PATTERN RECOGNITION IN CLASS IMBALANCED DATASETS

Class imbalanced datasets constitute a significant portion of the machine learning problems of interest, where recog­nizing the ‘rare class’ is the primary objective for most applications. Traditional linear machine learning algorithms are often not effective in recognizing the rare class. In this research work, a specifically optimized feed-forward artificial neural network (ANN) is proposed and developed to train from moderate to highly imbalanced datasets. The proposed methodology deals with the difficulty in classification task in multiple stages—by optimizing the training dataset, modifying kernel function to generate the gram matrix and optimizing the NN structure. First, the training dataset is extracted from the available sample set through an iterative process of selective under-sampling. Then, the proposed artificial NN comprises of a kernel function optimizer to specifically enhance class boundaries for imbalanced datasets by conformally transforming the kernel functions. Finally, a single hidden layer weighted neural network structure is proposed to train models from the imbalanced dataset. The proposed NN architecture is derived to effectively classify any binary dataset with even very high imbalance ratio with appropriate parameter tuning and sufficient number of processing elements. Effectiveness of the proposed method is tested on accuracy based performance metrics, achieving close to and above 90%, with several imbalanced datasets of generic nature and compared with state of the art methods. The proposed model is also used for classification of a 25GB computed tomographic colonography database to test its applicability for big data. Also the effectiveness of under-sampling, kernel optimization for training of the NN model from the modified kernel gram matrix representing the imbalanced data distribution is analyzed experimentally. Computation time analysis shows the feasibility of the system for practical purposes. This report is concluded with discussion of prospect of the developed model and suggestion for further development works in this direction

Brexit and its impact on New Zealand economy: A conceptual analysis / Saida Parvin … [et al.]

‘Brexit’ which means exit of United Kingdom (UK) from the European Union (EU) is not only a European issue but has varied implications throughout the world, especially amongst its trading nations. This conceptual paper first discusses pros and cons of Brexit on UK and EU. This is seen in the light of Brexit impact on trade, unemployment rate and inflation rate. It further discusses some of the immediate implication Brexit might have on New Zealand, which is the EU’s third largest trading partner. The discussion is based on the impact post-Brexit may have in terms of trade, employment, GDP and the tourism sector in New Zealand

Model Selection of Discrete Classifiers under Class and Cost Distribution Change: An Empirical Study

A variety of important machine learning applications require predictions on test data with different characteristics than the data on which a model was trained and validated. In particular, test data may have a different relative frequency of positives and negatives (i.e., class distribution) and/or different mislabeling costs of false positive and false negative errors (i.e., cost distribution) than the training data. Selecting models that have been built in conditions that are substantially different from the conditions under which they will be applied is more challenging than selecting models for identical conditions. Several approaches to this problem exist, but they have mostly been studied in theoretical contexts. This paper presents an empirical evaluation of approaches for model selection under class and cost distribution change, based on Receiver Operating Characteristic (ROC) analysis. The analysis compares the ROC Convex Hull (ROCCH) method with other candidate approaches for selecting discrete classifiers for several UCI Machine Learning Repository and simulated datasets. Surprisingly, the ROCCH method did not perform well in the experiments, despite being developed for this task. Instead, the results indicate that a reliable approach for selecting discrete classifiers on the ROC convex hull is to select the model that optimizes the cost metric of interest on the validation data (which has the same characteristics as the training data) but weighted by the class and/or cost distributions anticipated at test time

A novel cortical biomarker signature for predicting pain sensitivity : protocol for the PREDICT longitudinal analytical validation study

Introduction: Temporomandibular disorder is a common musculoskeletal pain condition with development of chronic symptoms in 49% of patients. Although a number of biological factors have shown an association with chronic temporomandibular disorder in cross-sectional and case control studies, there are currently no biomarkers that can predict the development of chronic symptoms. The PREDICT study aims to undertake analytical validation of a novel peak alpha frequency (PAF) and corticomotor excitability (CME) biomarker signature using a human model of the transition to sustained myofascial temporomandibular pain (masseter intramuscular injection of nerve growth factor [NGF]). This article describes, a priori, the methods and analysis plan. Methods: This study uses a multisite longitudinal, experimental study to follow individuals for a period of 30 days as they progressively develop and experience complete resolution of NGF-induced muscle pain. One hundred fifty healthy participants will be recruited. Participants will complete twice daily electronic pain diaries from day 0 to day 30 and undergo assessment of pressure pain thresholds, and recording of PAF and CME on days 0, 2, and 5. Intramuscular injection of NGF will be given into the right masseter muscle on days 0 and 2. The primary outcome is pain sensitivity. Perspective: PREDICT is the first study to undertake analytical validation of a PAF and CME biomarker signature. The study will determine the sensitivity, specificity, and accuracy of the biomarker signature to predict an individual's sensitivity to pain

