Browsing by Author "Pinar, S. Mesut"

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An Expert Classification System of Pollen of Onopordum Using a Rough Set Approach
(Elsevier, 2013) Kaya, Yilmaz; Pinar, S. Mesut; Erez, M. Emre; Fidan, Mehmet
Although pollen grains have a complicated 3-dimensional structure, they can be distinguished from one another by their specific and distinctive characteristics. Using microscopic differences between the pollen grains, it may be possible to identify them by family or even at the genus level. However for the identification of pollen grains at the taxon level, we require expert computer systems. For this purpose, we used 20 different pollen types, obtained from the genus Onopordum L (Asteraceae). For each pollen grain, 30 different images were photographed by microscope system and 11 different characteristic features (polar axis, equatorial axis, P/E ratio, colpus length, colpus weight, exine, intine, tectum, nexine, columellea, and echinae length) were measured for the analysis. The data set was formed from 600 samples, obtained from 20 different taxa, with 30 different images. The 440 samples were used for training and the remaining 160 samples were used for testing. The proposed method, a rough set-based expert system, has properly identified 145 of 160 pollen grains correctly. The success of the method for the identification of pollen grains was obtained at 90.625% (145/160). We can expect to achieve more efficient results with different genuses and families, considering the successful results in the same genus. Moreover, using computer-based systems in revision studies will lead us to more accurate and efficient results, and will identify which characters will be more effective for pollen identification. According to the literature, this is the first study for the identification and comparison of pollen of the same genus by using the measurements of distinctive characteristics with computer systems. (C) 2012 Elsevier B.V. All rights reserved.
Identification of Onopordum Pollen Using the Extreme Learning Machine, a Type of Artificial Neural Network
(Taylor & Francis inc, 2014) Kaya, Yilmaz; Pinar, S. Mesut; Erez, M. Emre; Fidan, Mehmet; Riding, James B.
Pollen grains are complex three-dimensional structures, and are identified using specific distinctive morphological characteristics. An efficient automatic system for the accurate and rapid identification of pollen grains would significantly enhance the consistency, objectivity, speed and perhaps accuracy of pollen analysis. This study describes the development and testing of an expert system for the identification of pollen grains based on their respective morphologies. The extreme learning machine (ELM) is a type of artificial neural network, and has been used for automatic pollen identification. To test the equipment and the method, pollen grains from 10 species of Onopordum (a thistle genus) from Turkey were used. In total, 30 different images were acquired for each of the 10 species studied. The images were then used to measure 11 morphological parameters; these were the colpus length, the colpus width, the equatorial axis (E), the polar axis (P), the P/E ratio, the columellae length, the echinae length, and the thicknesses of the exine, intine, nexine and tectum. Pollen recognition was performed using the ELM for the 50-50%, 70-30% and 80-20% training-test partitions of the overall dataset. The classification accuracies of these three training-test partitions of were 84.67%, 91.11% and 95.00%, respectively. Therefore, the ELM exhibited a very high success rate for identifying the pollen types considered here. The use of computer-based systems for pollen recognition has great potential in all areas of palynology for the accurate and rapid accumulation of data.
Onopordum Myriacanthum Subsp Arachnoideum Comb. & Stat. Nov (Asteraceae: Cardueae)
(Bangladesh Assoc Plant Taxonomists, 2014) Pinar, S. Mesut; Behcet, Lutfi
Turkish endemic taxon Onopordum bracteatzun Boiss. & Heldr. var. arachnoideunz Erik & Stimbill is transferred to O. myriacanthum Boiss. as O. myriacanthun7 subsp. arachnoideurn (Erik & Stimbtil) Pinar & Behcet comb. & stat. nov. It is characterized by the phyllaries with densely and persistently arachnoid hairs both inside and outside, and upper stem leaves are 2-8 cm far from capitulum. In addition, the pollen characteristics and achene features are presented. The conservation status of O. myriacanthurn subsp. arachnoideum has been assessed according to IUCN criteria. A distribution map of O. myriacanthum subsp. aracknoideum and its related taxa is also presented.