Machine learning based on structural and FTIR spectroscopic datasets for seed autoclassification

Abstract

In machine learning, traditional single datasets often cause confusion between species, mixing features to form a mixed dataset will solve this challenge.

Aprendizado de máquina para o diagnóstico da hipersensibilidade dentinária utilizando espectroscopia no infravermelho por transformada de Fourier

“…A taxa de aprendizado fixa de 0,16, utilizada no algoritmo BiLSTM, é relativamente alta em comparac ¸ão com valores típicos, que geralmente estão na faixa de 0,1 ou menores [Zlotogorski-Hurvitz et al 2019, Kiser et al 2023. Embora a taxa de aprendizado ideal possa variar dependendo do conjunto de dados e da arquitetura da rede, como no trabalho de [Wang et al 2022], que utilizou o valor de 0,3 com bons resultados.…”

A Review of Machine Learning for Near-Infrared Spectroscopy

“…The exhaustive search required by wrapper-based methods to prevent overfitting also makes them computationally expensive. Wrapper-based algorithms used in NIR include particle swarm optimization (PSO) and binary particle swarm optimization (BPSO) [28], genetic algorithms (GA) [29][30][31][32][33], variable combination population analysis (VCPA) [32,34], the variable iterative space shrinkage approach (VISSA) [35], bootstrapping soft shrinkage (BOSS) [36], iteratively retaining informative variables (IRIV) [32], competitive adaptive reweighted sampling (CARS) [37][38][39], the successive projection algorithm (SPA) [40], uninformative variable elimination (UVE) [41], Monte Carlo uninformative variable elimination (MCUVE) [35], partial least squares feature selection approaches [42], the randomization test (RT) [43], variable importance in the projection (VIP) [44], and the jackknife procedure.…”

“…These steps are iteratively repeated until the stopping criteria, such as the number of maximum iterations or the desired cross-validation accuracy, are reached. CARS was used to determine the quality of oilseed [37], rice [38], and seeds [39]. To find the rice-grain moisture NIR spectroscopy [38], the partial least squares (PLS) and competitive adaptive reweighted squares (CARS) models were used to model and analyze the spectral data.…”