Knowledge discovery for scheduling in computational grids

Fölling, Alexander; Lepping, Joachim

doi:10.1002/widm.1060

Cited by 3 publications

(2 citation statements)

References 34 publications

(73 reference statements)

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“…A decision tree classifier is designed and applied on the extracted features to detect mycelium and conidium from Pap smear sample images (Fölling & Lepping, ). The decision rules that classified conidium among all objects are: 0.8 < Area < 6.7, 1 < Major Axis < 7, 0.68 < Eccentricity < 0.91, 0.6 < Minor Axis < 1.6 and 15 < Compactness < 24 that demonstrated as flowcharts in Figure .…”

Section: Methodsmentioning

confidence: 99%

Computer‐aided diagnosis software for vulvovaginal candidiasis detection from Pap smear images

Momenzadeh

Vard

Talebi

et al. 2017

Microscopy Res & Technique

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Vulvovaginal candidiasis (VVC) is a common gynecologic infection and it occurs when there is overgrowth of the yeast called Candida. VVC diagnosis is usually done by observing a Pap smear sample under a microscope and searching for the conidium and mycelium components of Candida.This manual method is time consuming, subjective and tedious. Any diagnosis tools that detect VVC, semi-or full-automatically, can be very helpful to pathologists. This article presents a computer aided diagnosis (CAD) software to improve human diagnosis of VVC from Pap smear samples. The proposed software is designed based on phenotypic and morphology features of the Candida in Pap smear sample images. This software provide a user-friendly interface which consists of a set of image processing tools and analytical results that helps to detect Candida and determine severity of illness. The software was evaluated on 200 Pap smear sample images and obtained specificity of 91.04% and sensitivity of 92.48% to detect VVC. As a result, the use of the proposed software reduces diagnostic time and can be employed as a second objective opinion for pathologists.

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Section: Methodsmentioning

confidence: 99%

Computer‐aided diagnosis software for vulvovaginal candidiasis detection from Pap smear images

Momenzadeh

Vard

Talebi

et al. 2017

Microscopy Res & Technique

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“…The performance of online grid-scheduling algorithms highly depends on their adaptivity and flexibility in changing environments (Flling and Lepping 2012). In practice, the problem of online scheduling is often simplified by converting it into a series of offline problems (Flling and Lepping 2012).…”

Section: Introductionmentioning

confidence: 99%

Scheduling parallel batch jobs in grids with evolutionary metaheuristics

Switalski

Seredyński

2014

J Sched

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In this paper we propose an efficient offline job scheduling algorithm working in a grid environment that is based on a relatively new evolutionary metaheuristic called generalized extremal optimization (GEO). We compare our experimental results with those obtained using a very popular evolutionary metaheuristic, the genetic algorithm (GA). The scheduling algorithm implies two-stage scheduling. In the first stage, the algorithm allocates jobs to suitable machines of a grid; GEO/GA is used for this purpose. In the second stage, jobs are independently scheduled on each machine; this is performed with a variant of a list scheduling algorithm. Both GEO and GA belong to the class of evolutionary algorithms, but GEO is much simpler and requires the tuning of only one parameter, whereas GA requires the tuning of several parameters. The results of the experimental study show that GEO, despite its simplicity, outperforms the GA in a whole range of scheduling instances and is much easier to use.

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Automatic diagnosis of vulvovaginal candidiasis from Pap smear images

Momenzadeh

Sehhati

Dehnavi

et al. 2017

Journal of Microscopy

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Vulvovaginal candidiasis (VVC) is the most common genital infections that are seen every day in clinics. This infection is due to excessive growth of Candida that are normally present in the vagina in small numbers. Diagnosis of VVC is routinely done by direct microscopy of Pap smear samples and searching for the Candida in the Pap smear glass slides. This manual method is subjective, time consuming, labour-intensive and tedious. This study presents a computer-aided diagnostic (CAD) method to improve human diagnosis of VVC. The proposed CAD method reduces the diagnostic time and also can be worked as a second objective opinion for pathologists. Our main objective is detection and extraction of mycelium and conidium of Candida fungus from microscopic images of Pap smear samples. In this regard, the proposed method is composed of three main phases, namely preprocessing, segmentation, feature extraction and classification. At the first phase, bottom-hat filtering is used for elimination of the cervical cells and separating the background. Then decorrelation stretching and colour K-means clustering are used for Candida segmentation. Finally the extracted features used by a decision tree classifier to detect Candida from other parts of smear. The proposed method was evaluated on 200 Pap smear images and showed specificity of 99.83% and 99.62% and sensitivity of 92.18% and 94.53% for detection of mycelium and conidium, respectively.

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Knowledge discovery for scheduling in computational grids

Cited by 3 publications

References 34 publications

Computer‐aided diagnosis software for vulvovaginal candidiasis detection from Pap smear images

Computer‐aided diagnosis software for vulvovaginal candidiasis detection from Pap smear images

Scheduling parallel batch jobs in grids with evolutionary metaheuristics

Automatic diagnosis of vulvovaginal candidiasis from Pap smear images

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