Machine learning in incident categorization automation

IT incident management process requires a correct categorization to attribute incident tickets to the right resolution group and obtain an operational system as quickly as possible, having the lowest possible impact on the business and costumers. In this work, we introduce a module to automatically categorize incident tickets, turning the responsible teams for incident management more productive. This module can be integrated as an extension into an incident ticket system (ITS), which contributes to reduce the time wasted on incident ticket route and reduce the amount of errors on incident categorization. To automate the classification, we use a support vector machine (SVM), obtaining an accuracy of 89%, approximately, on a dataset of real-world incident tickets.info:eu-repo/semantics/acceptedVersio

Predictors of health-related quality of life in type II diabetic patients in Greece

<p>Abstract</p> <p>Background</p> <p>Diabetes Mellitus (DM) is a major cause of morbidity and mortality affecting millions of people worldwide, while placing a noteworthy strain on public health funding. The aim of this study was to assess health-related quality of life (HRQOL) of Greek Type II DM patients and to identify significant predictors of the disease in this patient population.</p> <p>Methods</p> <p>The sample (N = 229, 52.8% female, 70.0 years mean age) lived in a rural community of Lesvos, an island in the northeast of the Aegean Archipelagos. The generic SF-36 instrument, administered by trainee physicians, was used to measure HRQOL. Scale scores were compared with non-parametric Mann-Whitney and Kruskal-Wallis tests and multivariate stepwise linear regression analyses were used to investigate the effect of sociodemographic and diabetes-related variables on HRQOL.</p> <p>Results</p> <p>The most important predictors of impaired HRQOL were female gender, diabetic complications, non-diabetic comorbidity and years with diabetes. Older age, lower education, being unmarried, obesity, hypertension and hyperlipidaemia were also associated with impaired HRQOL in at least one SF-36 subscale. Multivariate regression analyses produced models explaining significant portions of the variance in SF-36 subscales, especially physical functioning (R²= 42%), and also showed that diabetes-related indicators were more important disease predictors, compared to sociodemographic variables.</p> <p>Conclusion</p> <p>The findings could have implications for health promotion in rural medical practice in Greece. In order to preserve a good HRQOL, it is obviously important to prevent diabetes complications and properly manage concomitant chronic diseases. Furthermore, the gender difference is interesting and requires further elucidation. Modifying screening methods and medical interventions or formulating educational programs for the local population appear to be steps in the correct direction.</p

Post-war British Fiction as 'Metaphysical Ethography'

UBL - phd migration 201

A hybrid colour image segmentation scheme

grantor: University of TorontoAn autonomous, hybrid-based segmentation scheme has been developed that efficiently and robustly partitions a colour image into different regions that we homogeneous with respect to colour. As with human visual perception, image segmentation is an important aspect of any type of image or scene analysis. The scheme is band on the HSI colour space. It combines both region- and pixel-based techniques. A pixel classification algorithm is used to categorise the pixels in the image as either chromatic or achromatic. A seed determination algorithm is then used to find geed pixels in the image. The region growing algorithm starts with the set of seed pixels and from these grow regions by appending to each seed pixel those neighbouring pixels that satisfy a homegeneity criterion. After regions are grown they are further processed with a region merging algorithm. Regions that an similar in colour are merged.M.A.Sc

Clinical and kinematic characteristics of cursive handwriting in elementary age children

The purpose of this research was to study the clinical and kinematic characteristics of cursive handwriting in healthy third and fifth grade children. One hundred-nine children participated in this study; 53 were in grade three and 56 were in grade five. Five commonly used clinical assessments were selected addressing strength, sensorimotor and coordination characteristics specific to handwriting. Two handwriting assessments, the Evaluation Tool of Children\u27s Handwriting–Cursive, and the writing subtest of the Jebsen Test of Hand Function, assessed speed and/or legibility of handwriting. A simple cursive writing task was also produced on a digitized tablet and analyzed for kinematic features. Multiple T–Tests were used to determine significant gender differences and the effects of maturation on handwriting. Logistic regression analysis was used to determine if clinical or kinematic characteristics were predictors of legibility in cursive handwriting. Multiple linear regression analyses were used to determine if clinical or kinematic characteristics of handwriting contributed to handwriting speed and legibility. Results of this study indicate that in all groups, boys had less legible handwriting than girls. With maturation, healthy children in the third and fifth grades improve in their ability to smoothly write in the up and down direction, which is complemented by improved hand steadiness and coordination. The strong association between the grooved pegboard and legibility suggest that improving a child\u27s in-hand manipulation skills may contribute to improvement in handwriting skills. The Jebsen and grooved pegboard contributed to handwriting speed and legibility. The findings of this study will guide Occupational Therapists in improving their understanding of the clinical and kinematic mechanisms underlying handwriting, which are critical to the development of appropriate intervention paradigms

Performance

evaluation of clustering algorithms on microcalcifications as mammography finding