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Abbreviations MOX: Metal oxide PCA: Principal component analysis KNN: K-nearest neighbor ANN: Artificial neural network PLS: Partial least squares MLPCR: Maximum likelihood principal components regression FFT: Fast Fourrier transform LDA: Linear discriminant analysis ZnO: Zinc oxide WO 3 : Tungsten trioxide TiO 2 : Titanium dioxide POMD: Partially observable markov decision processes EMD: Empirical mode decomposition VMD: Variational mode decomposition WPT: Wavelet pocket transform VOC: Volatile organic compounds SFS: Sequential forward selection LVW: Las Vegas wrapper FFNN: Feed forward neural network GMM: Gaussian mixture model DCNN: Deep convolutional neural network LCD: Low confidence decision CO: Carbon monoxide AI: Artificial intelligence SVM: Support vector machine CNN: Convolutional neural network LSTM: Long short-term memory EMD: Empirical mode decomposition DWT: Discret wavelet transform SnO 2 : Tin (IV) oxide In 2 O 3 : Indium(III) oxide CdO: Cadmium oxide VOCs: Volatile organic compounds DQN: Deep Q network EEMD: Ensemble empirical mode decomposition STFT: Short-time Fourier transform PDS: Power density spectrum LASSO: Least absolute shrinkage and selection operator SBS: Sequential backward selection GA: Genetic algorithm FBNN: FischerBurmeister: function neural network MLP: multilayer perceptron GTM-TT: Generative topographic mapping through time HCD: High confidence decision References Araujo I, Gamboa J, Silva A (2019) Deep learning models for classification of gases detected by sensor arrays of artificial nose, Anais do XVI Encontro Nacional de Inteligencia Artificial e Computacional Bajcsy R (1998) Active perception

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MR analysis could compensate for the shortcomings of observational studies, which are susceptible to reverse causation and confounding factors

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