scholarly journals A REAL PERSONNEL SELECTION PROBLEM USING THE GENERALIZED CHOQUET INTEGRAL METHODOLOGY

2018 ◽  
Vol 6 (2) ◽  
pp. 694-716
Author(s):  
Yavuz ÖZDEMİR ◽  
Kemal Gökhan NALBANT
Keyword(s):  
Human Resources ◽  
Personnel Selection ◽  
Selection Criteria ◽  
Choquet Integral ◽  
Human Resources Management ◽  
Selection Problem ◽  
Resources Management ◽  
Selection For ◽  
Selection Of

The main objective in the selection of personnel is to select the most appropriate candidate for a job. Personnel selection for human resources management is a very important issue.The aim of this paper is to determine the best-performing personnel for promotion using an application of a Multi Criteria Decision Making(MCDM) method, generalized Choquet integral, to a real personnel selection problem of a case study in Turkey and 17 alternatives are ranked according to personnel selection criteria (22 subcriteria are classified under 5 main criteria). The main contribution of this paper is to determine the interdependency among main criteria and subcriteria, the nonlinear relationship among them and the environmental uncertainties while selecting personnel alternatives using the generalized Choquet integral method with the experts’ view. To the authors’ knowledge, this will be the first study which uses the generalized Choquet Integral methodology for human resources. 

scholarly journals Individual tree crown delineation and tree species classification with hyperspectral and LiDAR data

PeerJ ◽  
2019 ◽  
Vol 6 ◽  
pp. e6227 ◽  
Author(s):  
Michele Dalponte ◽  
Lorenzo Frizzera ◽  
Damiano Gianelle
Keyword(s):  
Remote Sensing ◽  
Tree Species ◽  
Data Science ◽  
Remote Sensing Data ◽  
Tree Crown ◽  
Species Classification ◽  
Individual Tree ◽  
Sensing Data ◽  
Tree Species Classification ◽  
Selection Of

An international data science challenge, called National Ecological Observatory Network—National Institute of Standards and Technology data science evaluation, was set up in autumn 2017 with the goal to improve the use of remote sensing data in ecological applications. The competition was divided into three tasks: (1) individual tree crown (ITC) delineation, for identifying the location and size of individual trees; (2) alignment between field surveyed trees and ITCs delineated on remote sensing data; and (3) tree species classification. In this paper, the methods and results of team Fondazione Edmund Mach (FEM) are presented. The ITC delineation (Task 1 of the challenge) was done using a region growing method applied to a near-infrared band of the hyperspectral images. The optimization of the parameters of the delineation algorithm was done in a supervised way on the basis of the Jaccard score using the training set provided by the organizers. The alignment (Task 2) between the delineated ITCs and the field surveyed trees was done using the Euclidean distance among the position, the height, and the crown radius of the ITCs and the field surveyed trees. The classification (Task 3) was performed using a support vector machine classifier applied to a selection of the hyperspectral bands and the canopy height model. The selection of the bands was done using the sequential forward floating selection method and the Jeffries Matusita distance. The results of the three tasks were very promising: team FEM ranked first in the data science competition in Task 1 and 2, and second in Task 3. The Jaccard score of the delineated crowns was 0.3402, and the results showed that the proposed approach delineated both small and large crowns. The alignment was correctly done for all the test samples. The classification results were good (overall accuracy of 88.1%, kappa accuracy of 75.7%, and mean class accuracy of 61.5%), although the accuracy was biased toward the most represented species.

scholarly journals Genome Wide Search to Identify Reference Genes candidates for Gene Expression Analysis in Gossypium hirsutum

2019 ◽  
Author(s):  
Smitha P K ◽  
Vishnupriyan K ◽  
Ananya S. Kar ◽  
Anil Kumar M ◽  
Christopher Bathula ◽  
...  
Keyword(s):  
Gossypium Hirsutum ◽  
Reference Genes ◽  
Data Science ◽  
Natural Fiber ◽  
Quantitative Polymerase Chain Reaction ◽  
Critical Role ◽  
Genome Wide ◽  
Cotton Species ◽  
Genome Wide Search ◽  
Selection Of

Abstract Background: Cotton is one of the most important commercial crops as the source of natural fiber, oil and fodder. To protect it from harmful pest populations number of newer transgenic lines have been developed. For quick expression checks in successful agriculture qPCR (quantitative polymerase chain reaction) have become extremely popular. The selection of appropriate reference genes plays a critical role in the outcome of such experiments as the method quantifies expression of the target gene in comparison with the reference. Traditionally most commonly used reference genes are the “ house-keeping genes”, involved in basic cellular processes. However, expression levels of such genes often vary in response to experimental conditions, forcing the researchers to validate the reference genes for every experimental platform. This study presents a data science driven unbiased genome-wide search for the selection of reference genes by assessing variation of >50,000 genes in a publicly available RNA- seq dataset of cotton species Gossypium hirsutum . Result: Five genes ( TMN5, TBL6, UTR5B, AT1g65240 and CYP76B6 ) identified by data-science driven analysis, along with two commonly used reference genes found in literature ( PP2A1 and UBQ14 ) were taken through qPCR in a set of 33 experimental samples consisting of different tissues (leaves, square, stem and root), different stages of leaf (young and mature) and square development (small, medium and large) in both transgenic and non-transgenic plants. Expression stability of the genes was evaluated using four algorithms - geNorm , BestKeeper , NormFinder and RefFinder. Conclusion: Based on the results we recommend the usage of TMN5 and TBL6 as the optimal candidate reference genes in qPCR experiments with normal and transgenic cotton plant tissues. AT1g65240 and PP2A1 can also be used if expression study includes squares. This study, for the first time successfully displays a data science driven genome-wide search method followed by experimental validation as a method of choice for selection of stable reference genes over the selection based on function alone.

