This article presents a study on lung cancer detection based on electronic nose technology. The pattern recognition algorithm is extremely crucial for an electronic nose system, but the customary learning algorithms usually prefer the majority class for class imbalance learning due to the assumption of equal misclassification costs. To address this challenge, in this article, we propose a novel classification method named weighted discriminative extreme learning machine (WDELM) for lung cancer diagnosis. First, the WDELM assigns a different weight to each particular sample by using a flexible weighting strategy, which enables it to perform classification tasks with the unbalanced class distribution. Then, an alternating iterative algorithm is proposed to solve the convex objective function with a theoretical analysis presented. Finally, the effectiveness of the proposed method has been evaluated on lung cancer datasets and public datasets by comparing it with conventional methods. Experimental results have confirmed that the WDELM surpasses conventional methods and is competent for lung cancer diagnosis.

Increasing the specific superficial area of gas sensing layer is a direction to improve the performance of sensors. Considering the special features, high specific superficial area of fractal geometry and the fractal properties of thin film in the deposition and growth progress, many studies have explored the relationship between fractal geometry and gas sensing characteristics of the sensors. In this paper, the application and development of fractal geometry in gas sensors are reviewed, the superiority of fractal geometry in the application of gas sensors, as well as the formation mechanism and influencing factors of fractal geometry are indicated. The advantages of fractal analysis in the characterization of film morphology and properties are also discussed. This review demonstrates that fractal geometry can be successfully used in the design of macro and micro structure of gas sensors to improve the performance of sensors. Films with appropriate fractal structure and best gas sensing performance can be obtained by controlling the deposition and parameters. 3D printing and MEMS technologies provide an implementation scheme to prepare sensors with fractal structure, such as electrodes and heater based on Hilbert curve and substrates based on Menger sponge.

In order to improve the detection effect and simplify the number of sensors of electronic nose system for wound infection detection, the sensor arrays are optimized based on Hilbert-Schmidt independence criterion (HSIC) in this brief. Specifically, the HSIC optimization algorithm of Gaussian kernel function is exploited. The criterion of Hilbert-Schmidt independence is obtained by empirical estimation of norm of Hilbert-Schmidt cross-covariance operator. The main idea of its application in sensor array optimization is that the larger the empirical norm estimates of Hilbert-Schmidt cross-covariance operator, the stronger the correlation between sensor features and labels, the greater the role of sensor features in classification and recognition. The experimental results show that the HSIC optimization method (Gaussian kernel function) performs better than the linear discriminant analysis (LDA) method and the principal component analysis (PCA).