Scopus İndeksli Yayınlar Koleksiyonu

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

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search.filters.applied.f.rights: search.filters.rights.info:eu-repo/semantics/closedAccessSubject: search.filters.subject.Artificial neural networks

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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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    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.