Variable Precision Rough Set Model for Incomplete Information Systems and Its Beta-Reducts

As the original rough set model is quite sensitive to noisy data, Ziarko proposed the variable precision rough set (VPRS) model to deal with noisy data and uncertain information. This model allowed for some degree of uncertainty and misclassification in the mining process. In this paper, the variable precision rough set model for an incomplete information system is proposed by combining the VPRS model and incomplete information system, and the beta-lower and beta-upper approximations are defined. Considering that classical VPRS model lacks a feasible method to determine the precision parameter beta when calculating the beta-reducts, we present an approach to determine the parameter beta. Then, by calculating discernibility matrix and discernibility functions based on beta-lower approximation, the beta-reducts and the generalized decision rules are obtained. Finally, a concrete example is given to explain the validity and practicability of beta-reducts which is proposed in this paper

Bike skills training for children with cerebral palsy: protocol for a randomised controlled trial

INTRODUCTION: Two-wheel bike riding can be a goal for children with cerebral palsy (CP) and a means of participating in physical activity. It is possible for some children with CP to ride a two-wheel bike; however, currently far fewer can ride compared with their typically developing peers. Evidence supports training targeted towards goals of the child with CP and their family; yet there is little evidence to guide best-practice bike skills training. Task-specific training may lead to attainment of two-wheel bike-specific goals. This study aims to determine if a novel task-specific approach to training two-wheel bike skills is more effective than a parent-led home programme for attaining individualised two-wheel bike-specific goals in independently ambulant children with CP aged 6-15 years. METHODS AND ANALYSIS: Sixty eligible children with CP (Gross Motor Function Classification System levels I-II) aged 6-15 years with goals relating to riding a two-wheel bike will be randomised to either a novel task-specific centre-based group programme (intervention) or a parent-led home-based programme (comparison), both involving a 1-week intervention period. The primary outcome is goal attainment in the week following the intervention period (T1). Secondary outcomes include: goal attainment and participation in physical activity at 3&thinsp;months postintervention (T2) and bike skills, attendance and involvement in bike riding, self-perception and functional skills at T1 and T2. Economic appraisal will involve cost-effectiveness and cost-utility analyses. Adherence of clinicians and parents to the intervention and comparison protocols will be assessed. Linear and logistic regression will be used to assess the effect of the intervention, adjusted for site as used in the randomisation process. ETHICS AND DISSEMINATION: This study was approved by the Human Research and Ethics Committees at The Royal Children\u27s Hospital (#36209). Results will be disseminated via peer-reviewed publications and conference presentations.<br /

VPRS-based regional decision fusion of CNN and MRF classifications for very fine resolution remotely sensed images

Recent advances in computer vision and pattern recognition have demonstrated the superiority of deep neural networks using spatial feature representation, such as convolutional neural networks (CNN), for image classification. However, any classifier, regardless of its model structure (deep or shallow), involves prediction uncertainty when classifying spatially and spectrally complicated very fine spatial resolution (VFSR) imagery. We propose here to characterise the uncertainty distribution of CNN classification and integrate it into a regional decision fusion to increase classification accuracy. Specifically, a variable precision rough set (VPRS) model is proposed to quantify the uncertainty within CNN classifications of VFSR imagery, and partition this uncertainty into positive regions (correct classifications) and non-positive regions (uncertain or incorrect classifications). Those “more correct” areas were trusted by the CNN, whereas the uncertain areas were rectified by a Multi-Layer Perceptron (MLP)-based Markov random field (MLP-MRF) classifier to provide crisp and accurate boundary delineation. The proposed MRF-CNN fusion decision strategy exploited the complementary characteristics of the two classifiers based on VPRS uncertainty description and classification integration. The effectiveness of the MRF-CNN method was tested in both urban and rural areas of southern England as well as Semantic Labelling datasets. The MRF-CNN consistently outperformed the benchmark MLP, SVM, MLP-MRF and CNN and the baseline methods. This research provides a regional decision fusion framework within which to gain the advantages of model-based CNN, while overcoming the problem of losing effective resolution and uncertain prediction at object boundaries, which is especially pertinent for complex VFSR image classification

Reducing the Memory Size of a Fuzzy Case-Based Reasoning System Applying Rough Set Techniques

Early work on case-based reasoning (CBR) reported in the literature shows the importance of soft computing techniques applied to different stages of the classical four-step CBR life cycle. This correspondence proposes a reduction technique based on rough sets theory capable of minimizing the case memory by analyzing the contribution of each case feature. Inspired by the application of the minimum description length principle, the method uses the granularity of the original data to compute the relevance of each attribute. The rough feature weighting and selection method is applied as a preprocessing step prior to the generation of a fuzzy rule system, which is employed in the revision phase of the proposed CBR system. Experiments using real oceanographic data show that the rough sets reduction method maintains the accuracy of the employed fuzzy rules, while reducing the computational effort needed in its generation and increasing the explanatory strength of the fuzzy rules

