Global Air Quality: Assessing the Impact of Pollution on Health and the Environment
DOI:
https://doi.org/10.13052/spee1048-5236.45110Keywords:
global air pollution, stochastic gradient descent, k nearest neighbour classification, arithmetic optimization algorithm, manta ray foraging optimizationAbstract
The article proposes improvements to Machine Learning (ML) algorithms, specifically Stochastic Gradient Descent (SGD) and K K-nearest neighbour Classification (KNNC), with two improved optimization techniques: Arithmetic Optimization Algorithm (AOA) and Manta Ray Foraging Optimization (MRFO). The two nature-inspired optimization algorithms are used to tune the hyperparameters of SGD and KNNC toward the goals of maximizing prediction precision and computational cost reduction. SGD, being one of the popular algorithms used for loss function minimization in ML, is typically susceptible to careful fine-tuning of its parameters to prevent low convergence, among other problems, as well as overfitting. Likewise, while KNNC is cherished for convenience alongside performance in a majority of applications, well-tuned parameter values can substantially enhance its classification accuracy. AOA and MRFO, inspired by nature and the behavior of animals, present novel concepts for hyperparameter space exploration with better optimizing in a style than typical styles. Experience confirms that SGD and KNNC models work quite effectively about efficiency gains on various diversified datasets based on these optimization approaches. The study highlights the value of applying bio-inspired algorithms in ML processes that offer a flexible framework to address tough classification problems in diverse fields. SGD was the superior model since it showed superior accuracy, with the capability to handle enormous as well as intricate datasets with great precision, and also recovered complex patterns at lesser error rates. On the contrary, although KNNC was very accurate for small and localized datasets, it did not perform well with large and complex ones, thereby being the weaker model herein. All these findings validate the strength as well as effectiveness of SGD in handling different as well as sophisticated datasets, especially in air pollution prediction.
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