Acute cholangitis: a state-of-the-art review

Acute cholangitis is a potentially life-threatening bacterial infection of the intra and/or extrahepatic bile ducts. It remains the second and third cause of community-acquired and hospital-acquired bacteremia, respectively, and is associated with mortality rates of up to 15%, despite advances in broad-spectrum antimicrobial therapy and improved access to emergency biliary tract decompression procedures. Even though not much has changed in recent years in terms of diagnosis or treatment, new data have emerged regarding multidrug-resistant bacteria that serve as etiologic agents of cholangitis. Moreover, different approaches in antibiotic regimes depending on severity grading and bile sample cultures as well as novel minimally invasive endoscopic procedures that can help when consecrated treatments such as endoscopic retrograde cholangiopancreatography (ERCP) fail, cannot be performed, or are unavailable have been proposed. This state-of-the-art review aims to offer a complete and updated assessment of the epidemiology, novel diagnostic and therapeutic methods, complications, and prognostic variables of acute cholangitis. The authors will review the prognostic implications of unusual complications, the relevance of regular bile samples and antibiograms, and their new role in guiding antibiotic therapy and limiting antibiotic resistance to present an organized and comprehensive approach to the care of acute cholangitis

A framework for the successful implementation of food traceability systems in China

Implementation of food traceability systems in China faces many challenges due to the scale, diversity and complexity of China’s food supply chains. This study aims to identify critical success factors specific to the implementation of traceability systems in China. Twenty-seven critical success factors were identified in the literature. Interviews with managers at four food enterprises in a pre-study helped identify success criteria and five additional critical success factors. These critical success factors were tested through a survey of managers in eighty-three food companies. This study identifies six dimensions for critical success factors: laws, regulations and standards; government support; consumer knowledge and support; effective management and communication; top management and vendor support; and information and system quality

Brain Segmentation From Computed Tomography of Healthy Aging and Geriatric Concussion at Variable Spatial Resolutions

When properly implemented and processed, anatomic T1-weighted magnetic resonance imaging (MRI) can be ideal for the noninvasive quantification of white matter (WM) and gray matter (GM) in the living human brain. Although MRI is more suitable for distinguishing GM from WM than computed tomography (CT), the growing clinical use of the latter technique has renewed interest in head CT segmentation. Such interest is particularly strong in settings where MRI is unavailable, logistically unfeasible or prohibitively expensive. Nevertheless, whereas MRI segmentation is a sophisticated and technically-mature research field, the task of automatically classifying soft brain tissues from CT remains largely unexplored. Furthermore, brain segmentation methods for MRI hold considerable potential for adaptation and application to CT image processing. Here we demonstrate this by combining probabilistic, atlas-based classification with topologically-constrained tissue boundary refinement to delineate WM, GM and cerebrospinal fluid (CSF) from head CT images. The feasibility and utility of this approach are revealed by comparison of MRI-only vs. CT-only segmentations in geriatric concussion victims with both MRI and CT scans. Comparison of the two segmentations yields mean Sørensen-Dice coefficients of 85.5 ± 4.6% (WM), 86.7 ± 5.6% (GM) and 91.3 ± 2.8% (CSF), as well as average Hausdorff distances of 3.76 ± 1.85 mm (WM), 3.43 ± 1.53 mm (GM) and 2.46 ± 1.27 mm (CSF). Bootstrapping results suggest that the segmentation approach is sensitive enough to yield WM, GM and CSF volume estimates within ~5%, ~4%, and ~3% of their MRI-based estimates, respectively. To our knowledge, this is the first 3D segmentation approach for CT to undergo rigorous within-subject comparison with high-resolution MRI. Results suggest that (1) standard-quality CT allows WM/GM/CSF segmentation with reasonable accuracy, and that (2) the task of soft brain tissue classification from CT merits further attention from neuroimaging researchers

Spatio-temporal patterns of pre-eclampsia and eclampsia in relation to drinking water salinity at the district level in Bangladesh from 2016 to 2018

This analysis examines whether salinity in drinking water is associated with pre-eclampsia and eclampsia (PE/E), a leading cause of maternal morbidity and mortality. Bangladesh’s national health information system data were extracted at the district level (n = 64) to assess PE/E rates, and these were overlaid with three environmental measures approximating drinking water salinity, remotely sensed low-elevation coastal zone (LECZ), monthly rainfall data, and electrical conductivity of groundwater (i.e., water salinity). Results from a negative binomial fixed effects model suggest PE/E rates are higher with less rainfall (dry season), lower population density, and that district level rates of PE/E increase with higher groundwater salinity and in the high risk LECZ category closest to the coast. Results suggest that drinking water salinity may be associated with PE/E and that using national health surveillance data can improve understanding of this association. This approach can potentially be leveraged in the future to inform targeted interventions to high risk regions and times