A statistical learning method to fast generalised rule induction directly from raw measurements

Induction of descriptive models is one of the most important technologies in data mining. The expressiveness of descriptive models are of paramount importance in applications that examine the causality of relationships between variables. Most of the work on descriptive models has concentrated on less expressive approaches such as clustering algorithms or rule-based approaches that are limited to a particular type of data, such as association rule mining for binary data. However, in many applications its important to understand the structure of the produced model for further human evaluation. In this research we present a novel generalised rule induction method that allows the induction of descriptive and expressive rules directly from both categorical and numerical features

Optimized superpixel and AdaBoost classifier for human thermal face recognition

Infrared spectrum-based human recognition systems offer straightforward and robust solutions for achieving an excellent performance in uncontrolled illumination. In this paper, a human thermal face recognition model is proposed. The model consists of four main steps. Firstly, the grey wolf optimization algorithm is used to find optimal superpixel parameters of the quick-shift segmentation method. Then, segmentation-based fractal texture analysis algorithm is used for extracting features and the rough set-based methods are used to select the most discriminative features. Finally, the AdaBoost classifier is employed for the classification process. For evaluating our proposed approach, thermal images from the Terravic Facial infrared dataset were used. The experimental results showed that the proposed approach achieved (1) reasonable segmentation results for the indoor and outdoor thermal images, (2) accuracy of the segmented images better than the non-segmented ones, and (3) the entropy-based feature selection method obtained the best classification accuracy. Generally, the classification accuracy of the proposed model reached to 99% which is better than some of the related work with around 5%

Modification of supported lipid membranes by atomic force microscopy

The atomic force microscope (AFM) was used to structurally modify supported lipid bilayers in a controlled quantitative manner. By increasing the force applied by the AFM tip, lipid was removed from the scanned area, leaving a cut through the lipid bilayer. Cuts were repaired with the AFM by scanning the region with a controlled force and driving lipid back into the cut. A slow self-annealing of cuts was also observed

Kaleidoscopic associations between life outside home and the technological environment that shape occupational injustice as revealed through cross-sectional statistical modelling

BACKGROUND: Everyday life outside home and accessing a variety of places are central to occupation. Technology is ever more taken for granted, even outside home, and for some may culminate in occupational injustice. This study aims to explore the association between everyday technologies (ET), particularly out of home, and the number of places older adults with and without dementia go to, in rural and urban environments. METHOD: The Everyday Technology Use Questionnaire, and Participation in Activities and Places Outside Home Questionnaire, were administered with 128 people in England. Six logistic regression models explored the association between ET and the number of places people went to, with other demographic factors (i.e., rurality, diagnosis, deprivation). RESULTS: The amount of out of home technologies a person perceived relevant and relative levels of neighbourhood deprivation were most persistently associated with the number of places people went to. Associations with ability to use technology, diagnosis, and education were more tentative. In no model was rurality significant. All models explained a low proportion of variance and lacked sensitivity to predict the outcome. CONCLUSION: For a minority of people, perceptions of the technological environment are associated with other personal and environmental dimensions. Viewed kaleidoscopically, these associations assemble to generate an impermanent, fragmented view of occupational injustice that may jeopardise opportunities outside home. However, there will be other influential factors not identified in this study. Greater attention to the intersections between specific environmental dimensions may deepen understanding of how modifications can be made to deliver occupational justice