Personality Determination of an Individual Through Neural Networks

被引:0
|
作者
Sanchez, J. R. [1 ]
Capel, M. I. [2 ]
Jimenez, Celina [3 ]
Rodriguez-Fraile, Gonzalo [4 ]
Pegalajar, M. C. [1 ]
机构
[1] Univ Granada, Dept Comp Sci & Artificial Intelligence, E-18071 Granada, Spain
[2] Univ Granada, Software Engn Dept, ETSI Informat & Telecommun, E-18071 Granada, Spain
[3] Psychol Clin Altea, Altea, Spain
[4] Univ Granada, Fdn Dev Consciousness & Dev, Granada, Spain
关键词
Machine Learning; Eneagrama; 16PF; Psychology; Regression; Neural networks;
D O I
10.1007/978-3-319-91473-2_5
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The use of neural networks is proposed in this article as a means of determining the personality of an individual. This research work comes in view of the necessity of combining two psychological tests for carrying out personnel selection. From the assessment of the first test known as 16 Personality Factor we can directly obtain an appraisal of the individual's personality type as the one given by the Enneagram Test, which now does not need to be done. The two chosen tests are highly accepted by Human Resources Department in big companies as useful tools for selecting personnel when new recruitment comes up, for personnel promotion internal to the firm, for employees' personal development and growing as a person. The (mathematical/computer science) model chosen to attain the research objectives is based on Artificial Neuron Networks.
引用
收藏
页码:52 / 61
页数:10
相关论文
共 50 条
  • [31] Designing neural networks through neuroevolution
    Kenneth O. Stanley
    Jeff Clune
    Joel Lehman
    Risto Miikkulainen
    [J]. Nature Machine Intelligence, 2019, 1 : 24 - 35
  • [32] Cybervictim vs. cyberaggressor. profile determination and comparison through artificial neural networks
    Ortiz-Marcos, Jose Manuel
    Solano-Sanchez, Miguel-Angel
    Lendinez-Turon, Ana
    Tome-Fernandez, Maria
    [J]. VULNERABLE CHILDREN AND YOUTH STUDIES, 2024, 19 (02) : 288 - 308
  • [33] Handwritten Texts for Personality Identification Using Convolutional Neural Networks
    Valdez-Rodriguez, Jose E.
    Calvo, Hiram
    Felipe-Riveron, Edgardo M.
    [J]. PATTERN RECOGNITION AND INFORMATION FORENSICS, 2019, 11188 : 140 - 145
  • [34] Neural networks approach to the determination of the machining parameters
    Choi, K
    [J]. KSME JOURNAL, 1996, 10 (04): : 389 - 395
  • [35] PEDIATRIC SKELETAL AGE - DETERMINATION WITH NEURAL NETWORKS
    GROSS, GW
    BOONE, JM
    BISHOP, DM
    [J]. RADIOLOGY, 1995, 195 (03) : 689 - 695
  • [36] Determination of size distribution using neural networks
    Stevens, JH
    Nijhuis, JAG
    Spaanenburg, L
    [J]. COMPUTATIONAL INTELLIGENCE FOR MODELLING, CONTROL & AUTOMATION - NEURAL NETWORKS & ADVANCED CONTROL STRATEGIES, 1999, 54 : 40 - 45
  • [37] The applicability of neural networks in the determination of soil profiles
    Caglar, Naci
    Arman, Hasan
    [J]. BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT, 2007, 66 (03) : 295 - 301
  • [38] Determination of weights for relaxation recurrent neural networks
    Serpen, G
    Livingston, DL
    [J]. NEUROCOMPUTING, 2000, 34 : 145 - 168
  • [39] Determination of bearing clearance by the application of neural networks
    Meier, Nicolas
    Biyani, Yashvardhan
    Georgiadis, Anthimos
    [J]. 2018 IEEE SENSORS, 2018, : 1620 - 1623
  • [40] Determination of the CMSSM parameters using neural networks
    Bornhauser, Nicki
    Drees, Manuel
    [J]. PHYSICAL REVIEW D, 2013, 88 (07)