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  1. Article ; Online: A cost analysis with the discrete-event simulation application in nurse and doctor employment management.

    Atalan, Abdulkadir

    Journal of nursing management

    2022  Volume 30, Issue 3, Page(s) 733–741

    Abstract: Aim: This study aimed to analyse the treatment cost of a patient, depending on the number of patients treated, patient waiting times, and the number of nurses and doctors employed in an emergency department of a private hospital.: Background: Within ... ...

    Abstract Aim: This study aimed to analyse the treatment cost of a patient, depending on the number of patients treated, patient waiting times, and the number of nurses and doctors employed in an emergency department of a private hospital.
    Background: Within health systems, changes in health care resources can be very costly, especially if these changes are long-term. The discrete-event simulation method described in this paper allows for the monitoring and analysis of complicated changes in real systems by using computer-based modelling.
    Method: The discrete event simulation model was derived from nine scenarios according to the number of nurses and doctors, and a comparison was made between the results of the scenarios and the actual results.
    Results: Among the scenarios, scenario 6 provided the lowest treatment cost for a patient by employing three doctors and two nurses with the best performance. The cost of treatment for a patient varies between ŧ9.00 and ŧ11.00 depending on the value of δ, and the daily cost of these resources to the hospital is ŧ1300.77.
    Conclusions: This study provides a clear picture of a cost analysis comparison based on changes made about the actual health system in the computer-based simulated environment.
    Implications for nursing management: The workforce data of nurses and doctors offers enough detail for cost analysis in health care settings to calculate the cost of treatment for a patient.
    MeSH term(s) Computer Simulation ; Costs and Cost Analysis ; Emergency Service, Hospital ; Employment ; Hospitals ; Humans
    Language English
    Publishing date 2022-01-23
    Publishing country England
    Document type Journal Article
    ZDB-ID 1162321-4
    ISSN 1365-2834 ; 0966-0429
    ISSN (online) 1365-2834
    ISSN 0966-0429
    DOI 10.1111/jonm.13547
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 3805: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  2. Article ; Online: Forecasting drinking milk price based on economic, social, and environmental factors using machine learning algorithms

    Atalan, Abdulkadir

    Agribusiness. 2023 Jan., v. 39, no. 1 p.214-241

    2023  

    Abstract: The study aimed to describe and test machine learning (ML)‐based algorithms to evaluate the unit price of drinking milk. The algorithms were applied to the data collected over 8 years in 2014 and 2021 related to the price of drinking milk in Turkey. The ... ...

    Abstract The study aimed to describe and test machine learning (ML)‐based algorithms to evaluate the unit price of drinking milk. The algorithms were applied to the data collected over 8 years in 2014 and 2021 related to the price of drinking milk in Turkey. The economic, social, and environmental factors that have an impact on the unit price of drinking milk were evaluated. Five ML algorithms, including random forest, gradient boosting, support vector machine (SVM), neural network, and AdaBoost algorithms, were utilized to predict the drinking milk unit price. ML also applied hyperparameter tuning with nested cross‐validation to calculate the prediction accuracy for each algorithm. The results show that the random forest algorithm based on the features of the ML algorithms has the best performance, with the accuracy of 99.30% for training and 98.10% for testing the dataset. The average accuracy of gradient boosting, SVM, neural network, and AdaBoost are obtained as 97.30%, 96.15%, 95.65%, and 96.05%, respectively. Random forest performed best as the target variable with the lowest deviation values of mean squared error (MSE) (0.004), root mean square error (RMSE) (0.060), and mean absolute error (MAE) (0.029) in the training and MSE (0.009), RMSE (0.096), and MA (0.055) in the testing dataset. This study presents an interesting perspective with practical potential to adopt ML methods in the dairy industry. The developed ML algorithms can provide dairy investors and policymakers with important decision‐support information. [EconLit Citations: C13, C53, L66, C88].
    Keywords agribusiness ; dairy industry ; data collection ; milk ; milk prices ; prediction ; support vector machines
    Language English
    Dates of publication 2023-01
    Size p. 214-241.
    Publishing place John Wiley & Sons, Ltd
    Document type Article ; Online
    Note JOURNAL ARTICLE
    ZDB-ID 743656-7
    ISSN 0742-4477
    ISSN 0742-4477
    DOI 10.1002/agr.21773
    Database NAL-Catalogue (AGRICOLA)

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    In stock of ZB MED Bonn / Germany

    Z 4188: Show issues

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  3. Article: Correspondence about "Is the lockdown important to prevent the COVID-19 pandemic? Effects on psychology, environment and economy-perspective".

