To tackle the challenges in genomic data analysis caused by their tens of thousands of dimensions while having a small number of examples and unbalanced examples between classes, the technique of unsupervised feature selection based on standard deviation and cosine similarity is proposed in this paper. We refer to this idea as SCFS (Standard deviation and Cosine similarity based Feature Selection). It defines the discernibility and independence of a feature to value its distinguishable capability between classes and its redundancy to other features, respectively. A 2-dimensional space is constructed using discernibility as x-axis and independence as y-axis to represent all features where the upper right corner features have both comparatively high discernibility and independence. The importance of a feature is defined as the product of its discernibility and its independence (i.e., the area of the rectangular enclosed by the feature’s coordinate lines and axes). The upper right corner features are by far the most important, comprising the optimal feature subset. Based on different definitions of independence using cosine similarity, there are three feature selection algorithms derived from SCFS. These are SCEFS (Standard deviation and Exponent Cosine similarity based Feature Selection), SCRFS (Standard deviation and Reciprocal Cosine similarity based Feature Selection) and SCAFS (Standard deviation and Anti-Cosine similarity based Feature Selection), respectively. The KNN and SVM classifiers are built based on the optimal feature subsets detected by these feature selection algorithms, respectively. The experimental results on 18 genomic datasets of cancers demonstrate that the proposed unsupervised feature selection algorithms SCEFS, SCRFS and SCAFS can detect the stable biomarkers with strong classification capability. This shows that the idea proposed in this paper is powerful. The functional analysis of these biomarkers show that the occurrence of the cancer is closely related to the biomarker gene regulation level. This fact will benefit cancer pathology research, drug development, early diagnosis, treatment and prevention.

Today, a multitude of tourism websites are placing online services and information at users’ fingertips. Usability is an important factor in determining the success of such websites. This study adopts a user-centred approach to empirically assess the usability of tourism websites. Our results reveal serious usability weaknesses, including poor navigation, difficulty in completing tasks, overwhelming presentation of options, inconsistent naming of objects, and lack of warning messages. We leverage our findings to propose usability guidelines for the design of improved tourism websites.

Mobile app usability has been attracting the attention of academics and practitioners for sometime because well-designed mobile apps can build a close link between businesses and users in addition to enhancing user experiences. To better understand mobile app usability, this study develops a unified hierarchical approach that characterizes mobile app usability from the high level of “usability principles” to the intermediary level of “usability attributes” to the detailed level of “usability features” to assess the usability of current mobile apps from different categories. Using an online survey, we identified a set of usability design features that are common to all categories of apps. Furthermore, the study identifies a set of important and less important usability features within specific mobile app categories. In addition to the practical implications consisting of insights for mobile app design, the study found important relationships among usability principles, attributes, and features.