Flight Ticket Prediction Using Gradient Boosting Regressor Compared With Linear Regression

Rao, Nikhil; Thangaraj, S. John Justin; Kumari, V.

doi:10.1109/iconstem56934.2023.10142428

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Cited by 3 publications

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Leveraging Machine Learning Techniques to Estimate Airline Ticket Pricing

Kumar,

Ponnala

2023

2023 International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT)

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Leveraging Machine Learning Techniques to Estimate Airline Ticket Pricing

Kumar,

Ponnala

2023

2023 International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT)

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Predicting Diffusion Coefficients in Nafion Membranes during the Soaking Process Using a Machine Learning Approach

Malashin,

Daibagya,

Tynchenko

et al. 2024

Polymers

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Nafion, a versatile polymer used in electrochemistry and membrane technologies, exhibits complex behaviors in saline environments. This study explores Nafion membrane’s IR spectra during soaking and subsequent drying processes in salt solutions at various concentrations. Utilizing the principles of Fick’s second law, diffusion coefficients for these processes are derived via exponential approximation. By harnessing machine learning (ML) techniques, including the optimization of neural network hyperparameters via a genetic algorithm (GA) and leveraging various regressors, we effectively pinpointed the optimal model for predicting diffusion coefficients. Notably, for the prediction of soaking coefficients, our model is composed of layers with 64, 64, 32, and 16 neurons, employing ReLU, ELU, sigmoid, and ELU activation functions, respectively. Conversely, for drying coefficients, our model features two hidden layers with 16 and 12 neurons, utilizing sigmoid and ELU activation functions, respectively.

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Assessment of Global Forest Coverage through Machine Learning Algorithms

Metkewar,

Chauhan,

Prasanth

et al. 2024

ICST Transactions on Scalable Information Systems

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This exploration of paper presents an investigation of the Forest Region Inclusion Dataset that gives data on the backwoods inclusion of different nations overall from 1990 to 2020. The dataset contains country-wise information on population, population density, population development rate, total population rate, and forest region inclusion. We examined this dataset to decide the patterns in woodland region inclusion across various nations and mainlands, as well as the connection among populace and backwoods region inclusion. Our discoveries show that while certain nations have essentially expanded their forest region inclusion, others have encountered a decline. Besides, we found that population density and development rate are adversely related with forest area coverage. Authors have implemented four machine learning algorithms that are Linear Regression, Decision Tree, Random Forest and Support Vector Machine on the dataset.

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Flight Ticket Prediction Using Gradient Boosting Regressor Compared With Linear Regression

Cited by 3 publications

References 5 publications

Leveraging Machine Learning Techniques to Estimate Airline Ticket Pricing

Leveraging Machine Learning Techniques to Estimate Airline Ticket Pricing

Predicting Diffusion Coefficients in Nafion Membranes during the Soaking Process Using a Machine Learning Approach

Assessment of Global Forest Coverage through Machine Learning Algorithms

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