    Atalan, Abdulkadir

    Annals of medicine and surgery (2012)

    2021  Volume 65, Page(s) 102232

    Language English
    Publishing date 2021-04-02
    Publishing country England
    Document type Journal Article
    ZDB-ID 2745440-X
    ISSN 2049-0801
    ISSN 2049-0801
    DOI 10.1016/j.amsu.2021.102232
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Integration of the Machine Learning Algorithms and I-MR Statistical Process Control for Solar Energy

    Yasemin Ayaz Atalan / Abdulkadir Atalan

    Sustainability, Vol 15, Iss 13782, p

    2023  Volume 13782

    Abstract: The importance of solar power generation facilities, as one of the renewable energy types, is increasing daily. This study proposes a two-way validation approach to verify the validity of the forecast data by integrating solar energy production quantity ... ...

    Abstract The importance of solar power generation facilities, as one of the renewable energy types, is increasing daily. This study proposes a two-way validation approach to verify the validity of the forecast data by integrating solar energy production quantity with machine learning (ML) and I-MR statistical process control (SPC) charts. The estimation data for the amount of solar energy production were obtained by using random forest (RF), linear regression (LR), gradient boosting (GB), and adaptive boost or AdaBoost (AB) algorithms from ML models. Data belonging to eight independent variables consisting of environmental and geographical factors were used. This study consists of approximately two years of data on the amount of solar energy production for 636 days. The study consisted of three stages: First, descriptive statistics and analysis of variance tests of the dependent and independent variables were performed. In the second stage of the method, estimation data for the amount of solar energy production, representing the dependent variable, were obtained from AB, RF, GB, and LR algorithms and ML models. The AB algorithm performed best among the ML models, with the lowest RMSE, MSE, and MAE values and the highest R 2 value for the forecast data. For the estimation phase of the AB algorithm, the RMSE, MSE, MAE, and R 2 values were calculated as 0.328, 0.107, 0.134, and 0.909, respectively. The RF algorithm performed worst with performance scores for the prediction data. The RMSE, MSE, MAE, and R 2 values of the RF algorithm were calculated as 0.685, 0.469, 0.503, and 0.623, respectively. In the last stage, the estimation data were tested with I-MR control charts, one of the statistical control tools. At the end of all phases, this study aimed to validate the results obtained by integrating the two techniques. Therefore, this study offers a critical perspective to demonstrate a two-way verification approach to whether a system’s forecast data are under control for the future.
    Keywords solar energy ; machine learning ; random forest ; AdaBoost ; gradient boosting ; linear regression ; Environmental effects of industries and plants ; TD194-195 ; Renewable energy sources ; TJ807-830 ; Environmental sciences ; GE1-350
    Subject code 310 ; 670
    Language English
    Publishing date 2023-09-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article: Erratum to "Is the lockdown important to prevent the COVID-19 pandemic? Effects on psychology, environment and economy-perspective" [Ann. Med. Surg. 56 (2020) 38-42].

    Atalan, Abdulkadir

    Annals of medicine and surgery (2012)

    2020  Volume 56, Page(s) 217

    Abstract: This corrects the article DOI: 10.1016/j.amsu.2020.06.010.]. ...

    Abstract [This corrects the article DOI: 10.1016/j.amsu.2020.06.010.].
    Keywords covid19
    Language English
    Publishing date 2020-07-09
    Publishing country England
    Document type Journal Article ; Published Erratum
    ZDB-ID 2745440-X
    ISSN 2049-0801
    ISSN 2049-0801
    DOI 10.1016/j.amsu.2020.07.001
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article: Is the lockdown important to prevent the COVID-19 pandemic? Effects on psychology, environment and economy-perspective.

