Determination of Vital Cancer Sites in Malaysian Colorectal Cancer Dataset by Using A Fuzzy Feature Selection Method

In Malaysia, Colorectal Cancer (CRC) is one of the most common cancers that occur in both men and women. Early detection is very crucial and it can significantly increase the rate of survival for the patients and if left untreated can lead to death. With the lack of high-quality CRC data, expert systems and machine learning analysis are burdened with the presence of irrelevant features, outliers, and noise. This can reduce the classification accuracy for data analysis. Accordingly, it is essential to find a reliable feature selection method that can identify and remove any irrelevant feature while being resistant to noise and outliers. In this paper, Fuzzy Principal Component Analysis (FPCA) was tested for the classification of Malaysian’s CRC dataset. With the utilization of fuzzy membership in FPCA, the experimental results showed that the proposed method produces higher accuracy compared to PCA and SVM by almost 2% and 5% respectively. Empirical results showed that FPCA is a reliable feature selection method that can find the most informative features in the CRC dataset that could assist medical practitioners in making an informed decision.

Fuzzy Principal Component Analysis Model on Evaluating Innovation Service Capability

This article mainly evaluates the regional innovation service capacity through the TOPSIS method. Firstly, a regional collaborative innovation network is constructed and the Yangtze River Delta region is selected for analysis. Secondly, an evaluation index is constructed for innovation service capability, fuzzy principal component analysis is used to refine quantitative and qualitative index data of innovation service capability, and the index weight is calculated. Then the region of the Yangtze River Delta is selected and TOPSIS method is used to assist in the effective decision-making process of the evaluation of innovative service capabilities. Due to the large amount of data in this article, MATLAB programming is used. Finally, through the comparative analysis of the results, countermeasures and suggestions are put forward from the perspective of the improvement of collaborative innovation service capabilities.

Age-related differences in prey-handling efficiency and feeding habitat utilization of Squalius carolitertii (Cyprinidae) according to prey trait analysis

Multivariate prey trait analysis is a functional approach to understand predator-prey relationships. Here, seven macroinvertebrate ecological traits have been used for the analysis of trophic ecology of co-occurring age classes of Northern Iberian chub Squalius carolitertii, a cyprinid fish species. The present study identified several key factors in the handling efficiency and habitat utilization for feeding of S. carolitertii that may have a wider application, particularly for other cyprinid species. The results revealed a remarkable similarity in the feeding behaviour among age classes, suggesting a foraging behaviour convergence among them in both prey-handling efficiency and feeding habitat utilization. Nevertheless, some age classes showed clear preferences for particular categories of ecological trait; for example, age 1 showed a clear ability to feed on flattened prey items, whereas ages 2 and 3 were able to feed on preys with different body shape due to their general distribution in the fuzzy principal component analysis (FPCA). Finally, this study shows how multivariate approaches can complement traditional diet analyses, and the method has wide applicability across life-stages of cyprinid species.