…The conclusion about the sources of social exclusion and stigmatization in society is made due to the similarity between natural and artificial neural networks functioning. The authors suggest that it is the principles of neurotraining in a “natural” society that lead not only to discrimination at the macro level, but also cause vivid negative reactions towards representatives of the exclusive groups, for example, interethnic hatred, homophobia, sexism, etc. …”
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Improved Set-point Tracking Control of an Unmanned Aerodynamic MIMO System Using Hybrid Neural Networks by Oduetse Matsebe, David Mohammed Ezekiel, Ravi Samikannu

“…Artificial neural networks (ANN), an Artificial Intelligence (AI) technique, are both bio-inspired and nature-inspired models that mimic the operations of the human brain and the central nervous system that is capable of learning. …”
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Identifying and Evaluating Chaotic Behavior in Hydro-Meteorological Processes by Soojun Kim, Yonsoo Kim, Jongso Lee, Hung Soo Kim

“…The generated time series from summation of sine functions were fitted to each series and used for investigating the hypotheses. Then artificial neural networks had been built for modeling the reservoir system and the correlation dimension was analyzed for the evaluation of chaotic behavior between inputs and outputs. …”
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Mathematical and computational modeling for organic and insect frass fertilizer production: A systematic review. by Malontema Katchali, Edward Richard, Henri E Z Tonnang, Chrysantus M Tanga, Dennis Beesigamukama, Kennedy Senagi

“…Mathematical models such as simulation, regression, dynamics, and kinetics have been applied while computational data driven machine learning models such as random forest, support vector machines, gradient boosting, and artificial neural networks have also been applied as well. These models have been used in quantifying nutrients concentration/release, effects of nutrients in agro-production, and fertilizer treatment. …”
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Key risk factors of generalized anxiety disorder in adolescents: machine learning study by Yonghwan Moon, Hyekyung Woo, Hyekyung Woo

“…Predictive models using Random Forest and Artificial Neural Networks demonstrated that the XGBoost feature selection method effectively identified key factors and showed strong performance. …”
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Integrating Machine Learning and Material Feeding Systems for Competitive Advantage in Manufacturing by Müge Sinem Çağlayan, Aslı Aksoy

“…The research employs six machine learning (ML) algorithms—logistic regression (LR), decision trees (DT), random forest (RF), support vector machines (SVM), K-nearest neighbors (K-NN), and artificial neural networks (ANN)—to develop a multi-class classification model for material feeding system selection. …”
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