Efficient android electronic nose design for recognition and perception of fruit odors using Kernel Extreme Learning Machines

- It is presented a novel android electronic nose construction by using KELMs.
- It is developed the KELM classifier as an efficient recognition method for odor recognition.
- It is newly introduced simple feature extraction steps without using any dimension reduction method.
- A comparative study is generated on K-NNs, SVMs, LSSVMs, and ELMs.
- The experimental results exhibit that the KELM system is strides over and above the K-NN, SVM, LSSVM, and ELM systems in terms of higher testing recognition rates and lower training and testing times for recognizing fruit odors.

This study presents a novel android electronic nose construction using Kernel Extreme Learning Machines (KELMs). The construction consists of two parts. In the first part, an android electronic nose with fast and accurate detection and low cost are designed using Metal Oxide Semiconductor (MOS) gas sensors. In the second part, the KELMs are implemented to get the electronic nose to achieve fast and high accuracy recognition. The proposed algorithm is designed to recognize the odor of six fruits. Fruits at two concentration levels are placed to the sample chamber of the electronic nose to ensure the features invariant with the concentration. Odor samples in the form of time series are collected and preprocessed. This is a newly introduced simple feature extraction step that does not use any dimension reduction method. The obtained salient features are imported to the inputs of the KELMs. Additionally, K-Nearest Neighbor (K-NN) classifiers, the Support Vector Machines (SVMs), Least-Squares Support Vector Machines (LSSVMs), and Extreme Learning Machines (ELMs) are used for comparison. According to the comparative results for the proposed experimental setup, the KELMs produced good odor recognition performance in terms of the high test accuracy and fast response. In addition, odor concentration level was visualized on an android platform.

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