3detector - Lidar Nokta Bulutu Veri̇si̇nden Otomati̇k Ağaç Konumu Beli̇rleme Programinin Geli̇şti̇ri̇lmesi̇

Güzel, Cengiz; Şahin, Büşra; Baş, Sıla; Türker, Mustafa

doi:10.15659/uzalcbs2018.7016

Cited by 2 publications

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“…Due to stress, tourist guides can experience neuroticism and their job attitudes and job satisfaction can be adversely affected. Imbalance in wages, lack of holiday opportunities, late retirement, lack of health benefits and most importantly working without job security and lack of state support in difficult times can be regarded as actors that can cause stress (Güzel et al, 2014). For this reason, working conditions of tourist guides should be improved and sector representatives and authorities should endorse the tourist guides.…”

Section: Discussion Conclusion and Implicationsmentioning

confidence: 99%

Effects of Tourıst Guıdes’ Personal Traıts on Job Attıtude and Job Satısfactıon: A Study in Turkey

Pelit¹,

Kabakulak²

2019

PDM

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Objective: The aim of the study is to determine effects of tourist guides' personality traits on their job attitudes and job satisfactions.Methodology / Approach: The study was structured in quantitative research. As data collection method, survey technique was used. In order to measure personality traits, job attitude and job satisfaction, 600 questionnaires were distributed and 462 questionnaires were collected. Questionnaires with missing or incorrect data were removed and 453 questionnaires were used in total. Reliability, arithmetic mean, correlation and regression analyzes were used in the study.Originality / Relevance: This study has originality due to the lack of similar studies, as no other article or thesis was found with the same variables or sample belongs to tourist guides.Main results: Results showed that among personality traits; tourist guides' responsibility and agreeableness were surpassing. Also, tourist guides' attitude towards their profession was found to be generally in good and job satisfaction levels were detected as moderate. In addition, as a result of the correlation and regression analyzes, it was determined that personality traits of the tourist guides have had a significant effect on their job attitudes and job satisfaction.Theoretical / methodological contributions: The findings are important to guiding profession and tourist guides career path and tourist guides' continue to profession and raise vocational commitment and notify relevant persons about tourist guides situations.

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Section: Discussion Conclusion and Implicationsmentioning

confidence: 99%

Effects of Tourıst Guıdes’ Personal Traıts on Job Attıtude and Job Satısfactıon: A Study in Turkey

Pelit¹,

Kabakulak²

2019

PDM

View full text Add to dashboard Cite

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Classification of UAV point clouds by random forest machine learning algorithm

Zeybek

2021

Turkish Journal of Engineering

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Unmanned aerial vehicle Point cloud Classification Random forestToday, unmanned aerial vehicle (UAV)-based images have become an important data sources for researchers who deals with mapping from various disciplines on photogrammetry and remote sensing. Reconstruction of an area with three-dimensional (3D) point clouds from UAV-based images are an essential process to be used for traditional 2D cadastral maps or to produce a topographic maps. Point clouds should be classified since they subjected to various analyses for extraction for further information from direct point cloud data. Due to the high density of point clouds, data processing and gathering information makes the classification of point clouds a challenging task and may take a long time. Therefore, the classification processing allows an optimal solution to acquire valuable information. In this study, random forest machine learning algorithm for classification processing is applied with radiometric features (Red band, Green band and Blue band) and geometric characteristics derived from covariance feature (curvature, omnivariance, flatness, linearity, surface variance, anisotropy and normalized terrain surface) of points. In addition, the case study is presented in order to test applicability of the proposed methodology to acquire an accuracy and performance of random forest method on the UAV based point cloud. After the classification processing, a class assigned each point from the model was compared with the reference data class. Lastly, the overall accuracy of the classification was achieved as 96% and the Kappa index was reached to 91% on data set.

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3detector - Lidar Nokta Bulutu Veri̇si̇nden Otomati̇k Ağaç Konumu Beli̇rleme Programinin Geli̇şti̇ri̇lmesi̇

Cited by 2 publications

References 0 publications

Effects of Tourıst Guıdes’ Personal Traıts on Job Attıtude and Job Satısfactıon: A Study in Turkey

Effects of Tourıst Guıdes’ Personal Traıts on Job Attıtude and Job Satısfactıon: A Study in Turkey

Classification of UAV point clouds by random forest machine learning algorithm

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