Parameterizing neural networks for disease classification

Neural networks are one option to implement decision support systems for health care applications. In this paper, we identify optimal settings of neural networks for medical diagnoses: The study involves the application of supervised machine learning using an artificial neural network to distinguish between gout and leukaemia patients. With the objective to improve the base accuracy (calculated from the initial set-up of the neural network model), several enhancements are analysed, such as the use of hyperbolic tangent activation function instead of the sigmoid function, the use of two hidden layers instead of one, and transforming the measurements with linear regression to obtain a smoothened data set. Another setting we study is the impact on the accuracy when using a data set of reduced size but with higher data quality. We also discuss the tradeoff between accuracy and runtime efficiency

Car make and model recognition under limited lighting conditions at night

Car make and model recognition (CMMR) has become an important part of intelligent transport systems. Information provided by CMMR can be utilized when license plate numbers cannot be identified or fake number plates are used. CMMR can also be used when a certain model of a vehicle is required to be automatically identified by cameras. The majority of existing CMMR methods are designed to be used only in daytime when most of the car features can be easily seen. Few methods have been developed to cope with limited lighting conditions at night where many vehicle features cannot be detected. The aim of this work was to identify car make and model at night by using available rear view features. This paper presents a one-class classifier ensemble designed to identify a particular car model of interest from other models. The combination of salient geographical and shape features of taillights and license plates from the rear view is extracted and used in the recognition process. The majority vote from support vector machine, decision tree, and k-nearest neighbors is applied to verify a target model in the classification process. The experiments on 421 car makes and models captured under limited lighting conditions at night show the classification accuracy rate at about 93 %

Rapid Diagnostic Algorithms as a Screening Tool for Tuberculosis: An Assessor Blinded Cross-Sectional Study

Background: A major obstacle to effectively treat and control tuberculosis is the absence of an accurate, rapid, and low-cost diagnostic tool. A new approach for the screening of patients for tuberculosis is the use of rapid diagnostic classification algorithms. Methods: We tested a previously published diagnostic algorithm based on four biomarkers as a screening tool for tuberculosis in a Central European patient population using an assessor-blinded cross-sectional study design. In addition, we developed an improved diagnostic classification algorithm based on a study population at a tertiary hospital in Vienna, Austria, by supervised computational statistics. Results: The diagnostic accuracy of the previously published diagnostic algorithm for our patient population consisting of 206 patients was 54% (CI: 47%–61%). An improved model was constructed using inflammation parameters and clinical information. A diagnostic accuracy of 86% (CI: 80%–90%) was demonstrated by 10-fold cross validation. An alternative model relying solely on clinical parameters exhibited a diagnostic accuracy of 85% (CI: 79%–89%). Conclusion: Here we show that a rapid diagnostic algorithm based on clinical parameters is only slightly improved by inclusion of inflammation markers in our cohort. Our results also emphasize the need for validation of new diagnostic algorithms in different settings and patient populations

Team diversity and categorization salience : capturing diversity-blind, intergroup biased, and multicultural perceptions

It is increasingly recognized that team diversity with respect to various social categories (e.g., gender, race) does not automatically result in the cognitive activation of these categories (i.e., categorization salience), and that factors influencing this relationship are important for the effects of diversity. Thus, it is a methodological problem that no measurement technique is available to measure categorization salience in a way that efficiently applies to multiple dimensions of diversity in multiple combinations. Based on insights from artificial intelligence research, we propose a technique to capture the salience of different social categorizations in teams that does not prime the salience of these categories. We illustrate the importance of such measurement by showing how it may be used to distinguish among diversity-blind responses (low categorization salience), multicultural responses (positive responses to categorization salience), and intergroup biased responses (negative responses to categorization salience) in a study of gender and race diversity and the gender by race faultline in 38 manufacturing teams comprising 239 members

Combining machine learning and metaheuristics algorithms for classification method PROAFTN

© Crown 2019. The supervised learning classification algorithms are one of the most well known successful techniques for ambient assisted living environments. However the usual supervised learning classification approaches face issues that limit their application especially in dealing with the knowledge interpretation and with very large unbalanced labeled data set. To address these issues fuzzy classification method PROAFTN was proposed. PROAFTN is part of learning algorithms and enables to determine the fuzzy resemblance measures by generalizing the concordance and discordance indexes used in outranking methods. The main goal of this chapter is to show how the combined meta-heuristics with inductive learning techniques can improve performances of the PROAFTN classifier. The improved PROAFTN classifier is described and compared to well known classifiers, in terms of their learning methodology and classification accuracy. Through this chapter we have shown the ability of the metaheuristics when embedded to PROAFTN method to solve efficiency the classification problems

Analyzing and predicting the spatial penetration of Airbnb in U.S. cities

In the hospitality industry, the room and apartment sharing platform of Airbnb has been accused of unfair competition. Detractors have pointed out the chronic lack of proper legislation. Unfortunately, there is little quantitative evidence about Airbnb's spatial penetration upon which to base such a legislation. In this study, we analyze Airbnb's spatial distribution in eight U.S. urban areas, in relation to both geographic, socio-demographic, and economic information. We find that, despite being very different in terms of population composition, size, and wealth, all eight cities exhibit the same pattern: that is, areas of high Airbnb presence are those occupied by the \newpart{``talented and creative''} classes, and those that are close to city centers. This result is consistent so much so that the accuracy of predicting Airbnb's spatial penetration is as high as 0.725