## 1 Introduction  
In modern society, various factors such as industrial processes, landfills, livestock production, agricultural activities, and automobile emissions contribute to the generation of unpleasant odors. These odors not only cause discomfort in daily life but also raise concerns about important issues in public health and the environment. To address odor-related problems, it is necessary to accurately analyze the causes of odor and the extent and nature of odor emissions. Recent research has applied machine learning and deep learning techniques to odor analysis, enabling accurate and efficient odor detection and classification. Previous studies have mainly used machine learning algorithms such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) to analyze odor data and evaluate their performances [1; 2; 3]. CNNs have been particularly effective in odor classification, as they extract data features and perform classification simultaneously, enabling rapid and accurate classification. Furthermore, CNNs can effectively process data by learning spatial relationships. However, CNNs require large amounts of data and high computational power, and they have low interpretability, making it difficult to understand the basis for classification [4]. Random Forest (RF) [5] evaluates the importance of various featuresand provides generalized results without overfitting the training data. RF demonstrates stable performance even with relatively small datasets. However, RF is relatively slow and memory intensive when dealing with large amounts of data, and its interpretability is limited. LSTM is specialized in handling time series data and is useful for predicting current odor patterns by remembering previous information [6]. LSTM performs well in modeling time series data and is suitable for handling odor data with temporal dependencies. However, LSTM requires high computational costs and is effective when trained on large-scale datasets. Additionally, interpreting the internal workings of LSTM is challenging [7]. Stacked Autoencoder (SAE) is an unsupervised learning technique that automatically learns features from input data and extracts latent patterns. SAE can remove noise and variability in data, enabling reliable odor classification [8]. However, SAE models perform effective learning on large-scale datasets but may show limited performance in the presence of data scarcity. Ensemble techniques combine multiple models to improve accuracy and generalization. CNN-based ensembles learn [9] various features and ensure model diversity, enhancing the accuracy of odor classification. However, ensemble models require more computational resources, and training and inference with multiple models may take more time [10]. Therefore, this study aims to propose a neural network architecture that combines CNN and Transformer, and evaluate its performance in odor analysis. Transformer provides efficient and parameter-effective modeling compared to traditional sequence modeling, and combining it with CNN is expected to build a more powerful odor analysis model. To assess the model's performance, it will be compared and evaluated against CNN+LSTM hybrid models, CNN models, LSTM models, ELM models, and the proposed CNN-Transformer hybrid model. Additionally, performance metrics such as accuracy, precision, recall, F1 score, and the model's predictive ability will be compared by measuring the RMSE value. To achieve this, this study will construct a CNN-Transformer hybrid model using publicly available odor data consisting of 120,000 samples and evaluate the performance of the model. The structure of this paper is as follows. Section 2 explains the basics of the CNN-Transformer hybrid model proposed in this study and the CNN+LSTM hybrid model, CNN model, LSTM model, ELM model, and the proposed CNN-Transformer for comparative analysis. In Section 3, this paper designs and applies the structures of the CNN+LSTM hybrid model, CNN model, LSTM model, ELM model, and the newly proposed CNN-Transformer hybrid model.