    Atalan, Abdulkadir

    Annals of medicine and surgery (2012)

    2020  Volume 56, Page(s) 38–42

    Abstract: COVID-19's daily increasing cases and deaths have led to worldwide lockdown, quarantine and some restrictions. This study aims to analyze the effect of lockdown days on the spread of coronavirus in countries. COVID-19 cases and lockdown days data were ... ...

    Abstract COVID-19's daily increasing cases and deaths have led to worldwide lockdown, quarantine and some restrictions. This study aims to analyze the effect of lockdown days on the spread of coronavirus in countries. COVID-19 cases and lockdown days data were collected for 49 countries that implemented the lockdown between certain dates (without interruption). The correlation tests were used for data analysis based on unconstrained (normal) and constrained (Tukey-lambda). The lockdown days was significantly correlated with COVID-19 pandemic based on unconstrained (r = -0.9126, F-ratio = 6.1654; t-ratio = 2.40; prob > .0203 with 49 observations) and based on Tukey-lambda (r = 0.7402, λ = 0.14). The lockdown, one of the social isolation restrictions, has been observed to prevent the COVID-19 pandemic, and showed that the spread of the virus can be significantly reduced by this preventive restriction in this study. This study offers initial evidence that the COVID-19 pandemic can be suppressed by a lockdown. The application of lockdown by governments is also thought to be effective on psychology, environment and economy besides having impact on Covid-19.
    Keywords covid19
    Language English
    Publishing date 2020-06-14
    Publishing country England
    Document type Journal Article
    ZDB-ID 2745440-X
    ISSN 2049-0801
    ISSN 2049-0801
    DOI 10.1016/j.amsu.2020.06.010
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article: Integration of Machine Learning Algorithms and Discrete-Event Simulation for the Cost of Healthcare Resources.

    Atalan, Abdulkadir / Şahin, Hasan / Atalan, Yasemin Ayaz

    Healthcare (Basel, Switzerland)

    2022  Volume 10, Issue 10

    Abstract: A healthcare resource allocation generally plays a vital role in the number of patients treated ( ...

    Abstract A healthcare resource allocation generally plays a vital role in the number of patients treated (
    Language English
    Publishing date 2022-09-30
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2721009-1
    ISSN 2227-9032
    ISSN 2227-9032
    DOI 10.3390/healthcare10101920
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Is the lockdown important to prevent the COVID-19 pandemic? Effects on psychology, environment and economy-perspective

    Atalan, Abdulkadir

    Annals of Medicine and Surgery

    2020  Volume 56, Page(s) 38–42

    Keywords covid19
    Language English
    Publisher Elsevier BV
    Publishing country us
    Document type Article ; Online
    ZDB-ID 2745440-X
    ISSN 2049-0801
    ISSN 2049-0801
    DOI 10.1016/j.amsu.2020.06.010
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article: Erratum to "Is the lockdown important to prevent the COVID-19 pandemic? Effects on psychology, environment and economy-perspective" [Ann. Med. Surg. 56 (2020) 38-42]

    Atalan, Abdulkadir

    Ann Med Surg (Lond)

    Abstract: This corrects the article DOI: 10.1016/j.amsu.2020.06.010.]. ...

    Abstract [This corrects the article DOI: 10.1016/j.amsu.2020.06.010.].
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #642638
    Database COVID19

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  10. Article ; Online: Erratum to “Is the lockdown important to prevent the COVID-19 pandemic? Effects on psychology, environment and economy-perspective” [Ann. Med. Surg. 56 (2020) 38–42]

    Atalan, Abdulkadir

    Annals of Medicine and Surgery

    2020  Volume 56, Page(s) 217

    Keywords covid19
    Language English
    Publisher Elsevier BV
    Publishing country us
    Document type Article ; Online
    ZDB-ID 2745440-X
    ISSN 2049-0801
    ISSN 2049-0801
    DOI 10.1016/j.amsu.2020.07.001
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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