Yazar "Yildiz, Mustafa" seçeneğine göre listele

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    Can Artificial Intelligence Identify Reading Fluency and Level? Comparison of Human and Machine Performance
    (Routledge Journals, Taylor & Francis Ltd, 2024) Yildiz, Mustafa; Keskin, Hasan Kagan; Oyucu, Saadin; Hartman, Douglas K.; Temur, Murat; Aydogmus, Mucahit
    This study examined whether an artificial intelligence-based automatic speech recognition system can accurately assess students' reading fluency and reading level. Participants were 120 fourth-grade students attending public schools in T & uuml;rkiye. Students read a grade-level text out loud while their voice was recorded. Two experts and the artificial intelligence-based automatic speech recognition system analyzed the recordings for reading errors. Following the analysis, a word error rate was calculated for both the experts and the artificial intelligence-based automatic speech recognition system. Word error rates were converted into reading accuracy rate scores. Inter-rater agreement and linear regression analyses were used to compare the raters' reading fluency scores, and logistic regression analyses were used to compare the classification of readers according to their reading levels. Results showed that the difference between the scores of the artificial intelligence-based automatic speech recognition system and the expert scores was minimal. This is because there was a very high level of agreement between the artificial intelligence-based automatic speech recognition system and the experts scores. Linear regression analyses showed that the artificial intelligence-based automatic speech recognition system significantly predicted the scores of experts. According to the logistic regression analysis results, the artificial intelligence-based automatic speech recognition system was at least 93% as successful as human raters in classifying readers as poor and good. These results give us hope that reading assessments at classroom, school, regional, national, and even international levels can be conducted more accurately and economically by using artificial intelligence-based systems in the coming years.
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    Magnetic field effect on breaking tuber dormancy, early sprouting, seedling growth, and tuber formation in potato (Solanum tuberosum L.)
    (Science Society Thailand, 2020) Bahadir, Anzel; Sahin, Nilufer Kocak; Beyaz, Ramazan; Yildiz, Mustafa
    Magnetic field (MF) treatment improves the germination of seeds and enhances the performance of various crops. In this study, the effects of different MF strengths (0-control, 75, 150, and 300 mT) and exposure time periods (0-control, 24, 48, and 72 h) on sprouting of dormant seed potato tubers, vegetative growth (emergence time and plant height), tuber formation (tuber number per plant and mean tuber weight), and total chlorophyll content in 2 potato (Solanum tuberosum L.) cultivars ('Necta' and 'Banba') were investigated in all parameters examined in both cultivars, the worst results were recorded in control treatment where no MF strength was used. Emergence times of sprouts were reduced significantly when seed potato tubers were exposed to 150 mT MF strength for 72 h in both cultivars. The fastest emergence times were recorded as 14.0 days in cv. 'Nectar' and 17.0 days in cv. 'Banba' when seed tubers were exposed to 150 mT MF strength for 72 h. In control treatment, emergence time of sprouts was noted as 31.8 days in cv. 'Nectar' and 39.5 days in cv. 'Banba'. The best results in other parameters (plant height, total chlorophyll content, tuber number per plant and mean tuber weight) were again obtained from seed tubers treated with 150 mT MF strength for 72 h whereas the worst results were noted in control treatment in both cultivars. Thus, MF pre-treatment can compensate for the negative effects of dormancy in seed potato tubers.