Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/11727/4809

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    Morphometric analysis of Crayfish - traditional and artificial intelligent approach
    (2022) Benzer, Semra; Benzer, Recep; 0000-0002-5339-0554; A-5050-2014
    Crayfish are crustaceans of cultural importance in most countries, traditionally consumed on important occasions for centuries, and of high economic value in the market. This study was carried out to analyze some morphological characteristics, the total length of total weight, the carapace length total length, the chela length total length, the abdomen length total length relationships, properties, and ratios of freshwater crayfish (Astacus leptodactylus Eschscholtz 1823) in Iznik Lake. Length-weight and length-length relationships with traditional methods and artificial neural networks, which are the most important subfields of artificial intelligence, have been evaluated. The total length-weight relationships for males, females and all individuals were found to be: W = 0.08197221 L (2.61) (R-2 = 0.941), W = 0.08252047 L (2.53) (R-2 = 0.948) and W = 0.06874014 L (2.65) (R-2 = 0.927), respectively. As a result, the morphometric relationships in Astacus leptodactylus examined in this study will provide information for future studies and monitoring management plans with traditional and artificial intelligence approaches.
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    Investigation of some machine learning algorithms in fish age classification
    (2021) Benzer, Semra; Garabaghi, Farid Hassanbaki; Benzer, Recep; Mehr, Homay Danaei; 0000-0002-5339-0554; A-5050-2014
    Marine and freshwater scientists use fish scales, vertebrae, otoliths and length-weights values to estimate fish age because reliable fish age estimation plays a very important role in fish stock management. The advances in technology and the widespread use of artificial intelligence have revealed the use of traditional observations and techniques in the fishing industry. The aim of this study was to evaluate the effectiveness of three disesteemed machine learning algorithms (NB, J48 DT, RF) in comparison with ANNs which has been widely used in such studies in the literature. In culmination, all three algorithms outperformed ANNs and can be considered as alternatives in case of coming across noisy and non-linear datasets. Moreover, among these three algorithms J48 DT and RF showed exceptional performance where the data for specific fish age groups weren't abundant.
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    Can we diagnose disk and facet degeneration in lumbar spine by acoustic analysis of spine sounds?
    (2020) Nabi, Vugar; Ayhan, Selim; Acaroglu, Emre; Ahi, Mustafa Arda; Toreyin, Hakan; Cetin, A. Enis; 0000-0003-0153-3012; U-5409-2018
    This study aims to investigate spine sounds from a perspective that would make their use for diagnostic purposes of any spinal pathology possible. People with spine problems can be determined using joint sounds collected from the involved area of the spinal columns of subjects. In our sound dataset, it is observed that a 'click' sound is detected in individuals who are suffering from low back pain. Recorded joint sounds are classified using automatic speech recognition algorithm. mel-frequency cepstrum coefficients (MFCC) are extracted from the sound signals as feature vectors. MFCC's are classified using an artificial neural networks, which is currently the state-of-the-art speech recognition tool. The algorithm has a high success rate of detecting 'click' sounds in a given sound signal and it can perfectly identify and differentiate healthy individuals from unhealthy subjects in our data set. Spine sounds have the potential of serving as a reliable marker of the spine health.