Tonya Barrier

A gender‐based categorization for conflict resolution

Purpose – As the workforce becomes increasingly diversified, it becomes increasingly important for managers to understand the conflict resolution attitudes brought to information systems (IS) by both men and women. This research was designed to investigate assumptions that may exist regarding the relationship between gender and conflict resolution. Specifically, the intent of this study was to compare the conflict resolution strategies of males and females majoring in IS in order to determine if gender‐based differences exist.Design/methodology/approach – The Thomas‐Kilmann Conflict Mode Instrument was utilized to assess the conflict resolution styles of 163 traditional‐age (18‐22) students enrolled in undergraduate IS courses at a large Midwestern university. Both ANOVA and t‐test analyses were utilized to investigate the relationship between gender and conflict resolution style.Findings – Results of this study indicate that, when compared with their male counterparts, women are more likely to utilize a ...

Different approaches for extracting information from the co-occurrence matrix.

In 1979 Haralick famously introduced a method for analyzing the texture of an image: a set of statistics extracted from the co-occurrence matrix. In this paper we investigate novel sets of texture descriptors extracted from the co-occurrence matrix; in addition, we compare and combine different strategies for extending these descriptors. The following approaches are compared: the standard approach proposed by Haralick, two methods that consider the co-occurrence matrix as a three-dimensional shape, a gray-level run-length set of features and the direct use of the co-occurrence matrix projected onto a lower dimensional subspace by principal component analysis. Texture descriptors are extracted from the co-occurrence matrix evaluated at multiple scales. Moreover, the descriptors are extracted not only from the entire co-occurrence matrix but also from subwindows. The resulting texture descriptors are used to train a support vector machine and ensembles. Results show that our novel extraction methods improve the performance of standard methods. We validate our approach across six medical datasets representing different image classification problems using the Wilcoxon signed rank test. The source code used for the approaches tested in this paper will be available at: http://www.dei.unipd.it/wdyn/?IDsezione=3314&IDgruppo_pass=124&preview=.

A comparison of methods for extracting information from the co-occurrence matrix for subcellular classification

Abstract In this paper we focus on cell phenotype image classification, a bioimaging problem that is concerned with finding the location of protein expressions within a cell. Protein localization is becoming increasingly critical in the diagnosis and prognosis of many diseases. In recent years several new approaches for describing a given image have been proposed. Some of the most significant developments have been based on binary encodings, such as local binary patterns and local phase quantization. In this paper we reexamine one of the oldest methods for representing an image that Haralick famously proposed in 1979 using the co-occurrence matrix for calculating a set of image statistics. Few methods have been proposed since that extract new features from the co-occurrence matrix. In this work we compare some recently proposed methods that are based on the co-occurrence matrix (CM) to classify cell phenotype images. We investigate the correlation among the different sets of features that can be extracted from the CM and then determine the best way to combine these different feature sets for optimizing system performance. Moreover, we combine our novel approach with state of the art descriptors to optimize performance. We validate our approach on various types of biological microscope images using five image databases for subcellular classification. We use these image features for training a stand-alone support vector machine and a random subspace of support vector machines to separate the classes in each dataset. The Matlab code for some of the approaches tested in this paper will be available at http://www.dei.unipd.it/wdyn/?IDsezione=3314&IDgruppo_pass=124&preview= >.

An assessment of information systems taxonomies: time to be re-evaluate?

Dramatic advances in technology, evolving organizational structures and increasing user influence are impacting the types of information systems (IS) being developed. These are not recognized adequately by existing taxonomies which tend to be incomplete, unclear, simplistic, unwieldy, inflexible or some combination of these. Fourteen models from the literature are described and discussed in light of todays myriad systems. A user-oriented two-dimensional taxonomy utilizing user type (institution or individual) and type of system support provided to the user (data, communication, information, or decision) is developed.

Ensemble of Local Phase Quantization Variants with Ternary Encoding

In this chapter, we present some variants of local phase quantization (LPQ), a novel texture descriptor that has been shown to perform well on a variety of classification tasks. After providing an extensive review of LPQ, we report experiments using several new LPQ derivatives obtained by varying LPQ parameters and by using a ternary rather than the binary encoding scheme. Multiple parameter sets are generated and each set is used to train a standard machine-learning classifier, a stand-alone support vector machine. The ensemble is then combined using the sum rule. Extensive experiments are conducted using six different datasets. Our method is compared along with the best state-of-the-art methods for solving each problem represented by the datasets. In each case, the best result is obtained using an ensemble with LPQ variants and ternary encoding. In this study, we also examine the distribution in the images of the most important bins of the LPQ histograms using Gabor filters. We find that incorporating this information into our best texture descriptor approach produces even better results.

Data mining based on intelligent systems for decision support systems in healthcare

In this paper we make an extensive study of Artificial Intelligence (AI) techniques that can be used in decision support systems in healthcare. In particular, we propose variants of ensemble methods (i.e., Rotation Forest and Input Decimated Ensembles) that are based on perturbing features, and we make a wide comparison among the ensemble approaches. We illustrate the power of these techniques by applying our approaches to different healthcare problems. Included in this chapter is extensive background material on the single classifier systems, ensemble methods, and feature transforms used in the experimental section.