scholarly journals NEON NIST data science evaluation challenge: methods and results of team FEM

2018 ◽  
Author(s):  
Michele Dalponte ◽  
Lorenzo Frizzera ◽  
Damiano Gianelle
Keyword(s):  
Data Science ◽  
Near Infrared ◽  
Remote Sensing Data ◽  
Region Growing ◽  
Support Vector ◽  
Infrared Band ◽  
Species Classification ◽  
Individual Tree ◽  
Data Alignment ◽  
Selection Of

An international data science challenge, called NEON NIST data science evaluation, was set up in autumn 2017 with the goal to improve the use of remote sensing data in ecological applications. The competition was divided into three tasks: 1) segmentation of tree crowns; 2) data alignment; and 3) tree species classification. In this paper the methods and results of team FEM in the NEON NIST data science evaluation challenge are presented. The individual tree crown (ITC) segmentation (Task 1 of the challenge) was done using a region growing method applied to a near-infrared band of the hyperspectral images. The optimization of the parameters of the segmentation algorithm was done in a supervised way on the basis of the Jaccard score using the training set provided by the organizers. The alignment (Task 2) between the segmented ITCs and the ground measured trees was done using an Euclidean distance among the position, the height, and the crown radius of the ITCs and the ground trees. The classification (Task 3) was performed using a Support Vector Machine classifier applied to a selection of the hyperspectral bands. The selection of the bands was done using a Sequential Forward Floating Selection method and the Jeffries Matusita distance. The results in the three tasks were very promising: team FEM ranked first in Task 1 and 2, and second in Task 3. The segmentation results showed that the proposed approach segmented both small and large crowns. The alignment was correctly done for all the test samples. The classification results were good, even if the accuracy was biased towards the most represented species.

scholarly journals A REAL PERSONNEL SELECTION PROBLEM USING THE GENERALIZED CHOQUET INTEGRAL METHODOLOGY

2018 ◽  
Vol 6 (2) ◽  
Author(s):  
Yavuz ÖZDEMİR ◽  
Kemal Gökhan NALBANT
Keyword(s):  
Human Resources ◽  
Personnel Selection ◽  
Selection Criteria ◽  
Choquet Integral ◽  
Human Resources Management ◽  
Selection Problem ◽  
Resources Management ◽  
Selection For ◽  
Selection Of

The main objective in the selection of personnel is to select the most appropriate candidate for a job. Personnel selection for human resources management is a very important issue.The aim of this paper is to determine the best-performing personnel for promotion using an application of a Multi Criteria Decision Making(MCDM) method, generalized Choquet integral, to a real personnel selection problem of a case study in Turkey and 17 alternatives are ranked according to personnel selection criteria (22 subcriteria are classified under 5 main criteria). The main contribution of this paper is to determine the interdependency among main criteria and subcriteria, the nonlinear relationship among them and the environmental uncertainties while selecting personnel alternatives using the generalized Choquet integral method with the experts’ view. To the authors’ knowledge, this will be the first study which uses the generalized Choquet Integral methodology for human resources. 

Probabilistic uncertain linguistic TODIM method based on the generalized Choquet integral and its application

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xu Xiuqin ◽  
Xie Jialiang ◽  
Yue Na ◽  
Wang Honghui
Keyword(s):  
Design Methodology ◽  
Distance Measure ◽  
Group Decision ◽  
Choquet Integral ◽  
Decision Makers ◽  
Rational Behavior ◽  
Content Type ◽  
Qualitative And Quantitative ◽  
Todim Method ◽  
Selection Of

PurposeThe purpose of this paper is to develop a probabilistic uncertain linguistic (PUL) TODIM method based on the generalized Choquet integral, with respect to the interdependencies between criteria, for the selection of the best alternate in the context of multiple criteria group decision-making (MCGDM).Design/methodology/approachOwing to decision makers (DMs) do not always show completely rational and may have the preference of bounded rational behavior, this may affect the result of the MCGDM. At the same time, criteria interaction is a focused issue in MCGDM. Hence, a novel TODIM method based on the generalized Choquet integral selects the best alternate using PUL evaluation, where the generalized Choquet integral is used to calculate the weight of criterion. The generalized PUL distance measure between two probabilistic uncertain linguistic elements (PULEs) is calculated and the perceived dominance degree matrices for each alternate relative to other alternates are obtained. Furthermore, the comprehensive perceived dominance degree of each alternate can be calculated to get the ranking.FindingsPotential application of the PUL-TODIM method is demonstrated through an evaluation example with sensitivity and comparative analysis.Originality/valueAs per author's concern, there are no TODIM methods with probabilistic uncertain linguistic sets (PULTSs) to solve MCGDM problems under uncertainty. Compared with the result of existing methods, the final judgment value of alternates using the extended TODIM methodology is highly corroborated, which proves its potential in solving MCGDM problems under qualitative and quantitative environments.

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