An artificial neural network for predicting air traffic demand based on socio-economic parameters

Alam, Md Shafiqul; Deb, Jayanta Bhusan; Amin, Abdullah Al; Chowdhury, Shilpa

doi:10.1016/j.dajour.2023.100382

Cited by 9 publications

(2 citation statements)

References 24 publications

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“…To make accurate predictions or respond appropriately to new and unknown inputs, machine learning algorithms must be able to recognize patterns and relationships within datasets [21,22]. One of the key components of machine learning that underpins its many applications in a variety of disciplines is its innate capacity to learn from data, generalize to new cases, and make acceptable outcomes without having explicit instructions [23][24][25]. Qi et al [26] utilized a decision tree model to forecast the mechanical properties of plastic reinforced with carbon fiber.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches for Predicting the Ablation Performance of Ceramic Matrix Composites

Deb,

Gou,

Song

et al. 2024

J. Compos. Sci.

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Materials used in aircraft engines, gas turbines, nuclear reactors, re-entry vehicles, and hypersonic structures are subject to severe environmental conditions that present significant challenges. With their remarkable properties, such as high melting temperatures, strong resistance to oxidation, corrosion, and ablation, minimal creep, and advantageous thermal cycling behavior, ceramic matrix composites (CMCs) show great promise as a material to meet the strict requirements in these kinds of environments. Furthermore, the addition of boron nitride nanoparticles with continuous fibers to the CMCs can offer thermal resistivity in harsh conditions, which will improve the composites’ strength and fracture toughness. Therefore, in extreme situations, it is crucial to understand the thermal resistivity period of composite materials. To forecast the ablation performance of composites, we developed six machine learning regression methods in this study: decision tree, random forest, support vector machine, gradient boosting, extreme gradient boosting, and adaptive boosting. When evaluating model performance using metrics including R2 score, root mean square error, mean absolute error, and mean absolute percentage error, the gradient boosting and extreme gradient boosting machine learning regression models performed better than the others. The effectiveness of machine learning models as a useful tool for forecasting the ablation behavior of ceramic matrix composites was effectively explained by this study.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches for Predicting the Ablation Performance of Ceramic Matrix Composites

Deb,

Gou,

Song

et al. 2024

J. Compos. Sci.

Self Cite

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“…An artificial neural network is a basic network consisting of an input layer, an output layer, and one or more intermediate neuron layers [5]. The main advantage of this network is that it evaluates input patterns after training [6]. For such a network, supervised learning can be organized based on examples where input and output are linked by an event, or, in other words, a cause forms an effect (for example, experimental results of physical phenomena or function values obtained by an unknown formula, etc.).…”

Section: Introductionmentioning

confidence: 99%

Validation Challenges in Data for Different Diesel Engine Performance Regimes Utilising HVO Fuel: A Study on the Application of Artificial Neural Networks for Emissions Prediction

Matijošius,

Rimkus,

Gruodis

2024

Machines

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Artificial neural networks (ANNs) provide supervised learning via input pattern assessment and effective resource management, thereby improving energy efficiency and predicting environmental fluctuations. The advanced technique of ANNs forecasts diesel engine emissions by collecting measurements during trial sessions. This study included experimental sessions to establish technical and ecological indicators for a diesel engine across several operational scenarios. VALLUM01, a novel tool, has been created with a user-friendly interface for data input/output, intended for the purposes of testing and prediction. There was a comprehensive collection of 12 input parameters and 10 output parameters that were identified as relevant and sufficient for the objectives of training, validation, and prediction. The proper value ranges for transforming into fuzzy sets for input/output to an ANN were found. Given that the ANN’s training session comprises 1,000,000 epochs and 1000 perceptrons within a single-hidden layer, its effectiveness can be considered high. Many statistical distributions, including Pearson, Spearman, and Kendall, validate the prediction accuracy. The accuracy ranges from 96% on average, and in some instances, it may go up to 99%.

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Project Cost Prognostication for Government Buildings Using Feed-Forward Backpropagation Neural Network

Maravilla,

Silva,

de Jesus

et al. 2025

Lecture Notes in Civil Engineering

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An artificial neural network for predicting air traffic demand based on socio-economic parameters

Cited by 9 publications

References 24 publications

Machine Learning Approaches for Predicting the Ablation Performance of Ceramic Matrix Composites

Machine Learning Approaches for Predicting the Ablation Performance of Ceramic Matrix Composites

Validation Challenges in Data for Different Diesel Engine Performance Regimes Utilising HVO Fuel: A Study on the Application of Artificial Neural Networks for Emissions Prediction

Project Cost Prognostication for Government Buildings Using Feed-Forward Backpropagation Neural Network

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