Joseph Akeroyd: rediscovering a prison reformer

School teacher Joseph Akeroyd was appointed Inspector General of Victoria&amp;rsquo;s prisons in 1924. He held this role until 1947 becoming the longest serving Inspector General in Victoria&amp;rsquo;s history. However Akeroyd&amp;rsquo;s reform and legacies were recognised only in part. Examination of his private papers within the context of popular criminological theories demonstrated that Akeroyd single-mindedly pursued a positivist agenda to reform approaches to prison and prisoner management. Akeroyd&amp;rsquo;s fought private and public battles in his drive to reform in the areas of education in prisons, classification, sentencing and punishment. The examination of Akeroyd&amp;rsquo;s influence in shaping prison and prisoner management reform in Victoria and the processes he used unearthed three broad key discoveries; there was significant reform activity in the Victorian prison system in which Joseph Akeroyd was pivotal in his role as Inspector General at that time; there was robust public debate about differing ways to manage crime and criminality; and there was an emergence of criminological thinking predating trends in USA and UK many years later. These discoveries contradicted previous claims there was little or no prison and prisoner management reform in this period. It is clear Joseph Akeroyd played a central role in laying foundation for long term prison and prisoner management legacies through his education led reform. This study provides a fresh perspective on the nature and extent of transparent and opaque reform in prison and prisoner management in Victoria in the period 1924 &amp;ndash; 1947 under Akeroyd&amp;rsquo;s education inspired leadership. Through access to his personal documentation, Akeroyd&amp;rsquo;s role in establishing Victoria&amp;rsquo;s unique relationship between education and prison management can now be recognised and acknowledged

Topics in Primary Care Medicine

The medical specialty of primary care addresses the basic and fundamental healthcare needs of individuals, the family, and the larger community. Its reach starts at pre-conception and extends to global health and medical issues. Primary care issues include chronic medical problems, surgery, and community-wide health threats such as worldwide global pandemics, terrorism in all of its forms, and domestic violence. This book reviews eight topics including chronic medical issues like chronic fatigue syndrome, the response of primary care clinicians to global pandemics, and how patients and physicians are symbolized in comics. From top experts in the field, this book will improve your ability to practice primary care and to appreciate the broad demands placed upon primary care clinicians

Deep learning for land cover and land use classification

Recent advances in sensor technologies have witnessed a vast amount of very fine spatial resolution (VFSR) remotely sensed imagery being collected on a daily basis. These VFSR images present fine spatial details that are spectrally and spatially complicated, thus posing huge challenges in automatic land cover (LC) and land use (LU) classification. Deep learning reignited the pursuit of artificial intelligence towards a general purpose machine to be able to perform any human-related tasks in an automated fashion. This is largely driven by the wave of excitement in deep machine learning to model the high-level abstractions through hierarchical feature representations without human-designed features or rules, which demonstrates great potential in identifying and characterising LC and LU patterns from VFSR imagery. In this thesis, a set of novel deep learning methods are developed for LC and LU image classification based on the deep convolutional neural networks (CNN) as an example. Several difficulties, however, are encountered when trying to apply the standard pixel-wise CNN for LC and LU classification using VFSR images, including geometric distortions, boundary uncertainties and huge computational redundancy. These technical challenges for LC classification were solved either using rule-based decision fusion or through uncertainty modelling using rough set theory. For land use, an object-based CNN method was proposed, in which each segmented object (a group of homogeneous pixels) was sampled and predicted by CNN with both within-object and between-object information. LU was, thus, classified with high accuracy and efficiency. Both LC and LU formulate a hierarchical ontology at the same geographical space, and such representations are modelled by their joint distribution, in which LC and LU are classified simultaneously through iteration. These developed deep learning techniques achieved by far the highest classification accuracy for both LC and LU, up to around 90% accuracy, about 5% higher than the existing deep learning methods, and 10% greater than traditional pixel-based and object-based approaches. This research made a significant contribution in LC and LU classification through deep learning based innovations, and has great potential utility in a wide range of geospatial applications

Data mining in soft computing framework: a survey

The present article provides a survey of the available literature on data mining using soft computing. A categorization has been provided based on the different soft computing tools and their hybridizations used, the data mining function implemented, and the preference criterion selected by the model. The utility of the different soft computing methodologies is highlighted. Generally fuzzy sets are suitable for handling the issues related to understandability of patterns, incomplete/noisy data, mixed media information and human interaction, and can provide approximate solutions faster. Neural networks are nonparametric, robust, and exhibit good learning and generalization capabilities in data-rich environments. Genetic algorithms provide efficient search algorithms to select a model, from mixed media data, based on some preference criterion/objective function. Rough sets are suitable for handling different types of uncertainty in data. Some challenges to data mining and the application of soft computing methodologies are indicated. An extensive bibliography is also included