Yazar "Şahin, Mehmet" seçeneğine göre listele

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    Application of extreme learning machine for estimating solar radiation from satellite data. Internatıonal Journal Of Energy Research, (2013) 38(2), 205-212
    (2013) Şahin, Mehmet; Kaya, Yılmaz; Uyar, Murat; Yıldırım, Selçuk
    In this paper, a simple and fast method based on extreme learning machine (ELM) for the estimation of solar radiation in Turkey was presented. To design the ELM model, satellite data of the National Oceanic and Atmospheric Administration advanced very high-resolution radiometer from 20 locations spread over Turkey were used. The satellite-based land surface temperature, altitude, latitude, longitude, month, and city were applied as input to the ELM, and the output variable is the solar radiation. To show the applicability of the ELM model, a performance comparison in terms of the estimation capability and the learning speed was made between the ELM model and conventional artificial neural network (ANN) model with backpropagation. The comparison results showed that the ELM model gave better estimation than the ANN model for the overall test locations. Moreover, the ELM model was about 23.5 times faster than the ANN model. The method could be used by researchers or scientists to design high-efficiency solar devices such as solar power plant and photovoltaic cell.
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    Comparison of ANN and MLR models for estimating solar radiation in Turkeyusing NOAA/AVHRR data. Advances in Space Research 51 (2013) 891- 904
    (2013) Şahin, Mehmet; Kaya, Yılmaz; Uyar, Murat
    In this paper, the estimation capacities of MLR and ANN are investigated to estimate monthly-average daily SR over Turkey. The satellite data are used for 73 different locations over Turkey. Land surface temperature, altitude, latitude, longitude and month are offered as the input variables for modeling ANN and MLR to get SR. Estimations of SR are evaluated with the meteorological values by using the statistical bases. The obtained results indicated that the ANN model could achieve a satisfactory performance when compared to the MLR model. Moreover, it is understood that more accurate results in estimation of SR are obtained in the use of satellite data, rather than the use of meteorological station data. Finally, the built ANN model is used to estimate the yearly average of daily SR over Turkey. As a result, satellite-based SR map for Turkey is generated.
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    EFFECTS OF POE ON PRE-SCHOOL STUDENTS’ CRITICAL THINKING AND POE SKILLS
    (2021) Kırıktaş, Halit; Şahin, Mehmet
    Apart from basic skills such as self-care and speaking, higher-order cognitive skills including inquiry, critical thinking, and scientific thinking can develop during early childhood. Thus, it is crucial to employ teaching methods that support the development of higher-order cognitive skills in these age groups and ensure that these children systematically use such skills.This study aimed to investigate the effects of the POE (Predict-Observe-Explain) method on the POE and critical thinking skills during early childhood. In this context, 27 pre-school students participated in the study, which involved an experimental design and was carried out for six weeks. In the study, the data collected by using quantitative and qualitative data collection tools were analyzed using appropriate analysis methods. The researchers compared the effects of scientific experiments prepared in line with the POE and gamification on students' critical thinking skills through philosophical inquiry (CTSPI) and POE skills, and skills that constitute the sub-dimensions of these skills. The findings revealed that in early childhood education the POE was more effective than gamification in developing students' critical thinking and POE skills. Similarly, teachers participating in the research process thought that the POE was more effective than gamification in motivating students and turning their attention to teaching processes. Considering the results, the POE is recommended to be used in early childhood education, especially in transferring scientific content in the fields of science.
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    Erratum to: Poster Abstracts, 17th Annual Meeting of the International Association of Medical Science Educators, St. Andrews, Scotland, UK, June 8–11, 2013. (Medical Science Educator, (2013), 23, S4, (668-725), 10.1007/BF03341701)
    (Springer, 2016) Deo, Ravinesh C.; Şahin, Mehmet
    Abstract 218—Team-Based and Interprofessional Education: The Learning of Anatomy by Medical Students from Different Backgrounds in a Graduate Entry Course was published without authorship. Authors should have been: Michelle Machado1 and Norman Eizenberg 1,2 1 Gippsland Medical School, Monash University 2 Department of Anatomy and Developmental Biology, Monash University, Clayton michelle.machado@monash.edu. © 2018, Springer Nature Limited.
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    Estimating solar radiation by machine learning methods
    (Institute of Electrical and Electronics Engineers Inc., 2015) Ertu?rul, Edip; Şahin, Mehmet; A?gün, Fikri
    Solar energy, which is clean and renewable energy source, is a popular subject. The estimation of solar radiation can be done instead of long term measurements. Therefore, the satellite and meteorological values of 53 different locations of Turkey were used for estimations of solar radiation. In this study a hybrid approach was proposed. The train dataset was reduced by employing two times similarity and the reduced dataset was utilized with support vector machine to predict global solar radiation. Additionally, the proposed method was validated by employing neural network, linear regression, k nearest neighbor, extreme learning machine, Gaussian process regression and kernel smooth regression. This study was showed that the machine learning methods can be used instead of long term measurement before investments. © 2015 IEEE.
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    Yapay sinir a?i ve uydu datalari kullanilarak güneş radyasyonunun tahmini
    (Institute of Electrical and Electronics Engineers Inc., 2017) Kuncan, Fatma; Şahin, Mehmet
    In this study, a model was developed to estimate monthly-average daily solar radiation over Turkey. Artificial neural network method was used in improved model. The solar radiation values of 53 different locations over Turkey were taken as data. Land surface temperature, altitude, latitude, longitude and month values were used as input variables for modeling artificial neural network and solar radiation has been estimated as output of artificial neural network model. The RMSE, MBE and correlation coefficient for the best developed model were calculated as 1.550 MJ/m2, -0.172 MJ/m2 and 0.972, respectively. © 2017 IEEE.